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

# Copyright 2026 Upstage AI.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and 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 SolarOpen2 model
compatible with HuggingFace weights."""

import typing
from collections.abc import Callable, Iterable
from itertools import islice

import torch
from einops import rearrange
from torch import nn

from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
from vllm.distributed import (
    get_ep_group,
    get_pp_group,
    get_tensor_model_parallel_world_size,
)
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.fla.ops.kda import fused_kda_gate
from vllm.model_executor.layers.fused_moe import (
    FusedMoE,
    fused_moe_make_expert_params_mapping,
)
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.gdn.kimi_gdn_linear_attn import (
    KimiGatedDeltaNetAttention,
)
from vllm.model_executor.layers.mamba.mamba_utils import (
    MambaStateCopyFunc,
    MambaStateCopyFuncCalculator,
    MambaStateDtypeCalculator,
    MambaStateShapeCalculator,
)
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 (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
from vllm.triton_utils.allocation import set_triton_allocator

from .interfaces import (
    HasInnerState,
    IsHybrid,
    MixtureOfExperts,
    SupportsLoRA,
    SupportsPP,
)
from .utils import (
    AutoWeightsLoader,
    PPMissingLayer,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)

logger = init_logger(__name__)


def _get_layer_num_experts(config, layer_idx: int) -> int:
    """Number of routed experts for a given layer.

    Supports non-uniform (globally pruned) checkpoints via the optional
    ``n_routed_experts_per_layer`` config field (a list of length
    ``num_hidden_layers``). Falls back to the scalar ``n_routed_experts``
    when the per-layer field is absent (uniform models).
    """
    per_layer = getattr(config, "n_routed_experts_per_layer", None)
    if per_layer is not None:
        return int(per_layer[layer_idx])
    return config.n_routed_experts


def _max_num_experts(config) -> int:
    """Maximum routed-expert count across all layers.

    Used as the upper bound for the global expert->param mapping during
    weight loading so that every per-layer expert id (0..max-1) has an entry.
    """
    per_layer = getattr(config, "n_routed_experts_per_layer", None)
    if per_layer is not None:
        return int(max(per_layer))
    return config.n_routed_experts


class SolarOpen2MLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: QuantizationConfig | None = None,
        reduce_results: bool = True,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=f"{prefix}.down_proj",
        )
        if hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
            )
        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class SolarOpen2MoE(nn.Module):
    def __init__(
        self,
        config,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
        enable_eplb: bool = False,
        num_experts: int | None = None,
    ):
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.routed_scaling_factor = config.routed_scaling_factor

        self.ep_group = get_ep_group().device_group
        self.ep_rank = get_ep_group().rank_in_group
        self.ep_size = self.ep_group.size()
        # `num_experts` is the per-layer routed-expert count for non-uniform
        # (globally pruned) checkpoints; falls back to the scalar config value.
        self.n_routed_experts: int = (
            num_experts if num_experts is not None else config.n_routed_experts
        )
        self.n_shared_experts: int = config.n_shared_experts

        if config.hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {config.hidden_act}. "
                "Only silu is supported for now."
            )
        # NOTE In the transformers implementation, the gate isn't an nn.Linear,
        # so we cannot use ReplicatedLinear here.
        # See: https://github.com/huggingface/transformers/blob/v4.55.1/src/transformers/models/glm4_moe/modeling_glm4_moe.py#L260
        self.gate = nn.Linear(
            config.hidden_size,
            self.n_routed_experts,
            bias=False,
            dtype=torch.float32,
        )
        self.gate.e_score_correction_bias = nn.Parameter(
            torch.empty(self.n_routed_experts, dtype=torch.float32)
        )

        # Load balancing settings.
        vllm_config = get_current_vllm_config()
        eplb_config = vllm_config.parallel_config.eplb_config
        self.enable_eplb = enable_eplb

        self.n_redundant_experts = eplb_config.num_redundant_experts
        self.n_logical_experts = self.n_routed_experts
        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
        self.n_local_physical_experts = self.n_physical_experts // self.ep_size

        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
        )

        if config.n_shared_experts is not None:
            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
            self.shared_experts = SolarOpen2MLP(
                hidden_size=config.hidden_size,
                intermediate_size=intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                reduce_results=False,
                prefix=f"{prefix}.shared_experts",
            )
        else:
            self.shared_experts = None

        # NOTE: v0.22.0 folded SharedFusedMoE into FusedMoE via the
        # `shared_experts` kwarg. `apply_routed_scale_to_output=True` makes
        # FusedMoE scale only the routed-expert output by routed_scaling_factor
        # before adding the (unscaled) shared-expert output and reducing across
        # TP — reproducing the old `routed * scale + shared` semantics.
        self.experts = FusedMoE(
            shared_experts=self.shared_experts,
            num_experts=self.n_routed_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            renormalize=config.norm_topk_prob,
            quant_config=quant_config,
            use_grouped_topk=True,
            num_expert_group=config.n_group,
            topk_group=config.topk_group,
            prefix=f"{prefix}.experts",
            scoring_func="sigmoid",
            routed_scaling_factor=self.routed_scaling_factor,
            apply_routed_scale_to_output=True,
            e_score_correction_bias=self.gate.e_score_correction_bias,
            enable_eplb=self.enable_eplb,
            num_redundant_experts=self.n_redundant_experts,
            router_logits_dtype=torch.float32,
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        num_tokens, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)

        # router_logits: (num_tokens, n_experts)
        router_logits = self.gate(hidden_states.to(dtype=torch.float32))

        final_hidden_states = self.experts(
            hidden_states=hidden_states, router_logits=router_logits
        )
        return final_hidden_states.view(num_tokens, hidden_dim)


class SolarOpen2Attention(nn.Module):
    def __init__(
        self,
        config,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        max_position_embeddings: int = 131072,
        head_dim: int | None = None,
        rms_norm_eps: float = 1e-05,
        qkv_bias: bool = False,
        use_qk_norm: bool = False,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_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 = head_dim or (hidden_size // self.total_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.max_position_embeddings = max_position_embeddings
        self.use_qk_norm = use_qk_norm

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=qkv_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

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

        self.use_rope = getattr(config, "use_rope", False)
        if self.use_rope:
            config.rope_parameters.setdefault("partial_rotary_factor", 0.5)
            self.rotary_emb = get_rope(
                self.head_dim,
                max_position=max_position_embeddings,
                rope_parameters=config.rope_parameters,
            )
        else:
            self.rotary_emb = None
        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",
        )

        if self.use_qk_norm:
            self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
            self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)

        self.use_gqa_gate = config.use_gqa_gate
        if self.use_gqa_gate:
            self.g_proj = ColumnParallelLinear(
                config.hidden_size,
                config.num_attention_heads * self.head_dim,
                bias=getattr(config, "use_gqa_gate_bias", False),
                quant_config=quant_config,
                prefix=f"{prefix}.g_proj",
            )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        output: torch.Tensor,
    ) -> None:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q, k, v = q.contiguous(), k.contiguous(), v.contiguous()
        if self.use_qk_norm:
            q = self.q_norm(q.reshape(-1, self.num_heads, self.head_dim)).reshape(
                q.shape
            )
            k = self.k_norm(k.reshape(-1, self.num_kv_heads, self.head_dim)).reshape(
                k.shape
            )

        if self.rotary_emb is not None:
            q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        if self.use_gqa_gate:
            gate, _ = self.g_proj(hidden_states)
            attn_output = attn_output * torch.sigmoid(gate)
        output[:], _ = self.o_proj(attn_output)


class SolarOpen2KimiDeltaAttention(KimiGatedDeltaNetAttention):
    """KimiGatedDeltaNetAttention with kda_use_full_proj support.

    When kda_use_full_proj=True, replaces the factored f_a_proj+f_b_proj and
    g_a_proj+g_b_proj with single full-rank f_proj and g_proj.
    """

    def __init__(self, config, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__(config, vllm_config, prefix)
        # FLA triton kernels (chunk_kda, solve_tril) need a PyTorch-backed
        # allocator for scratch memory required by triton >= 3.x autotuner.
        set_triton_allocator(current_platform.current_device())
        self.use_full_proj = getattr(config, "kda_use_full_proj", False)
        self.allow_neg_eigval = getattr(config, "kda_allow_neg_eigval", False)

        if self.use_full_proj:
            projection_size = self.head_dim * self.num_heads
            quant_config = self.quant_config
            prefix = self.prefix

            del self.f_a_proj
            del self.f_b_proj
            self.f_proj = ColumnParallelLinear(
                self.hidden_size,
                projection_size,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.f_proj",
            )

            del self.g_a_proj
            del self.g_b_proj
            self.g_proj = ColumnParallelLinear(
                self.hidden_size,
                projection_size,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.g_proj",
            )

    def forward(
        self,
        hidden_states: torch.Tensor,
        positions: torch.Tensor,
        output: torch.Tensor,
    ) -> None:
        num_tokens = hidden_states.size(0)
        q = self.q_proj(hidden_states)[0]
        k = self.k_proj(hidden_states)[0]
        v = self.v_proj(hidden_states)[0]

        beta = self.b_proj(hidden_states)[0].float().sigmoid()
        if self.allow_neg_eigval:
            beta = beta * 2.0
        if self.use_full_proj:
            g1 = self.f_proj(hidden_states)[0]
        else:
            g1 = self.f_b_proj(self.f_a_proj(hidden_states)[0])[0]
        g1 = fused_kda_gate(g1, self.A_log, self.head_dim, g_bias=self.dt_bias)
        beta = beta.unsqueeze(0)
        g1 = g1.unsqueeze(0)

        if self.use_full_proj:
            g_proj_states = self.g_proj(hidden_states)[0]
        else:
            g_proj_states = self.g_b_proj(self.g_a_proj(hidden_states)[0])[0]
        g2 = rearrange(g_proj_states, "... (h d) -> ... h d", d=self.head_dim)

        core_attn_out = torch.zeros(
            (1, num_tokens, self.local_num_heads, self.head_dim),
            dtype=hidden_states.dtype,
            device=hidden_states.device,
        )
        torch.ops.vllm.kda_attention(
            q,
            k,
            v,
            g1,
            beta,
            core_attn_out,
            self.prefix,
        )
        core_attn_out = self.o_norm(core_attn_out, g2)
        core_attn_out = rearrange(core_attn_out, "1 n h d -> n (h d)")
        output[:] = self.o_proj(core_attn_out)[0]


class SolarOpen2DecoderLayer(nn.Module):
    def __init__(
        self,
        config,
        vllm_config: VllmConfig,
        prefix: str = "",
    ) -> None:
        super().__init__()
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        enable_eplb = vllm_config.parallel_config.enable_eplb
        self.hidden_size = config.hidden_size
        max_position_embeddings = getattr(config, "max_position_embeddings", 131072)
        # DecoderLayers are created with `make_layers` which passes the prefix
        # with the layer's index.
        layer_idx = int(prefix.split(sep=".")[-1])
        self.layer_idx = layer_idx

        # Determine if this layer uses GQA: gqa_layers takes priority over gqa_interval
        gqa_layers = getattr(config, "gqa_layers", None)
        if gqa_layers is not None:
            use_gqa = layer_idx in gqa_layers
        else:
            use_gqa = (layer_idx + 1) % config.gqa_interval == 0

        if use_gqa:
            self.self_attn = SolarOpen2Attention(
                config=config,
                hidden_size=self.hidden_size,
                num_heads=config.num_attention_heads,
                num_kv_heads=config.num_key_value_heads,
                max_position_embeddings=max_position_embeddings,
                head_dim=config.head_dim,
                rms_norm_eps=config.rms_norm_eps,
                qkv_bias=config.attention_bias,
                cache_config=cache_config,
                quant_config=quant_config,
                prefix=f"{prefix}.self_attn",
                use_qk_norm=config.use_qk_norm,
            )
        else:
            self.self_attn = SolarOpen2KimiDeltaAttention(
                config,
                vllm_config,
                prefix=f"{prefix}.self_attn",
            )

        if (
            config.n_routed_experts is not None
            and layer_idx >= config.first_k_dense_replace
        ):
            self.mlp = SolarOpen2MoE(
                config=config,
                quant_config=quant_config,
                prefix=f"{prefix}.mlp",
                enable_eplb=enable_eplb,
                # Per-layer routed-expert count for non-uniform pruned models.
                num_experts=_get_layer_num_experts(config, layer_idx),
            )
        else:
            self.mlp = SolarOpen2MLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                prefix=f"{prefix}.mlp",
            )

        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
        self.routed_scaling_factor = config.routed_scaling_factor

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(hidden_states, residual)

        attn_output = torch.empty_like(hidden_states)
        self.self_attn(
            hidden_states=hidden_states,
            positions=positions,
            output=attn_output,
        )
        hidden_states = attn_output

        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
        hidden_states = self.mlp(hidden_states)
        return hidden_states, residual


@support_torch_compile(
    dynamic_arg_dims={
        "input_ids": 0,
        "positions": -1,
        "intermediate_tensors": 0,
        "inputs_embeds": 0,
    }
)
class SolarOpen2Model(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config
        self.config = config

        self.vocab_size = config.vocab_size

        if get_pp_group().is_first_rank:
            self.embed_tokens = VocabParallelEmbedding(
                config.vocab_size, config.hidden_size, prefix=f"{prefix}.embed_tokens"
            )
        else:
            self.embed_tokens = PPMissingLayer()

        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: SolarOpen2DecoderLayer(
                config,
                vllm_config,
                prefix=prefix,
            ),
            prefix=f"{prefix}.layers",
        )

        if get_pp_group().is_last_rank:
            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer()
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )

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

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

            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        for layer in islice(self.layers, self.start_layer, self.end_layer):
            hidden_states, residual = layer(positions, hidden_states, residual)

        if not get_pp_group().is_last_rank:
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )

        hidden_states, _ = self.norm(hidden_states, residual)
        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)
        # Use the maximum per-layer expert count so the global mapping covers
        # every expert id across all (possibly non-uniform) layers. Per-layer
        # FusedMoE modules only consume the ids present in their own shard.
        return fused_moe_make_expert_params_mapping(
            self,
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            num_experts=_max_num_experts(self.config),
        )

    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),
        ]

        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        expert_params_mapping = self.get_expert_mapping()
        for name, loaded_weight in weights:
            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
            if spec_layer is not None:
                continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                # Skip non-stacked layers and experts (experts handled below).
                if weight_name not in name:
                    continue
                # Skip KDA layers
                layer_idx = int(name.split(".")[1])
                gqa_layers = getattr(self.config, "gqa_layers", None)
                if gqa_layers is not None:
                    use_gqa = layer_idx in gqa_layers
                else:
                    use_gqa = (layer_idx + 1) % self.config.gqa_interval == 0
                if (not use_gqa) and (weight_name in ["q_proj", "k_proj", "v_proj"]):
                    continue
                # We have mlp.experts[0].gate_proj in the checkpoint.
                # Since we handle the experts below in expert_params_mapping,
                # we need to skip here BEFORE we update the name, otherwise
                # name will be updated to mlp.experts[0].gate_up_proj, which
                # will then be updated below in expert_params_mapping
                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
                if ("mlp.experts." in name) and name not in params_dict:
                    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
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                is_expert_weight = False
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue

                    # Anyway, this is an expert weight and should not be
                    # attempted to load as other weights later
                    is_expert_weight = True

                    # Do not modify `name` since the loop may continue here
                    # Instead, create a new variable
                    name_mapped = name.replace(weight_name, param_name)

                    if is_pp_missing_parameter(name_mapped, self):
                        continue

                    param = params_dict[name_mapped]
                    # We should ask the weight loader to return success or not
                    # here since otherwise we may skip experts with other
                    # available replicas.
                    weight_loader = typing.cast(
                        Callable[..., bool], param.weight_loader
                    )
                    success = weight_loader(
                        param,
                        loaded_weight,
                        name_mapped,
                        shard_id=shard_id,
                        expert_id=expert_id,
                        return_success=True,
                    )
                    if success:
                        name = name_mapped
                        break
                else:
                    if is_expert_weight:
                        # We've checked that this is an expert weight
                        # However it's not mapped locally to this rank
                        # So we simply skip it
                        continue

                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue

                    # Remapping the name of FP8 kv-scale.
                    name = maybe_remap_kv_scale_name(name, params_dict)
                    if name is None:
                        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 SolarOpen2MixtureOfExperts(MixtureOfExperts):
    def extract_moe_parameters(self, example_moe: SolarOpen2MoE | None) -> None:
        if example_moe is None:
            raise RuntimeError("No SolarOpen2MoE layer found in model.layers.")
        else:
            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_shared_experts = example_moe.n_shared_experts
            self.num_redundant_experts = example_moe.n_redundant_experts

    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 moe in self.moe_mlp_layers:
            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()


class SolarOpen2ForCausalLM(
    nn.Module,
    HasInnerState,
    SupportsPP,
    SupportsLoRA,
    SolarOpen2MixtureOfExperts,
    IsHybrid,
):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    fall_back_to_pt_during_load = False

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config
        self.quant_config = quant_config
        self.model = SolarOpen2Model(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
        if get_pp_group().is_last_rank:
            self.lm_head = ParallelLMHead(
                config.vocab_size,
                config.hidden_size,
                quant_config=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
        )
        self.expert_weights = []

        # Set MoE hyperparameters
        self.num_moe_layers = config.num_hidden_layers - config.first_k_dense_replace
        self.num_expert_groups = config.n_group

        self.moe_layers = []
        self.moe_mlp_layers: list[SolarOpen2MoE] = []

        example_moe = None
        for layer in self.model.layers:
            if isinstance(layer, PPMissingLayer):
                continue

            assert isinstance(layer, SolarOpen2DecoderLayer)
            if isinstance(layer.mlp, SolarOpen2MoE):
                # Pick last one layer since the first ones may be dense layers.
                example_moe = layer.mlp
                self.moe_mlp_layers.append(layer.mlp)
                self.moe_layers.append(layer.mlp.experts)

        self.extract_moe_parameters(example_moe)

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

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
        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, torch.dtype, torch.dtype]:
        return MambaStateDtypeCalculator.kda_state_dtype(
            vllm_config.model_config.dtype, vllm_config.cache_config.mamba_cache_dtype
        )

    @classmethod
    def get_mamba_state_shape_from_config(
        cls, vllm_config: "VllmConfig"
    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
        parallel_config = vllm_config.parallel_config
        hf_config = vllm_config.model_config.hf_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.kda_state_shape(
            tp_size,
            hf_config.linear_attn_config["num_heads"],
            hf_config.linear_attn_config["head_dim"],
            conv_kernel_size=hf_config.linear_attn_config["short_conv_kernel_size"],
            num_spec=num_spec,
        )

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

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

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

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


def get_spec_layer_idx_from_weight_name(config, weight_name: str) -> int | None:
    if hasattr(config, "num_nextn_predict_layers") and (
        config.num_nextn_predict_layers > 0
    ):
        layer_idx = config.num_hidden_layers
        for i in range(config.num_nextn_predict_layers):
            if f"layers.{layer_idx + i}." in weight_name:
                return layer_idx + i
    return None