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vllm_npu/models/qwen3_next.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# mypy: ignore-errors
+"""Inference-only Qwen3Next model."""
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+from einops import rearrange
+from torch import nn
+from transformers.activations import ACT2FN
+from vllm import envs
+from vllm.attention import AttentionBackend, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import (CacheConfig, ModelConfig, SpeculativeConfig,
+                         VllmConfig, get_current_vllm_config)
+from vllm.distributed import (divide, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.fla.ops import RMSNormGated
+from vllm.model_executor.layers.fla.ops.chunk import chunk_gated_delta_rule
+from vllm.model_executor.layers.fla.ops.fused_recurrent import \
+    fused_recurrent_gated_delta_rule
+from vllm.model_executor.layers.fused_moe import FusedMoE
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm.model_executor.layers.layernorm import \
+    GemmaRMSNorm as Qwen3NextRMSNorm
+# yapf: enable
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mamba.abstract import MambaBase
+from vllm.model_executor.layers.mamba.mamba_mixer2 import \
+    mamba_v2_sharded_weight_loader
+from vllm.model_executor.layers.mamba.mamba_utils import (
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
+from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
+    causal_conv1d_fn, causal_conv1d_update)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, sharded_weight_loader)
+from vllm.model_executor.models.qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP
+from vllm.model_executor.models.utils import (
+    PPMissingLayer, extract_layer_index, is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.transformers_utils.configs import Qwen3NextConfig
+from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
+
+from vllm.model_executor.models.qwen3_next import (  # isort: skip
+    Qwen3NextAttention, Qwen3NextDecoderLayer, Qwen3NextForCausalLM,
+    Qwen3NextGatedDeltaNet, Qwen3NextModel, Qwen3NextSparseMoeBlock,
+    fused_gdn_gating)
+
+
+class CustomQwen3NextGatedDeltaNet(Qwen3NextGatedDeltaNet, MambaBase):
+
+    @property
+    def mamba_type(self) -> str:
+        return "linear_attention"
+
+    def get_attn_backend(self) -> type["AttentionBackend"]:
+        from vllm.v1.attention.backends.gdn_attn import GDNAttentionBackend
+        return GDNAttentionBackend
+
+    def get_state_dtype(self) -> tuple[torch.dtype, torch.dtype]:
+        return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
+            self.model_config.dtype, self.cache_config.mamba_cache_dtype)
+
+    def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
+        return MambaStateShapeCalculator.gated_delta_net_state_shape(
+            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)
+
+    def __init__(
+        self,
+        config: Qwen3NextConfig,
+        model_config: Optional[ModelConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        speculative_config: Optional[SpeculativeConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        nn.Module.__init__(self)
+        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
+
+        self.config = config
+        self.model_config = model_config
+        self.cache_config = cache_config
+        self.quant_config = quant_config
+        self.speculative_config = speculative_config
+        self.num_spec = (self.speculative_config.num_speculative_tokens
+                         if self.speculative_config else 0)
+
+        # 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
+        self.projection_size_qkvz = self.key_dim * 2 + self.value_dim * 2
+        self.projection_size_ba = self.num_v_heads * 2
+        self.in_proj = MergedColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_sizes=[self.projection_size_qkvz, self.projection_size_ba],
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.in_proj",
+        )
+
+        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.tp_size, self.tp_rank)
+            })
+
+        # selective projection used to make dt, B and C input dependent
+
+        # time step projection (discretization)
+        # instantiate once and copy inv_dt in init_weights of PretrainedModel
+        self.dt_bias = nn.Parameter(
+            torch.ones(self.num_v_heads // self.tp_size), )
+        self.A_log = nn.Parameter(
+            torch.empty(
+                divide(self.num_v_heads, self.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,
+            norm_before_gate=True,
+            device="npu",
+        )
+
+        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")
+
+        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
+
+    def _forward(
+        self,
+        hidden_states: torch.Tensor,
+        output: torch.Tensor,
+    ):
+        forward_context = get_forward_context()
+        attn_metadata: AttentionMetadata = forward_context.attn_metadata
+
+        if attn_metadata is None:
+            # V1 profile run
+            return
+
+        assert isinstance(attn_metadata, dict)
+        attn_metadata = attn_metadata[self.prefix]
+        assert isinstance(attn_metadata, GDNAttentionMetadata)
+        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
+        spec_token_masks = attn_metadata.spec_token_masks
+        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
+        self_kv_cache = self.kv_cache[forward_context.virtual_engine]
+
+        conv_state = self_kv_cache[0].transpose(-1, -2)
+        ssm_state = self_kv_cache[1]
+
+        num_actual_tokens = (attn_metadata.num_prefill_tokens +
+                             attn_metadata.num_decode_tokens +
+                             attn_metadata.num_spec_decode_tokens)
+        num_accepted_tokens = attn_metadata.num_accepted_tokens
+
+        # 1. Set up dimensions for reshapes later
+        projected_states, _ = self.in_proj(hidden_states[:num_actual_tokens])
+        if spec_token_masks is not None:
+            spec_token_masks = spec_token_masks[:num_actual_tokens]
+        projected_states_qkvz, projected_states_ba = torch.split(
+            projected_states,
+            [
+                self.projection_size_qkvz // self.tp_size,
+                self.projection_size_ba // self.tp_size
+            ],
+            dim=-1,
+        )
+        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)
+
+        # 2. Convolution sequence transformation
+        conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
+                                               self.conv1d.weight.size(2))
+
+        if spec_sequence_masks is not None:
+            if (attn_metadata.num_prefills == 0
+                    and attn_metadata.num_decodes == 0):
+                mixed_qkv_spec = mixed_qkv
+                mixed_qkv_non_spec = None
+            else:
+                mixed_qkv_spec = mixed_qkv[spec_token_masks]
+                mixed_qkv_non_spec = mixed_qkv[~spec_token_masks]
+        else:
+            mixed_qkv_spec = None
+            mixed_qkv_non_spec = mixed_qkv
+
+        # 2.2: process the remaining part
+        if attn_metadata.num_prefills > 0:
+            # - "cache_indices" updates the conv_state cache in positions
+            #   pointed to by "mamba_cache_params.state_indices_tensor"
+            mixed_qkv_non_spec = causal_conv1d_fn(
+                mixed_qkv_non_spec.transpose(0, 1),
+                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,
+            ).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,
+                conv_state_indices=non_spec_state_indices_tensor[:attn_metadata
+                                                                 .num_decodes],
+                # validate_data=True,
+            )
+        else:
+            mixed_qkv_non_spec = None
+
+        query_spec, key_spec, value_spec = self.rearrange_mixed_qkv(
+            mixed_qkv_spec)
+        query_non_spec, key_non_spec, value_non_spec = self.rearrange_mixed_qkv(
+            mixed_qkv_non_spec)
+
+        beta = b.sigmoid()
+        g = fused_gdn_gating(self.A_log, a, self.dt_bias)
+        g, beta = map(lambda x: rearrange(x, 'l d -> 1 l d'), (g, beta))
+
+        if spec_sequence_masks is not None:
+            if (attn_metadata.num_prefills == 0
+                    and attn_metadata.num_decodes == 0):
+                g_spec = g
+                beta_spec = beta
+                g_non_spec = None
+                beta_non_spec = None
+            else:
+                g_spec = g[:, spec_token_masks]
+                beta_spec = beta[:, spec_token_masks]
+                g_non_spec = g[:, ~spec_token_masks]
+                beta_non_spec = beta[:, ~spec_token_masks]
+        else:
+            g_spec = None
+            beta_spec = None
+            g_non_spec = g
+            beta_non_spec = beta
+
+        # 3. Recurrent attention
+        # 3.1: process the mutlti-query part
+        if spec_sequence_masks is not None:
+            core_attn_out_spec, last_recurrent_state = (
+                fused_recurrent_gated_delta_rule(
+                    q=query_spec,
+                    k=key_spec,
+                    v=value_spec,
+                    g=g_spec,
+                    beta=beta_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,
+                ))
+        else:
+            core_attn_out_spec, last_recurrent_state = None, None
+
+        # 3.2: process the remaining part
+        if attn_metadata.num_prefills > 0:
+            initial_state = ssm_state[
+                non_spec_state_indices_tensor].contiguous()
+            initial_state[~has_initial_state, ...] = 0
+
+            batch_size = initial_state.shape[0]
+            core_attn_out = []
+            last_recurrent_state = []
+
+            for b_idx in range(batch_size):
+                start, end = non_spec_query_start_loc[
+                    b_idx], non_spec_query_start_loc[b_idx + 1]
+                cur_q = query_non_spec[:, start:end, ...]
+                cur_k = key_non_spec[:, start:end, ...]
+                cur_v = value_non_spec[:, start:end, ...]
+                cur_g = g_non_spec[:, start:end, ...]
+                cur_b = beta_non_spec[:, start:end, ...]
+                cur_state = initial_state[b_idx].unsqueeze(0)
+
+                (
+                    cur_core_attn_out_non_spec,
+                    cur_last_recurrent_state,
+                ) = chunk_gated_delta_rule(
+                    query=cur_q,
+                    key=cur_k,
+                    value=cur_v,
+                    g=cur_g,
+                    beta=cur_b,
+                    initial_state=cur_state,
+                    output_final_state=True,
+                    use_qk_l2norm_in_kernel=True,
+                )
+
+                core_attn_out.append(cur_core_attn_out_non_spec)
+                last_recurrent_state.append(cur_last_recurrent_state)
+
+            tar_dtype = core_attn_out[0].dtype
+            tar_device = core_attn_out[0].device
+            tar_shape = list(core_attn_out[0].shape)
+            tar_shape[1] = non_spec_query_start_loc[-1]
+            core_attn_out_non_spec = torch.empty(tar_shape,
+                                                 dtype=tar_dtype,
+                                                 device=tar_device)
+            for b_idx in range(batch_size):
+                cur_core_attn_out = core_attn_out[b_idx]
+                start, end = non_spec_query_start_loc[
+                    b_idx], non_spec_query_start_loc[b_idx + 1]
+                core_attn_out_non_spec[:, start:end, ...] = cur_core_attn_out
+            last_recurrent_state = torch.cat(last_recurrent_state, dim=0)
+
+            # Init cache
+            ssm_state[non_spec_state_indices_tensor] = last_recurrent_state.to(
+                ssm_state.dtype)
+        elif attn_metadata.num_decodes > 0:
+            core_attn_out_non_spec, last_recurrent_state = (
+                fused_recurrent_gated_delta_rule(
+                    q=query_non_spec,
+                    k=key_non_spec,
+                    v=value_non_spec,
+                    g=g_non_spec,
+                    beta=beta_non_spec,
+                    initial_state=ssm_state,
+                    inplace_final_state=True,
+                    cu_seqlens=non_spec_query_start_loc[:attn_metadata.
+                                                        num_decodes + 1],
+                    ssm_state_indices=non_spec_state_indices_tensor,
+                    use_qk_l2norm_in_kernel=True,
+                ))
+        else:
+            core_attn_out_non_spec, last_recurrent_state = None, None
+
+        # Merge core attention output
+        if (spec_sequence_masks is not None
+                and core_attn_out_non_spec is not None):
+            core_attn_out = torch.empty(
+                (1, num_actual_tokens, *core_attn_out_spec.shape[2:]),
+                dtype=core_attn_out_non_spec.dtype,
+                device=core_attn_out_non_spec.device,
+            )
+            core_attn_out[:, spec_token_masks] = core_attn_out_spec
+            core_attn_out[:, ~spec_token_masks] = core_attn_out_non_spec
+        elif spec_sequence_masks is not None:
+            core_attn_out = core_attn_out_spec
+        else:
+            core_attn_out = core_attn_out_non_spec
+
+        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_actual_tokens], _ = self.out_proj(core_attn_out)
+
+
+class CustomQwen3NextDecoderLayer(Qwen3NextDecoderLayer):
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        layer_type: str,
+        prefix: str = "",
+    ) -> None:
+        nn.Module.__init__(self)
+        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
+
+        self.layer_type = layer_type
+        self.layer_idx = extract_layer_index(prefix)
+
+        if self.layer_type == "linear_attention":
+            self.linear_attn = CustomQwen3NextGatedDeltaNet(
+                config,
+                model_config=model_config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                speculative_config=speculative_config,
+                prefix=f'{prefix}.linear_attn')
+        elif self.layer_type == "full_attention":
+            self.self_attn = Qwen3NextAttention(
+                config,
+                model_config=model_config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=f'{prefix}.self_attn',
+            )
+        else:
+            raise ValueError(f"Invalid layer_type {self.layer_type}")
+
+        mlp_only_layers = ([] if not hasattr(config, "mlp_only_layers") else
+                           config.mlp_only_layers)
+        if (self.layer_idx not in mlp_only_layers) and (
+                config.num_experts > 0 and
+            (self.layer_idx + 1) % config.decoder_sparse_step == 0):
+            self.mlp = Qwen3NextSparseMoeBlock(vllm_config=vllm_config,
+                                               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,
+            )
+
+        self.input_layernorm = Qwen3NextRMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Qwen3NextRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps)
+
+        self.layer_scale = getattr(config, "layer_scale", False)
+        if self.layer_scale:
+            self.attn_layer_scale = torch.nn.Parameter(
+                torch.zeros(
+                    1,
+                    1,
+                    config.hidden_size,
+                    dtype=config.torch_dtype,
+                ), )
+            self.ffn_layer_scale = torch.nn.Parameter(
+                torch.zeros(
+                    1,
+                    1,
+                    config.hidden_size,
+                    dtype=config.torch_dtype,
+                ), )
+
+
+@support_torch_compile
+class CustomQwen3NextModel(Qwen3NextModel):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        nn.Module.__init__(self)
+        config: Qwen3NextConfig = vllm_config.model_config.hf_config
+        parallel_config = vllm_config.parallel_config
+        lora_config = vllm_config.lora_config
+        eplb_config = parallel_config.eplb_config
+        self.num_redundant_experts = eplb_config.num_redundant_experts
+
+        self.config = config
+        lora_vocab = ((lora_config.lora_extra_vocab_size *
+                       (lora_config.max_loras or 1)) if lora_config else 0)
+        self.vocab_size = config.vocab_size + lora_vocab
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+
+        def get_layer(prefix: str):
+            return CustomQwen3NextDecoderLayer(
+                vllm_config,
+                layer_type=config.layer_types[extract_layer_index(prefix)],
+                prefix=prefix,
+            )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            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))
+
+        self.norm = Qwen3NextRMSNorm(config.hidden_size,
+                                     eps=config.rms_norm_eps)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        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),
+            ("in_proj", "in_proj_qkvz", 0),
+            ("in_proj", "in_proj_ba", 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
+
+            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.
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param,
+                                  loaded_weight,
+                                  name,
+                                  shard_id=shard_id,
+                                  expert_id=expert_id)
+                    break
+                else:
+                    # 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
+                    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
+
+
+class CustomQwen3NextForCausalLM(Qwen3NextForCausalLM):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        nn.Module.__init__(self)
+        config = vllm_config.model_config.hf_config
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
+        lora_config = vllm_config.lora_config
+        scheduler_config = vllm_config.scheduler_config
+        assert not cache_config.enable_prefix_caching, \
+            "Qwen3Next currently does not support prefix caching"
+        assert envs.VLLM_USE_V1, "Qwen3Next requires VLLM_USE_V1"
+        self.quant_config = vllm_config.quant_config
+        self.config = config
+        self.scheduler_config = scheduler_config
+        self.model = CustomQwen3NextModel(vllm_config=vllm_config,
+                                          prefix=maybe_prefix(prefix, "model"))
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE
+            # We need bigger padding if using lora for kernel
+            # compatibility
+            if not lora_config else lora_config.lora_vocab_padding_size,
+        )
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+        # Set MoE hyperparameters
+        self.expert_weights = []
+
+        self.moe_layers: list[FusedMoE] = []
+        example_layer = None
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+
+            assert isinstance(layer, Qwen3NextDecoderLayer)
+            if isinstance(layer.mlp, Qwen3NextSparseMoeBlock):
+                example_layer = layer.mlp
+                self.moe_layers.append(layer.mlp.experts)
+
+        if example_layer is None:
+            raise RuntimeError("No Qwen3Next layer found in the model.layers.")
+
+        self.num_moe_layers = len(self.moe_layers)
+        self.num_expert_groups = 1
+        self.num_shared_experts = 0
+        self.num_logical_experts = example_layer.n_logical_experts
+        self.num_physical_experts = example_layer.n_physical_experts
+        self.num_local_physical_experts = example_layer.n_local_physical_experts
+        self.num_routed_experts = example_layer.n_routed_experts
+        self.num_redundant_experts = example_layer.n_redundant_experts