# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# Copyright 2025 The vLLM team.
# Copyright 2025 The Qwen Team.
# Copyright 2025 The HuggingFace Inc. team.
# All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only Qwen3.5 Series compatible with HuggingFace weights."""

from collections.abc import Iterable

import torch
from torch import nn

from vllm._aiter_ops import rocm_aiter_ops
from vllm.compilation.decorators import support_torch_compile
from vllm.config import VllmConfig
from vllm.distributed import (
    get_pp_group,
)
from vllm.logger import init_logger
from vllm.model_executor.layers.layernorm import GemmaRMSNorm as Qwen3_5RMSNorm
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.gdn.qwen_gdn_linear_attn import (
    QwenGatedDeltaNetAttention,
)
from vllm.model_executor.layers.mamba.mamba_utils import (
    MambaStateCopyFunc,
    MambaStateCopyFuncCalculator,
    MambaStateDtypeCalculator,
    MambaStateShapeCalculator,
)
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.sequence import IntermediateTensors
from vllm.tokenizers.registry import cached_tokenizer_from_config
from vllm.transformers_utils.configs.qwen3_5 import Qwen3_5Config, Qwen3_5TextConfig
from vllm.transformers_utils.configs.qwen3_5_moe import (
    Qwen3_5MoeConfig,
    Qwen3_5MoeTextConfig,
)

from .interfaces import (
    HasInnerState,
    IsHybrid,
    MixtureOfExperts,
    MultiModalEmbeddings,
    SupportsEagle3,
    SupportsLoRA,
    SupportsMRoPE,
    SupportsPP,
    _require_is_multimodal,
)
from .qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP
from .qwen3_next import (
    Qwen3NextAttention,
    Qwen3NextDecoderLayer,
    Qwen3NextModel,
    Qwen3NextSparseMoeBlock,
    QwenNextMixtureOfExperts,
    _is_shared_expert_fse_compatible,
)
from .qwen3_vl import (
    Qwen3_VisionTransformer,
    Qwen3VLDummyInputsBuilder,
    Qwen3VLForConditionalGeneration,
    Qwen3VLMultiModalProcessor,
    Qwen3VLProcessingInfo,
)
from .utils import (
    AutoWeightsLoader,
    PPMissingLayer,
    WeightsMapper,
    _merge_multimodal_embeddings,
    extract_layer_index,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_fuse_shared_experts,
    maybe_prefix,
)

logger = init_logger(__name__)


class Qwen3_5ProcessingInfo(Qwen3VLProcessingInfo):
    def get_hf_config(self):
        return self.ctx.get_hf_config(Qwen3_5Config)


class Qwen3_5MoeProcessingInfo(Qwen3VLProcessingInfo):
    def get_hf_config(self):
        # transformers 5.x renames the top-level Qwen3.5-MoE config class to
        # Qwen3_5MoeTextConfig for text-only models, while transformers ≤4.x
        # returns Qwen3_5MoeConfig (the multimodal wrapper).  Accept both so
        # that vLLM works regardless of which transformers version is installed.
        return self.ctx.get_hf_config((Qwen3_5MoeConfig, Qwen3_5MoeTextConfig))


class Qwen3_5DecoderLayer(Qwen3NextDecoderLayer):
    def __init__(
        self,
        vllm_config: VllmConfig,
        layer_type: str,
        prefix: str = "",
    ) -> None:
        super(Qwen3NextDecoderLayer, self).__init__()

        config = vllm_config.model_config.hf_text_config
        model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
        parallel_config = vllm_config.parallel_config
        quant_config = vllm_config.quant_config

        self.layer_type = layer_type
        self.layer_idx = extract_layer_index(prefix)
        is_moe_layer = config.model_type == "qwen3_5_moe_text"
        self.use_attn_reduce_scatter_for_moe = (
            parallel_config.use_sequence_parallel_moe
            and parallel_config.pipeline_parallel_size == 1
            and is_moe_layer
        )

        if self.layer_type == "linear_attention":
            self.linear_attn = QwenGatedDeltaNetAttention(
                config=config,
                vllm_config=vllm_config,
                prefix=f"{prefix}.linear_attn",
                gqa_interleaved_layout=False,
                reduce_results=not self.use_attn_reduce_scatter_for_moe,
            )
        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",
                reduce_results=not self.use_attn_reduce_scatter_for_moe,
            )
        else:
            raise ValueError(f"Invalid layer_type {self.layer_type}")

        # NOTE: Determine the MLP type based on the model type
        # Qwen3.5 use all layers for MLP / Qwen3.5-MoE use sparse MoE blocks
        if config.model_type == "qwen3_5_moe_text":
            self.mlp = Qwen3NextSparseMoeBlock(
                vllm_config=vllm_config,
                prefix=f"{prefix}.mlp",
            )
        elif config.model_type == "qwen3_5_text":
            self.mlp = Qwen3NextMLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                prefix=f"{prefix}.mlp",
            )
        else:
            raise ValueError(f"Invalid model_type {config.model_type}")

        self.input_layernorm = Qwen3_5RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
        self.post_attention_layernorm = Qwen3_5RMSNorm(
            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,
                ),
            )
            self.ffn_layer_scale = torch.nn.Parameter(
                torch.zeros(
                    1,
                    1,
                    config.hidden_size,
                ),
            )


@support_torch_compile(
    dynamic_arg_dims={
        "input_ids": 0,
        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
        # otherwise (seq_len, ).
        "positions": -1,
        "intermediate_tensors": 0,
        "inputs_embeds": 0,
    }
)
class Qwen3_5Model(Qwen3NextModel):
    # Qwen3.5 ships the GDN in_proj checkpoints separately (qwen3-next
    # pre-fuses them); fuse them on top of the qwen3-next QKV/gate_up mapping.
    hf_to_vllm_mapper = Qwen3NextModel.hf_to_vllm_mapper | WeightsMapper(
        orig_to_new_stacked={
            ".in_proj_qkv": (".in_proj_qkvz", (0, 1, 2)),
            ".in_proj_z": (".in_proj_qkvz", 3),
            ".in_proj_b": (".in_proj_ba", 0),
            ".in_proj_a": (".in_proj_ba", 1),
        }
    )

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super(Qwen3NextModel, self).__init__()

        config: Qwen3_5TextConfig | Qwen3_5MoeTextConfig = (
            vllm_config.model_config.hf_text_config
        )
        parallel_config = vllm_config.parallel_config

        eplb_config = parallel_config.eplb_config
        self.num_redundant_experts = eplb_config.num_redundant_experts

        self.config = config
        self.quant_config = vllm_config.quant_config

        self.vocab_size = config.vocab_size

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

        def get_layer(prefix: str):
            return Qwen3_5DecoderLayer(
                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
        )

        if get_pp_group().is_last_rank:
            self.norm = Qwen3_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer()

        self.aux_hidden_state_layers: tuple[int, ...] = ()

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        # FSE must match construction (Qwen3NextSparseMoeBlock): reroute the
        # shared expert into the extra fused slot only when AITER FSE is both
        # requested and compatible with the quant spec.
        if "moe" in self.config.model_type:
            weights = maybe_fuse_shared_experts(
                weights,
                enabled=rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
                and _is_shared_expert_fse_compatible(self.quant_config),
                n_routed_experts=self.config.num_experts,
                n_shared_experts=1,
                ckpt_prefix="mlp.shared_expert",
            )
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)


class Qwen3_5ForCausalLMBase(
    nn.Module,
    HasInnerState,
    IsHybrid,
    SupportsEagle3,
    SupportsLoRA,
    SupportsMRoPE,
    SupportsPP,
):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": ["gate_proj", "up_proj"],
        # GDN fused projections.
        "in_proj_qkvz": ["in_proj_qkv", "in_proj_z"],
        "in_proj_ba": ["in_proj_b", "in_proj_a"],
    }

    # Some community text-only checkpoints keep the extraneous
    # `model.language_model.` prefix inherited from the VL training stack.
    # Strip it so both prefixed and clean checkpoints load correctly.
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={"model.language_model.": "model."},
    )

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        config = vllm_config.model_config.hf_text_config
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config

        scheduler_config = vllm_config.scheduler_config
        if cache_config.mamba_cache_mode == "all":
            raise NotImplementedError(
                "Qwen3.5 currently does not support 'all' prefix caching, "
                "please use '--mamba-cache-mode=align' instead"
            )
        self.quant_config = vllm_config.quant_config

        super().__init__()
        self.config = config
        self.scheduler_config = scheduler_config
        self.model = Qwen3_5Model(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )

        if get_pp_group().is_last_rank:
            if config.tie_word_embeddings:
                self.lm_head = self.model.embed_tokens
            else:
                self.lm_head = ParallelLMHead(
                    config.vocab_size,
                    config.hidden_size,
                    quant_config=self.quant_config,
                    prefix=maybe_prefix(prefix, "lm_head"),
                )
        else:
            self.lm_head = PPMissingLayer()

        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors
        )

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

    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
        self.model.aux_hidden_state_layers = layers

    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
        num_layers = len(self.model.layers)
        return (2, num_layers // 2, num_layers - 3)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs: object,
    ):
        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )

        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(
            vllm_config.model_config.dtype,
            vllm_config.cache_config.mamba_cache_dtype,
            vllm_config.cache_config.mamba_ssm_cache_dtype,
        )

    @classmethod
    def get_mamba_state_shape_from_config(
        cls, vllm_config: "VllmConfig"
    ) -> tuple[tuple[int, int], tuple[int, int]]:
        parallel_config = vllm_config.parallel_config
        hf_config = vllm_config.model_config.hf_text_config
        tp_size = parallel_config.tensor_parallel_size
        num_spec = (
            vllm_config.speculative_config.num_speculative_tokens
            if vllm_config.speculative_config
            else 0
        )
        return MambaStateShapeCalculator.gated_delta_net_state_shape(
            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,
        )

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

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        return self.logits_processor(self.lm_head, hidden_states)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(
            self,
            skip_prefixes=["mtp."],
        )
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

    def get_mrope_input_positions(
        self,
        input_tokens: list[int],
        mm_features: list[object],
    ) -> tuple[torch.Tensor, int]:
        positions = torch.arange(len(input_tokens), dtype=torch.long)
        return positions.unsqueeze(0).expand(3, -1), 0


class Qwen3_5ForCausalLM(Qwen3_5ForCausalLMBase):
    pass


class Qwen3_5MoeForCausalLM(Qwen3_5ForCausalLMBase, QwenNextMixtureOfExperts):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__(vllm_config=vllm_config, prefix=prefix)

        # set MoE hyperparameters
        self.set_moe_parameters()


########################################################
# Qwen3_5-Dense
########################################################


@MULTIMODAL_REGISTRY.register_processor(
    Qwen3VLMultiModalProcessor,
    info=Qwen3_5ProcessingInfo,
    dummy_inputs=Qwen3VLDummyInputsBuilder,
)
class Qwen3_5ForConditionalGeneration(Qwen3VLForConditionalGeneration, IsHybrid):
    supports_multimodal_pruning = True

    packed_modules_mapping = Qwen3VLForConditionalGeneration.packed_modules_mapping | {
        "in_proj_qkvz": ["in_proj_qkv", "in_proj_z"],
        "in_proj_ba": ["in_proj_b", "in_proj_a"],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
        # protocols have not __init__ method, so we need to use nn.Module.__init__
        nn.Module.__init__(self)
        config: Qwen3_5Config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config

        self.config = config
        self.model_config = vllm_config.model_config
        self.multimodal_config = multimodal_config
        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
        self.is_multimodal_pruning_enabled = (
            multimodal_config.is_multimodal_pruning_enabled()
        )
        self.video_pruning_rate = self.multimodal_config.video_pruning_rate
        self._tokenizer = cached_tokenizer_from_config(vllm_config.model_config)

        # attributes needed by EVS-related functions inherited from Qwen3-VL
        self.use_deepstack = hasattr(config.vision_config, "deepstack_visual_indexes")
        self.deepstack_num_level = (
            len(config.vision_config.deepstack_visual_indexes)
            if self.use_deepstack
            else 0
        )
        self.visual_dim = config.vision_config.out_hidden_size
        self.multiscale_dim = self.visual_dim * self.deepstack_num_level

        with self._mark_tower_model(vllm_config, {"image", "video"}):
            self.visual = Qwen3_VisionTransformer(
                config.vision_config,
                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "visual"),
            )

        with self._mark_language_model(vllm_config):
            self.language_model = Qwen3_5ForCausalLM(
                vllm_config=vllm_config, prefix=maybe_prefix(prefix, "language_model")
            )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

    def embed_input_ids(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: MultiModalEmbeddings | None = None,
        *,
        is_multimodal: torch.Tensor | None = None,
    ) -> torch.Tensor:
        inputs_embeds = self._embed_text_input_ids(
            input_ids,
            self.language_model.embed_input_ids,
            is_multimodal=is_multimodal,
        )

        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
            return inputs_embeds

        is_multimodal = _require_is_multimodal(is_multimodal)

        inputs_embeds = _merge_multimodal_embeddings(
            inputs_embeds=inputs_embeds,
            multimodal_embeddings=multimodal_embeddings,
            is_multimodal=is_multimodal,
        )

        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs: object,
    ) -> torch.Tensor | IntermediateTensors:
        """Run forward pass for Qwen3.5.

        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            positions: Flattened (concatenated) position ids corresponding to a
                batch.
                **NOTE**: If mrope is enabled (default setting for Qwen3VL
                opensource models), the shape will be `(3, seq_len)`,
                otherwise it will be `(seq_len,).
            intermediate_tensors: Intermediate tensors from previous pipeline
                stages.
            inputs_embeds: Pre-computed input embeddings.
            **kwargs: Additional keyword arguments including:
                - pixel_values: Pixel values to be fed to a model.
                    `None` if no images are passed.
                - image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in
                    LLM. `None` if no images are passed.
                - pixel_values_videos: Pixel values of videos to be fed to a
                    model. `None` if no videos are passed.
                - video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in
                    LLM. `None` if no videos are passed.
        """

        if intermediate_tensors is not None:
            inputs_embeds = None

        hidden_states = self.language_model.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )

        return hidden_states

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(
            self,
            skip_prefixes=["mtp."],
        )
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

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

    @classmethod
    def get_mamba_state_shape_from_config(
        cls, vllm_config: "VllmConfig"
    ) -> tuple[tuple[int, int], tuple[int, int]]:
        parallel_config = vllm_config.parallel_config
        hf_config = vllm_config.model_config.hf_text_config
        tp_size = parallel_config.tensor_parallel_size
        num_spec = (
            vllm_config.speculative_config.num_speculative_tokens
            if vllm_config.speculative_config
            else 0
        )
        return MambaStateShapeCalculator.gated_delta_net_state_shape(
            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,
        )

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


########################################################
# Qwen3_5-MoE
########################################################


class Qwen3_5_MoeMixtureOfExperts(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.language_model.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.moe_layers = []
        example_moe = None
        for layer in self.language_model.model.layers:
            if isinstance(layer, Qwen3_5DecoderLayer) and isinstance(
                layer.mlp, Qwen3NextSparseMoeBlock
            ):
                example_moe = layer.mlp
                self.moe_layers.append(layer.mlp.experts)

        if example_moe is None:
            raise RuntimeError(
                "No Qwen3_5 layer found in the language_model.model.layers."
            )

        # 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


@MULTIMODAL_REGISTRY.register_processor(
    Qwen3VLMultiModalProcessor,
    info=Qwen3_5MoeProcessingInfo,
    dummy_inputs=Qwen3VLDummyInputsBuilder,
)
class Qwen3_5MoeForConditionalGeneration(
    Qwen3_5ForConditionalGeneration, Qwen3_5_MoeMixtureOfExperts
):
    # For MoE LoRA weights loading
    is_3d_moe_weight: bool = True

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
        # protocols have not __init__ method, so we need to use nn.Module.__init__
        nn.Module.__init__(self)
        config: Qwen3_5MoeConfig = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config

        self.config = config
        self.model_config = vllm_config.model_config
        self.multimodal_config = multimodal_config
        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
        self.is_multimodal_pruning_enabled = (
            multimodal_config.is_multimodal_pruning_enabled()
        )
        self.video_pruning_rate = self.multimodal_config.video_pruning_rate
        self._tokenizer = cached_tokenizer_from_config(vllm_config.model_config)

        # attributes needed by EVS-related functions inherited from Qwen3-VL
        self.use_deepstack = hasattr(config.vision_config, "deepstack_visual_indexes")
        self.deepstack_num_level = (
            len(config.vision_config.deepstack_visual_indexes)
            if self.use_deepstack
            else 0
        )
        self.visual_dim = config.vision_config.out_hidden_size
        self.multiscale_dim = self.visual_dim * self.deepstack_num_level

        with self._mark_tower_model(vllm_config, {"image", "video"}):
            self.visual = Qwen3_VisionTransformer(
                config.vision_config,
                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "visual"),
            )

        with self._mark_language_model(vllm_config):
            self.language_model = Qwen3_5MoeForCausalLM(
                vllm_config=vllm_config, prefix=maybe_prefix(prefix, "language_model")
            )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

        # set MoE hyperparameters
        self.set_moe_parameters()