# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# 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.

import typing
from collections.abc import Callable, Iterable
from typing import Any

import torch
from torch import nn
from transformers import PretrainedConfig

from vllm.attention.layer import Attention, AttentionType
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, ParallelConfig, VllmConfig
from vllm.distributed import (
    get_ep_group,
    get_pp_group,
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
    get_tp_group,
    tensor_model_parallel_all_gather,
)
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention.static_sink_attention import (
    StaticSinkAttention,
)
from vllm.model_executor.layers.fused_moe import SharedFusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mla import MLAModules, MultiHeadLatentAttentionWrapper
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.model_executor.models.interfaces import (
    MixtureOfExperts,
    SupportsLoRA,
    SupportsPP,
)
from vllm.model_executor.models.utils import (
    AutoWeightsLoader,
    PPMissingLayer,
    extract_layer_index,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
    sequence_parallel_chunk,
)
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.config import set_default_rope_theta
from vllm.v1.attention.backends.flash_attn_diffkv import FlashAttentionDiffKVBackend


def check_ffn_act_fn(act_fn: str):
    if act_fn != "silu":
        raise ValueError(
            f"Unsupported activation: {act_fn}. Only silu is supported for now."
        )


class OpenPanguMLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: QuantizationConfig | None = None,
        bias: bool = False,
        reduce_results: bool = True,
        is_sequence_parallel=False,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=bias,
            quant_config=quant_config,
            disable_tp=is_sequence_parallel,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=bias,
            quant_config=quant_config,
            reduce_results=reduce_results,
            disable_tp=is_sequence_parallel,
            prefix=f"{prefix}.down_proj",
        )

        check_ffn_act_fn(hidden_act)
        self.act_fn = SiluAndMul()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.down_proj(self.act_fn(self.gate_up_proj(x)[0]))[0]


class OpenPanguMoE(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        parallel_config: ParallelConfig,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.tp_rank = get_tp_group().rank_in_group

        self.routed_scaling_factor = config.routed_scaling_factor
        self.ep_group = get_ep_group().device_group
        self.ep_rank = self.ep_group.rank()
        self.ep_size = self.ep_group.size()
        self.n_routed_experts: int = config.n_routed_experts
        self.n_shared_experts: int = config.n_shared_experts

        self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe
        check_ffn_act_fn(config.hidden_act)

        self.gate = ReplicatedLinear(
            config.hidden_size,
            config.n_routed_experts,
            bias=False,
            quant_config=None,
            prefix=f"{prefix}.gate",
        )
        if (
            hasattr(config, "router_enable_expert_bias")
            and config.router_enable_expert_bias
        ):
            self.gate.e_score_correction_bias = nn.Parameter(
                torch.empty(self.n_routed_experts, dtype=torch.float32)
            )
        else:
            self.gate.e_score_correction_bias = None

        # Load balancing settings.
        eplb_config = parallel_config.eplb_config
        self.enable_eplb = parallel_config.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 = OpenPanguMLP(
                hidden_size=config.hidden_size,
                intermediate_size=intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                is_sequence_parallel=self.is_sequence_parallel,
                reduce_results=False,
                prefix=f"{prefix}.shared_experts",
            )
        else:
            self.shared_experts = None

        self.experts = SharedFusedMoE(
            shared_experts=self.shared_experts,
            num_experts=config.n_routed_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            reduce_results=False,
            renormalize=config.norm_topk_prob,
            quant_config=quant_config,
            use_grouped_topk=True,
            num_expert_group=1,
            topk_group=1,
            prefix=f"{prefix}.experts",
            scoring_func="sigmoid",
            # we do scaling outside, set factor to 1.0 to avoid double mul
            routed_scaling_factor=1.0,
            e_score_correction_bias=self.gate.e_score_correction_bias,
            enable_eplb=self.enable_eplb,
            num_redundant_experts=self.n_redundant_experts,
            is_sequence_parallel=self.is_sequence_parallel,
        )

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

        if self.is_sequence_parallel:
            hidden_states = sequence_parallel_chunk(hidden_states)

        router_logits, _ = self.gate(hidden_states)

        fused_moe_out = self.experts(
            hidden_states=hidden_states, router_logits=router_logits
        )

        shared_output, final_hidden_states = fused_moe_out
        if self.shared_experts is None:
            assert shared_output is None

        if hidden_states.dtype != torch.float16:
            final_hidden_states *= self.routed_scaling_factor
        elif self.shared_experts is not None:
            assert shared_output is not None
            shared_output *= 1.0 / self.routed_scaling_factor

        if self.shared_experts is not None:
            assert shared_output is not None
            final_hidden_states += shared_output

        if self.is_sequence_parallel:
            final_hidden_states = tensor_model_parallel_all_gather(
                final_hidden_states, 0
            )
            final_hidden_states = final_hidden_states[:num_tokens]
        elif self.tp_size > 1:
            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
                final_hidden_states
            )

        return final_hidden_states.view(num_tokens, hidden_dim)


class OpenPanguMLAAttention(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        hidden_size: int,
        num_heads: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        v_head_dim: int,
        q_lora_rank: int | None,
        kv_lora_rank: int,
        max_position_embeddings: int = 8192,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_rope_head_dim = qk_rope_head_dim
        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.q_lora_rank = q_lora_rank
        self.kv_lora_rank = kv_lora_rank
        self.tp_size = get_tensor_model_parallel_world_size()
        if num_heads % self.tp_size != 0:
            raise ValueError(
                f"num_heads {num_heads} is not divisible by tp_size {self.tp_size}."
            )
        self.num_local_heads = num_heads // self.tp_size

        self.scaling = self.qk_head_dim**-0.5
        self.max_position_embeddings = max_position_embeddings

        self.prefix = prefix

        if self.q_lora_rank is not None:
            self.fused_qkv_a_proj = MergedColumnParallelLinear(
                self.hidden_size,
                [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.fused_qkv_a_proj",
                disable_tp=True,
            )
            self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
            self.q_b_proj = ColumnParallelLinear(
                q_lora_rank,
                self.num_heads * self.qk_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.q_b_proj",
            )
        else:
            self.q_proj = ColumnParallelLinear(
                self.hidden_size,
                self.num_heads * self.qk_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.q_proj",
            )
            self.kv_a_proj_with_mqa = ReplicatedLinear(
                self.hidden_size,
                self.kv_lora_rank + self.qk_rope_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.kv_a_proj_with_mqa",
            )

        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
        self.kv_b_proj = ColumnParallelLinear(
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.kv_b_proj",
        )

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

        # TODO: remove hard coding
        set_default_rope_theta(config, default_theta=10000)
        rope_parameters = {
            "rope_theta": config.rope_parameters["rope_theta"],
            "beta_fast": 32,
            "beta_slow": 1,
            "factor": 1,
            "mscale": 1.0,
            "mscale_all_dim": 1.0,
            "original_max_position_embeddings": max_position_embeddings,
            "type": "yarn",
            "rope_type": "deepseek_yarn",
        }
        self.rotary_emb = get_rope(
            qk_rope_head_dim,
            max_position=max_position_embeddings,
            rope_parameters=rope_parameters,
            is_neox_style=False,
        )

        mla_modules = MLAModules(
            kv_a_layernorm=self.kv_a_layernorm,
            kv_b_proj=self.kv_b_proj,
            rotary_emb=self.rotary_emb,
            o_proj=self.o_proj,
            fused_qkv_a_proj=self.fused_qkv_a_proj
            if self.q_lora_rank is not None
            else None,
            kv_a_proj_with_mqa=self.kv_a_proj_with_mqa
            if self.q_lora_rank is None
            else None,
            q_a_layernorm=self.q_a_layernorm if self.q_lora_rank is not None else None,
            q_b_proj=self.q_b_proj if self.q_lora_rank is not None else None,
            q_proj=self.q_proj if self.q_lora_rank is None else None,
            indexer=None,
            is_sparse=False,
            topk_indices_buffer=None,
        )

        self.mla_attn = MultiHeadLatentAttentionWrapper(
            self.hidden_size,
            self.num_local_heads,
            self.scaling,
            self.qk_nope_head_dim,
            self.qk_rope_head_dim,
            self.v_head_dim,
            self.q_lora_rank,
            self.kv_lora_rank,
            mla_modules,
            cache_config,
            quant_config,
            prefix,
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        return self.mla_attn(positions, hidden_states)


class OpenPanguEmbeddedAttention(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        max_position_embeddings: int = 8192,
        quant_config: QuantizationConfig | None = None,
        bias: bool = False,
        bias_o_proj: bool = False,
        cache_config: CacheConfig | None = None,
        prefix: str = "",
        attn_type: str = AttentionType.DECODER,
    ) -> None:
        super().__init__()
        layer_idx = extract_layer_index(prefix)
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        if self.total_num_heads % tp_size != 0:
            raise ValueError(
                f"total_num_heads {self.total_num_heads} "
                f"is not divisible by tp_size {tp_size}."
            )
        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 and self.total_num_kv_heads % tp_size != 0:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel ranks.
            raise ValueError(
                "Number of KV heads is greater than TP size, "
                f"but total_num_kv_heads {self.total_num_kv_heads} "
                f"is not divisible by tp_size {tp_size}."
            )
        elif (
            self.total_num_kv_heads < tp_size and tp_size % self.total_num_kv_heads != 0
        ):
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel ranks.
            raise ValueError(
                f"Number of KV heads is less than TP size, but tp_size {tp_size} "
                f"is not divisible by total_num_kv_heads {self.total_num_kv_heads}."
            )
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        head_dim = getattr(config, "head_dim", None)
        if head_dim is None:
            head_dim = self.hidden_size // self.total_num_heads
        self.head_dim = head_dim
        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.qkv_proj = QKVParallelLinear(
            hidden_size=hidden_size,
            head_size=self.head_dim,
            total_num_heads=self.total_num_heads,
            total_num_kv_heads=self.total_num_kv_heads,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

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

        self._init_rotary_emb(config, quant_config=quant_config)

        if hasattr(config, "interleaved_sliding_window"):
            interleaved_sliding_window = config.interleaved_sliding_window
            if isinstance(interleaved_sliding_window, int):
                sliding_window = interleaved_sliding_window
            elif isinstance(interleaved_sliding_window, list):
                sw_idx = layer_idx % len(interleaved_sliding_window)
                sliding_window = interleaved_sliding_window[sw_idx]
            else:
                raise ValueError(
                    f"{type(interleaved_sliding_window)} "
                    "for interleaved_sliding_window is not supported."
                )
        else:
            sliding_window = 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,
            per_layer_sliding_window=sliding_window,
            attn_type=attn_type,
            prefix=f"{prefix}.attn",
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output

    def _init_rotary_emb(
        self,
        config: PretrainedConfig,
        quant_config: QuantizationConfig | None,
    ) -> None:
        is_neox_style = True
        is_gguf = quant_config and quant_config.get_name() == "gguf"
        if is_gguf and config.model_type == "PanguEmbedded":
            is_neox_style = False

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


class OpenPanguSinkAttention(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        rope_parameters: dict[str, Any] | None = None,
        max_position_embeddings: int = 8192,
        quant_config: QuantizationConfig | None = None,
        bias: bool = False,
        bias_o_proj: bool = False,
        cache_config: CacheConfig | None = None,
        prefix: str = "",
        attn_type: str = AttentionType.DECODER,
    ) -> None:
        super().__init__()
        layer_idx = extract_layer_index(prefix)
        self.hidden_size = hidden_size
        self.tp_size = get_tensor_model_parallel_world_size()
        self.tp_rank = get_tensor_model_parallel_rank()
        self.total_num_heads = num_heads
        if self.total_num_heads % self.tp_size != 0:
            raise ValueError(
                f"total_num_heads {self.total_num_heads} "
                f"is not divisible by tp_size {self.tp_size}."
            )
        self.num_heads = self.total_num_heads // self.tp_size
        self.total_num_kv_heads = num_kv_heads
        if (
            self.total_num_kv_heads > self.tp_size
            and self.total_num_kv_heads % self.tp_size != 0
        ):
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel ranks.
            raise ValueError(
                "Number of KV heads is greater than TP size, "
                f"but total_num_kv_heads {self.total_num_kv_heads} "
                f"is not divisible by tp_size {self.tp_size}."
            )
        elif self.total_num_kv_heads < self.tp_size:
            # TODO: Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel ranks.
            raise ValueError(
                f"Number of KV heads {self.total_num_kv_heads} is less than "
                f"TP size {self.tp_size}, KV heads replication is not support yet."
            )
        self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
        self.qk_nope_dim = getattr(config, "qk_nope_dim", None)
        self.qk_rope_dim = getattr(config, "qk_rope_dim", None)
        self.v_channels = getattr(config, "v_channels", None)
        self.head_dim = self.qk_rope_dim + self.qk_nope_dim
        self.q_size = self.num_heads * self.head_dim
        self.k_size = self.num_kv_heads * self.head_dim
        self.v_size = self.num_kv_heads * self.v_channels
        self.scaling = self.head_dim**-0.5
        self.max_position_embeddings = max_position_embeddings

        self.param_sink_number = getattr(config, "param_sink_number", 0)
        self.param_sink_with_value = getattr(config, "param_sink_with_value", False)
        self.param_sink_scalar = getattr(config, "param_sink_scalar", None)
        self.param_sink_of_head_num = getattr(config, "param_sink_of_head_dim", False)

        self.qkv_proj = MergedColumnParallelLinear(
            input_size=hidden_size,
            output_sizes=[
                self.q_size * self.tp_size,
                self.k_size * self.tp_size,
                self.v_size * self.tp_size,
            ],
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

        self.o_proj = RowParallelLinear(
            input_size=self.total_num_heads * self.v_channels,
            output_size=hidden_size,
            bias=bias_o_proj,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        self.k_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)

        self._init_rotary_emb(
            config, rope_parameters=rope_parameters, quant_config=quant_config
        )

        if hasattr(config, "interleaved_sliding_window"):
            interleaved_sliding_window = config.interleaved_sliding_window
            if isinstance(interleaved_sliding_window, int):
                sliding_window = interleaved_sliding_window
            elif isinstance(interleaved_sliding_window, list):
                sw_idx = layer_idx % len(interleaved_sliding_window)
                sliding_window = interleaved_sliding_window[sw_idx]
            else:
                raise ValueError(
                    f"{type(interleaved_sliding_window)} "
                    "for interleaved_sliding_window is not supported."
                )
        else:
            sliding_window = None

        FlashAttentionDiffKVBackend.set_head_size_v(self.v_channels)
        self.attn = StaticSinkAttention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            sink_len=self.param_sink_number,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            per_layer_sliding_window=sliding_window,
            attn_type=attn_type,
            prefix=f"{prefix}.attn",
            attn_backend=FlashAttentionDiffKVBackend,
            head_size_v=self.v_channels,
        )

        if self.param_sink_number > 0:
            self.param_sink_key = torch.nn.Parameter(
                torch.empty(
                    (
                        self.param_sink_number,
                        self.num_kv_heads,
                        self.head_dim,
                    ),
                    device=current_platform.current_device(),
                    dtype=config.torch_dtype,
                )
            )
            set_weight_attrs(
                self.param_sink_key,
                {
                    "output_dim": 1,
                    "weight_loader": self.weight_loader,
                },
            )

            if self.param_sink_with_value:
                self.param_sink_value = torch.nn.Parameter(
                    torch.empty(
                        (
                            self.param_sink_number,
                            self.num_kv_heads,
                            self.v_channels,
                        ),
                        device=current_platform.current_device(),
                        dtype=config.torch_dtype,
                    )
                )
                set_weight_attrs(
                    self.param_sink_value,
                    {
                        "output_dim": 1,
                        "weight_loader": self.weight_loader,
                    },
                )
            else:
                self.param_sink_value = torch.zeros(
                    (
                        self.param_sink_number,
                        self.num_kv_heads,
                        self.v_channels,
                    ),
                    device=current_platform.current_device(),
                    dtype=config.torch_dtype,
                )
        # To enable dummy run with out weight
        self.post_weight_load()

    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
        output_dim = getattr(param, "output_dim", None)

        is_sharded_weight = getattr(param, "is_sharded_weight", False)
        use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
        # bitsandbytes loads the weights of the specific portion
        # no need to narrow
        is_sharded_weight = is_sharded_weight or use_bitsandbytes_4bit

        # Special case for GGUF
        is_gguf_weight = getattr(param, "is_gguf_weight", False)
        is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
        if is_gguf_weight_type:
            param.weight_type = loaded_weight.item()

        # Materialize GGUF UninitializedParameter
        if is_gguf_weight and isinstance(param, nn.UninitializedParameter):
            final_shape = list(loaded_weight.shape)
            if output_dim is not None:
                assert final_shape[output_dim] % self.tp_size == 0
                final_shape[output_dim] = final_shape[output_dim] // self.tp_size
            param.materialize(final_shape, dtype=loaded_weight.dtype)

        param_data = param.data
        if output_dim is not None and not is_sharded_weight:
            shard_size = param_data.shape[output_dim]
            start_idx = self.tp_rank * shard_size
            loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)

        # Special case for loading scales off disk, which often do not
        # have a shape (such as in the case of AutoFP8).
        if len(loaded_weight.shape) == 0:
            loaded_weight = loaded_weight.reshape(1)

        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1)
        k = self.k_layernorm(k.view(-1, self.num_kv_heads, self.head_dim))
        q, k = self.rotary_emb(positions, q, k)

        q = q.view(-1, self.q_size)
        k = k.view(-1, self.k_size)

        attn_output = self.attn(
            q,
            k,
            v,
            output_shape=torch.Size(
                [q.shape[0], q.shape[1] // self.head_dim * self.v_channels]
            ),
        )
        output, _ = self.o_proj(attn_output)
        return output

    def _init_rotary_emb(
        self,
        config: PretrainedConfig,
        rope_parameters: dict[str, Any] | None,
        quant_config: QuantizationConfig | None,
    ) -> None:
        is_neox_style = False
        rope_parameters = {"partial_rotary_factor": self.qk_rope_dim / self.head_dim}

        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=self.max_position_embeddings,
            rope_parameters=rope_parameters,
            is_neox_style=is_neox_style,
        )

    def post_weight_load(self) -> None:
        if hasattr(self, "k_layernorm") and self.k_layernorm is not None:
            param_sink_key = self.k_layernorm(self.param_sink_key)
        else:
            param_sink_key = self.param_sink_key

        self.attn.update_sink_kv(param_sink_key, self.param_sink_value)


class OpenPanguDecoderLayer(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        prefix: str,
        vllm_config: VllmConfig,
    ) -> None:
        super().__init__()

        if config is None:
            config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        parallel_config = vllm_config.parallel_config

        self.hidden_size = config.hidden_size
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)

        layer_idx = int(prefix.split(sep=".")[-1])
        self.layer_idx = layer_idx

        self.use_mla = (
            hasattr(config, "qk_nope_head_dim")
            and hasattr(config, "qk_rope_head_dim")
            and hasattr(config, "v_head_dim")
            and hasattr(config, "kv_lora_rank")
        )
        self.use_sink_attention = (
            hasattr(config, "param_sink_number") and config.param_sink_number > 0
        )
        if self.use_mla:
            self.self_attn = OpenPanguMLAAttention(
                config=config,
                hidden_size=self.hidden_size,
                num_heads=config.num_attention_heads,
                qk_nope_head_dim=config.qk_nope_head_dim,
                qk_rope_head_dim=config.qk_rope_head_dim,
                v_head_dim=config.v_head_dim,
                q_lora_rank=(
                    config.q_lora_rank if hasattr(config, "q_lora_rank") else None
                ),
                kv_lora_rank=config.kv_lora_rank,
                max_position_embeddings=max_position_embeddings,
                cache_config=cache_config,
                quant_config=quant_config,
                prefix=f"{prefix}.self_attn",
            )
        elif self.use_sink_attention:
            attention_bias = getattr(config, "attention_bias", False) or getattr(
                config, "bias", False
            )
            bias_o_proj = attention_bias
            if hasattr(config, "qkv_bias"):
                attention_bias = config.qkv_bias
            if getattr(config, "is_causal", True):
                attn_type = AttentionType.DECODER
            else:
                raise ValueError(
                    f"is_causal={config.is_causal} is not support "
                    "for attention with sink"
                )
            rope_parameters = getattr(config, "rope_scaling", None)
            if rope_parameters is None:
                rope_parameters = {
                    "rope_type": "default",
                    "rope_theta": config.rope_theta,
                }
            self.self_attn = OpenPanguSinkAttention(
                config=config,
                hidden_size=self.hidden_size,
                num_heads=config.num_attention_heads,
                num_kv_heads=getattr(
                    config, "num_key_value_heads", config.num_attention_heads
                ),
                rope_parameters=rope_parameters,
                max_position_embeddings=max_position_embeddings,
                quant_config=quant_config,
                bias=attention_bias,
                bias_o_proj=bias_o_proj,
                cache_config=cache_config,
                prefix=f"{prefix}.self_attn",
                attn_type=attn_type,
            )
        else:
            attention_bias = getattr(config, "attention_bias", False) or getattr(
                config, "bias", False
            )
            bias_o_proj = attention_bias
            if hasattr(config, "qkv_bias"):
                attention_bias = config.qkv_bias
            # By default, PanguEmbedded uses causal attention
            # as it is a decoder-only model.
            # You can override the HF config with `is_causal=False` to enable
            # bidirectional attention, which is used in some embedding models
            if getattr(config, "is_causal", True):
                attn_type = AttentionType.DECODER
            else:
                attn_type = AttentionType.ENCODER_ONLY
            self.self_attn = OpenPanguEmbeddedAttention(
                config=config,
                hidden_size=self.hidden_size,
                num_heads=config.num_attention_heads,
                num_kv_heads=getattr(
                    config, "num_key_value_heads", config.num_attention_heads
                ),
                max_position_embeddings=max_position_embeddings,
                quant_config=quant_config,
                bias=attention_bias,
                bias_o_proj=bias_o_proj,
                cache_config=cache_config,
                prefix=f"{prefix}.self_attn",
                attn_type=attn_type,
            )

        if (
            getattr(config, "n_routed_experts", None) is not None
            and layer_idx >= config.first_k_dense_replace
        ):
            self.mlp = OpenPanguMoE(
                config=config,
                parallel_config=parallel_config,
                quant_config=quant_config,
                prefix=f"{prefix}.mlp",
            )
        else:
            self.mlp = OpenPanguMLP(
                hidden_size=self.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                bias=getattr(config, "mlp_bias", False),
                prefix=f"{prefix}.mlp",
            )
        self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
        self.num_hidden_layers = config.num_hidden_layers
        self.first_k_dense_replace = getattr(
            config, "first_k_dense_replace", self.num_hidden_layers
        )

        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.tp_group = get_tp_group().device_group
        self.sandwich_norm = getattr(config, "sandwich_norm", False)
        if self.sandwich_norm:
            self.pre_mlp_layernorm = RMSNorm(
                config.hidden_size, eps=config.rms_norm_eps
            )
            self.post_mlp_layernorm = RMSNorm(
                config.hidden_size, eps=config.rms_norm_eps
            )

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

        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
        )

        if (
            self.routed_scaling_factor is not None
            and hidden_states.dtype == torch.float16
        ):
            # Fix FP16 overflow
            # We scale both hidden_states and residual before
            # rmsnorm, and rmsnorm result would not affect by scale.
            hidden_states *= 1.0 / self.routed_scaling_factor
            if self.layer_idx == 0:
                # The residual is shared by all layers, we only scale it on
                # first layer.
                residual *= 1.0 / self.routed_scaling_factor

        if self.sandwich_norm:
            hidden_states = self.post_attention_layernorm(hidden_states)
            hidden_states, residual = self.pre_mlp_layernorm(hidden_states, residual)
        else:
            hidden_states, residual = self.post_attention_layernorm(
                hidden_states, residual
            )

        # Fully Connected
        hidden_states = self.mlp(hidden_states)

        if (
            self.routed_scaling_factor is not None
            and isinstance(self.mlp, OpenPanguMLP)
            and hidden_states.dtype == torch.float16
        ):
            hidden_states *= 1.0 / self.routed_scaling_factor

        if self.sandwich_norm:
            hidden_states = self.post_mlp_layernorm(hidden_states)

        return hidden_states, residual


@support_torch_compile
class OpenPanguModel(nn.Module):
    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
        eplb_config = vllm_config.parallel_config.eplb_config
        self.config = config
        self.num_redundant_experts = eplb_config.num_redundant_experts

        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        if get_pp_group().is_first_rank or (
            config.tie_word_embeddings and get_pp_group().is_last_rank
        ):
            self.embed_tokens = VocabParallelEmbedding(
                config.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
                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: OpenPanguDecoderLayer(config, prefix, vllm_config),
            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,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | 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 i in range(self.start_layer, self.end_layer):
            layer = self.layers[i]
            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 load_attn_mlp_weight(
        self,
        attn_mlp_replace_mapping: list[tuple[str, str, int]],
        params_dict: dict[str, Any],
        weight_name: str,
        loaded_weight: torch.Tensor,
        loaded_params: set[str],
    ) -> bool:
        for param_name, origin_name, shard_id in attn_mlp_replace_mapping:
            if origin_name not in weight_name or (
                ("mlp.experts." in weight_name) and weight_name not in params_dict
            ):
                continue
            weight_name_mapped = weight_name.replace(origin_name, param_name)
            if (
                param_name == "fused_qkv_a_proj"
                and weight_name_mapped not in params_dict
            ):
                continue
            else:
                weight_name = weight_name_mapped
            if weight_name.endswith(".bias") and weight_name not in params_dict:
                continue
            if is_pp_missing_parameter(weight_name, self):
                continue

            param = params_dict[weight_name]
            weight_loader = param.weight_loader
            weight_loader(param, loaded_weight, shard_id)
            loaded_params.add(weight_name)
            return True
        return False

    def load_expert_weight(
        self,
        expert_merge_mapping: list[tuple[str, str, int, str]],
        params_dict: dict[str, Any],
        weight_name: str,
        loaded_weight: torch.Tensor,
        loaded_params: set[str],
        flag_dict: dict[str, bool],
    ) -> bool:
        for mapping in expert_merge_mapping:
            param_name, origin_name, expert_id, shard_id = mapping
            if origin_name not in weight_name:
                continue
            flag_dict["is_expert_weight"] = True
            weight_name_mapped = weight_name.replace(origin_name, param_name)
            if is_pp_missing_parameter(weight_name_mapped, self):
                continue
            param = params_dict[weight_name_mapped]
            weight_loader = typing.cast(Callable[..., bool], param.weight_loader)
            success = weight_loader(
                param,
                loaded_weight,
                weight_name_mapped,
                shard_id=shard_id,
                expert_id=expert_id,
                return_success=True,
            )
            if success:
                weight_name = weight_name_mapped
                loaded_params.add(weight_name_mapped)
                return True
        return False

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        attn_mlp_replace_mapping = [
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
            (".fused_qkv_a_proj", ".q_a_proj", 0),
            (".fused_qkv_a_proj", ".kv_a_proj_with_mqa", 1),
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]
        has_experts = hasattr(self.config, "n_routed_experts")
        if has_experts:
            expert_merge_mapping = SharedFusedMoE.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=self.config.n_routed_experts,
                num_redundant_experts=self.num_redundant_experts,
            )

        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            if self.config.tie_word_embeddings and "lm_head.weight" in name:
                continue

            if (
                "layers" in name
                and hasattr(self.config, "num_nextn_predict_layers")
                and (self.config.num_nextn_predict_layers > 0)
            ):
                layer_idx = int(name.split("layers.")[-1].split(".")[0])
                mtp_idx = layer_idx - self.config.num_hidden_layers
                if mtp_idx >= 0 and mtp_idx < self.config.num_nextn_predict_layers:
                    continue  # skip spec decode layers for main model

            flag_dict = {"is_expert_weight": False}
            if (
                self.load_attn_mlp_weight(
                    attn_mlp_replace_mapping,
                    params_dict,
                    name,
                    loaded_weight,
                    loaded_params,
                )
                or has_experts
                and self.load_expert_weight(
                    expert_merge_mapping,
                    params_dict,
                    name,
                    loaded_weight,
                    loaded_params,
                    flag_dict,
                )
            ):
                continue
            else:
                if flag_dict["is_expert_weight"]:
                    continue
                if name.endswith(".bias") and name not in params_dict:
                    continue
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name.endswith("e_score_correction_bias"):
                    name = name.replace(
                        "e_score_correction_bias", "gate.e_score_correction_bias"
                    )
                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)

        self.post_weight_load()
        return loaded_params

    def post_weight_load(self) -> None:
        for name, module in self.named_modules():
            if module is self:
                continue
            if hasattr(module, "post_weight_load"):
                module.post_weight_load()


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

    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.fuse_qkv_a_proj = (
            hasattr(config, "q_lora_rank") and config.q_lora_rank is not None
        )
        if self.fuse_qkv_a_proj:
            self.packed_modules_mapping["fused_qkv_a_proj"] = [
                "q_a_proj",
                "kv_a_proj_with_mqa",
            ]

        self.model = OpenPanguModel(
            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"),
            )
            if config.tie_word_embeddings:
                self.lm_head.weight = self.model.embed_tokens.weight
        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 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

    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,
            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
        )
        return loader.load_weights(weights)


class OpenPanguMoEModel(OpenPanguModelBase, MixtureOfExperts):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__(vllm_config=vllm_config, prefix=prefix)
        config = vllm_config.model_config.hf_config

        # Set MoE hyperparameters
        self.expert_weights = []
        self.num_moe_layers = config.num_hidden_layers - config.first_k_dense_replace
        self.num_expert_groups = 1

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

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

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

        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.n_routed_experts = example_moe.n_routed_experts
        self.n_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 layer in self.model.layers:
            if isinstance(layer.mlp, OpenPanguMoE):
                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()


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


class PanguEmbeddedForCausalLM(OpenPanguEmbeddedModel):
    pass


class PanguUltraMoEForCausalLM(OpenPanguMoEModel):
    pass


class PanguProMoEV2ForCausalLM(OpenPanguMoEModel):
    pass