import enum
import logging
from typing import Any, Iterable, Optional, Set, Tuple

import torch
import triton
from torch import nn

from sglang.srt.configs.qwen3_next import Qwen3NextConfig
from sglang.srt.distributed import get_pp_group
from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
from sglang.srt.layers.attention.fla.layernorm_gated import RMSNorm as RMSNormGated
from sglang.srt.layers.attention.mamba.mamba import mamba_v2_sharded_weight_loader
from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
from sglang.srt.layers.dp_attention import (
    get_attention_tp_rank,
    get_attention_tp_size,
    is_dp_attention_enabled,
)
from sglang.srt.layers.layernorm import GemmaRMSNorm
from sglang.srt.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.radix_linear_attention import RadixLinearAttention
from sglang.srt.layers.rotary_embedding import get_rope
from sglang.srt.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.weight_utils import (
    default_weight_loader,
    sharded_weight_loader,
)
from sglang.srt.models.qwen2_moe import Qwen2MoeMLP, Qwen2MoeSparseMoeBlock
from sglang.srt.server_args import get_global_server_args
from sglang.srt.utils import (
    LazyValue,
    add_prefix,
    cpu_has_amx_support,
    is_cpu,
    is_cuda,
    is_npu,
    make_layers,
    set_weight_attrs,
)

logger = logging.getLogger(__name__)

from sglang.jit_kernel.triton.gdn_fused_proj import fused_qkvzba_split_reshape_cat
from sglang.srt.layers.attention.fla.fused_norm_gate import FusedRMSNormGated

_is_cuda = is_cuda()
_is_npu = is_npu()
_is_cpu = is_cpu()
_is_amx_available = cpu_has_amx_support()


class Qwen3GatedDeltaNet(nn.Module):
    def __init__(
        self,
        config: Qwen3NextConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        alt_stream: Optional[torch.cuda.Stream] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.attn_tp_rank = get_attention_tp_rank()
        self.attn_tp_size = get_attention_tp_size()
        self.hidden_size = config.hidden_size
        self.num_v_heads = (
            config.linear_num_value_heads
            if not _is_cpu
            else config.linear_num_value_heads_cpu
        )
        self.num_k_heads = (
            config.linear_num_key_heads
            if not _is_cpu
            else config.linear_num_key_heads_cpu
        )
        self.head_k_dim = config.linear_key_head_dim
        self.head_v_dim = config.linear_value_head_dim
        self.key_dim = self.head_k_dim * self.num_k_heads
        self.value_dim = self.head_v_dim * self.num_v_heads
        self.alt_stream = alt_stream

        self.conv_kernel_size = config.linear_conv_kernel_dim
        self.layer_id = layer_id
        self.activation = config.hidden_act
        self.layer_norm_epsilon = config.rms_norm_eps

        self.conv_dim = self.key_dim * 2 + self.value_dim
        self.conv1d = ColumnParallelLinear(
            input_size=self.conv_kernel_size,
            output_size=self.conv_dim,
            bias=False,
            quant_config=None,
            tp_rank=self.attn_tp_rank,
            tp_size=self.attn_tp_size,
            prefix=add_prefix("conv1d", prefix),
        )
        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)

        # projection of the input hidden states
        self.in_proj_qkvz = self.create_qkvz_proj(
            hidden_size=self.hidden_size,
            key_dim=self.key_dim,
            value_dim=self.value_dim,
            quant_config=quant_config,
            prefix=add_prefix("in_proj_qkvz", prefix),
            tp_rank=self.attn_tp_rank,
            tp_size=self.attn_tp_size,
        )

        self.in_proj_ba = MergedColumnParallelLinear(
            input_size=self.hidden_size,
            output_sizes=[self.num_v_heads] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=add_prefix("in_proj_ba", prefix),
            tp_rank=self.attn_tp_rank,
            tp_size=self.attn_tp_size,
        )

        # Override weight_loader for packed checkpoint format.
        # Must capture original_loader BEFORE overwriting.
        self.in_proj_qkvz.weight.weight_loader = self._make_packed_weight_loader(
            self.in_proj_qkvz
        )
        self.in_proj_ba.weight.weight_loader = self._make_packed_weight_loader(
            self.in_proj_ba
        )

        # Conv1d weight loader setup
        query_key_settings = (self.key_dim, 0, False)
        value_settings = (self.value_dim, 0, False)

        delattr(self.conv1d.weight, "weight_loader")
        set_weight_attrs(
            self.conv1d.weight,
            {
                "weight_loader": mamba_v2_sharded_weight_loader(
                    [
                        query_key_settings,
                        query_key_settings,
                        value_settings,
                    ],
                    self.attn_tp_size,
                    self.attn_tp_rank,
                )
            },
        )

        self.dt_bias = nn.Parameter(torch.zeros(self.num_v_heads // self.attn_tp_size))

        self.A_log = nn.Parameter(
            torch.zeros(self.num_v_heads // self.attn_tp_size, dtype=torch.float32)
        )

        set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(0)})
        set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
        self.norm = (
            RMSNormGated(
                self.head_v_dim,
                eps=self.layer_norm_epsilon,
                group_size=None,
                norm_before_gate=True,
                device=torch.get_device_module().current_device(),
                dtype=config.torch_dtype,
            )
            if not get_global_server_args().disable_piecewise_cuda_graph
            else FusedRMSNormGated(
                self.head_v_dim,
                eps=self.layer_norm_epsilon,
                activation=self.activation,
                device=torch.get_device_module().current_device(),
                dtype=config.torch_dtype,
            )
        )

        self.out_proj = RowParallelLinear(
            self.value_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            input_is_parallel=True,
            reduce_results=False,
            tp_rank=self.attn_tp_rank,
            tp_size=self.attn_tp_size,
            prefix=add_prefix("out_proj", prefix),
        )

        self.attn = RadixLinearAttention(
            layer_id=layer_id,
            num_q_heads=self.num_k_heads // self.attn_tp_size,
            num_k_heads=self.num_k_heads // self.attn_tp_size,
            num_v_heads=self.num_v_heads // self.attn_tp_size,
            head_q_dim=self.head_k_dim,
            head_k_dim=self.head_k_dim,
            head_v_dim=self.head_v_dim,
            conv_weights=self.conv1d.weight.squeeze(1),
            bias=self.conv1d.bias,
            activation=self.activation,
            A_log=self.A_log,
            dt_bias=self.dt_bias,
        )

    @staticmethod
    def _make_packed_weight_loader(module):
        """Create a weight_loader that does contiguous TP slicing for fused
        (packed-format) checkpoint weights (shard_id=None), and delegates
        to the standard MergedColumnParallelLinear loader for split checkpoint
        weights (shard_id=int/tuple)."""
        original_loader = module.weight.weight_loader

        def weight_loader(param, loaded_weight, loaded_shard_id=None):
            if loaded_shard_id is None:
                # Fused checkpoint: weight is in packed (per-head-group)
                # format. Do contiguous TP slice like ColumnParallelLinear.
                output_dim = getattr(param, "output_dim", None)
                if output_dim is not None and module.tp_size > 1:
                    shard_size = param.data.shape[output_dim]
                    start_idx = module.tp_rank * shard_size
                    loaded_weight = loaded_weight.narrow(
                        output_dim, start_idx, shard_size
                    )
                assert param.data.shape == loaded_weight.shape, (
                    f"Shape mismatch: param {param.data.shape} vs "
                    f"loaded {loaded_weight.shape}"
                )
                param.data.copy_(loaded_weight)
            else:
                # Split checkpoint (int or tuple shard_id) → standard path
                original_loader(param, loaded_weight, loaded_shard_id)

        return weight_loader

    def create_qkvz_proj(
        self,
        hidden_size: int,
        key_dim: int,
        value_dim: int,
        quant_config: QuantizationConfig | None,
        prefix: str,
        tp_rank: Optional[int] = None,
        tp_size: Optional[int] = None,
    ) -> MergedColumnParallelLinear:
        return MergedColumnParallelLinear(
            input_size=hidden_size,
            output_sizes=[key_dim, key_dim, value_dim, value_dim],
            bias=False,
            quant_config=quant_config,
            prefix=prefix,
            tp_rank=tp_rank,
            tp_size=tp_size,
        )

    def fix_query_key_value_ordering(
        self,
        mixed_qkvz: torch.Tensor,
        mixed_ba: torch.Tensor,
    ):
        """
        Derives `query`, `key` and `value` tensors from `mixed_qkvzba`.
        """
        new_tensor_shape_qkvz = mixed_qkvz.size()[:-1] + (
            self.num_k_heads // self.attn_tp_size,
            (
                self.head_k_dim
                + self.head_k_dim
                + (self.head_v_dim + self.head_v_dim)
                * self.num_v_heads
                // self.num_k_heads
            ),
        )
        new_tensor_shape_ba = mixed_ba.size()[:-1] + (
            self.num_k_heads // self.attn_tp_size,
            2 * self.num_v_heads // self.num_k_heads,
        )

        mixed_qkvz = mixed_qkvz.view(*new_tensor_shape_qkvz)
        mixed_ba = mixed_ba.view(*new_tensor_shape_ba)

        split_arg_list_qkvz = [
            self.head_k_dim,
            self.head_k_dim,
            (self.num_v_heads // self.num_k_heads * self.head_v_dim),
            (self.num_v_heads // self.num_k_heads * self.head_v_dim),
        ]
        split_arg_list_ba = [
            self.num_v_heads // self.num_k_heads,
            self.num_v_heads // self.num_k_heads,
        ]

        # [b, sq, ng, (hn + hn + np/ng * hn + np/ng + np/ng)]
        # --> [b, sq, ng, hn], [b, sq, ng, hn], [b, sq, ng, np/ng * hn], [b, sq, ng, np/ng * hn], [b, sq, ng, np/ng], [b, sq, ng, np/ng]
        query, key, value, z = torch.split(mixed_qkvz, split_arg_list_qkvz, dim=2)
        b, a = torch.split(mixed_ba, split_arg_list_ba, dim=2)

        # [b, sq, ng, np/ng * hn] -> [b, sq, np, hn]
        value = value.reshape(value.size(0), -1, self.head_v_dim)
        z = z.reshape(z.size(0), -1, self.head_v_dim)
        b = b.reshape(b.size(0), self.num_v_heads // self.attn_tp_size)
        a = a.reshape(a.size(0), self.num_v_heads // self.attn_tp_size)

        return query, key, value, z, b, a

    def _forward_input_proj(self, hidden_states: torch.Tensor):
        if (
            _is_cpu
            or _is_npu
            or not get_global_server_args().disable_piecewise_cuda_graph
        ):
            DUAL_STREAM_TOKEN_THRESHOLD = 0
        else:
            DUAL_STREAM_TOKEN_THRESHOLD = 1024

        seq_len, _ = hidden_states.shape
        if (
            self.alt_stream is not None
            and get_is_capture_mode()
            and seq_len < DUAL_STREAM_TOKEN_THRESHOLD
        ):
            current_stream = torch.cuda.current_stream()
            self.alt_stream.wait_stream(current_stream)
            projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states)
            with torch.cuda.stream(self.alt_stream):
                projected_states_ba, _ = self.in_proj_ba(hidden_states)
            current_stream.wait_stream(self.alt_stream)
        else:
            projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states)
            projected_states_ba, _ = self.in_proj_ba(hidden_states)
        return projected_states_qkvz, projected_states_ba

    def forward(
        self,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ):
        projected_states_qkvz, projected_states_ba = self._forward_input_proj(
            hidden_states
        )

        if self.num_v_heads // self.num_k_heads in [1, 2, 4] and not _is_cpu:
            mixed_qkv, z, b, a = fused_qkvzba_split_reshape_cat(
                projected_states_qkvz,
                projected_states_ba,
                triton.cdiv(self.num_k_heads, self.attn_tp_size),
                triton.cdiv(self.num_v_heads, self.attn_tp_size),
                self.head_k_dim,
                self.head_v_dim,
            )
        elif _is_cpu and _is_amx_available:
            mixed_qkv, z, b, a = (
                torch.ops.sgl_kernel.fused_qkvzba_split_reshape_cat_cpu(
                    projected_states_qkvz,
                    projected_states_ba,
                    self.num_k_heads // self.attn_tp_size,
                    self.num_v_heads // self.attn_tp_size,
                    self.head_k_dim,
                    self.head_v_dim,
                )
            )
        else:
            query, key, value, z, b, a = self.fix_query_key_value_ordering(
                projected_states_qkvz, projected_states_ba
            )
            query, key, value = map(
                lambda x: x.reshape(x.shape[0], -1), (query, key, value)
            )
            mixed_qkv = torch.cat((query, key, value), dim=-1)
        core_attn_out = self.attn(
            forward_batch,
            mixed_qkv=mixed_qkv,
            a=a,
            b=b,
        )

        z_shape_og = z.shape
        # reshape input data into 2D tensor
        core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
        z = z.reshape(-1, z.shape[-1])

        # Add padding for DP-Attn
        if core_attn_out.shape != z.shape:
            core_attn_out_pad = torch.zeros_like(z)
            core_attn_out_pad[: core_attn_out.shape[0], :] = core_attn_out
            core_attn_out = core_attn_out_pad

        core_attn_out = self.norm(core_attn_out, z)
        core_attn_out = core_attn_out.reshape(z_shape_og)
        core_attn_out = core_attn_out.reshape(*core_attn_out.shape[:-2], -1)

        output, _ = self.out_proj(core_attn_out)
        return output


class Qwen3HybridLinearDecoderLayer(nn.Module):

    def __init__(
        self,
        config: Qwen3NextConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
        is_nextn: bool = False,
    ) -> None:
        super().__init__()
        self.config = config
        self.linear_attn = Qwen3GatedDeltaNet(
            config, layer_id, quant_config, alt_stream, prefix
        )

        # Qwen3Next all layers are sparse and have no nextn now
        self.is_layer_sparse = True
        is_previous_layer_sparse = True
        is_next_layer_sparse = True
        self.layer_id = layer_id

        self.layer_scatter_modes = LayerScatterModes.init_new(
            layer_id=layer_id,
            num_layers=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 = Qwen2MoeSparseMoeBlock(
                layer_id=layer_id,
                config=config,
                quant_config=quant_config,
                alt_stream=alt_stream,
                prefix=add_prefix("mlp", prefix.replace(".linear_attn", "")),
                is_nextn=is_nextn,
            )
        else:
            self.mlp = Qwen2MoeMLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                prefix=add_prefix("mlp", prefix.replace(".linear_attn", "")),
            )
        self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = GemmaRMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
        self.layer_communicator = LayerCommunicator(
            layer_scatter_modes=self.layer_scatter_modes,
            input_layernorm=self.input_layernorm,
            post_attention_layernorm=self.post_attention_layernorm,
            allow_reduce_scatter=True,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        residual: Optional[torch.Tensor],
        captured_last_layer_outputs: Optional[list[torch.Tensor]] = None,
        **kwargs,
    ):
        forward_batch = kwargs.get("forward_batch", None)

        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,
            )
        )

        if not forward_batch.forward_mode.is_idle():
            hidden_states = self.linear_attn(
                hidden_states,
                forward_batch,
            )
        # Fully Connected
        hidden_states, residual = self.layer_communicator.prepare_mlp(
            hidden_states, residual, forward_batch
        )

        use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
            forward_batch
        )
        hidden_states = self.mlp(hidden_states, forward_batch, use_reduce_scatter)

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

        return hidden_states, residual


class Qwen3HybridAttentionDecoderLayer(nn.Module):

    def __init__(
        self,
        config: Qwen3NextConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        alt_stream: Optional[torch.cuda.Stream] = None,
        is_nextn: bool = False,
    ) -> None:
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.attn_tp_rank = get_attention_tp_rank()
        self.attn_tp_size = get_attention_tp_size()
        self.total_num_heads = config.num_attention_heads
        assert self.total_num_heads % self.attn_tp_size == 0
        self.num_heads = self.total_num_heads // self.attn_tp_size
        self.total_num_kv_heads = config.num_key_value_heads
        if self.total_num_kv_heads >= self.attn_tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % self.attn_tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert self.attn_tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // self.attn_tp_size)
        self.head_dim = config.head_dim or (self.hidden_size // self.num_heads)
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.rope_theta = getattr(config, "rope_theta", 10000)
        self.max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
        if "rope_parameters" in config:
            self.rope_scaling = getattr(config, "rope_parameters", None)
        else:
            self.rope_scaling = getattr(config, "rope_scaling", None)
        self.partial_rotary_factor = config.partial_rotary_factor
        self.layer_id = layer_id

        self.attn_output_gate = getattr(config, "attn_output_gate", True)
        if self.attn_output_gate:
            logger.warning_once("using attn output gate!")

        self.rotary_emb = get_rope(
            head_size=self.head_dim,
            rotary_dim=self.head_dim,
            max_position=self.max_position_embeddings,
            rope_scaling=self.rope_scaling,
            base=self.rope_theta,
            partial_rotary_factor=self.partial_rotary_factor,
            is_neox_style=True,
            dtype=torch.get_default_dtype(),  # see impl of get_rope
        )

        # qkv_proj is not quantized for fp4
        self.qkv_proj = QKVParallelLinear(
            config.hidden_size,
            self.head_dim,
            self.total_num_heads * (1 + self.attn_output_gate),
            self.total_num_kv_heads,
            bias=False,
            quant_config=(
                quant_config
                if quant_config is not None
                and quant_config.get_name() != "modelopt_fp4"
                else None
            ),
            tp_rank=self.attn_tp_rank,
            tp_size=self.attn_tp_size,
            prefix=add_prefix("qkv_proj", prefix),
        )

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

        self.attn = RadixAttention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            layer_id=layer_id,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
        )

        # Qwen3Next all layers are sparse and have no nextn now
        self.is_layer_sparse = True
        is_previous_layer_sparse = True
        is_next_layer_sparse = True

        self.layer_scatter_modes = LayerScatterModes.init_new(
            layer_id=layer_id,
            num_layers=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 = Qwen2MoeSparseMoeBlock(
                layer_id=layer_id,
                config=config,
                quant_config=quant_config,
                alt_stream=alt_stream,
                prefix=add_prefix("mlp", prefix.replace(".self_attn", "")),
                is_nextn=is_nextn,
            )
        else:
            self.mlp = Qwen2MoeMLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                prefix=add_prefix("mlp", prefix.replace(".self_attn", "")),
            )
        self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = GemmaRMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )

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

        self.layer_communicator = LayerCommunicator(
            layer_scatter_modes=self.layer_scatter_modes,
            input_layernorm=self.input_layernorm,
            post_attention_layernorm=self.post_attention_layernorm,
            allow_reduce_scatter=True,
        )

        self.alt_stream = alt_stream

    def _apply_qk_norm(
        self, q: torch.Tensor, k: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # overlap qk norm
        if self.alt_stream is not None and get_is_capture_mode():
            current_stream = torch.cuda.current_stream()
            self.alt_stream.wait_stream(current_stream)
            q_by_head = q.reshape(-1, self.head_dim)
            q_by_head = self.q_norm(q_by_head)
            with torch.cuda.stream(self.alt_stream):
                k_by_head = k.reshape(-1, self.head_dim)
                k_by_head = self.k_norm(k_by_head)
            current_stream.wait_stream(self.alt_stream)
        else:
            q_by_head = q.reshape(-1, self.head_dim)
            q_by_head = self.q_norm(q_by_head)
            k_by_head = k.reshape(-1, self.head_dim)
            k_by_head = self.k_norm(k_by_head)
        q = q_by_head.view(q.shape)
        k = k_by_head.view(k.shape)
        return q, k

    def self_attention(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)

        if self.attn_output_gate:
            q_gate, k, v = qkv.split(
                [self.q_size * 2, self.kv_size, self.kv_size], dim=-1
            )
            orig_shape = q_gate.shape[:-1]
            q_gate = q_gate.view(*orig_shape, self.num_heads, -1)
            q, gate = torch.chunk(q_gate, 2, dim=-1)
            q = q.reshape(*orig_shape, -1)
            gate = gate.reshape(*orig_shape, -1)
        else:
            q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)

        q, k = self._apply_qk_norm(q, k)

        q, k = self.rotary_emb(positions, q, k)

        attn_output = self.attn(q, k, v, forward_batch)

        if self.attn_output_gate:
            gate = torch.sigmoid(gate)
            attn_output = attn_output * gate

        output, _ = self.o_proj(attn_output)
        return output

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: Optional[torch.Tensor],
        forward_batch: ForwardBatch,
        captured_last_layer_outputs: Optional[list[torch.Tensor]] = None,
        **kwargs: Any,
    ):
        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,
            )
        )

        if not forward_batch.forward_mode.is_idle():
            hidden_states = self.self_attention(
                positions=positions,
                hidden_states=hidden_states,
                forward_batch=forward_batch,
            )

        # Fully Connected
        hidden_states, residual = self.layer_communicator.prepare_mlp(
            hidden_states, residual, forward_batch
        )
        use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
            forward_batch
        )
        hidden_states = self.mlp(hidden_states, forward_batch, use_reduce_scatter)

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

        return hidden_states, residual


ALL_DECODER_LAYER_TYPES = {
    "attention": Qwen3HybridAttentionDecoderLayer,
    "linear_attention": Qwen3HybridLinearDecoderLayer,
}


class Qwen3NextModel(nn.Module):
    def __init__(
        self,
        config: Qwen3NextConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        is_nextn: bool = False,
    ) -> None:
        super().__init__()
        self.config = config

        alt_stream = torch.cuda.Stream() if _is_cuda else None

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
            use_attn_tp_group=is_dp_attention_enabled(),
        )

        def get_layer(idx: int, prefix: str):
            layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[idx]]
            if config.layers_block_type[idx] == "attention":
                prefix = add_prefix("self_attn", prefix)
            else:
                prefix = add_prefix("linear_attn", prefix)
            return layer_class(
                config,
                idx,
                quant_config=quant_config,
                prefix=prefix,
                alt_stream=alt_stream,
                is_nextn=is_nextn,
            )

        self.layers = make_layers(
            config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers"
        )

        self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.infer_count = 0

        # For EAGLE3 support
        self.layers_to_capture = []

    def set_eagle3_layers_to_capture(self, layers_to_capture: list[int]):
        self.layers_to_capture = layers_to_capture
        for layer_id in self.layers_to_capture:
            setattr(self.layers[layer_id], "_is_layer_to_capture", True)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        # mamba_cache_params: MambaCacheParams,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:

        # pass a sequence index tensor, that is required for
        # proper continuous batching computation including
        # chunked prefill
        if inputs_embeds is not None:
            hidden_states = inputs_embeds
        else:
            hidden_states = self.embed_tokens(input_ids)

        residual = None
        aux_hidden_states = []
        for i in range(len(self.layers)):
            layer = self.layers[i]
            with get_global_expert_distribution_recorder().with_current_layer(i):
                hidden_states, residual = layer(
                    layer_id=i,
                    positions=positions,
                    hidden_states=hidden_states,
                    residual=residual,
                    forward_batch=forward_batch,
                    captured_last_layer_outputs=(
                        aux_hidden_states
                        if getattr(layer, "_is_layer_to_capture", False)
                        else None
                    ),
                )

        if not forward_batch.forward_mode.is_idle():
            if residual is None:
                hidden_states = self.norm(hidden_states)
            else:
                hidden_states, _ = self.norm(hidden_states, residual)

        if len(aux_hidden_states) == 0:
            return hidden_states

        return hidden_states, aux_hidden_states


class HybridLayerType(enum.Enum):
    full_attention = "attention"
    swa_attention = "swa_attention"
    linear_attention = "linear_attention"
    mamba2 = "mamba"


class Qwen3NextForCausalLM(nn.Module):
    fall_back_to_pt_during_load = False

    # Map fused module names to their checkpoint (unfused) counterparts.
    # This is needed so the quantization exclusion logic can match
    # checkpoint-style names (e.g. "q_proj") against the fused sglang
    # module names (e.g. "qkv_proj").
    packed_modules_mapping = {
        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
        "gate_up_proj": ["gate_proj", "up_proj"],
    }

    def __init__(
        self,
        config: Qwen3NextConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.pp_group = get_pp_group()
        assert self.pp_group.is_first_rank and self.pp_group.is_last_rank

        # The quant config's packed_modules_mapping may be None if it wasn't
        # in the checkpoint config. The base class (QuantizationConfig) intends
        # for models to set this. We need it so is_layer_skipped can unfuse
        # "qkv_proj" into ["q_proj","k_proj","v_proj"] when checking exclusions.
        if quant_config is not None and hasattr(quant_config, "packed_modules_mapping"):
            quant_config.packed_modules_mapping = self.packed_modules_mapping

        self.quant_config = quant_config
        self.model = Qwen3NextModel(
            config, quant_config, prefix=add_prefix("model", prefix)
        )
        self.lm_head = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
            quant_config=quant_config,
            org_num_embeddings=config.vocab_size,
            prefix=add_prefix("lm_head", prefix),
            use_attn_tp_group=get_global_server_args().enable_dp_lm_head,
        )
        self.logits_processor = LogitsProcessor(config)
        # For EAGLE3 support
        self.capture_aux_hidden_states = False

        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, Qwen2MoeSparseMoeBlock)
            }
        )

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

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs,
    ):
        hidden_states = self.model(input_ids, positions, forward_batch, inputs_embeds)

        aux_hidden_states = None
        if self.capture_aux_hidden_states:
            hidden_states, aux_hidden_states = hidden_states

        return self.logits_processor(
            input_ids, hidden_states, self.lm_head, forward_batch, aux_hidden_states
        )

    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()

    def get_embed(self):
        return self.model.embed_tokens.weight

    def set_embed(self, embed):
        # NOTE: If draft hidden size != target hidden size, the embed weight cannot be shared for EAGLE3
        if (
            hasattr(self.config, "target_hidden_size")
            and self.config.target_hidden_size != self.config.hidden_size
        ):
            return
        del self.model.embed_tokens.weight
        self.model.embed_tokens.weight = embed
        torch.cuda.empty_cache()
        torch.cuda.synchronize()

    def load_weights(
        self, weights: Iterable[Tuple[str, torch.Tensor]], is_mtp: bool = False
    ) -> Set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            # self attention
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            # mlp
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
            # GDN
            ("in_proj_qkvz.", "in_proj_qkv.", (0, 1, 2)),
            ("in_proj_qkvz.", "in_proj_z.", 3),
            ("in_proj_ba.", "in_proj_b.", 0),
            ("in_proj_ba.", "in_proj_a.", 1),
        ]

        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        expert_params_mapping = FusedMoE.make_expert_params_mapping(
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            num_experts=self.config.num_experts,
        )

        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()
        for name, loaded_weight in weights:

            if is_mtp:

                if "mtp" not in name:
                    continue

                if name in [
                    "mtp.fc.weight",
                    "mtp.pre_fc_norm_embedding.weight",
                    "mtp.pre_fc_norm_hidden.weight",
                ]:
                    name = name.replace("mtp.", "")
                else:
                    name = name.replace("mtp", "model")

            if not is_mtp and "mtp" in name:
                continue

            if "rotary_emb.inv_freq" in name:
                continue

            if ".self_attn." in name:
                name = name.replace(".self_attn", "")

            # Remap modelopt FP8 KV cache scale names:
            # checkpoint: k_proj.k_scale / v_proj.v_scale
            # model:      attn.k_scale   / attn.v_scale
            if name.endswith(".k_proj.k_scale"):
                name = name.replace(".k_proj.k_scale", ".attn.k_scale")
            elif name.endswith(".v_proj.v_scale"):
                name = name.replace(".v_proj.v_scale", ".attn.v_scale")

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue

                # TODO(fix mtp loading)
                if "mlp.experts" in name:
                    continue

                replaced_name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if replaced_name.endswith(".bias") and replaced_name not in params_dict:
                    continue
                # Skip layers on other devices.
                # if is_pp_missing_parameter(name, self):
                #     continue
                if replaced_name not in params_dict:
                    continue
                name = replaced_name
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader")
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue
                    replaced_name = name.replace(weight_name, param_name)
                    # Skip layers on other devices.
                    # if is_pp_missing_parameter(name, self):
                    #     continue
                    # Skip loading extra bias for GPTQ models.
                    if (
                        replaced_name.endswith(".bias")
                        or replaced_name.endswith("_bias")
                    ) and replaced_name not in params_dict:
                        continue
                    name = replaced_name
                    param = params_dict[name]

                    weight_loader = getattr(param, "weight_loader")
                    weight_loader(
                        param,
                        loaded_weight,
                        name,
                        shard_id=shard_id,
                        expert_id=expert_id,
                    )
                    break
                else:
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    # if is_pp_missing_parameter(name, self):
                    #     continue

                    if name.endswith("_scale") and name not in params_dict:
                        assert (
                            abs(loaded_weight.item() - 1.0) < 1e-6
                        ), f"Expected 1.0, got {loaded_weight.item()} in skipped {name}"
                        continue
                    param = params_dict[name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params

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

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

        self.capture_aux_hidden_states = True
        if layer_ids is None:
            num_layers = self.config.num_hidden_layers
            self.model.set_eagle3_layers_to_capture(
                [
                    2,
                    num_layers // 2,
                    num_layers - 3,
                ]
            )  # Specific layers for EAGLE3 support
        else:
            self.model.set_eagle3_layers_to_capture([val + 1 for val in layer_ids])


EntryClass = Qwen3NextForCausalLM
