qwen3_next.py 65.2 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Inference-only Qwen3Next model."""
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from collections.abc import Iterable
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from itertools import islice
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import torch
from einops import rearrange
from torch import nn
from transformers.activations import ACT2FN

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from vllm import envs
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import (
    CacheConfig,
    ModelConfig,
    SpeculativeConfig,
    VllmConfig,
    get_current_vllm_config,
)
from vllm.distributed import (
    divide,
    get_ep_group,
    get_pp_group,
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
    tensor_model_parallel_all_gather,
)
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from vllm.forward_context import ForwardContext, get_forward_context
from vllm.logger import init_logger
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from vllm.model_executor.custom_op import CustomOp
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from vllm.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.fla.ops import (
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    chunk_gated_delta_rule as fla_chunk_gated_delta_rule,
)
from vllm.model_executor.layers.fla.ops import (
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    fused_recurrent_gated_delta_rule_packed_decode,
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    fused_sigmoid_gating_delta_rule_update,
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)
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from vllm.model_executor.layers.fla.ops.chunk import l2norm_fwd
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from vllm.model_executor.layers.fused_moe import SharedFusedMoE
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from vllm.model_executor.layers.layernorm import (
    GemmaRMSNorm as Qwen3NextRMSNorm,
)
from vllm.model_executor.layers.layernorm import RMSNormGated
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from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
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    MergedColumnParallelLinear,
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    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.abstract import MambaBase
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from vllm.model_executor.layers.mamba.mamba_mixer2 import mamba_v2_sharded_weight_loader
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from vllm.model_executor.layers.mamba.mamba_utils import (
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    MambaStateCopyFunc,
    MambaStateCopyFuncCalculator,
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    MambaStateDtypeCalculator,
    MambaStateShapeCalculator,
)
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from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
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    causal_conv1d_fn,
    causal_conv1d_update,
)
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from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
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    ParallelLMHead,
    VocabParallelEmbedding,
)
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from vllm.model_executor.model_loader.weight_utils import (
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    default_weight_loader,
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    maybe_remap_kv_scale_name,
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    sharded_weight_loader,
)
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from vllm.model_executor.models.qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP
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from vllm.model_executor.models.utils import sequence_parallel_chunk
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from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs.qwen3_next import Qwen3NextConfig
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from vllm.triton_utils import tl, triton
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from vllm.utils.multi_stream_utils import maybe_execute_in_parallel
from vllm.utils.torch_utils import (
    aux_stream,
    direct_register_custom_op,
)
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from vllm.v1.attention.backend import AttentionMetadata
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata

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from .interfaces import (
    HasInnerState,
    IsHybrid,
    MixtureOfExperts,
    SupportsLoRA,
    SupportsPP,
)
from .utils import (
    AutoWeightsLoader,
    PPMissingLayer,
    extract_layer_index,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)
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logger = init_logger(__name__)

KVCache = tuple[torch.Tensor, torch.Tensor]


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def fi_chunk_gated_delta_rule(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    g: torch.Tensor,
    beta: torch.Tensor,
    initial_state: torch.Tensor,
    output_final_state: bool,
    cu_seqlens: torch.LongTensor | None = None,
    use_qk_l2norm_in_kernel: bool = True,
):
    from flashinfer.gdn_prefill import (
        chunk_gated_delta_rule as chunk_gated_delta_rule_fi,
    )

    if use_qk_l2norm_in_kernel:
        q = l2norm_fwd(q)
        k = l2norm_fwd(k)

    # use flashinfer implementation
    q = q.squeeze(0).contiguous()
    k = k.squeeze(0).contiguous()
    v = v.squeeze(0).contiguous()

    g = g.squeeze(0).contiguous()
    beta = beta.squeeze(0).contiguous()
    fi_state = initial_state.to(torch.float32)
    fi_g = g.to(torch.float32)
    fi_beta = beta.to(torch.float32)
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    result = chunk_gated_delta_rule_fi(
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        q=q,
        k=k,
        v=v,
        g=torch.exp(fi_g),
        beta=fi_beta,
        initial_state=fi_state,
        output_final_state=output_final_state,
        cu_seqlens=cu_seqlens,
    )
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    # FlashInfer returns (output, state) when output_final_state=True,
    # or just output when output_final_state=False.
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    # Unsqueeze back to 4D (1, L, H, D) to match fla output format
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    if output_final_state:
        output, final_state = result
        return output.unsqueeze(0), final_state
    else:
        return result.unsqueeze(0), None
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@CustomOp.register("chunk_gated_delta_rule")
class ChunkGatedDeltaRule(CustomOp):
    def __init__(self) -> None:
        super().__init__()
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        backend = (
            str(
                get_current_vllm_config().additional_config.get(
                    "gdn_prefill_backend", "auto"
                )
            )
            .strip()
            .lower()
        )
        supports_flashinfer = (
            current_platform.is_cuda() and current_platform.is_device_capability(90)
        )

        if backend == "flashinfer":
            use_flashinfer = supports_flashinfer
            if not use_flashinfer:
                logger.warning_once(
                    "GDN prefill backend 'flashinfer' is selected but "
                    "cannot use this kernel on the current platform. "
                    "Falling back to Triton/FLA."
                )
        elif backend == "triton":
            use_flashinfer = False
        else:
            use_flashinfer = supports_flashinfer

        if use_flashinfer:
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            logger.info_once("Using FlashInfer GDN prefill kernel", scope="local")
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            logger.info_once(
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                "FlashInfer GDN prefill kernel is JIT-compiled; first run may "
                "take a while to compile. Set `--gdn-prefill-backend triton` to "
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                "avoid JIT compile time.",
                scope="local",
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            )
        else:
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            logger.info_once("Using Triton/FLA GDN prefill kernel", scope="local")
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        self._forward_method = (
            self.forward_cuda if use_flashinfer else self.forward_native
        )
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    def forward_cuda(
        self,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        g: torch.Tensor,
        beta: torch.Tensor,
        initial_state: torch.Tensor,
        output_final_state: bool,
        cu_seqlens: torch.LongTensor | None = None,
        use_qk_l2norm_in_kernel: bool = True,
    ):
        return fi_chunk_gated_delta_rule(
            q=q,
            k=k,
            v=v,
            g=g,
            beta=beta,
            initial_state=initial_state,
            output_final_state=output_final_state,
            cu_seqlens=cu_seqlens,
            use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
        )

    def forward_native(
        self,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        g: torch.Tensor,
        beta: torch.Tensor,
        initial_state: torch.Tensor,
        output_final_state: bool,
        cu_seqlens: torch.LongTensor | None = None,
        use_qk_l2norm_in_kernel: bool = True,
    ):
        return fla_chunk_gated_delta_rule(
            q=q,
            k=k,
            v=v,
            g=g,
            beta=beta,
            initial_state=initial_state,
            output_final_state=output_final_state,
            cu_seqlens=cu_seqlens,
            use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
        )


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class Qwen3NextSparseMoeBlock(nn.Module):
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    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
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        super().__init__()
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        config = vllm_config.model_config.hf_text_config
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        parallel_config = vllm_config.parallel_config
        quant_config = vllm_config.quant_config

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        self.tp_size = get_tensor_model_parallel_world_size()

        self.ep_group = get_ep_group().device_group
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        self.ep_rank = get_ep_group().rank_in_group
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        self.ep_size = self.ep_group.size()
        self.n_routed_experts = config.num_experts

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        self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe

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        if self.tp_size > config.num_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
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                f"the number of experts {config.num_experts}."
            )
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        # Load balancing settings.
        vllm_config = get_current_vllm_config()
        eplb_config = vllm_config.parallel_config.eplb_config
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        self.enable_eplb = parallel_config.enable_eplb
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        self.n_logical_experts = self.n_routed_experts
        self.n_redundant_experts = eplb_config.num_redundant_experts
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        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
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        self.n_local_physical_experts = self.n_physical_experts // self.ep_size

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        self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
        self.physical_expert_end = (
            self.physical_expert_start + self.n_local_physical_experts
        )

        self.gate = ReplicatedLinear(
            config.hidden_size,
            config.num_experts,
            bias=False,
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            quant_config=None,
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            prefix=f"{prefix}.gate",
        )
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        self.shared_expert_gate = ReplicatedLinear(
            config.hidden_size,
            1,
            bias=False,
            quant_config=None,
            prefix=f"{prefix}.shared_expert_gate",
        )
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        if config.shared_expert_intermediate_size > 0:
            self.shared_expert = Qwen3NextMLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.shared_expert_intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
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                reduce_results=False,
                expert_gate=self.shared_expert_gate,
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                prefix=f"{prefix}.shared_expert",
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            )
        else:
            self.shared_expert = None
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        self.experts = SharedFusedMoE(
            shared_experts=self.shared_expert,
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            gate=self.gate,
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            num_experts=self.n_routed_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            reduce_results=False,
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            renormalize=getattr(config, "norm_topk_prob", True),
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            quant_config=quant_config,
            prefix=f"{prefix}.experts",
            enable_eplb=self.enable_eplb,
            num_redundant_experts=self.n_redundant_experts,
            is_sequence_parallel=self.is_sequence_parallel,
        )
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    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        # NOTE: hidden_states can have either 1D or 2D shape.
        orig_shape = hidden_states.shape
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        num_tokens, hidden_dim = hidden_states.shape
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        hidden_states = hidden_states.view(-1, hidden_dim)

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        if self.is_sequence_parallel:
            hidden_states = sequence_parallel_chunk(hidden_states)

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        if self.experts.is_internal_router:
            # In this case, the gate/router runs inside the FusedMoE class
            final_hidden_states = self.experts(
                hidden_states=hidden_states, router_logits=hidden_states
            )
        else:
            # router_logits: (num_tokens, n_experts)
            router_logits, _ = self.gate(hidden_states)
            final_hidden_states = self.experts(
                hidden_states=hidden_states, router_logits=router_logits
            )
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        if self.shared_expert is not None:
            final_hidden_states = final_hidden_states[0] + final_hidden_states[1]
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        if self.is_sequence_parallel:
            final_hidden_states = tensor_model_parallel_all_gather(
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                final_hidden_states, 0
            )
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            final_hidden_states = final_hidden_states[:num_tokens]
        elif self.tp_size > 1:
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            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
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                final_hidden_states
            )
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        return final_hidden_states.view(orig_shape)


class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
    @property
    def mamba_type(self) -> str:
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        return "gdn_attention"
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    def get_state_dtype(self) -> tuple[torch.dtype, torch.dtype]:
        return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
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            self.model_config.dtype,
            self.cache_config.mamba_cache_dtype,
            self.cache_config.mamba_ssm_cache_dtype,
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        )
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    def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
        return MambaStateShapeCalculator.gated_delta_net_state_shape(
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            self.tp_size,
            self.num_k_heads,
            self.num_v_heads,
            self.head_k_dim,
            self.head_v_dim,
            self.conv_kernel_size,
            self.num_spec,
        )
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    def __init__(
        self,
        config: Qwen3NextConfig,
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        model_config: ModelConfig | None = None,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        speculative_config: SpeculativeConfig | None = None,
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        prefix: str = "",
    ) -> None:
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.tp_rank = get_tensor_model_parallel_rank()
        self.hidden_size = config.hidden_size
        self.num_v_heads = config.linear_num_value_heads
        self.num_k_heads = config.linear_num_key_heads
        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.conv_kernel_size = config.linear_conv_kernel_dim
        self.layer_idx = extract_layer_index(prefix)
        self.activation = config.hidden_act
        self.act = ACT2FN[config.hidden_act]
        self.layer_norm_epsilon = config.rms_norm_eps
        self.prefix = prefix
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        self.aux_stream = aux_stream()
        self.events = (
            [torch.cuda.Event(), torch.cuda.Event()]
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            if current_platform.is_cuda_alike()
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            else [None, None]
        )
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        self.config = config
        self.model_config = model_config
        self.cache_config = cache_config
        self.quant_config = quant_config
        self.speculative_config = speculative_config
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        self.num_spec = (
            self.speculative_config.num_speculative_tokens
            if self.speculative_config
            else 0
        )
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        # QKV
        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,
            prefix=f"{prefix}.conv1d",
        )
        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)

        # projection of the input hidden states
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        # Qwen3-Next and Qwen3.5 has a different qkv_proj layout,
        # we need to create qkvz_proj adaptively here.
        self.in_proj_qkvz = self.create_qkvz_proj(
            hidden_size=self.hidden_size,
            key_dim=self.key_dim,
            value_dim=self.value_dim,
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            quant_config=quant_config,
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            prefix=f"{prefix}.in_proj_qkvz",
        )
        # ba_proj doesn't support blockwise fp8 quantization.
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        # Qwen3-Next and Qwen3.5 have different in_proj_ba checkpoint
        # layouts, so we use a factory method to create the projection.
        self.in_proj_ba = self.create_ba_proj(
            hidden_size=self.hidden_size,
            num_v_heads=self.num_v_heads,
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            quant_config=quant_config,
            prefix=f"{prefix}.in_proj_ba",
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        )

        query_key_settings = (self.key_dim, 0, False)
        value_settings = (self.value_dim, 0, False)

        delattr(self.conv1d.weight, "weight_loader")
        set_weight_attrs(
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            self.conv1d.weight,
            {
                "weight_loader": mamba_v2_sharded_weight_loader(
                    [
                        query_key_settings,
                        query_key_settings,
                        value_settings,
                    ],
                    self.tp_size,
                    self.tp_rank,
                )
            },
        )
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        # selective projection used to make dt, B and C input dependent
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        # time step projection (discretization)
        # instantiate once and copy inv_dt in init_weights of PretrainedModel
        self.dt_bias = nn.Parameter(
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            torch.ones(self.num_v_heads // self.tp_size),
        )
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        self.A_log = nn.Parameter(
            torch.empty(
                divide(self.num_v_heads, self.tp_size),
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            )
        )
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        set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(0)})
        set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
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        self.norm = RMSNormGated(
            self.head_v_dim,
            eps=self.layer_norm_epsilon,
            group_size=None,
            norm_before_gate=True,
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            device=current_platform.current_device(),
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        )

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        self.out_proj = RowParallelLinear(
            self.value_dim,
            self.hidden_size,
            bias=False,
            input_is_parallel=True,
            quant_config=quant_config,
            prefix=f"{prefix}.out_proj",
        )
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        self.chunk_gated_delta_rule = ChunkGatedDeltaRule()
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        self.enable_packed_recurrent_decode = (
            envs.VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE
        )
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        compilation_config = get_current_vllm_config().compilation_config
        if prefix in compilation_config.static_forward_context:
            raise ValueError(f"Duplicate layer name: {prefix}")
        compilation_config.static_forward_context[prefix] = self

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

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    def create_ba_proj(
        self,
        hidden_size: int,
        num_v_heads: int,
        quant_config: QuantizationConfig | None,
        prefix: str,
    ) -> MergedColumnParallelLinear:
        # Qwen3-Next stores in_proj_ba as a single fused weight with an
        # interleaved GQA layout: [b_g0, a_g0, b_g1, a_g1, ...] where
        # each group corresponds to a key-head group. We must use a single
        # output shard so that ColumnParallel sharding preserves this
        # interleaved structure across TP ranks.
        # Qwen3.5 overrides this to use [num_v_heads, num_v_heads] since
        # its checkpoint has separate in_proj_b and in_proj_a weights.
        return MergedColumnParallelLinear(
            input_size=hidden_size,
            output_sizes=[num_v_heads * 2],
            bias=False,
            quant_config=quant_config,
            prefix=prefix,
        )

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

        # [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]
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        (query, key, value, z) = torch.split(mixed_qkvz, split_arg_list_qkvz, dim=2)
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        (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.tp_size)
        a = a.reshape(a.size(0), self.num_v_heads // self.tp_size)

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

    def rearrange_mixed_qkv(self, mixed_qkv):
        if mixed_qkv is None:
            return None, None, None
        query, key, value = torch.split(
            mixed_qkv,
            [
                self.key_dim // self.tp_size,
                self.key_dim // self.tp_size,
                self.value_dim // self.tp_size,
            ],
            dim=-1,
        )
        query, key = map(
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            lambda x: rearrange(x, "l (h d) -> 1 l h d", d=self.head_k_dim),
            (query, key),
        )
        value = rearrange(value, "l (h d) -> 1 l h d", d=self.head_v_dim)
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        return query.contiguous(), key.contiguous(), value.contiguous()
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    def forward(
        self,
        hidden_states: torch.Tensor,
        output: torch.Tensor,
    ):
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        """
        Forward pass with three parts:
        1. Input projection
        2. Core attention (custom op)
        3. Output projection
        """
        num_tokens = hidden_states.size(0)

        # ============================================================
        # Part 1: Input Projection
        # ============================================================
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        projected_states_qkvz, projected_states_ba = torch.ops.vllm.gdn_in_proj(
            hidden_states,
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            sum(self.in_proj_qkvz.output_sizes) // self.tp_size,
            sum(self.in_proj_ba.output_sizes) // self.tp_size,
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            self.prefix,
        )
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        query, key, value, z, b, a = self.fix_query_key_value_ordering(
            projected_states_qkvz, projected_states_ba
        )
        query, key, value = map(
            lambda x: rearrange(x, "l p d -> l (p d)"), (query, key, value)
        )
        mixed_qkv = torch.cat((query, key, value), dim=-1)

        # ============================================================
        # Part 2: Core Attention (Custom Op)
        # ============================================================
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        # Note: we should not use torch.empty here like other attention backends,
        # see discussions in https://github.com/vllm-project/vllm/pull/28182
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        core_attn_out = torch.zeros(
            (num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
            dtype=hidden_states.dtype,
            device=hidden_states.device,
        )

        torch.ops.vllm.gdn_attention_core(
            mixed_qkv,
            b,
            a,
            core_attn_out,
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            self.prefix,
        )

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        # ============================================================
        # Part 3: Output Projection
        # ============================================================
        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])
        core_attn_out = self.norm(core_attn_out, z)
        core_attn_out = core_attn_out.reshape(z_shape_og)
        core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
        output[:num_tokens], _ = self.out_proj(core_attn_out)

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    def _warmup_prefill_kernels(self, mixed_qkv: torch.Tensor) -> None:
        """Warm up GDN prefill kernels during V1 profiling.

        During V1 profile runs, ``_forward_core`` returns early because
        ``attn_metadata`` is ``None``, so the autotuned kernels used by
        ``chunk_gated_delta_rule`` (e.g. ``solve_tril``,
        ``chunk_scaled_dot_kkt``) are never invoked.  After profiling,
        vLLM allocates KV cache using most of the remaining GPU memory.
        When the first real inference triggers the autotuner it OOMs
        because there is not enough memory left for benchmarking.

        This method runs minimal forward passes through
        ``chunk_gated_delta_rule`` with small dummy tensors to force
        autotuning while GPU memory is still plentiful.  The autotuner
        results are cached globally, so only the first layer incurs
        actual benchmarking cost.

        Most kernels use a fixed ``BT = chunk_size`` (64), but
        ``chunk_fwd_kernel_o`` recomputes ``BT`` from the sequence
        length: ``min(64, max(16, next_power_of_2(T)))``.  Since ``BT``
        is part of its autotune key, we run warmup passes with T = 16,
        32, and 64 to cover all possible ``BT`` values.

        The decode path uses ``fused_sigmoid_gating_delta_rule_update``
        which has fixed kernel parameters (no autotuning), so only the
        prefill (chunked) path needs warming up.
        """
        if hasattr(self, "_prefill_kernels_warmed_up"):
            return
        self._prefill_kernels_warmed_up = True

        device = mixed_qkv.device
        dtype = mixed_qkv.dtype
        num_k_heads = self.num_k_heads // self.tp_size
        num_v_heads = self.num_v_heads // self.tp_size
        _, state_dtype = self.get_state_dtype()

        # Run warmup for each possible BT value of chunk_fwd_kernel_o:
        #   T=16 → BT=16, T=32 → BT=32, T=64 → BT=64.
        # Other kernels always use BT=chunk_size(64), so their autotune
        # cache is populated on the first pass and reused thereafter.
        for T in (16, 32, 64):
            q = torch.randn(
                1, T, num_k_heads, self.head_k_dim, device=device, dtype=dtype
            )
            k = torch.randn(
                1, T, num_k_heads, self.head_k_dim, device=device, dtype=dtype
            )
            v = torch.randn(
                1, T, num_v_heads, self.head_v_dim, device=device, dtype=dtype
            )
            g = torch.randn(1, T, num_v_heads, device=device, dtype=dtype)
            beta = torch.randn(1, T, num_v_heads, device=device, dtype=dtype)
            state = torch.zeros(
                1,
                num_v_heads,
                self.head_v_dim,
                self.head_k_dim,
                device=device,
                dtype=state_dtype,
            )
            cu_seqlens = torch.tensor([0, T], device=device, dtype=torch.long)

            try:
                self.chunk_gated_delta_rule(
                    q=q,
                    k=k,
                    v=v,
                    g=g,
                    beta=beta,
                    initial_state=state,
                    output_final_state=False,
                    cu_seqlens=cu_seqlens,
                    use_qk_l2norm_in_kernel=True,
                )
            except Exception:
                logger.warning(
                    "GDN prefill kernel warmup (T=%d) failed for "
                    "layer %s. First inference may OOM due to "
                    "autotuner.",
                    T,
                    self.prefix,
                    exc_info=True,
                )
            else:
                logger.debug(
                    "GDN prefill kernel warmup (T=%d) completed for layer %s",
                    T,
                    self.prefix,
                )
            finally:
                del q, k, v, g, beta, state, cu_seqlens

        torch.accelerator.empty_cache()

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    def _forward_in_proj(
        self, hidden_states: torch.Tensor
    ) -> tuple[torch.Tensor, torch.Tensor]:
        projected_states_qkvz, projected_states_ba = maybe_execute_in_parallel(
            lambda: self.in_proj_qkvz(hidden_states)[0],
            lambda: self.in_proj_ba(hidden_states)[0],
            self.events[0],
            self.events[1],
            self.aux_stream,
        )
        return projected_states_qkvz, projected_states_ba

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    def _forward_core(
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        self,
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        mixed_qkv: torch.Tensor,
        b: torch.Tensor,
        a: torch.Tensor,
        core_attn_out: torch.Tensor,
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    ):
        forward_context = get_forward_context()
        attn_metadata: AttentionMetadata = forward_context.attn_metadata

        if attn_metadata is None:
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            # V1 profile run — warm up prefill kernels so that
            # autotuning completes before KV cache allocation.
            self._warmup_prefill_kernels(mixed_qkv)
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            return

        assert isinstance(attn_metadata, dict)
        attn_metadata = attn_metadata[self.prefix]
        assert isinstance(attn_metadata, GDNAttentionMetadata)
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        if (
            self.enable_packed_recurrent_decode
            and attn_metadata.spec_sequence_masks is None
            and attn_metadata.num_prefills == 0
            and attn_metadata.num_decodes > 0
        ):
            return self._forward_core_decode_non_spec(
                mixed_qkv=mixed_qkv,
                b=b,
                a=a,
                core_attn_out=core_attn_out,
                attn_metadata=attn_metadata,
            )

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        has_initial_state = attn_metadata.has_initial_state
        spec_query_start_loc = attn_metadata.spec_query_start_loc
        non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
        spec_sequence_masks = attn_metadata.spec_sequence_masks
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        spec_token_indx = attn_metadata.spec_token_indx
        non_spec_token_indx = attn_metadata.non_spec_token_indx
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        spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor  # noqa: E501
        non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor  # noqa: E501
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        self_kv_cache = self.kv_cache[0]
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        conv_state = self_kv_cache[0].transpose(-1, -2)
        ssm_state = self_kv_cache[1]
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        num_actual_tokens = attn_metadata.num_actual_tokens
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        num_accepted_tokens = attn_metadata.num_accepted_tokens
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        mixed_qkv = mixed_qkv[:num_actual_tokens]
        b = b[:num_actual_tokens]
        a = a[:num_actual_tokens]
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        # 1. Convolution sequence transformation
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        conv_weights = self.conv1d.weight.view(
            self.conv1d.weight.size(0), self.conv1d.weight.size(2)
        )
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        if spec_sequence_masks is not None:
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            if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
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                mixed_qkv_spec = mixed_qkv
                mixed_qkv_non_spec = None
            else:
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                mixed_qkv_spec = mixed_qkv.index_select(0, spec_token_indx)
                mixed_qkv_non_spec = mixed_qkv.index_select(0, non_spec_token_indx)
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        else:
            mixed_qkv_spec = None
            mixed_qkv_non_spec = mixed_qkv

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        # 1.1: Process the multi-query part
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        if spec_sequence_masks is not None:
            mixed_qkv_spec = causal_conv1d_update(
                mixed_qkv_spec,
                conv_state,
                conv_weights,
                self.conv1d.bias,
                self.activation,
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                conv_state_indices=spec_state_indices_tensor[:, 0][
                    : attn_metadata.num_spec_decodes
                ],
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                num_accepted_tokens=num_accepted_tokens,
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                query_start_loc=spec_query_start_loc,
                max_query_len=spec_state_indices_tensor.size(-1),
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                validate_data=False,
            )

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        # 1.2: Process the remaining part
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        if attn_metadata.num_prefills > 0:
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            mixed_qkv_non_spec_T = mixed_qkv_non_spec.transpose(0, 1)
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            # - "cache_indices" updates the conv_state cache in positions
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            #   pointed to by "state_indices_tensor"
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            mixed_qkv_non_spec = causal_conv1d_fn(
904
                mixed_qkv_non_spec_T,
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                conv_weights,
                self.conv1d.bias,
                activation=self.activation,
                conv_states=conv_state,
                has_initial_state=has_initial_state,
                cache_indices=non_spec_state_indices_tensor,
                query_start_loc=non_spec_query_start_loc,
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                metadata=attn_metadata,
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            ).transpose(0, 1)
        elif attn_metadata.num_decodes > 0:
            mixed_qkv_non_spec = causal_conv1d_update(
                mixed_qkv_non_spec,
                conv_state,
                conv_weights,
                self.conv1d.bias,
                self.activation,
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                conv_state_indices=non_spec_state_indices_tensor[
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                    : attn_metadata.num_actual_tokens
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                ],
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                validate_data=True,
            )
        else:
            mixed_qkv_non_spec = None

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        query_spec, key_spec, value_spec = self.rearrange_mixed_qkv(mixed_qkv_spec)
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        query_non_spec, key_non_spec, value_non_spec = self.rearrange_mixed_qkv(
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            mixed_qkv_non_spec
        )
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        if attn_metadata.num_prefills > 0:
            g, beta = fused_gdn_gating(self.A_log, a, b, self.dt_bias)
            if spec_sequence_masks is not None:
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                g_non_spec = g.index_select(1, non_spec_token_indx)
                beta_non_spec = beta.index_select(1, non_spec_token_indx)
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            else:
                g_non_spec = g
                beta_non_spec = beta
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        else:
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            g_non_spec = None
            beta_non_spec = None
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        # 2. Recurrent attention
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        # 2.1: Process the multi-query part
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        if spec_sequence_masks is not None:
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            core_attn_out_spec, last_recurrent_state = (
                fused_sigmoid_gating_delta_rule_update(
                    A_log=self.A_log,
                    a=a,
                    b=b,
                    dt_bias=self.dt_bias,
                    q=query_spec,
                    k=key_spec,
                    v=value_spec,
                    initial_state=ssm_state,
                    inplace_final_state=True,
                    cu_seqlens=spec_query_start_loc[
                        : attn_metadata.num_spec_decodes + 1
                    ],
                    ssm_state_indices=spec_state_indices_tensor,
                    num_accepted_tokens=num_accepted_tokens,
                    use_qk_l2norm_in_kernel=True,
                )
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            )
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        else:
            core_attn_out_spec, last_recurrent_state = None, None

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        # 2.2: Process the remaining part
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        if attn_metadata.num_prefills > 0:
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            initial_state = ssm_state[non_spec_state_indices_tensor].contiguous()
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            initial_state[~has_initial_state, ...] = 0
            (
                core_attn_out_non_spec,
                last_recurrent_state,
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            ) = self.chunk_gated_delta_rule(
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                q=query_non_spec,
                k=key_non_spec,
                v=value_non_spec,
                g=g_non_spec,
                beta=beta_non_spec,
                initial_state=initial_state,
                output_final_state=True,
                cu_seqlens=non_spec_query_start_loc,
                use_qk_l2norm_in_kernel=True,
            )
            # Init cache
            ssm_state[non_spec_state_indices_tensor] = last_recurrent_state.to(
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                ssm_state.dtype
            )
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        elif attn_metadata.num_decodes > 0:
            core_attn_out_non_spec, last_recurrent_state = (
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                fused_sigmoid_gating_delta_rule_update(
                    A_log=self.A_log,
                    a=a,
                    b=b,
                    dt_bias=self.dt_bias,
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                    q=query_non_spec,
                    k=key_non_spec,
                    v=value_non_spec,
                    initial_state=ssm_state,
                    inplace_final_state=True,
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                    cu_seqlens=non_spec_query_start_loc[
                        : attn_metadata.num_decodes + 1
                    ],
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                    ssm_state_indices=non_spec_state_indices_tensor,
                    use_qk_l2norm_in_kernel=True,
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                )
            )
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        else:
            core_attn_out_non_spec, last_recurrent_state = None, None

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        # 3. Merge core attention output
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        if spec_sequence_masks is not None and core_attn_out_non_spec is not None:
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            merged_out = torch.empty(
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                (1, num_actual_tokens, *core_attn_out_spec.shape[2:]),
                dtype=core_attn_out_non_spec.dtype,
                device=core_attn_out_non_spec.device,
            )
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            merged_out.index_copy_(1, spec_token_indx, core_attn_out_spec)
            merged_out.index_copy_(1, non_spec_token_indx, core_attn_out_non_spec)
            core_attn_out[:num_actual_tokens] = merged_out.squeeze(0)
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        elif spec_sequence_masks is not None:
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            core_attn_out[:num_actual_tokens] = core_attn_out_spec.squeeze(0)
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        else:
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            core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(0)
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    def _forward_core_decode_non_spec(
        self,
        mixed_qkv: torch.Tensor,
        b: torch.Tensor,
        a: torch.Tensor,
        core_attn_out: torch.Tensor,
        attn_metadata: GDNAttentionMetadata,
    ):
        """
        Core attention computation with a packed non-spec decode fast path.
        """
        non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor  # noqa: E501
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        self_kv_cache = self.kv_cache[0]
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        conv_state = self_kv_cache[0].transpose(-1, -2)
        ssm_state = self_kv_cache[1]
        num_actual_tokens = attn_metadata.num_actual_tokens

        mixed_qkv = mixed_qkv[:num_actual_tokens]
        b = b[:num_actual_tokens]
        a = a[:num_actual_tokens]

        conv_weights = self.conv1d.weight.view(
            self.conv1d.weight.size(0), self.conv1d.weight.size(2)
        )
        mixed_qkv_non_spec = causal_conv1d_update(
            mixed_qkv,
            conv_state,
            conv_weights,
            self.conv1d.bias,
            self.activation,
            conv_state_indices=non_spec_state_indices_tensor[:num_actual_tokens],
            validate_data=False,
        )
        out_buf = core_attn_out[:num_actual_tokens].unsqueeze(1)
        fused_recurrent_gated_delta_rule_packed_decode(
            mixed_qkv=mixed_qkv_non_spec,
            a=a,
            b=b,
            A_log=self.A_log,
            dt_bias=self.dt_bias,
            scale=self.head_k_dim**-0.5,
            initial_state=ssm_state,
            out=out_buf,
            ssm_state_indices=non_spec_state_indices_tensor[:num_actual_tokens],
            use_qk_l2norm_in_kernel=True,
        )
        return

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class Qwen3NextAttention(nn.Module):
    def __init__(
        self,
        config: Qwen3NextConfig,
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        model_config: ModelConfig | None = None,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = config.num_attention_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = config.num_key_value_heads
        if self.total_num_kv_heads >= 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 % 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 tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // 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.dual_chunk_attention_config = getattr(
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            config, "dual_chunk_attention_config", None
        )
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        self.attn_output_gate = getattr(config, "attn_output_gate", True)

        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=getattr(config, "qkv_bias", False),
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            config.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        self.rotary_emb = get_rope(
            head_size=self.head_dim,
            max_position=config.max_position_embeddings,
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            rope_parameters=config.rope_parameters,
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            dual_chunk_attention_config=self.dual_chunk_attention_config,
        )

        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
            **{
                "layer_idx": extract_layer_index(prefix),
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                "dual_chunk_attention_config": self.dual_chunk_attention_config,
            }
            if self.dual_chunk_attention_config
            else {},
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        )

        self.q_norm = Qwen3NextRMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = Qwen3NextRMSNorm(self.head_dim, eps=config.rms_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        output: torch.Tensor,
        hidden_states: torch.Tensor,
    ):
        qkv, _ = self.qkv_proj(hidden_states)

        if self.attn_output_gate:
            q_gate, k, v = qkv.split(
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                [self.q_size * 2, self.kv_size, self.kv_size], dim=-1
            )
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            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:
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            q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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        q = self.q_norm(q.view(-1, self.num_heads, self.head_dim)).view(
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            -1, self.num_heads * self.head_dim
        )
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        k = self.k_norm(k.view(-1, self.num_kv_heads, self.head_dim)).view(
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            -1, self.num_kv_heads * self.head_dim
        )
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        q, k = self.rotary_emb(positions, q, k)

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

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

        output[:], _ = self.o_proj(attn_output)


class Qwen3NextDecoderLayer(nn.Module):
    def __init__(
        self,
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        vllm_config: VllmConfig,
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        layer_type: str,
        prefix: str = "",
    ) -> None:
        super().__init__()
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        config = vllm_config.model_config.hf_config
        model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        speculative_config = vllm_config.speculative_config
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        self.layer_type = layer_type
        self.layer_idx = extract_layer_index(prefix)

        if self.layer_type == "linear_attention":
            self.linear_attn = Qwen3NextGatedDeltaNet(
                config,
                model_config=model_config,
                cache_config=cache_config,
                quant_config=quant_config,
                speculative_config=speculative_config,
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                prefix=f"{prefix}.linear_attn",
            )
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        elif self.layer_type == "full_attention":
            self.self_attn = Qwen3NextAttention(
                config,
                model_config=model_config,
                cache_config=cache_config,
                quant_config=quant_config,
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                prefix=f"{prefix}.self_attn",
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            )
        else:
            raise ValueError(f"Invalid layer_type {self.layer_type}")

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        mlp_only_layers = (
            [] if not hasattr(config, "mlp_only_layers") else config.mlp_only_layers
        )
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        if (self.layer_idx not in mlp_only_layers) and (
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            config.num_experts > 0
            and (self.layer_idx + 1) % config.decoder_sparse_step == 0
        ):
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            self.mlp = Qwen3NextSparseMoeBlock(
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                vllm_config=vllm_config,
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                prefix=f"{prefix}.mlp",
            )
        else:
            self.mlp = Qwen3NextMLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
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                prefix=f"{prefix}.mlp",
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            )

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        self.input_layernorm = Qwen3NextRMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
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        self.post_attention_layernorm = Qwen3NextRMSNorm(
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            config.hidden_size, eps=config.rms_norm_eps
        )
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        self.layer_scale = getattr(config, "layer_scale", False)
        if self.layer_scale:
            self.attn_layer_scale = torch.nn.Parameter(
                torch.zeros(
                    1,
                    1,
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                    config.hidden_size,
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                ),
            )
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            self.ffn_layer_scale = torch.nn.Parameter(
                torch.zeros(
                    1,
                    1,
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                    config.hidden_size,
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                ),
            )
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    def forward(
        self,
        hidden_states: torch.Tensor,
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        residual: torch.Tensor | None,
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        positions: torch.Tensor = None,
        **kwargs: object,
    ):
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
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            hidden_states, residual = self.input_layernorm(hidden_states, residual)
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        self_attention_output = torch.empty_like(hidden_states)
        if self.layer_type == "linear_attention":
            self.linear_attn(
                hidden_states=hidden_states,
                output=self_attention_output,
            )
        elif self.layer_type == "full_attention":
            self.self_attn(
                hidden_states=hidden_states,
                output=self_attention_output,
                positions=positions,
            )
        else:
            raise ValueError("Invalid layer_type")
        hidden_states = self_attention_output

        if self.layer_scale:
            if len(hidden_states.shape) == 2:
                hidden_states = hidden_states * (
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                    self.attn_layer_scale.to(hidden_states.dtype)[0] + 1
                )
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            else:
                hidden_states = hidden_states * (
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                    self.attn_layer_scale.to(hidden_states.dtype) + 1
                )
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        # Fully Connected
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        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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        hidden_states = self.mlp(hidden_states)

        if self.layer_scale:
            if len(hidden_states.shape) == 2:
                hidden_states = hidden_states * (
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                    self.ffn_layer_scale.to(hidden_states.dtype)[0] + 1
                )
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            else:
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                assert len(hidden_states.shape) == len(self.ffn_layer_scale.shape), (
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                    f"shape must be the same {len(hidden_states.shape)}, "
                    f"{len(self.ffn_layer_scale.shape)}"
                )
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                hidden_states = hidden_states * (
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                    self.ffn_layer_scale.to(hidden_states.dtype) + 1
                )
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        return hidden_states, residual


@support_torch_compile
class Qwen3NextModel(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

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        config: Qwen3NextConfig = vllm_config.model_config.hf_text_config
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        parallel_config = vllm_config.parallel_config
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        eplb_config = parallel_config.eplb_config
        self.num_redundant_experts = eplb_config.num_redundant_experts

        self.config = config
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        self.vocab_size = config.vocab_size
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        self.embed_tokens = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
        )

        def get_layer(prefix: str):
            return Qwen3NextDecoderLayer(
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                vllm_config,
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                layer_type=config.layer_types[extract_layer_index(prefix)],
                prefix=prefix,
            )

        self.start_layer, self.end_layer, self.layers = make_layers(
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            config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers"
        )
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )
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        if get_pp_group().is_last_rank:
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            self.norm = Qwen3NextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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        else:
            self.norm = PPMissingLayer()
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        self.aux_hidden_state_layers: tuple[int, ...] = ()

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    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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        return self.embed_tokens(input_ids)

    def forward(
        self,
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        input_ids: torch.Tensor | None,
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        positions: torch.Tensor,
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        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
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    ) -> torch.Tensor | IntermediateTensors | tuple[torch.Tensor, list[torch.Tensor]]:
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        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
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                hidden_states = self.embed_input_ids(input_ids)
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            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

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        aux_hidden_states = []
        for layer_idx, layer in enumerate(
            islice(self.layers, self.start_layer, self.end_layer),
            start=self.start_layer,
        ):
            if layer_idx in self.aux_hidden_state_layers:
                aux_hidden_states.append(
                    hidden_states + residual if residual is not None else hidden_states
                )
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            hidden_states, residual = layer(
                positions=positions,
                hidden_states=hidden_states,
                residual=residual,
            )

        if not get_pp_group().is_last_rank:
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            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
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        hidden_states, _ = self.norm(hidden_states, residual)
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        if aux_hidden_states:
            return hidden_states, aux_hidden_states
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        return hidden_states

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
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        return SharedFusedMoE.make_expert_params_mapping(
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            self,
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            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
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            num_experts=getattr(self.config, "num_experts", 0),
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            num_redundant_experts=self.num_redundant_experts,
        )
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    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        expert_params_mapping = self.get_expert_mapping()
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue

            if name.startswith("mtp."):
                continue

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            # Remapping the name of FP8 kv-scale.
            if name.endswith("scale"):
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue

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

                if "mlp.experts" in name:
                    continue

                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                # Skip layers on other devices.
                if is_pp_missing_parameter(name, self):
                    continue
                # name = apply_attn_prefix(name, params_dict)
                if name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue
                    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.
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                    if (
                        name.endswith(".bias") or name.endswith("_bias")
                    ) and name not in params_dict:
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                        continue
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                    if name not in params_dict:
                        continue
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                    param = params_dict[name]
                    weight_loader = param.weight_loader
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                    weight_loader(
                        param,
                        loaded_weight,
                        name,
                        shard_id=shard_id,
                        expert_id=expert_id,
                    )
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                    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
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                    if name not in params_dict:
                        logger.warning_once(
                            f"Parameter {name} not found in params_dict, skip loading"
                        )
                        continue
1521
                    param = params_dict[name]
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                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
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                    weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


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class QwenNextMixtureOfExperts(MixtureOfExperts):
    def update_physical_experts_metadata(
        self,
        num_physical_experts: int,
        num_local_physical_experts: int,
    ) -> None:
        assert self.num_local_physical_experts == num_local_physical_experts
        self.num_physical_experts = num_physical_experts
        self.num_local_physical_experts = num_local_physical_experts
        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
        for layer in self.model.layers:
            if isinstance(layer.mlp, Qwen3NextSparseMoeBlock):
                moe = layer.mlp
                moe.n_local_physical_experts = num_local_physical_experts
                moe.n_physical_experts = num_physical_experts
                moe.n_redundant_experts = self.num_redundant_experts
                moe.experts.update_expert_map()

    def set_moe_parameters(self):
        self.expert_weights = []

        self.moe_layers = []
        example_moe = None
        for layer in self.model.layers:
            if isinstance(layer, Qwen3NextDecoderLayer) and isinstance(
                layer.mlp, Qwen3NextSparseMoeBlock
            ):
                example_moe = layer.mlp
                self.moe_layers.append(layer.mlp.experts)

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        if example_moe is None:
            raise RuntimeError("No Qwen3Next layer found in the model.layers.")
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        # Set MoE hyperparameters
        self.num_moe_layers = len(self.moe_layers)
        self.num_expert_groups = 1
        self.num_shared_experts = 0
        self.num_logical_experts = example_moe.n_logical_experts
        self.num_physical_experts = example_moe.n_physical_experts
        self.num_local_physical_experts = example_moe.n_local_physical_experts
        self.num_routed_experts = example_moe.n_routed_experts
        self.num_redundant_experts = example_moe.n_redundant_experts


1574
class Qwen3NextForCausalLM(
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1580
    nn.Module,
    HasInnerState,
    SupportsLoRA,
    SupportsPP,
    QwenNextMixtureOfExperts,
    IsHybrid,
1581
):
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1587
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
1588
        "gate_up_proj": ["gate_proj", "up_proj"],
1589
1590
        "in_proj_qkvz": ["in_proj_qkvz"],
        "in_proj_ba": ["in_proj_ba"],
1591
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1593
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
1594
        config = vllm_config.model_config.hf_text_config
1595
1596
1597
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
1598

1599
        scheduler_config = vllm_config.scheduler_config
1600
1601
1602
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1604
        if cache_config.mamba_cache_mode == "all":
            raise NotImplementedError(
                "Qwen3Next currently does not support 'all' prefix caching, "
                "please use '--mamba-cache-mode=align' instead"
            )
1605
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1609
        self.quant_config = vllm_config.quant_config

        super().__init__()
        self.config = config
        self.scheduler_config = scheduler_config
1610
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1612
        self.model = Qwen3NextModel(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
1613

1614
        self.lm_head = ParallelLMHead(
1615
            config.vocab_size,
1616
            config.hidden_size,
1617
1618
            prefix=maybe_prefix(prefix, "lm_head"),
        )
1619
        self.logits_processor = LogitsProcessor(config.vocab_size)
1620
        self.make_empty_intermediate_tensors = (
1621
1622
            self.model.make_empty_intermediate_tensors
        )
1623
1624

        # Set MoE hyperparameters
1625
        self.set_moe_parameters()
1626

1627
1628
    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)
1629
1630
1631

    def forward(
        self,
1632
        input_ids: torch.Tensor | None,
1633
        positions: torch.Tensor,
1634
1635
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
1636
1637
        **kwargs: object,
    ):
1638
1639
1640
        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )
1641
1642
1643
1644
1645
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1647
1648
1649

        return hidden_states

    @classmethod
    def get_mamba_state_dtype_from_config(
        cls,
        vllm_config: "VllmConfig",
    ) -> tuple[torch.dtype, torch.dtype]:
        return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
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1652
            vllm_config.model_config.dtype,
            vllm_config.cache_config.mamba_cache_dtype,
            vllm_config.cache_config.mamba_ssm_cache_dtype,
1653
        )
1654
1655
1656

    @classmethod
    def get_mamba_state_shape_from_config(
1657
        cls, vllm_config: "VllmConfig"
1658
1659
    ) -> tuple[tuple[int, int], tuple[int, int]]:
        parallel_config = vllm_config.parallel_config
1660
        hf_config = vllm_config.model_config.hf_text_config
1661
        tp_size = parallel_config.tensor_parallel_size
1662
1663
1664
1665
1666
        num_spec = (
            vllm_config.speculative_config.num_speculative_tokens
            if vllm_config.speculative_config
            else 0
        )
1667
        return MambaStateShapeCalculator.gated_delta_net_state_shape(
1668
1669
1670
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1673
1674
1675
            tp_size,
            hf_config.linear_num_key_heads,
            hf_config.linear_num_value_heads,
            hf_config.linear_key_head_dim,
            hf_config.linear_value_head_dim,
            hf_config.linear_conv_kernel_dim,
            num_spec,
        )
1676

1677
1678
1679
1680
    @classmethod
    def get_mamba_state_copy_func(cls) -> tuple[MambaStateCopyFunc, MambaStateCopyFunc]:
        return MambaStateCopyFuncCalculator.gated_delta_net_state_copy_func()

1681
1682
1683
    def compute_logits(
        self,
        hidden_states: torch.Tensor,
1684
    ) -> torch.Tensor | None:
1685
        return self.logits_processor(self.lm_head, hidden_states)
1686

1687
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
        loader = AutoWeightsLoader(
            self,
            skip_prefixes=["mtp."],
        )
        return loader.load_weights(weights)

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        return self.model.get_expert_mapping()


1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
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1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
def gdn_in_proj(
    hidden_states: torch.Tensor,
    qkvz_output_size: int,
    ba_output_size: int,
    layer_name: str,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Custom op for the input projection.
    """
    forward_context: ForwardContext = get_forward_context()
    self = forward_context.no_compile_layers[layer_name]
    return self._forward_in_proj(hidden_states)


def gdn_in_proj_fake(
    hidden_states: torch.Tensor,
    qkvz_output_size: int,
    ba_output_size: int,
    layer_name: str,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Fake implementation for torch.compile."""
    return hidden_states.new_empty(
        hidden_states.shape[0], qkvz_output_size
    ), hidden_states.new_empty(hidden_states.shape[0], ba_output_size)


1724
1725
1726
1727
1728
def gdn_attention_core(
    mixed_qkv: torch.Tensor,
    b: torch.Tensor,
    a: torch.Tensor,
    core_attn_out: torch.Tensor,
1729
1730
    layer_name: str,
) -> None:
1731
1732
1733
1734
1735
    """
    Custom op for the core attention computation.
    Only handles the convolution + recurrent attention part.
    Input/output projections are handled outside this op.
    """
1736
1737
    forward_context: ForwardContext = get_forward_context()
    self = forward_context.no_compile_layers[layer_name]
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
    self._forward_core(
        mixed_qkv=mixed_qkv,
        b=b,
        a=a,
        core_attn_out=core_attn_out,
    )


def gdn_attention_core_fake(
    mixed_qkv: torch.Tensor,
    b: torch.Tensor,
    a: torch.Tensor,
    core_attn_out: torch.Tensor,
1751
1752
    layer_name: str,
) -> None:
1753
    """Fake implementation for torch.compile."""
1754
1755
1756
    return


1757
1758
1759
1760
1761
1762
direct_register_custom_op(
    op_name="gdn_in_proj",
    op_func=gdn_in_proj,
    fake_impl=gdn_in_proj_fake,
)

1763
direct_register_custom_op(
1764
1765
1766
1767
    op_name="gdn_attention_core",
    op_func=gdn_attention_core,
    mutates_args=["core_attn_out"],
    fake_impl=gdn_attention_core_fake,
1768
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1770
1771
1772
1773
)


@triton.jit
def fused_gdn_gating_kernel(
    g,
1774
    beta_output,
1775
1776
    A_log,
    a,
1777
    b,
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
    dt_bias,
    seq_len,
    NUM_HEADS: tl.constexpr,
    beta: tl.constexpr,
    threshold: tl.constexpr,
    BLK_HEADS: tl.constexpr,
):
    i_b, i_s, i_d = tl.program_id(0), tl.program_id(1), tl.program_id(2)
    head_off = i_d * BLK_HEADS + tl.arange(0, BLK_HEADS)
    off = i_b * seq_len * NUM_HEADS + i_s * NUM_HEADS + head_off
    mask = head_off < NUM_HEADS
    blk_A_log = tl.load(A_log + head_off, mask=mask)
    blk_a = tl.load(a + off, mask=mask)
1791
    blk_b = tl.load(b + off, mask=mask)
1792
1793
1794
    blk_bias = tl.load(dt_bias + head_off, mask=mask)
    # If the model is loaded in fp16, without the .float() here, A might be -inf
    x = blk_a.to(tl.float32) + blk_bias.to(tl.float32)
1795
1796
1797
    softplus_x = tl.where(
        beta * x <= threshold, (1 / beta) * tl.log(1 + tl.exp(beta * x)), x
    )
1798
1799
    blk_g = -tl.exp(blk_A_log.to(tl.float32)) * softplus_x
    tl.store(g + off, blk_g.to(g.dtype.element_ty), mask=mask)
1800
    # compute beta_output = sigmoid(b)
1801
1802
1803
1804
    blk_beta_output = tl.sigmoid(blk_b.to(tl.float32))
    tl.store(
        beta_output + off, blk_beta_output.to(beta_output.dtype.element_ty), mask=mask
    )
1805
1806
1807
1808
1809


def fused_gdn_gating(
    A_log: torch.Tensor,
    a: torch.Tensor,
1810
    b: torch.Tensor,
1811
1812
1813
    dt_bias: torch.Tensor,
    beta: float = 1.0,
    threshold: float = 20.0,
1814
1815
1816
1817
1818
1819
1820
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Fused computation of g and beta for Gated Delta Net.
    g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias)
    beta_output = b.sigmoid()
    TODO maybe use torch.compile to replace this triton kernel
    """
1821
1822
1823
    batch, num_heads = a.shape
    seq_len = 1
    grid = (batch, seq_len, triton.cdiv(num_heads, 8))
1824
    g = torch.empty(1, batch, num_heads, dtype=torch.float32, device=a.device)
1825
    beta_output = torch.empty(1, batch, num_heads, dtype=b.dtype, device=b.device)
1826
    fused_gdn_gating_kernel[grid](
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
        g,
        beta_output,
        A_log,
        a,
        b,
        dt_bias,
        seq_len,
        num_heads,
        beta,
        threshold,
        8,
        num_warps=1,
1839
    )
1840
    return g, beta_output