sgl-project--sglang/python/sglang/srt/models/deepseek_v2.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2023-2024 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.
# ==============================================================================

# Adapted from:
# https://github.com/vllm-project/vllm/blob/fb6af8bc086328ca6659e72d11ffd4309ce4de22/vllm/model_executor/models/deepseek_v2.py
"""Inference-only DeepseekV2 model."""

from __future__ import annotations

import logging
from contextlib import nullcontext
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.jit_kernel.dsv4 import (
    silu_and_mul_clamp,
    silu_and_mul_contig_post_quant,
)
from sglang.srt.batch_overlap.single_batch_overlap import SboFlags, compute_overlap_args
from sglang.srt.batch_overlap.two_batch_overlap import (
    MaybeTboDeepEPDispatcher,
    model_forward_maybe_tbo,
)
from sglang.srt.configs.model_config import (
    compute_mla_mscale_scaling,
    dsa_layer_skips_topk,
    get_dsa_index_head_dim,
    get_dsa_index_n_heads,
    get_dsa_index_topk,
    is_deepseek_dsa,
)
from sglang.srt.distributed import (
    divide,
    get_pp_group,
    tensor_model_parallel_all_reduce,
)
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 import deep_gemm_wrapper
from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.amx_utils import PackWeightMethod
from sglang.srt.layers.attention.dsa.dsa_indexer import Indexer
from sglang.srt.layers.attention.dsa.utils import (
    can_dsa_cp_split,
    dsa_use_prefill_cp,
    is_dsa_enable_prefill_cp,
)
from sglang.srt.layers.communicator import (
    LayerCommunicator,
    LayerScatterModes,
    enable_moe_dense_fully_dp,
    get_attn_tp_context,
)
from sglang.srt.layers.communicator_dsa_cp import (
    DSACPLayerCommunicator,
    maybe_prefetch_next_full_attention_kv,
)
from sglang.srt.layers.dcp.planner import (
    prepare_decode_context_parallel_metadata,
)
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.moe import (
    get_moe_a2a_backend,
    get_moe_runner_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.hash_topk import HashTopK
from sglang.srt.layers.moe.kt_ep_wrapper import KTEPWrapperMethod
from sglang.srt.layers.moe.token_dispatcher.base import (
    BaseDispatcher,
    CombineInput,
    DispatchOutput,
)
from sglang.srt.layers.moe.topk import BypassedTopKOutput, TopK, TopKOutputFormat
from sglang.srt.layers.moe.utils import (
    RoutingMethodType,
    filter_moe_weight_param_global_expert,
    has_per_rank_fused_shared_slots,
    is_deepep_class_backend,
    is_sbo_enabled,
    is_tbo_enabled,
)
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.quantization.fp8 import Fp8Config
from sglang.srt.layers.quantization.fp8_kernel import (
    create_per_token_group_quant_fp8_output_scale,
)
from sglang.srt.layers.quantization.fp8_utils import (
    materialize_bpreshuffle_fp8_scale,
)
from sglang.srt.layers.quantization.mxfp4_flashinfer_trtllm_moe import (
    maybe_fuse_routed_scale_and_shared_add,
)
from sglang.srt.layers.quantization.unquant import get_bf16_gemm_backend
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.rotary_embedding import get_rope_wrapper
from sglang.srt.layers.utils import PPMissingLayer
from sglang.srt.layers.utils.cp_utils import (
    can_cp_split,
    cp_all_gather_rerange_output,
    cp_split_and_rebuild_data,
    cp_split_and_rebuild_position,
    is_prefill_context_parallel_enabled,
    mla_use_prefill_cp,
    prepare_context_parallel_metadata,
)
from sglang.srt.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
    get_embedding_tp_kwargs,
)
from sglang.srt.model_executor.cuda_graph_config import (
    Backend,
    Phase,
    check_cuda_graph_backend,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
from sglang.srt.model_executor.forward_context import get_attn_backend
from sglang.srt.model_executor.runner import get_is_capture_mode
from sglang.srt.model_executor.runner_backend_utils.breakable_cuda_graph.context import (
    is_in_breakable_cuda_graph,
)
from sglang.srt.model_executor.runner_backend_utils.tc_piecewise_cuda_graph import (
    get_tc_piecewise_forward_context,
    is_in_tc_piecewise_cuda_graph,
)
from sglang.srt.models.deepseek_common.attention_backend_handler import (
    AttentionBackendRegistry,
)
from sglang.srt.models.deepseek_common.attention_forward_methods import (
    AttnForwardMethod,
    DeepseekMHAForwardMixin,
    DeepseekMLACpuForwardMixin,
    DeepseekMLAForwardMixin,
    DeepseekMLARocmForwardMixin,
)
from sglang.srt.models.deepseek_common.deepseek_weight_loader import (
    DeepseekV2WeightLoaderMixin,
)
from sglang.srt.models.deepseek_common.utils import (
    _device_sm,
    _get_llama_4_scaling,
    _is_cpu,
    _is_cpu_amx_available,
    _is_cuda,
    _is_gfx95_supported,
    _is_hip,
    _is_musa,
    _is_npu,
    _is_xpu,
    _use_aiter,
    _use_aiter_bpreshuffle_gfx95,
    _use_aiter_gfx95,
)
from sglang.srt.runtime_context import (
    get_flags,
    get_forward,
    get_parallel,
    get_server_args,
)
from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
from sglang.srt.utils import (
    BumpAllocator,
    LazyValue,
    add_prefix,
    is_non_idle_and_non_empty,
    log_info_on_rank0,
    make_layers,
    use_intel_amx_backend,
)
from sglang.srt.utils.custom_op import register_custom_op

if _use_aiter:
    from sglang.srt.layers.rocm_linear_utils import aiter_dsv3_router_gemm

if _use_aiter_gfx95:
    from sglang.srt.layers.rocm_linear_utils import (
        get_dsv3_gemm_output_zero_allocator_size,
    )

if _use_aiter:
    pass

if _is_cuda:
    from sglang.jit_kernel.dsv3_router_gemm import (
        dsv3_router_gemm as _jit_dsv3_router_gemm,
    )
    from sglang.jit_kernel.fused_a_gemm import dsv3_fused_a_gemm
elif _is_npu:
    from sglang.srt.hardware_backend.npu.modules.deepseek_v2_attention_mla_npu import (
        forward_dsa_core_npu,
        forward_dsa_prepare_npu,
        forward_mha_core_npu,
        forward_mha_prepare_npu,
        forward_mla_core_npu,
        forward_mla_prepare_npu,
    )
elif _is_musa:
    from sgl_kernel import dsv3_fused_a_gemm
else:
    pass

logger = logging.getLogger(__name__)

_enable_pcg_dsv2_dual_stream = (
    _is_cuda and envs.SGLANG_ENABLE_PCG_DSV2_DUAL_STREAM.get()
)


class DeepseekV2MLP(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,
        swiglu_limit: Optional[float] = None,
    ) -> None:
        super().__init__()
        self.tp_size = tp_size
        self.swiglu_limit = swiglu_limit

        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 not hasattr(self.gate_up_proj, "weight") and hasattr(
            self.gate_up_proj, "weight_packed"
        ):
            self.gate_up_proj.weight = self.gate_up_proj.weight_packed
        if not hasattr(self.down_proj, "weight") and hasattr(
            self.down_proj, "weight_packed"
        ):
            self.down_proj.weight = self.down_proj.weight_packed
        if hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {hidden_act}. "
                "Only silu is supported for now."
            )
        self.act_fn = SiluAndMul()
        self.use_fused_clamp_act_mul = (
            _is_hip and envs.SGLANG_OPT_USE_FUSED_CLAMP_ACT_MUL.get()
        )
        self._fused_clamp_fp8_checked = False
        self._fused_clamp_use_fp8 = False

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

        if (
            getattr(self, "_enable_nvfp4_gemm_swiglu_fusion", False)
            and self.swiglu_limit is None
            and not isinstance(x, tuple)
        ):
            from sglang.srt.layers.quantization.fp4_utils import fp4_quantize
            from sglang.srt.layers.quantization.nvfp4_gemm_swiglu_nvfp4_quant import (
                nvfp4_gemm_swiglu_nvfp4_quant,
            )

            x_fp4, x_scale = fp4_quantize(
                x, self.gate_up_proj.input_scale_inv, enable_pdl=True
            )
            out_fp4, out_scale = nvfp4_gemm_swiglu_nvfp4_quant(
                x_fp4,
                x_scale,
                self.gate_up_proj.weight_swiglu_interleaved,
                self.gate_up_proj.weight_scale_swiglu_interleaved,
                self.gate_up_proj.alpha,
                self.down_proj.input_scale_inv,
                enable_pdl=True,
            )
            out, _ = self.down_proj((out_fp4, out_scale))
            return out

        if (
            gemm_output_zero_allocator is not None
            and x.shape[0] <= 256
            and self.gate_up_proj.weight.dtype == torch.uint8
        ):
            y = gemm_output_zero_allocator.allocate(
                x.shape[0] * self.gate_up_proj.output_size_per_partition
            ).view(x.shape[0], self.gate_up_proj.output_size_per_partition)
            x = (x, None, y)

        gate_up, _ = self.gate_up_proj(x)
        # Fast path: fused silu+clamp+fp8_quant+deepgemm when conditions met.
        # Only valid when down_proj does NOT need an all-reduce and its weights
        # are fp8 (uint8 storage with weight_scale_inv).
        if (
            self.swiglu_limit is not None
            and not self.down_proj.reduce_results
            and self.down_proj.weight.dtype == torch.uint8
            and hasattr(self.down_proj, "weight_scale_inv")
        ):
            M, N = gate_up.shape
            down_input_fp8 = gate_up.new_empty((M, N // 2), dtype=torch.float8_e4m3fn)
            scale_block_size = 128
            down_input_scale = create_per_token_group_quant_fp8_output_scale(
                x_shape=(M, N // 2),
                device=gate_up.device,
                group_size=scale_block_size,
                column_major_scales=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
                scale_tma_aligned=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
                scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
            )
            silu_and_mul_contig_post_quant(
                input=gate_up,
                output=down_input_fp8,
                output_scale=down_input_scale,
                quant_group_size=scale_block_size,
                scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
                transposed=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
                swiglu_limit=float(self.swiglu_limit),
            )
            down_output = gate_up.new_empty(
                (M, self.down_proj.output_size), dtype=torch.bfloat16
            )
            deep_gemm_wrapper.gemm_nt_f8f8bf16(
                (down_input_fp8, down_input_scale),
                (self.down_proj.weight, self.down_proj.weight_scale_inv),
                down_output,
            )
            return down_output

        if self.use_fused_clamp_act_mul and self.swiglu_limit is not None:
            from aiter.ops.triton.fusions.fused_clamp_act_mul import (
                fused_clamp_act_mul,
            )

            if not self._fused_clamp_fp8_checked:
                from sglang.srt.layers.quantization.fp8 import Fp8LinearMethod

                qm = getattr(self.down_proj, "quant_method", None)
                self._fused_clamp_use_fp8 = (
                    isinstance(qm, Fp8LinearMethod) and qm.block_quant
                )
                self._fused_clamp_fp8_checked = True

            if self._fused_clamp_use_fp8:
                from aiter import dtypes

                x_fp8, x_scale = fused_clamp_act_mul(
                    gate_up,
                    swiglu_limit=self.swiglu_limit,
                    activation="silu",
                    dtype_quant=dtypes.fp8,
                    transpose_scale=False,
                )
                if _use_aiter_bpreshuffle_gfx95:
                    x_scale = materialize_bpreshuffle_fp8_scale(x_scale)
                x = (x_fp8, x_scale)
            else:
                x = fused_clamp_act_mul(
                    gate_up,
                    swiglu_limit=self.swiglu_limit,
                    activation="silu",
                )

        # Fallback: fused silu+clamp kernel (still faster than unfused)
        elif self.swiglu_limit is not None:
            if _is_npu:
                _g, _u = gate_up.chunk(2, dim=-1)
                _lim = float(self.swiglu_limit)
                gate_up = torch.cat(
                    [_g.clamp(max=_lim), _u.clamp(min=-_lim, max=_lim)], dim=-1
                )
                x = self.act_fn(gate_up)
            else:
                M, N = gate_up.shape
                x = gate_up.new_empty((M, N // 2))
                silu_and_mul_clamp(gate_up, x, float(self.swiglu_limit))
        else:
            x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class MoEGate(nn.Module):
    def __init__(
        self,
        config,
        quant_config,
        prefix: str = "",
        is_nextn: bool = False,
        is_hash_moe: bool = False,
        is_deepseek_v4: bool = False,
        dsa_enable_prefill_cp: bool = False,
        mla_enable_prefill_cp: bool = False,
    ):
        super().__init__()
        self.is_nextn = is_nextn
        self.is_deepseek_v4 = is_deepseek_v4
        self.weight = nn.Parameter(
            torch.empty((config.n_routed_experts, config.hidden_size))
        )

        if config.topk_method == "noaux_tc" and not is_hash_moe:
            correction_bias_dtype = torch.float32
            if quant_config is not None:
                if _use_aiter and quant_config.get_name() in (
                    "fp8",
                    "compressed_tensors",
                    "quark",
                ):
                    correction_bias_dtype = torch.bfloat16
            self.e_score_correction_bias = nn.Parameter(
                torch.empty((config.n_routed_experts), dtype=correction_bias_dtype)
            )
        else:
            self.e_score_correction_bias = None
        if _is_cpu and _is_cpu_amx_available:
            self.quant_method = PackWeightMethod(weight_names=["weight"])
        self.use_dsa = is_deepseek_dsa(config)
        self.dsa_enable_prefill_cp = dsa_enable_prefill_cp
        self.mla_enable_prefill_cp = mla_enable_prefill_cp

    def forward(
        self,
        hidden_states,
        gemm_output_zero_allocator: BumpAllocator = None,
        forward_batch: ForwardBatch = None,
    ):
        if use_intel_amx_backend(self):
            return torch.ops.sgl_kernel.weight_packed_linear(
                hidden_states,
                self.weight,
                None,  # bias
                True,  # is_vnni
            )

        if get_server_args().enable_deterministic_inference:
            return F.linear(hidden_states, self.weight, None)

        if (
            not self.is_deepseek_v4
            and forward_batch is not None
            and (
                dsa_use_prefill_cp(forward_batch, self.dsa_enable_prefill_cp)
                or mla_use_prefill_cp(forward_batch, self.mla_enable_prefill_cp)
            )
        ):
            if _is_cuda:
                from sglang.jit_kernel.dsv4 import linear_bf16_fp32

                return linear_bf16_fp32(hidden_states, self.weight)
            return F.linear(hidden_states, self.weight, None)
        else:
            # NOTE(b8zhong): this threshold has been empirically verified
            max_router_gemm_tokens = 4 if _device_sm in (100, 103) else 16
            if (
                _is_cuda
                and hidden_states.shape[0] <= max_router_gemm_tokens
                and hidden_states.shape[1] % 1024 == 0
                and (self.weight.shape[0] == 256 or self.weight.shape[0] == 384)
                and _device_sm >= 90
            ):
                logits = _jit_dsv3_router_gemm(
                    hidden_states, self.weight, out_dtype=torch.float32
                )

            elif _use_aiter:
                logits = aiter_dsv3_router_gemm(hidden_states, self.weight)
            elif not _is_cuda:
                logits = F.linear(hidden_states, self.weight, None)
            else:
                # cuBLAS bf16 x bf16 -> fp32 GEMM (torch.mm's out_dtype kwarg is CUDA-only)
                from sglang.jit_kernel.dsv4 import linear_bf16_fp32

                logits = linear_bf16_fp32(hidden_states, self.weight)

        return logits


class DeepseekV2MoE(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,
        is_deepseek_v4: bool = False,
        dsa_enable_prefill_cp: bool = False,
        mla_enable_prefill_cp: bool = False,
    ):
        super().__init__()
        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

        n_shared_experts = (
            0 if config.n_shared_experts is None else int(config.n_shared_experts)
        )
        _fusion_disabled = get_server_args().disable_shared_experts_fusion

        # num_fused_shared_experts drives weight remapping in deepseek_weight_loader:
        # mlp.shared_experts → mlp.experts.256 when > 0.
        self.num_fused_shared_experts = 0 if _fusion_disabled else n_shared_experts

        # DeepEP and MegaMOE shared expert fusion: shared expert is fused into
        # the same MoE kernel as a local expert at each EP rank. Expert layout
        # is expanded from 256 routed to 256+EP_size (e.g. 272 for EP=16).
        _uses_per_rank_shared_slots = has_per_rank_fused_shared_slots(
            self.num_fused_shared_experts
        )

        if _uses_per_rank_shared_slots:
            # 256 routed + EP_size shared slots = 272 experts total (for EP=16)
            num_experts_for_moe = config.n_routed_experts + self.moe_ep_size
            top_k_for_moe = config.num_experts_per_tok + 1  # 8 routed + 1 shared
            # Interleaving for DeepEP/MegaMOE dispatch is handled by TopK internally.
        else:
            num_experts_for_moe = (
                config.n_routed_experts + self.num_fused_shared_experts
            )
            top_k_for_moe = config.num_experts_per_tok + self.num_fused_shared_experts

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

        n_hash_layers = getattr(config, "num_hash_layers", 0)
        self.is_hash = layer_id < n_hash_layers and not (is_deepseek_v4 and 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 = MoEGate(
            config=config,
            quant_config=quant_config,
            prefix=add_prefix("gate", prefix),
            is_nextn=is_nextn,
            is_hash_moe=self.is_hash,
            is_deepseek_v4=is_deepseek_v4,
            dsa_enable_prefill_cp=dsa_enable_prefill_cp,
            mla_enable_prefill_cp=mla_enable_prefill_cp,
        )

        # scaling factor for fused shared experts on AMD-platform.
        # DeepEP/MegaMOE doesn't need this: shared expert is only computed on home rank
        # (not all-reduced), so no 1/ep_size correction is needed.
        fused_shared_experts_scaling_factor = None
        if (
            self.moe_ep_size > 1
            and self.num_fused_shared_experts > 0
            and not _uses_per_rank_shared_slots
        ):
            # if enable_ep_moe tp_szie == ep_size, every gpu get shared experts gemm output
            # so we scale with 1 / self.moe_ep_size in ep mode which will make it equalation as in tp mode
            # with fused_shared_experts
            fused_shared_experts_scaling_factor = 1.0 / float(self.moe_ep_size)

        self.experts = get_moe_impl_class(quant_config)(
            num_experts=num_experts_for_moe
            + get_server_args().ep_num_redundant_experts,
            num_fused_shared_experts=self.num_fused_shared_experts,
            top_k=top_k_for_moe,
            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,
            routing_method_type=getattr(
                config, "routing_method_type", RoutingMethodType.DeepSeekV3
            ),
            swiglu_limit=getattr(config, "swiglu_limit", None),
            prefix=add_prefix("experts", prefix),
        )

        if self.is_hash and not (is_nextn and is_deepseek_v4):
            self.topk = HashTopK(
                topk=config.num_experts_per_tok + self.num_fused_shared_experts,
                num_experts=config.n_routed_experts,
                num_fused_shared_experts=self.num_fused_shared_experts,
                vocab_size=config.vocab_size,
                scoring_func=config.scoring_func,
                routed_scaling_factor=self.routed_scaling_factor,
                apply_routed_scaling_factor_on_output=self.experts.should_fuse_routed_scaling_factor_in_topk,
                layer_id=self.layer_id,
            )
        else:
            # Default: grouped noaux_tc top-k. Covers V3/V3.2/GLM-5/Glm4MoeLite.
            topk_kwargs = dict(
                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,
                scoring_func=config.scoring_func,
                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,
                fused_shared_experts_scaling_factor=fused_shared_experts_scaling_factor,
                # 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 (except trtllm). We use quant_config to determine the output format.
                output_format=(
                    TopKOutputFormat.STANDARD
                    if (quant_config is None)
                    and (not get_moe_runner_backend().is_flashinfer_trtllm())
                    else None
                ),
            )
            # DSV4 override: ungrouped sqrtsoftplus + fp4 expert layout flag.
            if is_deepseek_v4:
                topk_kwargs.update(
                    use_grouped_topk=False,
                    scoring_func=config.scoring_func,
                    is_fp4_experts=getattr(quant_config, "is_fp4_experts", False),
                    apply_routed_scaling_factor_on_output=(
                        True
                        if _use_aiter
                        else self.experts.should_fuse_routed_scaling_factor_in_topk
                    ),
                )
            self.topk = TopK(**topk_kwargs)

        self.shared_experts_is_int8 = False
        self.shared_experts_is_fp8 = False
        self.shared_experts_weight_block_size = None
        self._shared_expert_tp1 = False
        # Shared experts: skip when fused into MoE kernel
        # (self.num_fused_shared_experts > 0) or when DeepEP/MegaMOE fusion is enabled.
        if (
            config.n_shared_experts is not None
            and config.n_shared_experts > 0
            and self.num_fused_shared_experts == 0
            and not _uses_per_rank_shared_slots
        ):
            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
            # Disable TP for shared experts for A2A/FP4 allgather paths, or when
            # explicitly requested for DSV4 checkpoints whose shared scales are
            # not divisible by the global TP size.
            _shared_expert_use_tp1 = (
                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 get_moe_a2a_backend().is_megamoe()
                or should_use_flashinfer_cutlass_moe_fp4_allgather()
                or envs.SGLANG_SHARED_EXPERT_TP1.get()
            )
            self.shared_experts = DeepseekV2MLP(
                hidden_size=config.hidden_size,
                intermediate_size=intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                reduce_results=False,
                swiglu_limit=getattr(config, "swiglu_limit", None),
                prefix=add_prefix("shared_experts", prefix),
                **(dict(tp_rank=0, tp_size=1) if _shared_expert_use_tp1 else {}),
            )
            # Flags must be set before weight load so
            # process_weights_after_loading sees them and builds the
            # [Up, Gate]-interleaved weight + scale.
            from sglang.srt.layers.quantization.modelopt_quant import (
                ModelOptFp4LinearMethod,
            )
            from sglang.srt.utils.common import is_sm100_supported

            fc1_n = self.shared_experts.gate_up_proj.output_size_per_partition
            if (
                envs.SGLANG_ENABLE_NVFP4_GEMM_SWIGLU_FUSION.get()
                and is_sm100_supported()
                and isinstance(
                    self.shared_experts.gate_up_proj.quant_method,
                    ModelOptFp4LinearMethod,
                )
                and isinstance(
                    self.shared_experts.down_proj.quant_method,
                    ModelOptFp4LinearMethod,
                )
                and fc1_n % 128 == 0
                and not check_cuda_graph_backend(Phase.PREFILL, Backend.TC_PIECEWISE)
            ):
                self.shared_experts.gate_up_proj._interleave_for_swiglu_fusion = True
                self.shared_experts._enable_nvfp4_gemm_swiglu_fusion = True
                self.shared_experts.down_proj._accepts_prequantized_fp4 = True
            self._shared_expert_tp1 = _shared_expert_use_tp1
            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):
        # EPLB only rebalances physical routed experts. Fused shared expert
        # slots live after each rank's routed slots and must stay stable.
        num_local_experts_for_eplb = (
            self.experts.num_local_experts - self.num_fused_shared_experts
        )

        return [
            x.data[:num_local_experts_for_eplb]
            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 _can_dual_stream_graph(
        self, hidden_states: torch.Tensor, server_args=None
    ) -> bool:
        if server_args is None:
            server_args = get_server_args()
        return (
            _enable_pcg_dsv2_dual_stream
            and (is_in_tc_piecewise_cuda_graph() or is_in_breakable_cuda_graph())
            and get_moe_runner_backend().is_flashinfer_trtllm()
            and self.alt_stream is not None
            and self.num_fused_shared_experts == 0
            and hidden_states.shape[0] > 0
            and hasattr(self, "shared_experts")
            and getattr(self.experts, "use_flashinfer_trtllm_moe", False)
            and not self._enable_a2a_moe
            and not self._fuse_shared_experts_inside_sbo
            and not getattr(self, "is_hash", False)
            and not server_args.enable_eplb
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        forward_batch: Optional[ForwardBatch] = None,
        gemm_output_zero_allocator: BumpAllocator = None,
        input_ids: Optional[torch.Tensor] = None,
        input_ids_global: Optional[torch.Tensor] = None,
        skip_shared_experts: bool = False,
    ) -> torch.Tensor:
        from sglang.srt.layers.moe.mega_moe import forward_mega_moe, should_use_mega_moe

        if should_use_mega_moe(self, hidden_states):
            return forward_mega_moe(
                self,
                hidden_states,
                forward_batch,
                input_ids_global=input_ids_global,
            )

        if not self._enable_a2a_moe:
            server_args = get_server_args()
            if self._can_dual_stream_graph(hidden_states, server_args):
                return dsv2_flashinfer_moe_dual_stream_graph(
                    hidden_states,
                    self.layer_id,
                )
            elif (
                self.alt_stream is not None
                and self.num_fused_shared_experts == 0
                and hidden_states.shape[0] > 0
                and get_is_capture_mode()
                and not (
                    get_flags().capture.enable_torch_compile
                    and hidden_states.shape[0]
                    <= server_args.torch_compile_max_bs
                    * (server_args.speculative_num_draft_tokens or 1)
                )
            ):
                return self.forward_normal_dual_stream(
                    hidden_states,
                    gemm_output_zero_allocator,
                    input_ids,
                    input_ids_global=input_ids_global,
                )
            else:
                return self.forward_normal(
                    hidden_states,
                    gemm_output_zero_allocator,
                    input_ids,
                    input_ids_global=input_ids_global,
                    skip_shared_experts=skip_shared_experts,
                )
        else:
            return self.forward_deepep(
                hidden_states, forward_batch, input_ids_global=input_ids_global
            )

    def forward_normal_dual_stream(
        self,
        hidden_states: torch.Tensor,
        gemm_output_zero_allocator: BumpAllocator = None,
        input_ids: Optional[torch.Tensor] = None,
        input_ids_global: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        # Note(kpham-sgl): issue order satisfies 3 constraints:
        # - no stream explosion: main (routed) issued before alt block -> capture reuses 1 alt stream;
        # - PDL overlap: routed is the last main-stream kernel (fuses w/ residual add);
        # - dispose_tensor: disabled during capture (CaptureFlags.disable_dispose_tensor) so the routed
        #   deep_gemm does not free hidden_states, which the shared expert reads on the alt stream.
        use_flashinfer_trtllm_bypass = get_forward().flashinfer_trtllm_bypass
        current_stream = torch.cuda.current_stream()
        self.alt_stream.wait_stream(current_stream)
        has_shared_output = (
            hidden_states.shape[0] > 0 and self.num_fused_shared_experts == 0
        )
        server_args = get_server_args()
        dispatch_info = (
            ExpertLocationDispatchInfo.init_new(layer_id=self.layer_id)
            if server_args.enable_eplb and not self.is_nextn
            else None
        )
        # router_logits: (num_tokens, n_experts)
        router_logits = self.gate(hidden_states, gemm_output_zero_allocator)
        if use_flashinfer_trtllm_bypass:
            topk_output = BypassedTopKOutput(
                hidden_states=hidden_states,
                router_logits=router_logits,
                topk_config=self.topk.topk_config,
            )
        else:
            topk_kwargs = (
                {"input_ids": input_ids_global}
                if getattr(self, "is_hash", False)
                else {}
            )
            topk_output = self.topk(
                hidden_states,
                router_logits,
                expert_location_dispatch_info=dispatch_info,
                **topk_kwargs,
            )
        deferred_finalize = (
            has_shared_output
            and not self._shared_expert_tp1
            and topk_output.format == TopKOutputFormat.BYPASSED
            and self.experts.supports_deferred_finalize
        )
        if deferred_finalize:
            final_hidden_states = self.experts.forward_deferred_finalize(
                hidden_states, topk_output
            )
        elif use_flashinfer_trtllm_bypass:
            final_hidden_states = self.experts.forward_impl(hidden_states, topk_output)
        else:
            final_hidden_states = self.experts(hidden_states, topk_output)
        if (
            not _is_cuda
            and not _is_musa
            and not _use_aiter
            or isinstance(self.experts.quant_method, KTEPWrapperMethod)
        ):
            final_hidden_states *= self.routed_scaling_factor

        # Shared expert on alt stream, issued AFTER the main (routed) branch. See note above.
        with torch.cuda.stream(self.alt_stream):
            shared_output = self._forward_shared_experts(
                hidden_states, gemm_output_zero_allocator
            )

        current_stream.wait_stream(self.alt_stream)

        if deferred_finalize:
            from sglang.srt.layers.moe.moe_runner.flashinfer_trtllm import (
                finalize_flashinfer_trtllm_deferred_output,
            )

            final_hidden_states = finalize_flashinfer_trtllm_deferred_output(
                final_hidden_states,
                shared_output,
            )
        else:
            final_hidden_states = maybe_fuse_routed_scale_and_shared_add(
                self.experts,
                final_hidden_states,
                None if self._shared_expert_tp1 else shared_output,
                self.routed_scaling_factor,
            )

        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)
        # TP1 shared experts are replicated, so add them after all-reduce to
        # avoid summing the same shared output once per TP rank.
        if self._shared_expert_tp1:
            final_hidden_states += shared_output
        return final_hidden_states

    def forward_normal(
        self,
        hidden_states: torch.Tensor,
        gemm_output_zero_allocator: BumpAllocator = None,
        input_ids: Optional[torch.Tensor] = None,
        input_ids_global: Optional[torch.Tensor] = None,
        skip_shared_experts: bool = False,
    ) -> torch.Tensor:
        if hasattr(self, "shared_experts") and use_intel_amx_backend(
            self.shared_experts.gate_up_proj
        ):
            return self.forward_cpu(hidden_states)
        server_args = get_server_args()
        dispatch_info = (
            ExpertLocationDispatchInfo.init_new(layer_id=self.layer_id)
            if server_args.enable_eplb and not self.is_nextn
            else None
        )
        defer_shared = not self.experts.moe_runner_config.inplace
        # PoC (SGLANG_DP_SHARED_EXPERT_LOCAL): shared expert is computed on the LOCAL
        # hidden in the decoder layer (before the dp gather) and added after the
        # reduce_scatterv. When set, never compute/add it here (on the global buffer).
        shared_output = None
        if hidden_states.shape[0] > 0:
            if (
                not defer_shared
                and not self._fuse_shared_experts_inside_sbo
                and not skip_shared_experts
            ):
                shared_output = self._forward_shared_experts(
                    hidden_states, gemm_output_zero_allocator
                )
            # router_logits: (num_tokens, n_experts)
            router_logits = self.gate(hidden_states, gemm_output_zero_allocator)
            topk_kwargs = (
                {"input_ids": input_ids_global}
                if getattr(self, "is_hash", False)
                else {}
            )
            topk_output = self.topk(
                hidden_states,
                router_logits,
                expert_location_dispatch_info=dispatch_info,
                **topk_kwargs,
            )
        else:
            shared_output = None
            topk_output = self.topk.empty_topk_output(
                hidden_states.device, layer_id=self.layer_id
            )

        if self._fuse_shared_experts_inside_sbo and not skip_shared_experts:
            shared_output = None

            def _pre_combine_hook(
                dispatcher: BaseDispatcher, combine_input: CombineInput
            ):

                nonlocal shared_output
                self.alt_stream.wait_stream(torch.cuda.current_stream())
                with torch.cuda.stream(self.alt_stream):
                    shared_output = self._forward_shared_experts(
                        hidden_states, gemm_output_zero_allocator
                    )

                pre_combine_hook_handle.remove()

            def _post_combine_hook(
                dispatcher: BaseDispatcher, hidden_states: torch.Tensor
            ):
                nonlocal shared_output
                torch.cuda.current_stream().wait_stream(self.alt_stream)
                post_combine_hook_handle.remove()

            pre_combine_hook_handle = self.experts.dispatcher.register_pre_combine_hook(
                _pre_combine_hook
            )
            post_combine_hook_handle = (
                self.experts.dispatcher.register_post_combine_hook(_post_combine_hook)
            )

        final_hidden_states = self.experts(
            hidden_states,
            topk_output,
        )
        if (
            not _is_cuda
            and not _is_musa
            and not _is_xpu
            and not _use_aiter
            or isinstance(self.experts.quant_method, KTEPWrapperMethod)
        ):
            # fused in biased_grouped_topk so we can skip here
            final_hidden_states *= self.routed_scaling_factor

        if (
            defer_shared
            and hidden_states.shape[0] > 0
            and not self._fuse_shared_experts_inside_sbo
            and not skip_shared_experts
        ):
            shared_output = self._forward_shared_experts(
                hidden_states, gemm_output_zero_allocator
            )

        final_hidden_states = maybe_fuse_routed_scale_and_shared_add(
            self.experts,
            final_hidden_states,
            None if self._shared_expert_tp1 else shared_output,
            self.routed_scaling_factor,
        )

        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)
        # TP1 shared experts are replicated, so add them after all-reduce to
        # avoid summing the same shared output once per TP rank.
        if shared_output is not None and self._shared_expert_tp1:
            final_hidden_states += shared_output
        return final_hidden_states

    def forward_cpu(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        # router_logits: (num_tokens, n_experts)
        router_logits = self.gate(hidden_states)
        topk_output = self.topk(hidden_states, router_logits)
        fused_experts_out = self.experts(
            hidden_states=hidden_states, topk_output=topk_output
        )

        assert use_intel_amx_backend(
            self.shared_experts.gate_up_proj
        ) == use_intel_amx_backend(self.shared_experts.down_proj)
        # [Note] inplace should be False in fused_experts.
        # If inplace is True in fused_experts (self.experts), hidden_states will be changed after fused_experts
        # While hidden_states is still needed in shared_expert.
        final_hidden_states = torch.ops.sgl_kernel.shared_expert_cpu(
            hidden_states,
            self.shared_experts.gate_up_proj.weight,
            self.shared_experts.down_proj.weight,
            fused_experts_out,
            self.routed_scaling_factor,
            True,  # inplace
            self.shared_experts_is_int8,  # use_int8_w8a8
            self.shared_experts_is_fp8,  # use_fp8_w8a16
            (
                self.shared_experts.gate_up_proj.weight_scale
                if self.shared_experts_is_int8
                else (
                    self.shared_experts.gate_up_proj.weight_scale_inv
                    if self.shared_experts_is_fp8
                    else None
                )
            ),  # w1_scale
            (
                self.shared_experts.down_proj.weight_scale
                if self.shared_experts_is_int8
                else (
                    self.shared_experts.down_proj.weight_scale_inv
                    if self.shared_experts_is_fp8
                    else None
                )
            ),  # w2_scale
            (
                self.shared_experts_weight_block_size
                if self.shared_experts_is_fp8
                else None
            ),  # block_size
            True,  # is_vnni
        )
        if self.tp_size > 1 and not get_forward().fuse_mlp_allreduce:
            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,
        input_ids_global: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        shared_output = None
        sbo_enabled_flag = self._fuse_shared_experts_inside_sbo and not self.is_nextn
        sbo_overlap_dispatch_flag = (
            sbo_enabled_flag and SboFlags.enable_dispatch_shared_one_stream_overlap()
        )
        sbo_overlap_combine_flag = (
            sbo_enabled_flag and SboFlags.enable_combine_shared_two_stream_overlap()
        )

        if hidden_states.shape[0] > 0:
            # router_logits: (num_tokens, n_experts)
            router_logits = self.gate(hidden_states, forward_batch=forward_batch)
            if not sbo_enabled_flag and self.num_fused_shared_experts == 0:
                if self.alt_stream is not None:
                    self.alt_stream.wait_stream(torch.cuda.current_stream())
                    with torch.cuda.stream(self.alt_stream):
                        shared_output = self._forward_shared_experts(hidden_states)
                        shared_output.record_stream(self.alt_stream)
                        shared_event = self.alt_stream.record_event()
                else:
                    shared_output = self._forward_shared_experts(hidden_states)
            topk_kwargs = (
                {"input_ids": input_ids_global}
                if getattr(self, "is_hash", False)
                else {}
            )
            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,
                    )
                    if not self.is_nextn
                    else None
                ),
                **topk_kwargs,
            )
        else:
            topk_output = self.topk.empty_topk_output(
                hidden_states.device, layer_id=self.layer_id
            )

        if sbo_overlap_dispatch_flag:
            shared_output = None

            def _deepep_dispatch_hook(dispatcher: BaseDispatcher):
                nonlocal shared_output
                shared_output = self._forward_shared_experts(hidden_states)
                for handle in deepep_dispatch_hook_handle:
                    handle.remove()

            def _post_dispatch_hook(
                dispatcher: BaseDispatcher, dispatch_output: DispatchOutput
            ):
                combine_overlap_args, down_gemm_overlap_args, meta_overlap_args = (
                    compute_overlap_args(dispatch_output, self.alt_stream)
                )
                dispatcher.set_overlap_args(
                    combine_overlap_args=combine_overlap_args,
                    meta_overlap_args=meta_overlap_args,
                )
                self.experts.set_overlap_args(
                    down_gemm_overlap_args=down_gemm_overlap_args,
                    meta_overlap_args=meta_overlap_args,
                )
                post_dispatch_hook_handle.remove()

            def _post_combine_hook(
                dispatcher: BaseDispatcher, hidden_states: torch.Tensor
            ):
                dispatcher.clear_overlap_args()
                self.experts.clear_overlap_args()
                post_combine_hook_handle.remove()

            assert isinstance(self.experts.dispatcher, MaybeTboDeepEPDispatcher)
            deepep_dispatch_hook_handle = (
                self.experts.dispatcher.register_deepep_dispatch_hook(
                    _deepep_dispatch_hook
                )
            )
            post_dispatch_hook_handle = (
                self.experts.dispatcher.register_post_dispatch_hook(_post_dispatch_hook)
            )
            post_combine_hook_handle = (
                self.experts.dispatcher.register_post_combine_hook(_post_combine_hook)
            )

        elif sbo_overlap_combine_flag:
            shared_output = None

            def _post_dispatch_hook(
                dispatcher: BaseDispatcher, dispatch_output: DispatchOutput
            ):

                combine_overlap_args, down_gemm_overlap_args, meta_overlap_args = (
                    compute_overlap_args(dispatch_output, self.alt_stream)
                )
                dispatcher.set_overlap_args(
                    combine_overlap_args=combine_overlap_args,
                    meta_overlap_args=meta_overlap_args,
                )
                self.experts.set_overlap_args(
                    down_gemm_overlap_args=down_gemm_overlap_args,
                    meta_overlap_args=meta_overlap_args,
                )

                post_dispatch_hook_handle.remove()

            def _pre_combine_hook(
                dispatcher: BaseDispatcher, combine_input: CombineInput
            ):

                nonlocal shared_output

                if (
                    e := dispatcher.meta_overlap_args.get("record_event_after_down")
                ) is not None:
                    e.record()

                # TODO reduce sm for non-deepgemm
                with deep_gemm_wrapper.configure_deep_gemm_num_sms(
                    dispatcher.meta_overlap_args["compute_num_sms"]
                ):
                    shared_output = self._forward_shared_experts(hidden_states)

                pre_combine_hook_handle.remove()

            def _post_combine_hook(
                dispatcher: BaseDispatcher, hidden_states: torch.Tensor
            ):
                dispatcher.clear_overlap_args()
                self.experts.clear_overlap_args()
                post_combine_hook_handle.remove()

            post_dispatch_hook_handle = (
                self.experts.dispatcher.register_post_dispatch_hook(_post_dispatch_hook)
            )
            pre_combine_hook_handle = self.experts.dispatcher.register_pre_combine_hook(
                _pre_combine_hook
            )
            post_combine_hook_handle = (
                self.experts.dispatcher.register_post_combine_hook(_post_combine_hook)
            )
        elif envs.SGLANG_BLACKWELL_OVERLAP_SHARED_EXPERTS_OUTSIDE_SBO.get():
            # On GB200: Shared experts overlapped on alt_stream, down gemm overlapped with DeepEP Combine

            def _post_dispatch_hook(
                dispatcher: BaseDispatcher, dispatch_output: DispatchOutput
            ):

                combine_overlap_args, down_gemm_overlap_args, meta_overlap_args = (
                    compute_overlap_args(dispatch_output, self.alt_stream)
                )
                dispatcher.set_overlap_args(
                    combine_overlap_args=combine_overlap_args,
                    meta_overlap_args=meta_overlap_args,
                )
                self.experts.set_overlap_args(
                    down_gemm_overlap_args=down_gemm_overlap_args,
                    meta_overlap_args=meta_overlap_args,
                )

                post_dispatch_hook_handle.remove()

            def _pre_combine_hook(
                dispatcher: BaseDispatcher, combine_input: CombineInput
            ):
                if (
                    e := dispatcher.meta_overlap_args.get("record_event_after_down")
                ) is not None:
                    e.record()
                pre_combine_hook_handle.remove()

            def _post_combine_hook(
                dispatcher: BaseDispatcher, hidden_states: torch.Tensor
            ):
                dispatcher.clear_overlap_args()
                self.experts.clear_overlap_args()
                post_combine_hook_handle.remove()

            post_dispatch_hook_handle = (
                self.experts.dispatcher.register_post_dispatch_hook(_post_dispatch_hook)
            )
            pre_combine_hook_handle = self.experts.dispatcher.register_pre_combine_hook(
                _pre_combine_hook
            )
            post_combine_hook_handle = (
                self.experts.dispatcher.register_post_combine_hook(_post_combine_hook)
            )

        final_hidden_states = self.experts(
            hidden_states=hidden_states,
            topk_output=topk_output,
        )

        if (
            hidden_states.shape[0] > 0
            and not sbo_enabled_flag
            and self.num_fused_shared_experts == 0
            and self.alt_stream is not None
        ):
            torch.cuda.current_stream().wait_event(shared_event)

        if shared_output is not None:
            x = shared_output
            # aiter moe call will handle routed_scaling_factor in the function
            # so add _use_aiter condition to eliminate to use self.routed_scaling_factor in add_ call
            if self.experts.should_fuse_routed_scaling_factor_in_topk or _use_aiter:
                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 or _use_aiter
            ):
                final_hidden_states *= self.routed_scaling_factor

        return final_hidden_states

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

    def op_gate(self, state):
        if state.hidden_states_mlp_input.shape[0] > 0:
            # 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

        # Hash MoE layers (e.g. DeepSeek-V4) route on input_ids; forward_deepep
        # passes them as a topk kwarg. The per-ubatch forward_batch.input_ids is
        # already sliced+padded to match hidden_states rows (and equals the
        # global ids under EP dp-attention). No-op for non-hash models.
        topk_kwargs = {}
        if getattr(self, "is_hash", False):
            topk_kwargs["input_ids"] = state.forward_batch.input_ids

        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,
                        )
                        if not self.is_nextn
                        else None
                    ),
                    **topk_kwargs,
                )
        else:
            state.topk_output = self.topk.empty_topk_output(
                hidden_states.device, layer_id=self.layer_id
            )

    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 DeepseekV2AttentionMLA(
    nn.Module,
    DeepseekMHAForwardMixin,
    DeepseekMLAForwardMixin,
    DeepseekMLARocmForwardMixin,
    DeepseekMLACpuForwardMixin,
):

    def __init__(
        self,
        config: PretrainedConfig,
        hidden_size: int,
        num_heads: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        v_head_dim: int,
        q_lora_rank: int,
        kv_lora_rank: int,
        rope_theta: float = 10000,
        rope_scaling: Optional[Dict[str, Any]] = None,
        max_position_embeddings: int = 8192,
        quant_config: Optional[QuantizationConfig] = None,
        reduce_results: bool = True,
        layer_id: int = None,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
        skip_rope: bool = False,
        is_nextn: bool = False,
        dsa_enable_prefill_cp: bool = False,
        mla_enable_prefill_cp: bool = False,
    ) -> None:
        super().__init__()
        self.layer_id = layer_id
        self.hidden_size = hidden_size
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_rope_head_dim = qk_rope_head_dim
        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.q_lora_rank = q_lora_rank
        self.kv_lora_rank = kv_lora_rank
        self.quant_config = quant_config
        self.is_nextn = is_nextn
        attn_tp_rank = get_parallel().attn_tp_rank
        attn_tp_size = get_parallel().attn_tp_size
        self.use_dsa = is_deepseek_dsa(config)
        self.dsa_enable_prefill_cp = dsa_enable_prefill_cp
        self.mla_enable_prefill_cp = mla_enable_prefill_cp
        if self.dsa_enable_prefill_cp:
            assert self.use_dsa, "CP currently only supports deepseek v3.2 model"
        # cp reuses the attn_tp comm group but needs to duplicate the weights;
        # store cp_size whenever either CP flavor is active so rebuild_cp_kv_cache
        # and the FA3 MLA wrapper can reach it on the dense MLA path too.
        if self.dsa_enable_prefill_cp or self.mla_enable_prefill_cp:
            self.cp_size = get_parallel().attn_cp_size
        self.num_heads = num_heads
        assert num_heads % attn_tp_size == 0
        self.num_local_heads = num_heads // attn_tp_size
        self.scaling = self.qk_head_dim**-0.5
        self.rope_theta = rope_theta
        self.max_position_embeddings = max_position_embeddings
        self.kv_cache_dtype = get_server_args().kv_cache_dtype

        # NOTE modification to rope_scaling must be done early enough, b/c e.g. Indexer needs it
        if rope_scaling:
            rope_scaling["rope_type"] = "deepseek_yarn"

        # For tensor parallel attention
        if self.q_lora_rank is not None:
            self.fused_qkv_a_proj_with_mqa = ReplicatedLinear(
                self.hidden_size,
                self.q_lora_rank + self.kv_lora_rank + self.qk_rope_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=add_prefix("fused_qkv_a_proj_with_mqa", prefix),
            )
            self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
            self.q_b_proj = ColumnParallelLinear(
                q_lora_rank,
                self.num_heads * self.qk_head_dim,
                bias=False,
                quant_config=self._get_q_b_proj_quant_config(quant_config),
                prefix=add_prefix("q_b_proj", prefix),
                tp_rank=attn_tp_rank,
                tp_size=attn_tp_size,
            )
        else:
            self.q_proj = ColumnParallelLinear(
                self.hidden_size,
                self.num_heads * self.qk_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=add_prefix("q_proj", prefix),
                tp_rank=attn_tp_rank,
                tp_size=attn_tp_size,
            )
            self.kv_a_proj_with_mqa = ReplicatedLinear(
                self.hidden_size,
                self.kv_lora_rank + self.qk_rope_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=add_prefix("kv_a_proj_with_mqa", prefix),
            )

        self.skip_topk = None
        self.next_skip_topk = None
        if self.use_dsa:
            is_neox_style = not getattr(config, "indexer_rope_interleave", False)
            self.indexer = Indexer(
                hidden_size=hidden_size,
                index_n_heads=get_dsa_index_n_heads(config),
                index_head_dim=get_dsa_index_head_dim(config),
                rope_head_dim=qk_rope_head_dim,
                index_topk=get_dsa_index_topk(config),
                q_lora_rank=q_lora_rank,
                max_position_embeddings=max_position_embeddings,
                rope_theta=rope_theta,
                scale_fmt="ue8m0",
                block_size=128,
                rope_scaling=rope_scaling,
                is_neox_style=is_neox_style,
                prefix=add_prefix("indexer", prefix),
                quant_config=quant_config,
                layer_id=layer_id,
                alt_stream=alt_stream,
                config=config,
            )
            # Refer: https://arxiv.org/abs/2603.12201 for more details.
            # skip_topk: when True, this layer will skip computation and reuse previous layer's topk indices.
            # next_skip_topk: when True, the next layer will skip computation and reuse this layer's topk indices.
            if is_nextn:
                self.skip_topk = True
                self.next_skip_topk = True
            else:
                index_cli_factor = getattr(config, "cli_factor", 1)
                if index_cli_factor > 1:
                    self.skip_topk = layer_id % index_cli_factor != 0
                    self.next_skip_topk = (layer_id + 1) % index_cli_factor != 0
                else:
                    self.skip_topk = dsa_layer_skips_topk(config, layer_id)
                    self.next_skip_topk = dsa_layer_skips_topk(config, layer_id + 1)

        self.kv_b_proj = ColumnParallelLinear(
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
            bias=False,
            quant_config=quant_config,
            prefix=add_prefix("kv_b_proj", prefix),
            tp_rank=attn_tp_rank,
            tp_size=attn_tp_size,
        )
        # O projection.
        self.o_proj = RowParallelLinear(
            self.num_heads * self.v_head_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=add_prefix("o_proj", prefix),
            tp_rank=attn_tp_rank,
            tp_size=attn_tp_size,
        )
        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)

        if not skip_rope:
            is_neox_style = not getattr(config, "rope_interleave", True)
            self.rotary_emb = get_rope_wrapper(
                qk_rope_head_dim,
                rotary_dim=qk_rope_head_dim,
                max_position=max_position_embeddings,
                base=rope_theta,
                rope_scaling=rope_scaling,
                is_neox_style=is_neox_style,
                device=get_server_args().device,
            )

            if rope_scaling and rope_scaling.get("apply_yarn_scaling", True):
                self.scaling = compute_mla_mscale_scaling(rope_scaling, self.scaling)
        else:
            self.rotary_emb = None
        self.use_deepseek_yarn_rope = rope_scaling is not None

        self.attn_mqa = RadixAttention(
            self.num_local_heads,
            self.kv_lora_rank + self.qk_rope_head_dim,
            self.scaling,
            num_kv_heads=1,
            layer_id=layer_id,
            v_head_dim=self.kv_lora_rank,
            quant_config=quant_config,
            prefix=add_prefix("attn_mqa", prefix),
        )
        # use num_local_heads * dcp_world_size because q_nope, q_rope is all gathered from dcp ranks
        if get_parallel().dcp_enabled:
            self.attn_mqa_for_dcp_decode = RadixAttention(
                self.num_local_heads * get_parallel().attn_dcp_size,
                self.kv_lora_rank + self.qk_rope_head_dim,
                self.scaling,
                num_kv_heads=1,
                layer_id=layer_id,
                v_head_dim=self.kv_lora_rank,
                quant_config=quant_config,
                prefix=add_prefix("attn_mqa", prefix),
            )

        self.attn_mha = RadixAttention(
            self.num_local_heads,
            self.qk_nope_head_dim + self.qk_rope_head_dim,
            self.scaling,
            num_kv_heads=self.num_local_heads,
            layer_id=layer_id,
            v_head_dim=self.v_head_dim,
            quant_config=quant_config,
            prefix=add_prefix("attn_mha", prefix),
        )

        self.alt_stream = alt_stream
        self.attn_mha.kv_b_proj = None

        self.w_kc = None
        self.w_vc = None
        self.w_scale = 1.0

        self.w_scale_k = None
        self.w_scale_v = None
        self.use_deep_gemm_bmm = False

        self.current_attention_backend = (
            None  # Attention backend used by current forward batch
        )

        self.has_fused_proj = hasattr(self, "fused_qkv_a_proj_with_mqa")
        self.is_packed_weight = (
            self.has_fused_proj
            and hasattr(self.fused_qkv_a_proj_with_mqa.quant_method, "quant_config")
            and self.fused_qkv_a_proj_with_mqa.quant_method.quant_config.get_name()
            in {"awq", "awq_marlin", "moe_wna16"}
        )
        self.use_min_latency_fused_a_gemm = (
            self.has_fused_proj
            and not self.is_packed_weight
            and self.fused_qkv_a_proj_with_mqa.weight.dtype == torch.bfloat16
            and self.fused_qkv_a_proj_with_mqa.weight.shape[0] % 16 == 0
            and self.fused_qkv_a_proj_with_mqa.weight.shape[1] % 256 == 0
            and _is_cuda
            and _device_sm >= 90
        )
        self.fused_a_gemm_backend = "auto"

        self.init_mha_forward()
        self.init_mla_forward()
        self.init_mla_fused_rope_rocm_forward()
        self.init_mla_fused_rope_cpu_forward()

    def dispatch_attn_forward_method(
        self, forward_batch: ForwardBatch
    ) -> AttnForwardMethod:
        # Determine attention backend name for current forward batch: prefer the
        # name stamped per-runner on the backend object, else resolve from server args.
        backend = get_attn_backend()
        server_args = get_server_args()
        default_prefill_str, default_decode_str = server_args.get_attention_backends()
        prefill_backend_str = (
            backend.prefill_attention_backend_str or default_prefill_str
        )
        decode_backend_str = backend.decode_attention_backend_str or default_decode_str
        if forward_batch.forward_mode.is_decode_or_idle():
            attention_backend = decode_backend_str
        elif (
            forward_batch.forward_mode.is_target_verify()
            or forward_batch.forward_mode.is_draft_extend_v2()
        ):
            # Use the specified backend for speculative operations (both verify and draft extend)
            if server_args.speculative_attention_mode == "decode":
                attention_backend = decode_backend_str
            else:  # default to prefill
                attention_backend = prefill_backend_str
        else:
            attention_backend = prefill_backend_str
        self.current_attention_backend = attention_backend

        handler = AttentionBackendRegistry.get_handler(attention_backend)
        return handler(self, forward_batch)

    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,
            zero_allocator=state.zero_allocator,
        )

    def op_core(self, state):
        result = self.forward_core(state.pop("attn_intermediate_state"))
        # forward_core may return (hidden_states, topk_indices) for DSA models
        # with index cache enabled. In the TBO path, topk_indices is not
        # propagated between layers, so we discard it here.
        if isinstance(result, tuple):
            state.hidden_states_after_attn = result[0]
        else:
            state.hidden_states_after_attn = result

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        zero_allocator: BumpAllocator,
        layer_scatter_modes: LayerScatterModes = None,
        llama_4_scaling: Optional[torch.Tensor] = None,
        prev_topk_indices: Optional[torch.Tensor] = None,
    ):
        s = self.forward_prepare(
            positions=positions,
            hidden_states=hidden_states,
            forward_batch=forward_batch,
            zero_allocator=zero_allocator,
            layer_scatter_modes=layer_scatter_modes,
            llama_4_scaling=llama_4_scaling,
            prev_topk_indices=prev_topk_indices,
        )
        return self.forward_core(s)

    def forward_prepare(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        zero_allocator: BumpAllocator,
        layer_scatter_modes: LayerScatterModes = None,
        llama_4_scaling: Optional[torch.Tensor] = None,
        prev_topk_indices: Optional[torch.Tensor] = None,
    ):
        if self.attn_mha.kv_b_proj is None:
            self.attn_mha.kv_b_proj = self.kv_b_proj

        # when hidden_states is a tuple of tensors, the tuple will include quantized weight and scale tensor
        if isinstance(hidden_states, tuple):
            if (
                not get_attn_tp_context().input_scattered
                and hidden_states[0].shape[0] == 0
            ):
                assert (
                    not self.o_proj.reduce_results
                ), "short-circuiting allreduce will lead to hangs"
                return hidden_states[0]
        else:
            if (
                not get_attn_tp_context().input_scattered
                and hidden_states.shape[0] == 0
            ):
                assert (
                    not self.o_proj.reduce_results
                ), "short-circuiting allreduce will lead to hangs"
                return hidden_states, None, forward_batch, None

        attn_forward_method = self.dispatch_attn_forward_method(forward_batch)
        if attn_forward_method == AttnForwardMethod.MHA:
            inner_state = self.forward_normal_prepare(
                positions, hidden_states, forward_batch, zero_allocator
            )
        elif attn_forward_method == AttnForwardMethod.MHA_CHUNKED_KV:
            inner_state = self.forward_normal_chunked_kv_prepare(
                positions, hidden_states, forward_batch, zero_allocator
            )
        elif attn_forward_method == AttnForwardMethod.MHA_ONE_SHOT:
            inner_state = self.forward_normal_one_shot_prepare(
                positions, hidden_states, forward_batch, zero_allocator
            )
        elif attn_forward_method == AttnForwardMethod.MLA:
            inner_state = self.forward_absorb_prepare(
                positions,
                hidden_states,
                forward_batch,
                zero_allocator,
                llama_4_scaling,
                prev_topk_indices,
            )
        elif attn_forward_method == AttnForwardMethod.MLA_FUSED_ROPE_ROCM:
            inner_state = self.forward_absorb_fused_mla_rope_prepare(
                positions, hidden_states, forward_batch, zero_allocator
            )
        elif attn_forward_method == AttnForwardMethod.MLA_FUSED_ROPE_CPU:
            inner_state = self.forward_absorb_fused_mla_rope_cpu_prepare(
                positions, hidden_states, forward_batch, zero_allocator
            )
        elif attn_forward_method == AttnForwardMethod.MHA_NPU:
            inner_state = forward_mha_prepare_npu(
                self,
                positions,
                hidden_states,
                forward_batch,
                zero_allocator,
                layer_scatter_modes,
            )
        elif attn_forward_method == AttnForwardMethod.MLA_NPU:
            inner_state = forward_mla_prepare_npu(
                self,
                positions,
                hidden_states,
                forward_batch,
                zero_allocator,
                layer_scatter_modes,
            )
        elif attn_forward_method == AttnForwardMethod.DSA_NPU:
            inner_state = forward_dsa_prepare_npu(
                self,
                positions,
                hidden_states,
                forward_batch,
                zero_allocator,
                layer_scatter_modes,
                prev_topk_indices,
            )
        else:
            raise NotImplementedError
        return None, attn_forward_method, forward_batch, inner_state

    def forward_core(self, intermediate_state):
        hidden_states, attn_forward_method, forward_batch, inner_state = (
            intermediate_state
        )
        if inner_state is None:
            return hidden_states

        if attn_forward_method == AttnForwardMethod.MHA:
            return self.forward_normal_core(*inner_state)
        elif attn_forward_method == AttnForwardMethod.MHA_CHUNKED_KV:
            return self.forward_normal_chunked_kv_core(*inner_state)
        elif attn_forward_method == AttnForwardMethod.MHA_ONE_SHOT:
            return self.forward_normal_one_shot_core(*inner_state)
        elif attn_forward_method == AttnForwardMethod.MLA:
            return self.forward_absorb_core(*inner_state)
        elif attn_forward_method == AttnForwardMethod.MLA_FUSED_ROPE_ROCM:
            return self.forward_absorb_fused_mla_rope_core(*inner_state)
        elif attn_forward_method == AttnForwardMethod.MLA_FUSED_ROPE_CPU:
            return self.forward_absorb_fused_mla_rope_cpu_core(*inner_state)
        elif attn_forward_method == AttnForwardMethod.MHA_NPU:
            return forward_mha_core_npu(self, *inner_state)
        elif attn_forward_method == AttnForwardMethod.MLA_NPU:
            return forward_mla_core_npu(self, *inner_state)
        elif attn_forward_method == AttnForwardMethod.DSA_NPU:
            return forward_dsa_core_npu(self, *inner_state)
        else:
            raise NotImplementedError

    def prepare_qkv_latent(
        self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
    ):
        assert self.q_lora_rank is not None
        # When the module is wrapped with LoRA, the fused GEMM fast-path would
        # bypass the adapter because it reads weight.T directly.
        lora_active = getattr(self.fused_qkv_a_proj_with_mqa, "set_lora", False)
        cutedsl_backend = get_bf16_gemm_backend().is_cutedsl()
        if cutedsl_backend:
            from sglang.jit_kernel.cutedsl_bf16_gemm import use_cutedsl_bf16_gemm
        if (
            (not isinstance(hidden_states, tuple))
            and hidden_states.shape[0] >= 1
            and hidden_states.shape[0] <= 16
            and self.use_min_latency_fused_a_gemm
            and not lora_active
            and not (
                cutedsl_backend
                and use_cutedsl_bf16_gemm(
                    hidden_states.shape[0],
                    self.fused_qkv_a_proj_with_mqa.weight.shape[0],
                    self.fused_qkv_a_proj_with_mqa.weight.shape[1],
                )
            )
        ):
            qkv_latent = dsv3_fused_a_gemm(
                hidden_states,
                self.fused_qkv_a_proj_with_mqa.weight.T,
                backend=self.fused_a_gemm_backend,
            )
        else:
            qkv_latent = self.fused_qkv_a_proj_with_mqa(hidden_states)[0]
        return qkv_latent

    def rebuild_cp_kv_cache(self, latent_cache, forward_batch, k_nope, k_pe):
        # support allgather+rerrange
        latent_cache[..., : self.kv_lora_rank] = k_nope.squeeze(1)
        latent_cache[..., self.kv_lora_rank :] = k_pe.squeeze(1)
        latent_cache_output = cp_all_gather_rerange_output(
            latent_cache.contiguous(),
            self.cp_size,
            forward_batch,
            torch.cuda.current_stream(),
        )
        k_nope = latent_cache_output[..., : self.kv_lora_rank].unsqueeze(1)
        k_pe = latent_cache_output[..., self.kv_lora_rank :].unsqueeze(1)
        return k_nope, k_pe

    @staticmethod
    def _get_q_b_proj_quant_config(quant_config):
        if envs.SGLANG_NVFP4_CKPT_FP8_GEMM_IN_ATTN.get():
            # refer to real DeepSeek V3 quant config
            return Fp8Config(
                is_checkpoint_fp8_serialized=True,
                weight_block_size=[128, 128],
            )
        else:
            return quant_config


class DeepseekV2DecoderLayer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        moe_quant_config_override: Optional[QuantizationConfig] = None,
        is_nextn: bool = False,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
        dsa_enable_prefill_cp: bool = False,
        mla_enable_prefill_cp: bool = False,
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        self.config = config
        if hasattr(config, "rope_parameters"):
            rope_theta = config.rope_parameters["rope_theta"]
            assert rope_theta is not None, f"rope_theta not found in config: {config}"
            rope_type = config.rope_parameters.get("rope_type")
            rope_scaling = config.rope_parameters if rope_type != "default" else None
        else:
            rope_theta = config.rope_theta
            rope_scaling = config.rope_scaling
        max_position_embeddings = config.max_position_embeddings
        self.speculative_algorithm = SpeculativeAlgorithm.from_string(
            get_server_args().speculative_algorithm
        )
        self.dsa_enable_prefill_cp = dsa_enable_prefill_cp
        self.mla_enable_prefill_cp = mla_enable_prefill_cp
        self.layer_id = layer_id
        self.is_nextn = is_nextn
        self.self_attn = DeepseekV2AttentionMLA(
            config=config,
            hidden_size=self.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 if hasattr(config, "q_lora_rank") else None
            ),
            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,
            layer_id=layer_id,
            reduce_results=False,
            prefix=add_prefix("self_attn", prefix),
            alt_stream=alt_stream,
            is_nextn=is_nextn,
            dsa_enable_prefill_cp=dsa_enable_prefill_cp,
            mla_enable_prefill_cp=mla_enable_prefill_cp,
        )
        if not hasattr(config, "q_lora_rank") and envs.SGLANG_USE_AG_AFTER_QLORA.get():
            raise ValueError(
                "SGLANG_USE_AG_AFTER_QLORA only supports the model with q_lora_rank"
            )

        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 = DeepseekV2MoE(
                config=config,
                quant_config=moe_quant_config_override or quant_config,
                prefix=add_prefix("mlp", prefix),
                layer_id=self.layer_id,
                alt_stream=alt_stream,
                is_nextn=is_nextn,
                dsa_enable_prefill_cp=dsa_enable_prefill_cp,
                mla_enable_prefill_cp=mla_enable_prefill_cp,
            )
        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 = DeepseekV2MLP(
                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,
                swiglu_limit=getattr(config, "swiglu_limit", None),
            )

        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()

        if self.dsa_enable_prefill_cp or self.mla_enable_prefill_cp:
            # DSACPLayerCommunicator is flavor-agnostic; its internal gates
            # read both dsa_use_prefill_cp and mla_use_prefill_cp. The rename
            # to CPLayerCommunicator is deferred to a cleanup PR.
            self.layer_communicator = DSACPLayerCommunicator(
                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,
            )
        else:
            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:
        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,
        gemm_output_zero_allocator: BumpAllocator = None,
        llama_4_scaling: Optional[torch.Tensor] = None,
        prev_topk_indices: Optional[torch.Tensor] = None,
        captured_last_layer_outputs: Optional[List[torch.Tensor]] = None,
        next_full_attention_layer_id: Optional[int] = None,
    ) -> torch.Tensor:
        hidden_states_orig = hidden_states
        hidden_states, residual = (
            self.layer_communicator.prepare_attn_and_capture_last_layer_outputs(
                hidden_states,
                residual,
                forward_batch,
                captured_last_layer_outputs=captured_last_layer_outputs,
                quant_format=getattr(self, "_gfx95_quant_format", ""),
            )
        )

        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            forward_batch=forward_batch,
            zero_allocator=zero_allocator,
            llama_4_scaling=llama_4_scaling,
            layer_scatter_modes=self.layer_scatter_modes,
            prev_topk_indices=prev_topk_indices,
        )
        if isinstance(hidden_states, tuple):
            hidden_states, topk_indices = hidden_states
        else:
            topk_indices = None
        get_attn_tp_context().clear_attn_inputs()

        maybe_prefetch_next_full_attention_kv(
            forward_batch, next_full_attention_layer_id
        )

        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
        )

        if isinstance(self.mlp, DeepseekV2MLP):
            gemm_output_zero_allocator = None

        if (
            isinstance(self.mlp, DeepseekV2MoE)
            and not self.mlp.experts.moe_runner_config.inplace
            and not torch.compiler.is_compiling()
        ):
            from sglang.srt.layers.moe.moe_runner.base import moe_output_buffer_ctx

            _mlp_ctx = moe_output_buffer_ctx(hidden_states_orig)
        else:
            _mlp_ctx = nullcontext()

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

        if (
            not (self.dsa_enable_prefill_cp or self.mla_enable_prefill_cp)
            and fuse_mlp_allreduce
        ):
            hidden_states._sglang_needs_allreduce_fusion = True

        if not fuse_mlp_allreduce:
            hidden_states, residual = self.layer_communicator.postprocess_layer(
                hidden_states, residual, forward_batch
            )

        return hidden_states, residual, topk_indices

    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 DeepseekV2Model(nn.Module):
    fall_back_to_pt_during_load = False

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.use_dsa = is_deepseek_dsa(config)
        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()
        self.dsa_enable_prefill_cp = is_dsa_enable_prefill_cp()
        self.mla_enable_prefill_cp = (
            is_prefill_context_parallel_enabled() and not self.use_dsa
        )
        if self.dsa_enable_prefill_cp or self.mla_enable_prefill_cp:
            self.cp_size = get_parallel().attn_cp_size
        else:
            self.cp_size = None

        if self.pp_group.is_first_rank:
            self.embed_tokens = VocabParallelEmbedding(
                config.vocab_size,
                config.hidden_size,
                **get_embedding_tp_kwargs(),
            )
        else:
            self.embed_tokens = PPMissingLayer()

        self.alt_stream = (
            torch.cuda.Stream()
            if (
                _is_cuda
                or _is_musa
                or envs.SGLANG_NPU_USE_MULTI_STREAM.get()
                or envs.SGLANG_ROCM_USE_MULTI_STREAM.get()
            )
            else None
        )

        self.layers, self.start_layer, self.end_layer = make_layers(
            config.num_hidden_layers,
            lambda idx, prefix: DeepseekV2DecoderLayer(
                config=config,
                layer_id=idx,
                quant_config=quant_config,
                prefix=prefix,
                alt_stream=self.alt_stream,
                dsa_enable_prefill_cp=self.dsa_enable_prefill_cp,
                mla_enable_prefill_cp=self.mla_enable_prefill_cp,
            ),
            pp_rank=self.pp_group.rank_in_group,
            pp_size=self.pp_group.world_size,
            prefix=add_prefix("layers", prefix),
            offloader_kwargs=dict(
                submodule_accessor=lambda layer: (
                    layer.mlp.experts
                    if isinstance(layer.mlp, DeepseekV2MoE)
                    else layer.mlp
                ),
                whitelist_param_names_creator=lambda module: (
                    [
                        "w13_weight",
                        "w2_weight",
                        # only for nvfp4
                        *(
                            [
                                "w13_blockscale_swizzled",
                                "w2_blockscale_swizzled",
                            ]
                            if hasattr(module, "w13_blockscale_swizzled")
                            else []
                        ),
                    ]
                    if isinstance(module, FusedMoE)
                    else []
                ),
            ),
        )

        local_layer_ids = list(range(self.start_layer, self.end_layer))
        self.next_full_attention_layer_id = dict(
            zip(local_layer_ids, local_layer_ids[1:])
        )
        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.gemm_output_zero_allocator_size = 0
        if (
            _use_aiter_gfx95
            and config.n_routed_experts == 256
            and self.embed_tokens.embedding_dim == 7168
        ):
            num_moe_layers = sum(
                [
                    1
                    for i in range(len(self.layers))
                    if isinstance(self.layers[i].mlp, DeepseekV2MoE)
                ]
            )

            allocate_size = 0
            for i in range(len(self.layers)):
                if isinstance(self.layers[i].mlp, DeepseekV2MoE):
                    # tp_size = get_parallel().tp_size
                    is_a2a_moe = is_deepep_class_backend()
                    tp_size = 1 if is_a2a_moe else get_parallel().tp_size
                    intermediate_size = (
                        config.moe_intermediate_size * config.n_shared_experts
                    )
                    share_expert_output_size_per_partition = divide(
                        intermediate_size * 2, tp_size
                    )
                    allocate_size = share_expert_output_size_per_partition
                    break

            self.gemm_output_zero_allocator_size = (
                get_dsv3_gemm_output_zero_allocator_size(
                    config.n_routed_experts,
                    num_moe_layers,
                    allocate_size,
                    self.embed_tokens.embedding_dim,
                )
            )
        self.layers_to_capture = []
        if get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mooncake():
            self.enable_a2a_moe = True
        else:
            self.enable_a2a_moe = False

        # llama_4_scaling: for supporting Mistral-Large-3 model
        self.llama_4_scaling_config = getattr(config, "llama_4_scaling", None)

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

    def _dsa_forward_uses_topk(self) -> bool:
        if not self.use_dsa:
            return False
        backend = get_attn_backend()
        backend = getattr(backend, "primary", backend)
        return not getattr(backend, "use_mha", False)

    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
        dsa_forward_uses_topk = self._dsa_forward_uses_topk()
        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"]
            topk_indices = pp_proxy_tensors.tensors.get("topk_indices")
            assert not (
                not forward_batch.forward_mode.is_idle()
                and hidden_states.shape[0] != 0
                and self.use_dsa
                and dsa_forward_uses_topk
                and dsa_layer_skips_topk(self.config, self.start_layer)
                and topk_indices is None
            ), (
                f"PP stage starting at layer {self.start_layer} requires DSA "
                "topk_indices from the previous stage."
            )
        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,
        )

        has_gemm_output_zero_allocator = hasattr(
            self, "gemm_output_zero_allocator_size"
        )

        gemm_output_zero_allocator = (
            BumpAllocator(
                buffer_size=self.gemm_output_zero_allocator_size,
                dtype=torch.float32,
                device=device,
            )
            if has_gemm_output_zero_allocator
            and self.gemm_output_zero_allocator_size > 0
            else None
        )

        if dsa_use_prefill_cp(
            forward_batch, self.dsa_enable_prefill_cp
        ) or mla_use_prefill_cp(forward_batch, self.mla_enable_prefill_cp):
            if self.pp_group.is_first_rank:
                hidden_states = cp_split_and_rebuild_data(forward_batch, hidden_states)
            positions = cp_split_and_rebuild_position(forward_batch, positions)

        # llama_4_scaling: for supporting Mistral-Large-3 model
        # Compute llama 4 scaling once per forward pass if enabled
        llama_4_scaling: Optional[torch.Tensor] = None
        if self.llama_4_scaling_config is not None:
            llama_4_scaling = _get_llama_4_scaling(
                original_max_position_embeddings=self.llama_4_scaling_config[
                    "original_max_position_embeddings"
                ],
                scaling_beta=self.llama_4_scaling_config["beta"],
                positions=positions,
            )

        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 = []
        if self.pp_group.is_first_rank:
            topk_indices = None
        for i in range(normal_start_layer, normal_end_layer):
            # NOTE: torch dynamo does not support graph break in context manager
            ctx = (
                nullcontext()
                if check_cuda_graph_backend(Phase.PREFILL, Backend.TC_PIECEWISE)
                else get_global_expert_distribution_recorder().with_current_layer(i)
            )
            with ctx:
                layer = self.layers[i]
                hidden_states, residual, topk_indices = layer(
                    positions,
                    hidden_states,
                    forward_batch,
                    residual,
                    zero_allocator,
                    gemm_output_zero_allocator,
                    llama_4_scaling,
                    prev_topk_indices=topk_indices,
                    captured_last_layer_outputs=(
                        aux_hidden_states if i in self.layers_to_capture else None
                    ),
                    next_full_attention_layer_id=self.next_full_attention_layer_id.get(
                        i
                    ),
                )

        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:
            proxy_tensors = {
                "hidden_states": hidden_states,
                "residual": residual,
            }
            if (
                self.use_dsa
                and dsa_forward_uses_topk
                and self.end_layer < self.config.num_hidden_layers
                and dsa_layer_skips_topk(self.config, self.end_layer)
            ):
                if (
                    not forward_batch.forward_mode.is_idle()
                    and hidden_states.shape[0] != 0
                ):
                    assert topk_indices is not None, (
                        f"PP stage ending at layer {self.end_layer} must forward "
                        "DSA topk_indices because the next stage starts on a "
                        "skip-topk layer."
                    )
                if topk_indices is None:
                    topk_indices = hidden_states.new_empty(
                        (0, get_dsa_index_topk(self.config)), dtype=torch.int32
                    )
                proxy_tensors["topk_indices"] = topk_indices
            return PPProxyTensors(proxy_tensors)
        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 self.pp_group.is_last_rank and (
            dsa_use_prefill_cp(forward_batch, self.dsa_enable_prefill_cp)
            or mla_use_prefill_cp(forward_batch, self.mla_enable_prefill_cp)
        ):
            # allgather + rerrange
            hidden_states = cp_all_gather_rerange_output(
                hidden_states,
                self.cp_size,
                forward_batch,
                torch.cuda.current_stream(),
            )
        if len(aux_hidden_states) == 0:
            return hidden_states
        return hidden_states, aux_hidden_states


class DeepseekV2ForCausalLM(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__()

        # for quark model load
        # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
        self.fuse_qkv_a_proj = (
            hasattr(config, "q_lora_rank") and config.q_lora_rank is not None
        )
        if self.fuse_qkv_a_proj:
            self.packed_modules_mapping["fused_qkv_a_proj_with_mqa"] = [
                "q_a_proj",
                "kv_a_proj_with_mqa",
            ]

        # Quant configs like Quark may rely on the model to provide fused-module
        # mappings so exclusion checks can unfuse derived names back to the
        # checkpoint's source layer names.
        if quant_config is not None:
            quant_config.update_packed_modules_mapping(self.packed_modules_mapping)

        self.pp_group = get_pp_group()
        self.config = config
        self.tp_size = get_parallel().tp_size
        self.quant_config = quant_config
        self.determine_num_fused_shared_experts()
        self.use_dsa = is_deepseek_dsa(config)
        self.model = DeepseekV2Model(
            config, quant_config, prefix=add_prefix("model", prefix)
        )

        if self.pp_group.is_last_rank:
            if self.pp_group.world_size == 1 and config.tie_word_embeddings:
                self.lm_head = self.model.embed_tokens
            else:
                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,
                )
        else:
            # ranks other than the last rank will have a placeholder layer
            self.lm_head = PPMissingLayer()
        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, DeepseekV2MoE)
            }
        )
        self.capture_aux_hidden_states = False

        self.dsa_enable_prefill_cp = is_dsa_enable_prefill_cp()
        self.mla_enable_prefill_cp = (
            is_prefill_context_parallel_enabled() and not is_deepseek_dsa(config)
        )
        if self.dsa_enable_prefill_cp or self.mla_enable_prefill_cp:
            self.cp_rank = get_parallel().attn_cp_rank
            self.cp_size = get_parallel().attn_cp_size
        else:
            self.cp_rank = self.cp_size = None

        q_lora_rank = config.q_lora_rank if hasattr(config, "q_lora_rank") else None
        get_attn_tp_context().init_context(q_lora_rank, is_deepseek_dsa(config))

    @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 = "DeepseekV3ForCausalLM"
    ):
        self.num_fused_shared_experts = 0
        server_args = get_server_args()

        if get_server_args().disable_shared_experts_fusion:
            return

        disable_reason = None
        if server_args.enforce_shared_experts_fusion:
            pass
        elif is_sbo_enabled() or is_tbo_enabled():
            disable_reason = "SBO/TBO enabled: incompatible with fusing shared expert into MoE kernel."
        elif is_deepep_class_backend():
            disable_reason = "DeepEP: fusion off by default (use --enforce-shared-experts-fusion to enable)."
        elif (
            self.config.architectures[0] != architecture
            # Allow-list of n_routed_experts values that have been validated
            # for shared-experts fusion under this code path. Currently:
            #   256 -> DeepSeek-V3 / R1
            #   384 -> Kimi-K2.5, only when the checkpoint is Quark MXFP4
            #          (amd/Kimi-K2.5-MXFP4); the standard
            #          moonshotai/Kimi-K2.5 (compressed-tensors) checkpoint
            #          stores the shared expert loose and is NOT pre-fused,
            #          so the fused path silently mis-loads it.
            or self.config.n_routed_experts not in (256, 384)
            or self.config.n_shared_experts != 1
            or (
                self.config.n_routed_experts == 384
                and (
                    self.quant_config is None or self.quant_config.get_name() != "quark"
                )
            )
        ):
            disable_reason = "Config does not support fused shared expert(s)."
        elif (
            (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))
            and (not _is_musa or torch.musa.get_device_capability("musa") < (3, 1))
        ):
            disable_reason = (
                "Only Deepseek V3/R1 on NV-platform with capability >= 80 "
                "or AMD-platform with capability >= gfx942(MI30x) can use shared experts fusion optimization."
                "or MT-platform with capability >= 31 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 Deepseek V3/R1 on AMD-platform with capability >= gfx942(MI30x) "
                "can use shared experts fusion optimization under expert parallelism."
            )
        elif self.quant_config and self.quant_config.get_name() == "w4afp8":
            disable_reason = "Deepseek V3/R1 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(
                "DeepseekV2ForCausalLM.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:
        # Minor fix for multi-modal model: input_ids is None
        len_input_ids = (
            input_ids.shape[0] if input_ids is not None else input_embeds.shape[0]
        )
        if self.dsa_enable_prefill_cp:
            if can_dsa_cp_split(
                len_input_ids, self.cp_size, self.use_dsa, forward_batch
            ):
                forward_batch.attn_cp_metadata = prepare_context_parallel_metadata(
                    len_input_ids,
                    self.cp_rank,
                    self.cp_size,
                    forward_batch.seq_lens_cpu.tolist(),
                    extend_seqs_len=forward_batch.extend_seq_lens_cpu,
                )
        elif self.mla_enable_prefill_cp:
            if can_cp_split(len_input_ids, self.cp_size, forward_batch):
                forward_batch.attn_cp_metadata = prepare_context_parallel_metadata(
                    len_input_ids,
                    self.cp_rank,
                    self.cp_size,
                    forward_batch.seq_lens_cpu.tolist(),
                    extend_seqs_len=forward_batch.extend_seq_lens_cpu,
                )

        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 load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=False):
        self.do_load_weights(weights, is_nextn)

    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 prepare_context_parallel_metadata_for_dcp(
        self,
        seq_lens: torch.Tensor,
        extend_prefix_lens: torch.Tensor,
        extend_prefix_lens_cpu: torch.Tensor,
        extend_seq_lens: torch.Tensor,
        req_pool_indices: torch.Tensor,
        req_to_token: torch.Tensor,
        seq_lens_sum: int,
        kv_buffer_shape: torch.Size,
        kv_cache_dtype,
        kv_cache_device,
        create_chunked_prefix_cache_kv_indices_fn,
    ):
        return prepare_decode_context_parallel_metadata(
            seq_lens=seq_lens,
            extend_prefix_lens=extend_prefix_lens,
            extend_prefix_lens_cpu=extend_prefix_lens_cpu,
            extend_seq_lens=extend_seq_lens,
            req_pool_indices=req_pool_indices,
            req_to_token=req_to_token,
            seq_lens_sum=seq_lens_sum,
            kv_buffer_shape=kv_buffer_shape,
            kv_cache_dtype=kv_cache_dtype,
            kv_cache_device=kv_cache_device,
            create_chunked_prefix_cache_kv_indices_fn=create_chunked_prefix_cache_kv_indices_fn,
        )


class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
    pass


class DeepseekV32ForCausalLM(DeepseekV2ForCausalLM):
    pass


@register_custom_op(out_shape="hidden_states")
def dsv2_flashinfer_moe_dual_stream_graph(
    hidden_states: torch.Tensor,
    layer_id: int,
) -> torch.Tensor:
    forward_context = get_tc_piecewise_forward_context()
    assert forward_context is not None
    assert forward_context.moe_fusions is not None

    moe_fusion = forward_context.moe_fusions[layer_id]
    assert moe_fusion is not None
    with get_forward().scoped(flashinfer_trtllm_bypass=True):
        return moe_fusion.forward_normal_dual_stream(hidden_states)


EntryClass = [DeepseekV2ForCausalLM, DeepseekV3ForCausalLM, DeepseekV32ForCausalLM]