linear.py

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

import itertools
from abc import abstractmethod
from typing import Any, Literal, Optional, Union, List
import vllm.envs as envs
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter, UninitializedParameter

from vllm import envs
from vllm.distributed import (divide, get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size,
                              split_tensor_along_last_dim,
                              tensor_model_parallel_all_gather,
                              tensor_model_parallel_all_reduce)
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig, QuantizeMethodBase)
from vllm.model_executor.layers.utils import dispatch_unquantized_gemm
# yapf: disable
from vllm.model_executor.parameter import (BasevLLMParameter,
                                           BlockQuantScaleParameter,
                                           PackedColumnParameter,
                                           PackedvLLMParameter,
                                           PerTensorScaleParameter,
                                           RowvLLMParameter)
# yapf: enable
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform

import os
from vllm.model_executor.utils import gemm_bank_conf

if envs.USE_FUSED_RMS_QUANT:
    try:
        from lmslim.quantize.quant_ops import lm_faster_rmsquant
    except Exception as e:
        print(f"Error: Import fused rmsquant error: {e}") 
if envs.USE_FUSED_SILU_MUL_QUANT:        
    try:
        # from lightop import fuse_silu_mul_quant
        from lmslim.quantize.quant_ops import lm_fuse_silu_mul_quant
    except Exception as e:
        print(f"Error: Import fused silu_mul_qunat error: {e}")
        
logger = init_logger(__name__)

WEIGHT_LOADER_V2_SUPPORTED = [
    "CompressedTensorsLinearMethod",
    "BitBLASLinearMethod",
    "GPTQBitBLASLinearMethod",
    "AWQMarlinLinearMethod",
    "AWQLinearMethod",
    "GPTQMarlinLinearMethod",
    "Fp8LinearMethod",
    "MarlinLinearMethod",
    "QQQLinearMethod",
    "GPTQMarlin24LinearMethod",
    "TPUInt8LinearMethod",
    "GPTQLinearMethod",
    "FBGEMMFp8LinearMethod",
    "ModelOptFp8LinearMethod",
    "IPEXAWQLinearMethod",
    "IPEXGPTQLinearMethod",
    "HQQMarlinMethod",
    "QuarkLinearMethod",
    "ModelOptNvFp4LinearMethod",
    "BlockInt8LinearMethod",
]


def adjust_bitblas_shard(param, shard_size, shard_offset):
    bitblas_tile_size = getattr(param, "bitblas_tile_size", None)
    if bitblas_tile_size is not None:
        return (shard_size // bitblas_tile_size,
                shard_offset // bitblas_tile_size)

    return shard_size, shard_offset


def adjust_marlin_shard(param, shard_size, shard_offset):
    marlin_tile_size = getattr(param, "marlin_tile_size", None)
    if marlin_tile_size is None:
        return shard_size, shard_offset

    return shard_size * marlin_tile_size, shard_offset * marlin_tile_size


def adjust_bitsandbytes_4bit_shard(param: Parameter,
                                   shard_offsets: dict[str, tuple[int, int]],
                                   loaded_shard_id: str) -> tuple[int, int]:
    """Adjust the quantization offsets and sizes for BitsAndBytes sharding."""

    total, _ = shard_offsets["total"]
    orig_offset, orig_size = shard_offsets[loaded_shard_id]

    quantized_total = param.data.shape[0]
    quantized_offset = orig_offset * quantized_total // total
    quantized_size = orig_size * quantized_total // total

    return quantized_size, quantized_offset


def adjust_scalar_to_fused_array(param, loaded_weight, shard_id):
    """For fused modules (QKV and MLP) we have an array of length
    N that holds 1 scale for each "logical" matrix. So the param
    is an array of length N. The loaded_weight corresponds to 
    one of the shards on disk. Here, we slice the param based on 
    the shard_id for loading.
    """
    qkv_idxs = {"q": 0, "k": 1, "v": 2}

    if isinstance(shard_id, str):
        shard_id = qkv_idxs[shard_id]
    elif not isinstance(shard_id, int):
        raise ValueError(f"Unknown Shard Id {shard_id}")

    # AutoFP8 scales do not have a shape
    # compressed-tensors scales do have a shape
    if len(loaded_weight.shape) != 0:
        assert loaded_weight.shape[0] == 1
        loaded_weight = loaded_weight[0]

    if envs.VLLM_USE_NN:
        return param[shard_id], loaded_weight.t()
    else:
        return param[shard_id], loaded_weight


# TODO(Isotr0py): We might need a more flexible structure to handle
# bitsandbytes shard offsets.
def left_shift_bitsandbytes_4bit_shard(bnb_weight_attrs: dict[str, Any]):
    """
    Separate the BitsAndBytes 4-bit shard.

    For example, given bnb weight attributes as below:
    {
        'bnb_shard_offsets': array([0, 4, 8, 16]), 
        'bnb_quant_state': {0: ..., 1: ..., 2: ...},
    }

    The function will return:
    {
        'bnb_shard_offsets': array([0, 4]), 
        'bnb_quant_state': {0: ...},
    }
    and
    {
        'bnb_shard_offsets': array([0, 4, 12]),
        'bnb_quant_state': {0: ..., 1: ...},
    }
    """
    shard_offsets = bnb_weight_attrs["bnb_shard_offsets"]
    offset_l = shard_offsets[:2]
    offset_r = shard_offsets[1:] - shard_offsets[1]
    quant_state_l = {0: bnb_weight_attrs["bnb_quant_state"][0]}
    quant_state_r = {
        i - 1: bnb_weight_attrs["bnb_quant_state"][i]
        for i in range(1,
                       len(shard_offsets) - 1)
    }
    left = dict(bnb_shard_offsets=offset_l, bnb_quant_state=quant_state_l)
    right = dict(bnb_shard_offsets=offset_r, bnb_quant_state=quant_state_r)
    return left, right


class LinearMethodBase(QuantizeMethodBase):
    """Base class for different (maybe quantized) linear methods."""

    @abstractmethod
    def create_weights(self, layer: torch.nn.Module,
                       input_size_per_partition: int,
                       output_partition_sizes: list[int], input_size: int,
                       output_size: int, params_dtype: torch.dtype,
                       **extra_weight_attrs):
        """Create weights for a linear layer. 
           The weights will be set as attributes of the layer.

        Args:
            layer: The layer that is using the LinearMethodBase factory.
            input_size_per_partition: Size of the weight input dim on rank X.
            output_partition_sizes: Sizes of the output dim of each logical 
                weight on rank X. E.g., output_partition_sizes for QKVLinear
                is a list contains the width of Wq, Wk, Wv on rank X.
            input_size: Size of the input dim of the weight across all ranks.
            output_size: Size of the output dim of the weight across all ranks.
            params_dtype: Datatype of the parameters.
        """
        raise NotImplementedError

    @abstractmethod
    def apply(self,
              layer: torch.nn.Module,
              x: torch.Tensor,
              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
        """Apply the weights in layer to the input tensor.
        Expects create_weights to have been called before on the layer."""
        raise NotImplementedError


class UnquantizedLinearMethod(LinearMethodBase):
    """Linear method without quantization."""
    
    def __init__(self):
        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
        self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
        
    def create_weights(self, layer: torch.nn.Module,
                       input_size_per_partition: int,
                       output_partition_sizes: list[int], input_size: int,
                       output_size: int, params_dtype: torch.dtype,
                       **extra_weight_attrs):
        if envs.VLLM_USE_NN:
            weight = Parameter(torch.empty(input_size_per_partition,
                                       sum(output_partition_sizes),
                                       dtype=params_dtype),
                           requires_grad=False)
        else:
            weight = Parameter(torch.empty(sum(output_partition_sizes),
                                           input_size_per_partition,
                                           dtype=params_dtype),
                           requires_grad=False)
        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
        layer.register_parameter("weight", weight)
        set_weight_attrs(weight, extra_weight_attrs)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        if current_platform.is_cpu() and envs.VLLM_CPU_SGL_KERNEL:
            N, K = layer.weight.size()
            dtype = layer.weight.dtype
            if (torch._C._cpu._is_amx_tile_supported()
                    and dtype == torch.bfloat16 and N % 32 == 0
                    and K % 32 == 0):
                packed_weight = torch.ops._C.convert_weight_packed(
                    layer.weight)
                assert packed_weight.size() == layer.weight.size()
                layer.weight.copy_(packed_weight)
                if layer.bias is not None:
                    layer.bias = Parameter(layer.bias.to(torch.float32),
                                           requires_grad=False)
                layer.use_cpu_sgl = True
            else:
                logger.warning(
                    "CPU SGL kernels require Intel AMX support,"
                    " bfloat16 weight, IC and OC are divisible by 32.")
                layer.use_cpu_sgl = False

    def apply(self,
              layer: torch.nn.Module,
              x: torch.Tensor,
              bias: Optional[torch.Tensor] = None,
              residual: Optional[torch.Tensor] = None,
              output: Optional[torch.Tensor] = None) -> torch.Tensor:
        if self.use_llama_nn:
            if gemm_bank_conf(layer.weight.shape[1] - 32) and os.environ['GEMM_PAD'] == '1':
                layer.weight = layer.weight[:,:-32]
                
            if bias is not None:
                if len(x.shape) == 2: 
                    return torch.addmm(bias, x, layer.weight)
                else:
                    return torch.matmul(x, layer.weight) + bias
            else:
                return torch.matmul(x, layer.weight)
        else:
            if envs.VLLM_USE_NN and x.shape[-1] == layer.weight.shape[0]:
                return dispatch_unquantized_gemm()(x, layer.weight.t(), bias)
            else:
                weight = layer.weight
                if residual is not None:
                    assert output is None or output is residual
                    if get_tensor_model_parallel_world_size(
                    ) > 1 and get_tensor_model_parallel_rank() != 0:
                        beta = 0.0
                    else:
                        beta = 1.0
                    # optimize cuda memory usage
                    if x.dim() == 2:
                        torch.addmm(residual, x, weight.t(), beta=beta, out=residual)
                    elif x.dim() >= 3:
                        hx = x.size(-1)
                        hr = residual.size(-1)
                        torch.addmm(residual.view(-1, hr),
                                    x.view(-1, hx),
                                    weight.t(),
                                    beta=beta,
                                    out=residual.view(-1, hr))
                    else:
                        raise AssertionError(
                            "unrecognized tensor dimensions: {}".format(x.dim()))
                    if bias is not None:
                        residual += bias
                    return residual
                else:
                    if output is not None:
                        if bias is not None:  # always separate bias add when output is provided
                            torch.matmul(x, weight.t(), out=output)
                            output.add_(bias)
                            return output
                        return torch.matmul(x, weight.t(), out=output)
                    else:
                        return dispatch_unquantized_gemm()(x, layer.weight, bias)
                # return dispatch_unquantized_gemm()(x, layer.weight, bias)


class UnquantizedMoELinearMethod(LinearMethodBase):
    """MoE Linear method without quantization.
    """

    def __init__(self):
        self.quant_config = None

    def create_weights(self,
                       layer: torch.nn.Module,
                       input_size_per_partition: int,
                       output_partition_sizes: List[int],
                       input_size: int,
                       output_size: int,
                       params_dtype: torch.dtype,
                       num_experts: Optional[int] = None,
                       **extra_weight_attrs):
        weight = Parameter(torch.empty(num_experts,
                                       sum(output_partition_sizes),
                                       input_size_per_partition,
                                       device=torch.cuda.current_device(),
                                       dtype=params_dtype),
                           requires_grad=False)
        set_weight_attrs(weight, {"input_dim": 2, "output_dim": 1})
        layer.register_parameter("weight", weight)
        set_weight_attrs(weight, extra_weight_attrs)

    def apply(self,
              layer: torch.nn.Module,
              x: torch.Tensor,
              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
        """Apply the weights to the input tensor."""
        raise NotImplementedError
    
    
class LinearBase(torch.nn.Module):
    """Base linear layer.

    Args:
        input_size: input dimension of the linear layer.
        output_size: output dimension of the linear layer.
        bias: If true, add bias.
        skip_bias_add: If true, skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
        quant_config: Quantization configure.
        return_bias: If true, return bias together with outputs in forward pass.
    """

    def __init__(
        self,
        input_size: int,
        output_size: int,
        skip_bias_add: bool = False,
        params_dtype: Optional[torch.dtype] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        *,
        return_bias: bool = True,
    ):
        super().__init__()

        # Keep input parameters
        self.input_size = input_size
        self.output_size = output_size
        self.skip_bias_add = skip_bias_add
        if params_dtype is None:
            params_dtype = torch.get_default_dtype()
        self.params_dtype = params_dtype
        if quant_config is None:
            self.quant_method: Optional[
                QuantizeMethodBase] = UnquantizedLinearMethod()
        else:
            self.quant_method = quant_config.get_quant_method(self,
                                                              prefix=prefix)
        self.return_bias = return_bias

    def forward(
        self, x: torch.Tensor
    ) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
        raise NotImplementedError


class ReplicatedLinear(LinearBase):
    """Replicated linear layer.

    Args:
        input_size: input dimension of the linear layer.
        output_size: output dimension of the linear layer.
        bias: If true, add bias.
        skip_bias_add: If true, skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
        quant_config: Quantization configure.
        prefix: The name of the layer in the state dict, including all parents
                        (e.g. model.layers.0.qkv_proj)
        return_bias: If true, return bias together with outputs in forward pass.
    """

    def __init__(
        self,
        input_size: int,
        output_size: int,
        bias: bool = True,
        skip_bias_add: bool = False,
        params_dtype: Optional[torch.dtype] = None,
        quant_config: Optional[QuantizationConfig] = None,
        eps: Optional[float] = 1e-6,
        prefix: str = "",
        *,
        return_bias: bool = True,
    ):
        super().__init__(input_size,
                         output_size,
                         skip_bias_add,
                         params_dtype,
                         quant_config,
                         prefix=prefix,
                         return_bias=return_bias)
        self.eps = eps

        # All the linear layer supports quant method.
        assert self.quant_method is not None
        self.quant_method.create_weights(self,
                                         self.input_size, [self.output_size],
                                         self.input_size,
                                         self.output_size,
                                         self.params_dtype,
                                         weight_loader=self.weight_loader)

        if bias:
            self.bias = Parameter(
                torch.empty(self.output_size, dtype=self.params_dtype))
            set_weight_attrs(self.bias, {
                "output_dim": 0,
                "weight_loader": self.weight_loader,
            })
        else:
            self.register_parameter("bias", None)

    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
        # If the weight on disk does not have a shape, give it one
        # (such scales for AutoFp8).
        # 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, UninitializedParameter):
            param.materialize(loaded_weight.shape, dtype=loaded_weight.dtype)

        if len(loaded_weight.shape) == 0:
            loaded_weight = loaded_weight.reshape(1)

        is_quantization = not isinstance(self.quant_method, UnquantizedLinearMethod)
        if envs.VLLM_USE_NN and not is_quantization:
            loaded_weight = loaded_weight.t()
            
        assert param.size() == loaded_weight.size(), (
            f"Tried to load weights of size {loaded_weight.size()}"
            f"to a parameter of size {param.size()}")
        param.data.copy_(loaded_weight)

    def forward(
        self, 
        input_: torch.Tensor,
        rms_weight: Optional[torch.Tensor] = None,
        residual: Optional[torch.Tensor] = None,
        quant_args: Optional[list] = None,
        update_hd: Optional[bool] = True
    ) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
        if envs.USE_FUSED_RMS_QUANT and (rms_weight is not None or quant_args is not None):
            if quant_args is not None:
                input_quant_args = quant_args
            
                bias = self.bias if not self.skip_bias_add else None
                assert self.quant_method is not None
                output = self.quant_method.apply(self, input_, bias, input_quant_args)
                output_bias = self.bias if self.skip_bias_add else None
                if not self.return_bias:
                    return output
                return output, output_bias

            else:
                i_q, _scales = lm_faster_rmsquant(input=input_,
                                                  rms_weight=rms_weight,
                                                  epsilon=self.eps,
                                                  quant_dtype=torch.int8,
                                                  residual=residual,
                                                  update_input=update_hd
                                                  )
            
                new_residual = residual
                input_quant_args = [i_q, _scales]
                
                bias = self.bias if not self.skip_bias_add else None
                assert self.quant_method is not None
                output = self.quant_method.apply(self, input_, bias, input_quant_args)
                output_bias = self.bias if self.skip_bias_add else None
                if not self.return_bias:
                    return output
                return output, new_residual, output_bias, input_quant_args

        else:
            bias = self.bias if not self.skip_bias_add else None
            assert self.quant_method is not None
            output = self.quant_method.apply(self, input_, bias)
            output_bias = self.bias if self.skip_bias_add else None
            if not self.return_bias:
                return output
            return output, output_bias

    def extra_repr(self) -> str:
        s = f"in_features={self.input_size}"
        s += f", output_features={self.output_size}"
        s += f", bias={self.bias is not None}"
        return s


class ColumnParallelLinear(LinearBase):
    """Linear layer with column parallelism.

    The linear layer is defined as Y = XA + b. A is parallelized along
    its second dimension as A = [A_1, ..., A_p].

    Args:
        input_size: first dimension of matrix A.
        output_size: second dimension of matrix A.
        bias: If true, add bias.
        gather_output: If true, call all-gather on output and make Y available
                       to all GPUs, otherwise, every GPU will have its output
                       which is Y_i = XA_i
        skip_bias_add: This was added to enable performance optimizations where
                       bias can be fused with other element-wise operations. we
                       skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
        quant_config: Quantization configure.
        output_sizes: list of output sizes packed into one output, like for QKV
                       the list would be size 3.
        prefix: The name of the layer in the state dict, including all parents
                        (e.g. model.layers.0.qkv_proj) 
    """

    def __init__(
        self,
        input_size: int,
        output_size: int,
        bias: bool = True,
        gather_output: bool = False,
        skip_bias_add: bool = False,
        params_dtype: Optional[torch.dtype] = None,
        quant_config: Optional[QuantizationConfig] = None,
        output_sizes: Optional[list[int]] = None,
        eps: Optional[float] = 1e-6,
        prefix: str = "",
        *,
        return_bias: bool = True,
    ):
        # Divide the weight matrix along the last dimension.
        self.tp_size = get_tensor_model_parallel_world_size()
        self.input_size_per_partition = input_size
        self.output_size_per_partition = divide(output_size, self.tp_size)
        self.output_partition_sizes = [self.output_size_per_partition]
        # If QKV or MergedColumn, use output size of each partition.
        if hasattr(self, "output_sizes"):
            self.output_partition_sizes = [
                divide(output_size, self.tp_size)
                for output_size in self.output_sizes
            ]

        super().__init__(input_size,
                         output_size,
                         skip_bias_add,
                         params_dtype,
                         quant_config,
                         prefix,
                         return_bias=return_bias)
        self.eps = eps
        self.gather_output = gather_output

        if output_sizes is None:
            output_sizes = [output_size]

        assert self.quant_method is not None
        self.quant_method.create_weights(
            layer=self,
            input_size_per_partition=self.input_size_per_partition,
            output_partition_sizes=self.output_partition_sizes,
            input_size=self.input_size,
            output_size=self.output_size,
            params_dtype=self.params_dtype,
            weight_loader=(
                self.weight_loader_v2 if self.quant_method.__class__.__name__
                in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader))
        if bias:
            self.bias = Parameter(
                torch.empty(self.output_size_per_partition,
                            dtype=params_dtype))
            set_weight_attrs(self.bias, {
                "output_dim": 0,
                "weight_loader": self.weight_loader,
            })
        else:
            self.register_parameter("bias", None)

    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
        tp_rank = get_tensor_model_parallel_rank()
        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
        is_quantization = not isinstance(self.quant_method, UnquantizedLinearMethod)

        # 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, UninitializedParameter):
            final_shape = list(loaded_weight.shape)
            if output_dim is not None:
                tp_size = get_tensor_model_parallel_world_size()
                assert final_shape[output_dim] % tp_size == 0
                final_shape[output_dim] = final_shape[output_dim] // 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:
            if not envs.VLLM_USE_NN or len(param_data.shape)==1 or is_quantization:
                shard_size = param_data.shape[output_dim] 
            else:
                shard_size = param_data.shape[int(not(output_dim))]
            start_idx = 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)

        if envs.VLLM_USE_NN and not is_quantization:
            loaded_weight = loaded_weight.t()
            
        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)

    def weight_loader_v2(self, param: Parameter, loaded_weight: torch.Tensor):
        # 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:
            assert loaded_weight.numel() == 1
            loaded_weight = loaded_weight.reshape(1)
        param.load_column_parallel_weight(loaded_weight=loaded_weight)

    def forward(
        self, input_,
        rms_weight: Optional[torch.Tensor] = None,
        residual: Optional[torch.Tensor] = None,
        update_hd: Optional[bool] = True,
        output: Optional[torch.Tensor] = None
    ) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
        if envs.USE_FUSED_RMS_QUANT and rms_weight is not None:
            input_quant_args = None
            assert rms_weight is not None 
            i_q, _scales = lm_faster_rmsquant(input=input_,
                                        rms_weight=rms_weight,
                                        epsilon=self.eps,
                                        quant_dtype=torch.int8,
                                        residual=residual,
                                        update_input=update_hd)
            new_residual = residual
            input_quant_args = [i_q, _scales]
        
            bias = self.bias if not self.skip_bias_add else None

            assert self.quant_method is not None
            output_parallel = self.quant_method.apply(self, input_, bias, input_quant_args)
            if self.gather_output:
                output = tensor_model_parallel_all_gather(output_parallel)
            else:
                output = output_parallel
            output_bias = self.bias if self.skip_bias_add else None
            if not self.return_bias:
                return output
            return output, new_residual, output_bias
        else:
            bias = self.bias if not self.skip_bias_add else None
            # Matrix multiply.
            assert self.quant_method is not None
            output_parallel = self.quant_method.apply(self, input_, bias, output=output)
            if self.gather_output:
                # All-gather across the partitions.
                output = tensor_model_parallel_all_gather(output_parallel)
            else:
                output = output_parallel
            output_bias = self.bias if self.skip_bias_add else None
            if not self.return_bias:
                return output
            return output, output_bias

    def extra_repr(self) -> str:
        s = f"in_features={self.input_size}"
        s += f", output_features={self.output_size_per_partition}"
        s += f", bias={self.bias is not None}"
        s += f", tp_size={get_tensor_model_parallel_world_size()}"
        s += f", gather_output={self.gather_output}"
        return s


class MergedColumnParallelLinear(ColumnParallelLinear):
    """Packed linear layers with column parallelism.

    Similar to ColumnParallelLinear, but the weight matrix is concatenated
    along the output dimension. When the weight matrix is loaded, the
    different partitions are sharded separately.

    Args:
        input_size: input dimension of the linear layer.
        output_sizes: list of output dimensions of the linear layer.
        bias: If true, add bias.
        gather_output: If true, call all-gather on output and make the output
                       available to all GPUs, otherwise, every GPU will have
                       its own output.
        skip_bias_add: This was added to enable performance optimizations where
                       bias can be fused with other element-wise operations. we
                       skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
        quant_config: Quantization configure.
        prefix: The name of the layer in the state dict, including all parents
                        (e.g. model.layers.0.qkv_proj)
        return_bias: If true, return bias together with outputs in forward pass.
    """

    def forward(
        self, input_,
        rms_weight: Optional[torch.Tensor] = None,
        residual: Optional[torch.Tensor] = None,
        update_hd: Optional[bool] = True
    ) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
        if envs.USE_FUSED_RMS_QUANT and rms_weight is not None:
            input_quant_args = None
            assert residual is not None and rms_weight is not None 
            i_q, _scales = lm_faster_rmsquant(input=input_,
                                        rms_weight=rms_weight,
                                        epsilon=self.eps,
                                        quant_dtype=torch.int8,
                                        residual=residual,
                                        update_input=update_hd)
            
            new_residual = residual
            input_quant_args = [i_q, _scales]
            
            
            bias = self.bias if not self.skip_bias_add else None
            assert self.quant_method is not None
            output_parallel = self.quant_method.apply(self, input_, bias, input_quant_args)
            
            if self.gather_output:
                # All-gather across the partitions.
                output = tensor_model_parallel_all_gather(output_parallel)
            else:
                output = output_parallel
            output_bias = self.bias if self.skip_bias_add else None
            if not self.return_bias:
                return output
            return output, new_residual, output_bias
        else: # not USE_FUSED_RMS_QUANT
            bias = self.bias if not self.skip_bias_add else None

            assert self.quant_method is not None
            output_parallel = self.quant_method.apply(self, input_, bias)
            if self.gather_output:
                # All-gather across the partitions.
                output = tensor_model_parallel_all_gather(output_parallel)
            else:
                output = output_parallel
            output_bias = self.bias if self.skip_bias_add else None
            if not self.return_bias:
                return output
            return output, output_bias
    
    def __init__(
        self,
        input_size: int,
        output_sizes: list[int],
        bias: bool = True,
        gather_output: bool = False,
        skip_bias_add: bool = False,
        params_dtype: Optional[torch.dtype] = None,
        quant_config: Optional[QuantizationConfig] = None,
        eps: Optional[float] = 1e-6,
        prefix: str = "",
        *,
        return_bias: bool = True,
    ):
        self.eps = eps
        self.output_sizes = output_sizes
        tp_size = get_tensor_model_parallel_world_size()
        assert all(output_size % tp_size == 0 for output_size in output_sizes)
        super().__init__(input_size=input_size,
                         output_size=sum(output_sizes),
                         bias=bias,
                         gather_output=gather_output,
                         skip_bias_add=skip_bias_add,
                         params_dtype=params_dtype,
                         quant_config=quant_config,
                         prefix=prefix,
                         return_bias=return_bias)

    def weight_loader(self,
                      param: Parameter,
                      loaded_weight: torch.Tensor,
                      loaded_shard_id: Optional[int] = None):

        # Special case for GGUF
        # initialize GGUF param after we know the quantize type
        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:
            if loaded_shard_id is not None:
                param.data[loaded_shard_id].copy_(loaded_weight)
                param.shard_weight_type[loaded_shard_id] = loaded_weight.item()
            else:
                param.shard_weight_type = {
                    i: loaded_weight.item()
                    for i, _ in enumerate(self.output_sizes)
                }
            return

        if is_gguf_weight:
            tp_size = get_tensor_model_parallel_world_size()
            tp_rank = get_tensor_model_parallel_rank()

            output_dim = getattr(param, "output_dim", None)
            shard_size = loaded_weight.size(output_dim) // tp_size
            start_idx = tp_rank * shard_size

            if loaded_shard_id is not None:
                loaded_weight = loaded_weight.narrow(output_dim, start_idx,
                                                     shard_size)
                param.shard_id.append(loaded_shard_id)
                param.shard_id_map[loaded_shard_id] = len(param.data_container)
                param.data_container.append(loaded_weight)
                if len(param.data_container) == 2:
                    self.qweight = param.materialize_nested()
                return

        param_data = param.data
        output_dim = getattr(param, "output_dim", None)
        # Special case for AQLM codebooks.
        is_metadata = getattr(param, "is_metadata", False)
        # Special case for per-tensor scale to load scalar into fused array.
        needs_scalar_to_array = getattr(param, "needs_scalar_to_array", False)
        is_quantization = not isinstance(self.quant_method, UnquantizedLinearMethod)

        if loaded_shard_id is None:
            # Loaded weight is already fused on disk (mlp).
            # (e.g., Phi-3's gate_up_proj).
            if output_dim is None:
                if needs_scalar_to_array:
                    param_data, loaded_weight = adjust_scalar_to_fused_array(
                        param_data, loaded_weight, 0)

                assert param_data.shape == loaded_weight.shape
                param_data.copy_(loaded_weight)
                return
            current_shard_offset = 0
            use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit",
                                            False)
            shard_offsets: list[tuple[int, int, int]] = []
            for i, output_size in enumerate(self.output_sizes):
                shard_offsets.append((i, current_shard_offset, output_size))
                current_shard_offset += output_size
            packed_dim = getattr(param, "packed_dim", None)
            for shard_id, shard_offset, shard_size in shard_offsets:
                # Special case for Quantization.
                # If quantized, we need to adjust the offset and size to account
                # for the packing.
                if packed_dim == output_dim:
                    shard_size = shard_size // param.pack_factor
                    shard_offset = shard_offset // param.pack_factor
                    # Special case for Marlin.
                    shard_size, shard_offset = adjust_marlin_shard(
                        param, shard_size, shard_offset)

                shard_size, shard_offset = adjust_bitblas_shard(
                    param, shard_size, shard_offset)

                if use_bitsandbytes_4bit:
                    index = list(itertools.accumulate([0] + self.output_sizes))
                    orig_offsets = {
                        str(i): (index[i], size)
                        for i, size in enumerate(self.output_sizes)
                    }
                    orig_offsets["total"] = (self.output_size, 0)
                    shard_size, shard_offset = adjust_bitsandbytes_4bit_shard(
                        param, orig_offsets, str(shard_id))

                loaded_weight_shard = loaded_weight.narrow(
                    output_dim, shard_offset, shard_size)
                self.weight_loader(param, loaded_weight_shard, shard_id)
            return

        assert loaded_shard_id < len(self.output_sizes)
        tp_rank = get_tensor_model_parallel_rank()
        tp_size = get_tensor_model_parallel_world_size()
        if output_dim is not None:
            shard_offset = sum(self.output_sizes[:loaded_shard_id]) // tp_size
            shard_size = self.output_sizes[loaded_shard_id] // tp_size
            # Special case for quantization.
            # If quantized, we need to adjust the offset and size to account
            # for the packing.
            packed_dim = getattr(param, "packed_dim", None)
            if packed_dim == output_dim:
                shard_size = shard_size // param.pack_factor
                shard_offset = shard_offset // param.pack_factor
                # Special case for Marlin.
                shard_size, shard_offset = adjust_marlin_shard(
                    param, shard_size, shard_offset)
            shard_size, shard_offset = adjust_bitblas_shard(
                param, shard_size, shard_offset)

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

            if use_bitsandbytes_4bit:
                shard_size = loaded_weight.shape[output_dim]
                shard_offset = loaded_weight.shape[output_dim] * \
                    loaded_shard_id
                    
            if not envs.VLLM_USE_NN or is_quantization:
                param_data = param_data.narrow(output_dim, shard_offset, shard_size)
            else:
                param_data = param_data.narrow(int(not(output_dim)), shard_offset, shard_size)

            start_idx = tp_rank * shard_size
            if not is_sharded_weight:
                loaded_weight = loaded_weight.narrow(output_dim, start_idx,
                                                     shard_size)
        # Special case for AQLM codebooks.
        elif is_metadata:
            # metadata indicates fixed size concatenated along dim 0
            shard_size = loaded_weight.shape[0]
            shard_offset = loaded_shard_id * shard_size
            param_data = param_data.narrow(0, shard_offset, shard_size)

        # Special case for per-tensor scales in fused case.
        elif needs_scalar_to_array:
            param_data, loaded_weight = adjust_scalar_to_fused_array(
                param_data, loaded_weight, loaded_shard_id)

        else:
            ignore_warning = getattr(param, "ignore_warning", False)
            if not ignore_warning:
                logger.warning(
                    "Loading a weight without `output_dim` attribute in "
                    "MergedColumnParallelLinear, assume the weight is "
                    "the same for all partitions.")

        if envs.VLLM_USE_NN and not is_quantization:
            loaded_weight = loaded_weight.t()
            
        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)

    def _load_fused_module_from_checkpoint(self, param: BasevLLMParameter,
                                           loaded_weight: torch.Tensor):
        """
        Handle special case for models where MLP layers are already
        fused on disk. In this case, we have no shard id. This function
        determmines the shard id by splitting these layers and then calls
        the weight loader using the shard id.

        An example of a model with these fused layers:
        https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
        """

        current_shard_offset = 0
        shard_offsets: list[tuple[int, int, int]] = []
        for i, output_size in enumerate(self.output_sizes):
            shard_offsets.append((i, current_shard_offset, output_size))
            current_shard_offset += output_size

        for shard_id, shard_offset, shard_size in shard_offsets:
            # Special case for Quantization.
            # If quantized, we need to adjust the offset and size to account
            # for the packing.
            if isinstance(param, (PackedColumnParameter, PackedvLLMParameter
                                  )) and param.packed_dim == param.output_dim:
                shard_size, shard_offset = \
                    param.adjust_shard_indexes_for_packing(
                    shard_size=shard_size, shard_offset=shard_offset)

            loaded_weight_shard = loaded_weight.narrow(param.output_dim,
                                                       shard_offset,
                                                       shard_size)
            self.weight_loader_v2(param, loaded_weight_shard, shard_id)

    def weight_loader_v2(self,
                         param: BasevLLMParameter,
                         loaded_weight: torch.Tensor,
                         loaded_shard_id: Optional[int] = None):
        if loaded_shard_id is None:
            if isinstance(param, PerTensorScaleParameter):
                param.load_merged_column_weight(loaded_weight=loaded_weight,
                                                shard_id=0)
                return
            elif type(param) in (RowvLLMParameter, BasevLLMParameter):
                param.load_merged_column_weight(loaded_weight=loaded_weight)
                return
            # TODO: @dsikka - move to parameter.py
            self._load_fused_module_from_checkpoint(param, loaded_weight)
            return

        assert loaded_shard_id < len(self.output_sizes)

        tp_size = get_tensor_model_parallel_world_size()

        if isinstance(param, BlockQuantScaleParameter):
            from vllm.model_executor.layers.quantization.fp8 import (
                Fp8LinearMethod, Fp8MoEMethod)
            
            from vllm.model_executor.layers.quantization.blockwise_int8 import (
                BlockInt8LinearMethod, BlockInt8MoEMethod)
            assert self.quant_method is not None
            assert isinstance(self.quant_method,
                              (Fp8LinearMethod, Fp8MoEMethod, BlockInt8LinearMethod, BlockInt8MoEMethod))
            weight_block_size = self.quant_method.quant_config.weight_block_size
            assert weight_block_size is not None
            block_n, _ = weight_block_size[0], weight_block_size[1]
            shard_offset = (
                (sum(self.output_sizes[:loaded_shard_id]) + block_n - 1) //
                block_n) // tp_size
            shard_size = ((self.output_sizes[loaded_shard_id] + block_n - 1) //
                          block_n // tp_size)
        else:
            shard_offset = sum(self.output_sizes[:loaded_shard_id]) // tp_size
            shard_size = self.output_sizes[loaded_shard_id] // tp_size

        param.load_merged_column_weight(loaded_weight=loaded_weight,
                                        shard_id=loaded_shard_id,
                                        shard_offset=shard_offset,
                                        shard_size=shard_size)


class MergedColumnParallelMoELinear(MergedColumnParallelLinear):

    def __init__(self,
                 num_experts: int,
                 input_size: int,
                 output_sizes: List[int],
                 params_dtype: Optional[torch.dtype] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = ""):
        torch.nn.Module.__init__(self)
        output_size = sum(output_sizes)
        self.num_experts = num_experts
        self.output_sizes = output_sizes
        self.input_size = input_size
        self.output_size = sum(output_sizes)
        tp_size = get_tensor_model_parallel_world_size()
        assert all(output_size % tp_size == 0 for output_size in output_sizes)
        self.output_size_per_partition = divide(self.output_size, tp_size)
        self.output_partition_sizes = [
            divide(output_size, tp_size) for output_size in self.output_sizes
        ]
        self.gather_output = False
        if output_sizes is None:
            output_sizes = [output_size]
        if params_dtype is None:
            params_dtype = torch.get_default_dtype()
        self.params_dtype = params_dtype
        if quant_config is None:
            self.quant_method = UnquantizedMoELinearMethod()
        else:
            self.quant_method = quant_config.get_quant_method(self,
                                                              prefix=prefix)
            # FIXME(ys): hack for moe
            if isinstance(self.quant_method, UnquantizedLinearMethod):
                self.quant_method = UnquantizedMoELinearMethod()

        assert self.quant_method is not None
        self.quant_method.create_weights(self,
                                         self.input_size,
                                         self.output_partition_sizes,
                                         self.input_size,
                                         self.output_size,
                                         self.params_dtype,
                                         self.num_experts,
                                         weight_loader=self.weight_loader)
        self.register_parameter("bias", None)

    def forward(self,
                input_,
                output: Optional[torch.Tensor] = None,
                expert_idx: int = -1):
        if isinstance(self.quant_method, UnquantizedMoELinearMethod):
            # use optimus moe_ffn outside
            return
        bias = None
        assert self.quant_method is not None

        output = self.quant_method.apply(self,
                                         input_,
                                         bias,
                                         expert_idx=expert_idx,
                                         output=output)
        return output


class QKVReplicatedLinear(ReplicatedLinear):

    def __init__(self,
                 hidden_size: int,
                 head_size: int,
                 total_num_heads: int,
                 total_num_kv_heads: Optional[int] = None,
                 bias: bool = True,
                 skip_bias_add: bool = False,
                 params_dtype: Optional[torch.dtype] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = "",
                 return_bias: bool = True):

        nn.Module.__init__(self)
        self.hidden_size = hidden_size
        self.head_size = head_size
        self.num_heads = total_num_heads
        self.num_kv_heads = total_num_kv_heads if total_num_kv_heads else total_num_heads
        self.input_size = self.hidden_size
        self.output_size = (self.num_heads +
                            2 * self.num_kv_heads) * self.head_size
        self.skip_bias_add = skip_bias_add
        self.return_bias = return_bias
        if params_dtype is None:
            params_dtype = torch.get_default_dtype()
        self.params_dtype = params_dtype
        if quant_config is None:
            self.quant_method: Optional[
                QuantizeMethodBase] = UnquantizedLinearMethod()
        else:
            self.quant_method = quant_config.get_quant_method(self,
                                                              prefix=prefix)

        assert self.quant_method is not None
        self.quant_method.create_weights(self,
                                         self.input_size, [self.output_size],
                                         self.input_size,
                                         self.output_size,
                                         self.params_dtype,
                                         weight_loader=self.weight_loader)
        if bias:
            self.bias = Parameter(
                torch.empty(self.output_size, dtype=self.params_dtype))
            set_weight_attrs(self.bias, {
                "output_dim": 0,
                "weight_loader": self.weight_loader
            })
        else:
            self.register_parameter("bias", None)

    def weight_loader(self,
                      param: Parameter,
                      loaded_weight: torch.Tensor,
                      loaded_shard_id: Optional[str] = None):
        param_data = param.data
        output_dim = getattr(param, "output_dim", None)
        is_quantization = not isinstance(self.quant_method, UnquantizedLinearMethod)
        if loaded_shard_id is None:
            # Loaded weight is already packed.
            assert param_data.shape == loaded_weight.shape
            param_data.copy_(loaded_weight)
            return

        assert loaded_shard_id in ["q", "k", "v"]
        if output_dim is not None:
            if loaded_shard_id == "q":
                shard_offset = 0
                shard_size = self.num_heads * self.head_size
            elif loaded_shard_id == "k":
                shard_offset = self.num_heads * self.head_size
                shard_size = self.num_kv_heads * self.head_size
            elif loaded_shard_id == "v":
                shard_offset = (self.num_heads +
                                self.num_kv_heads) * self.head_size
                shard_size = self.num_kv_heads * self.head_size
            # If quantized, we need to adjust the offset and size to account
            # for the packing.
            packed_dim = getattr(param, "packed_dim", None)
            if packed_dim == output_dim:
                shard_size = shard_size // param.pack_factor
                shard_offset = shard_offset // param.pack_factor
                
            if not envs.VLLM_USE_NN or is_quantization:
                param_data = param_data.narrow(output_dim, shard_offset,
                                           shard_size)
            else:
                param_data = param_data.narrow(int(not(output_dim)), shard_offset, 
                                           shard_size)
        else:
            ignore_warning = getattr(param, "ignore_warning", False)
            if not ignore_warning:
                logger.warning(
                    "Loading a weight without `output_dim` attribute in "
                    "QKVReplicatedLinear, assume the weight is the same "
                    "for all partitions.")
        
        if envs.VLLM_USE_NN and not is_quantization:
            loaded_weight = loaded_weight.t()
        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)
        
        
class QKVParallelLinear(ColumnParallelLinear):
    """Linear layers for the attention's QKV transformation.

    Linear layers for the linear transformation of the query, key, and value
    vectors in the attention layer. The weight matrix is concatenated along
    the output dimension. The layer is parallelized along the head dimension.
    When the number of key/value heads is smaller than the number of query
    heads (e.g., multi-query/grouped-query attention), the key/value head may
    be replicated while the query heads are partitioned.

    Args:
        hidden_size: input hidden state size of the transformer.
        head_size: size of each attention head.
        total_num_heads: total number of attention query heads.
        total_num_kv_heads: total number of attention key/value heads. If
                            None, assume total_num_kv_heads = total_num_heads.
        bias: If true, add bias.
        skip_bias_add: This was added to enable performance optimizations where
                       bias can be fused with other element-wise operations. we
                       skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
        quant_config: Quantization configure.
        prefix: The name of the layer in the state dict, including all parents
                        (e.g. model.layers.0.qkv_proj)
        return_bias: If true, return bias together with outputs in forward pass.
    """

    def __init__(
        self,
        hidden_size: int,
        head_size: int,
        total_num_heads: int,
        total_num_kv_heads: Optional[int] = None,
        bias: bool = True,
        skip_bias_add: bool = False,
        params_dtype: Optional[torch.dtype] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        *,
        return_bias: bool = True,
    ):
        self.hidden_size = hidden_size
        self.head_size = head_size
        self.total_num_heads = total_num_heads
        if total_num_kv_heads is None:
            total_num_kv_heads = total_num_heads
        self.total_num_kv_heads = total_num_kv_heads
        # Divide the weight matrix along the last dimension.
        tp_size = get_tensor_model_parallel_world_size()
        self.num_heads = divide(self.total_num_heads, tp_size)
        if tp_size >= self.total_num_kv_heads:
            self.num_kv_heads = 1
            self.num_kv_head_replicas = divide(tp_size,
                                               self.total_num_kv_heads)
        else:
            self.num_kv_heads = divide(self.total_num_kv_heads, tp_size)
            self.num_kv_head_replicas = 1
        input_size = self.hidden_size
        output_size = (self.num_heads +
                       2 * self.num_kv_heads) * tp_size * self.head_size
        self.output_sizes = [
            self.num_heads * self.head_size * tp_size,  # q_proj
            self.num_kv_heads * self.head_size * tp_size,  # k_proj
            self.num_kv_heads * self.head_size * tp_size,  # v_proj 
        ]

        super().__init__(input_size=input_size,
                         output_size=output_size,
                         bias=bias,
                         gather_output=False,
                         skip_bias_add=skip_bias_add,
                         params_dtype=params_dtype,
                         quant_config=quant_config,
                         prefix=prefix,
                         return_bias=return_bias)

    def _get_shard_offset_mapping(self, loaded_shard_id: str):
        shard_offset_mapping = {
            "q": 0,
            "k": self.num_heads * self.head_size,
            "v": (self.num_heads + self.num_kv_heads) * self.head_size,
            "total": (self.num_heads + 2 * self.num_kv_heads) * self.head_size
        }
        return shard_offset_mapping.get(loaded_shard_id)

    def _get_shard_size_mapping(self, loaded_shard_id: str):
        shard_size_mapping = {
            "q": self.num_heads * self.head_size,
            "k": self.num_kv_heads * self.head_size,
            "v": self.num_kv_heads * self.head_size,
        }
        return shard_size_mapping.get(loaded_shard_id)

    def _load_fused_module_from_checkpoint(self, param: BasevLLMParameter,
                                           loaded_weight: torch.Tensor):
        """
        Handle special case for models where QKV layers are already 
        fused on disk. In this case, we have no shard id. This function
        determmines the shard id by splitting these layers and then calls
        the weight loader using the shard id.

        An example of a model with these fused layers:
        https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
        """
        shard_offsets = [
            # (shard_id, shard_offset, shard_size)
            ("q", 0, self.total_num_heads * self.head_size),
            ("k", self.total_num_heads * self.head_size,
             self.total_num_kv_heads * self.head_size),
            ("v",
             (self.total_num_heads + self.total_num_kv_heads) * self.head_size,
             self.total_num_kv_heads * self.head_size),
        ]

        for shard_id, shard_offset, shard_size in shard_offsets:
            # Special case for Quantization.
            # If quantized, we need to adjust the offset and size to account
            # for the packing.
            if isinstance(param, (PackedColumnParameter, PackedvLLMParameter
                                  )) and param.packed_dim == param.output_dim:
                shard_size, shard_offset = \
                    param.adjust_shard_indexes_for_packing(
                    shard_size=shard_size, shard_offset=shard_offset)

            loaded_weight_shard = loaded_weight.narrow(param.output_dim,
                                                       shard_offset,
                                                       shard_size)
            self.weight_loader_v2(param, loaded_weight_shard, shard_id)

    def weight_loader_v2(self,
                         param: BasevLLMParameter,
                         loaded_weight: torch.Tensor,
                         loaded_shard_id: Optional[str] = None):
        if loaded_shard_id is None:  # special case for certain models
            if isinstance(param, PerTensorScaleParameter):
                param.load_qkv_weight(loaded_weight=loaded_weight, shard_id=0)
                return
            elif type(param) in (RowvLLMParameter, BasevLLMParameter):
                param.load_qkv_weight(loaded_weight=loaded_weight)
                return
            # TODO: @dsikka - move to parameter.py
            self._load_fused_module_from_checkpoint(param, loaded_weight)
            return

        assert loaded_shard_id in ["q", "k", "v"]

        shard_offset = self._get_shard_offset_mapping(loaded_shard_id)
        shard_size = self._get_shard_size_mapping(loaded_shard_id)

        # Note(simon): This is needed for Qwen3's fp8 quantization.
        if isinstance(param, BlockQuantScaleParameter):
            assert self.quant_method is not None
            assert hasattr(self.quant_method, "quant_config")
            weight_block_size = self.quant_method.quant_config.weight_block_size
            block_n, _ = weight_block_size[0], weight_block_size[1]
            shard_offset = (shard_offset + block_n - 1) // block_n
            shard_size = (shard_size + block_n - 1) // block_n

        param.load_qkv_weight(loaded_weight=loaded_weight,
                              num_heads=self.num_kv_head_replicas,
                              shard_id=loaded_shard_id,
                              shard_offset=shard_offset,
                              shard_size=shard_size)

    def weight_loader(self,
                      param: Parameter,
                      loaded_weight: torch.Tensor,
                      loaded_shard_id: Optional[str] = None):

        # Special case for GGUF
        # initialize GGUF param after we know the quantize type
        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:
            idx_map = {"q": 0, "k": 1, "v": 2}
            if loaded_shard_id is not None:
                param.data[idx_map[loaded_shard_id]].copy_(loaded_weight)
                param.shard_weight_type[loaded_shard_id] = loaded_weight.item()
            else:
                param.shard_weight_type = {
                    k: loaded_weight.item()
                    for k in idx_map
                }
            return

        if is_gguf_weight:
            tp_size = get_tensor_model_parallel_world_size()
            tp_rank = get_tensor_model_parallel_rank()

            output_dim = getattr(param, "output_dim", None)
            shard_size = loaded_weight.size(output_dim) // tp_size
            start_idx = tp_rank * shard_size

            if loaded_shard_id is not None:
                loaded_weight = loaded_weight.narrow(output_dim, start_idx,
                                                     shard_size)
                param.shard_id.append(loaded_shard_id)
                param.shard_id_map[loaded_shard_id] = len(param.data_container)
                param.data_container.append(loaded_weight)
                if len(param.data_container) == 3:
                    self.qweight = param.materialize_nested()
                return

        param_data = param.data
        output_dim = getattr(param, "output_dim", None)
        # Special case for AQLM codebooks.
        is_metadata = getattr(param, "is_metadata", False)

        # Special case for per-tensor scales in fused case.
        needs_scalar_to_array = getattr(param, "needs_scalar_to_array", False)
        is_quantization = not isinstance(self.quant_method, UnquantizedLinearMethod)

        if loaded_shard_id is None:
            # Loaded weight is already fused on disk (qkv).
            # (e.g., Phi-3's qkv_proj).
            if output_dim is None:
                if needs_scalar_to_array:
                    param_data, loaded_weight = adjust_scalar_to_fused_array(
                        param_data, loaded_weight, 0)

                assert param_data.shape == loaded_weight.shape
                param_data.copy_(loaded_weight)
                return
            shard_offsets = [
                # (shard_id, shard_offset, shard_size)
                ("q", 0, self.total_num_heads * self.head_size),
                ("k", self.total_num_heads * self.head_size,
                 self.total_num_kv_heads * self.head_size),
                ("v", (self.total_num_heads + self.total_num_kv_heads) *
                 self.head_size, self.total_num_kv_heads * self.head_size),
            ]
            use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit",
                                            False)

            packed_dim = getattr(param, "packed_dim", None)
            for shard_id, shard_offset, shard_size in shard_offsets:
                # Special case for Quantized Weights.
                # If quantized, we need to adjust the offset and size to account
                # for the packing.
                if packed_dim == output_dim:
                    shard_size = shard_size // param.pack_factor
                    shard_offset = shard_offset // param.pack_factor

                    # Special case for Marlin.
                    shard_size, shard_offset = adjust_marlin_shard(
                        param, shard_size, shard_offset)

                if use_bitsandbytes_4bit:
                    orig_qkv_offsets = {
                        "q": (0, self.total_num_heads * self.head_size),
                        "k": (self.total_num_heads * self.head_size,
                              self.total_num_kv_heads * self.head_size),
                        "v":
                        ((self.total_num_heads + self.total_num_kv_heads) *
                         self.head_size,
                         self.total_num_kv_heads * self.head_size),
                        "total":
                        ((self.total_num_heads + 2 * self.total_num_kv_heads) *
                         self.head_size, 0)
                    }

                    shard_size, shard_offset = adjust_bitsandbytes_4bit_shard(
                        param, orig_qkv_offsets, shard_id)

                loaded_weight_shard = loaded_weight.narrow(
                    output_dim, shard_offset, shard_size)
                self.weight_loader(param, loaded_weight_shard, shard_id)
            return

        tp_rank = get_tensor_model_parallel_rank()
        assert loaded_shard_id in ["q", "k", "v"]

        # If output dim is defined, use the default loading process.
        if output_dim is not None:
            if loaded_shard_id == "q":
                shard_offset = 0
                shard_size = self.num_heads * self.head_size
            elif loaded_shard_id == "k":
                shard_offset = self.num_heads * self.head_size
                shard_size = self.num_kv_heads * self.head_size
            elif loaded_shard_id == "v":
                shard_offset = (self.num_heads +
                                self.num_kv_heads) * self.head_size
                shard_size = self.num_kv_heads * self.head_size
            # Special case for Quantized Weights.
            # If quantized, we need to adjust the offset and size to account
            # for the packing.
            packed_dim = getattr(param, "packed_dim", None)
            if packed_dim == output_dim:
                shard_size = shard_size // param.pack_factor
                shard_offset = shard_offset // param.pack_factor

                # Special case for Marlin.
                shard_size, shard_offset = adjust_marlin_shard(
                    param, shard_size, shard_offset)

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

            if use_bitsandbytes_4bit:
                orig_qkv_offsets = {
                    "q": (0, self.num_heads * self.head_size),
                    "k": (self.num_heads * self.head_size,
                          self.num_kv_heads * self.head_size),
                    "v":
                    ((self.num_heads + self.num_kv_heads) * self.head_size,
                     self.num_kv_heads * self.head_size),
                    "total":
                    ((self.num_heads + 2 * self.num_kv_heads) * self.head_size,
                     0)
                }
                shard_size, shard_offset = adjust_bitsandbytes_4bit_shard(
                    param, orig_qkv_offsets, loaded_shard_id)

            if not envs.VLLM_USE_NN or len(param_data.shape)==1 or is_quantization:
                param_data = param_data.narrow(output_dim, shard_offset,
                                               shard_size)
            else:
                param_data = param_data.narrow(int(not(output_dim)), shard_offset,
                                               shard_size)
                
            if loaded_shard_id == "q":
                shard_id = tp_rank
            else:
                shard_id = tp_rank // self.num_kv_head_replicas
            start_idx = shard_id * shard_size

            if not is_sharded_weight:
                loaded_weight = loaded_weight.narrow(output_dim, start_idx,
                                                     shard_size)

        # Special case for for AQLM codebooks.
        elif is_metadata:
            # metadata indicates fixed size concatenated along dim 0
            shard_size = loaded_weight.shape[0]
            shard_index = ["q", "k", "v"].index(loaded_shard_id)
            param_data = param_data.narrow(0, shard_index * shard_size,
                                           shard_size)
        # Special case for per-tensor scales in fused case.
        elif needs_scalar_to_array:
            param_data, loaded_weight = adjust_scalar_to_fused_array(
                param_data, loaded_weight, loaded_shard_id)
        else:
            ignore_warning = getattr(param, "ignore_warning", False)
            if not ignore_warning:
                logger.warning(
                    "Loading a weight without `output_dim` attribute in "
                    "QKVParallelLinear, assume the weight is the same "
                    "for all partitions.")

        if envs.VLLM_USE_NN and not is_quantization:
            loaded_weight = loaded_weight.t()
            
        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)


class RowParallelLinear(LinearBase):
    """Linear layer with row parallelism.

    The linear layer is defined as Y = XA + b. A is parallelized along
    its first dimension and X along its second dimension as:
               -   -
              | A_1 |
              | .   |
          A = | .   |        X = [X_1, ..., X_p]
              | .   |
              | A_p |
               -   -
    Arguments:
        input_size: first dimension of matrix A.
        output_size: second dimension of matrix A.
        bias: If true, add bias. Note that bias is not parallelized.
        input_is_parallel: If true, we assume that the input is already
                           split across the GPUs and we do not split
                           again.
        skip_bias_add: This was added to enable performance optimization where
                       bias can be fused with other element-wise operations.
                       We skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
        reduce_results: If true, call all-reduce on output and make Y available
                       to all GPUs, otherwise, every GPU will have its output
                       which is Y = X_iA_i
        quant_config: Quantization configure.
        prefix: The name of the layer in the state dict, including all parents
                        (e.g. model.layers.0.down_proj)
        return_bias: If true, return bias together with outputs in forward pass.
    """

    def __init__(
        self,
        input_size: int,
        output_size: int,
        bias: bool = True,
        input_is_parallel: bool = True,
        skip_bias_add: bool = False,
        params_dtype: Optional[torch.dtype] = None,
        reduce_results: bool = True,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        *,
        return_bias: bool = True,
    ):
        # Divide the weight matrix along the first dimension.
        self.tp_rank = get_tensor_model_parallel_rank()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.input_size_per_partition = divide(input_size, self.tp_size)
        self.output_size_per_partition = output_size
        self.output_partition_sizes = [output_size]

        super().__init__(input_size,
                         output_size,
                         skip_bias_add,
                         params_dtype,
                         quant_config,
                         prefix,
                         return_bias=return_bias)

        self.input_is_parallel = input_is_parallel
        self.reduce_results = reduce_results

        assert self.quant_method is not None
        self.quant_method.create_weights(
            layer=self,
            input_size_per_partition=self.input_size_per_partition,
            output_partition_sizes=self.output_partition_sizes,
            input_size=self.input_size,
            output_size=self.output_size,
            params_dtype=self.params_dtype,
            weight_loader=(
                self.weight_loader_v2 if self.quant_method.__class__.__name__
                in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader))
        if not reduce_results and (bias and not skip_bias_add):
            raise ValueError("When not reduce the results, adding bias to the "
                             "results can lead to incorrect results")

        if bias:
            self.bias = Parameter(
                torch.empty(self.output_size, dtype=params_dtype))
            set_weight_attrs(self.bias, {
                "output_dim": 0,
                "weight_loader": self.weight_loader,
            })
        else:
            self.register_parameter("bias", None)
        from vllm.two_batch_overlap.two_batch_overlap import tbo_all_reduce
        self.tbo_all_reduce = tbo_all_reduce

    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
        tp_rank = get_tensor_model_parallel_rank()
        tp_size = get_tensor_model_parallel_world_size()
        input_dim = getattr(param, "input_dim", None)
        use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
        is_sharded_weight = getattr(param, "is_sharded_weight", 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, UninitializedParameter):
            weight_shape = list(loaded_weight.shape)
            if input_dim:
                weight_shape[input_dim] = weight_shape[input_dim] // tp_size
            param.materialize(tuple(weight_shape), dtype=loaded_weight.dtype)
            
        is_quantization = not isinstance(self.quant_method, UnquantizedLinearMethod)

        param_data = param.data
        if input_dim is not None and not is_sharded_weight:
            if not envs.VLLM_USE_NN or is_quantization:
                shard_size = param_data.shape[input_dim]
            else:
                shard_size = param_data.shape[int(not(input_dim))]
            start_idx = tp_rank * shard_size
            loaded_weight = loaded_weight.narrow(input_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)

        if envs.VLLM_USE_NN and not is_quantization:
            loaded_weight = loaded_weight.t()
            
        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)

    def weight_loader_v2(self, param: BasevLLMParameter,
                         loaded_weight: torch.Tensor):

        # 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:
            assert loaded_weight.numel() == 1
            loaded_weight = loaded_weight.reshape(1)

        param.load_row_parallel_weight(loaded_weight=loaded_weight)

    def forward(
        self, input_,
        use_fused_silu_mul_quant: Optional[bool] = False,
        residual=None,
        output=None,
        disable_allreduce=False
    ) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
        if self.input_is_parallel:
            input_parallel = input_
        else:
            tp_rank = get_tensor_model_parallel_rank()
            splitted_input = split_tensor_along_last_dim(
                input_, num_partitions=self.tp_size)
            input_parallel = splitted_input[tp_rank].contiguous()
            
        # only add residual to the first rank
        if residual is not None and self.tp_size > 1 and get_tensor_model_parallel_rank(
        ) != 0:
            residual *= 0

        # Matrix multiply.
        if output is not None:
            assert disable_allreduce or not self.reduce_results
        assert self.quant_method is not None
        # Only fuse bias add into GEMM for rank 0 (this ensures that
        # bias will not get added more than once in TP>1 case)
        bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
        if use_fused_silu_mul_quant:
            xq, xs = lm_fuse_silu_mul_quant(input_parallel)
            
            silu_quant_args = [xq, xs]
            output_parallel = self.quant_method.apply(self,
                                                      input_parallel,
                                                      bias=bias_,
                                                      silu_quant_args=silu_quant_args)
        else:
            output_parallel = self.quant_method.apply(self,
                                                      input_parallel,
                                                      residual=residual,
                                                      output=output)
        if self.reduce_results and self.tp_size > 1 and not disable_allreduce:
            if envs.VLLM_ENABLE_TBO:
                output_ = self.tbo_all_reduce(output_parallel)
            else:
                output_ = tensor_model_parallel_all_reduce(output_parallel)
        else:
            output_ = output_parallel

        if not self.skip_bias_add:
            output = output_ + self.bias if self.bias is not None else output_
            output_bias = None
        else:
            output = output_
            output_bias = self.bias
            
        # output_bias = self.bias if self.skip_bias_add else None

        # if not self.return_bias:
        #     return output
        
        return output, output_bias

    def extra_repr(self) -> str:
        s = f"input_features={self.input_size_per_partition}"
        s += f", output_features={self.output_size}"
        s += f", bias={self.bias is not None}"
        s += f", tp_size={self.tp_size}"
        s += f", reduce_results={self.reduce_results}"
        return s


class QKVCrossParallelLinear(LinearBase):
    """Linear layers for efficient cross-attention's QKV transformation.

    Args:
        hidden_size: input hidden state size of the transformer.
        head_size: size of each attention head.
        total_num_heads: total number of attention query heads.
        total_num_kv_heads: total number of attention key/value heads. If
                            None, assume total_num_kv_heads = total_num_heads.
        bias: If true, add bias.
        skip_bias_add: This was added to enable performance optimizations where
                       bias can be fused with other element-wise operations. we
                       skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
        quant_config: Quantization configure.
        prefix: The name of the layer in the state dict, including all parents
                        (e.g. model.layers.0.qkv_proj)
    """

    def __init__(self,
                 hidden_size: int,
                 head_size: int,
                 total_num_heads: int,
                 total_num_kv_heads: Optional[int] = None,
                 bias: bool = True,
                 skip_bias_add: bool = False,
                 params_dtype: Optional[torch.dtype] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = ""):
        # input_size and output_size are not used, just for alignment
        input_size = hidden_size
        output_size = (total_num_heads + (total_num_kv_heads or 0)) * head_size
        super().__init__(input_size=input_size,
                         output_size=output_size,
                         skip_bias_add=skip_bias_add,
                         params_dtype=params_dtype,
                         quant_config=quant_config,
                         prefix=prefix)

        self.quant_config = quant_config

        # Empty placeholders for loading as a single module.
        placeholder_size = 0
        assert self.quant_method is not None
        self.quant_method.create_weights(self,
                                         placeholder_size, [placeholder_size],
                                         placeholder_size,
                                         placeholder_size,
                                         self.params_dtype,
                                         weight_loader=self.weight_loader)

        # Use a dictionary to avoid submodules parameters auto-registration:
        # drop-in replacement for a `QKVParallelLinear` module.
        self.proj = dict()
        self.proj["q_proj_decoder"] = ColumnParallelLinear(
            input_size=hidden_size,
            output_size=total_num_heads * head_size,
            bias=bias,
            quant_config=quant_config,
            skip_bias_add=skip_bias_add,
            params_dtype=params_dtype,
            prefix=f"{prefix}.q_proj_decoder")

        self.proj["kv_proj_encoder"] = QKVParallelLinear(
            hidden_size=hidden_size,
            head_size=head_size,
            total_num_heads=0,
            total_num_kv_heads=total_num_kv_heads,
            bias=bias,
            quant_config=quant_config,
            skip_bias_add=skip_bias_add,
            params_dtype=params_dtype,
            prefix=f"{prefix}.kv_proj_encoder")

        # `kv_proj_encoder.num_kv_heads` accounts for sharding with tp>1.
        self.q_size = self.q_proj_decoder.output_size_per_partition
        self.kv_size = self.kv_proj_encoder.num_kv_heads * head_size

        if bias:
            self.bias = torch.nn.Parameter()
            set_weight_attrs(self.bias, {
                "output_dim": 0,
                "weight_loader": self.weight_loader,
            })
        else:
            self.bias = None

    def process_weights_after_loading(self):
        for layer in self.proj.values():
            if self.quant_method is not None:
                self.quant_method.process_weights_after_loading(layer)

    @property
    def q_proj_decoder(self) -> ColumnParallelLinear:
        layer = self.proj["q_proj_decoder"]
        for name, param in self.named_parameters():
            target_param = getattr(layer, name, None)
            if target_param is not None:
                self.sync_weight_attrs(param,
                                       target_param,
                                       mode="q_proj_decoder")
        return layer

    @property
    def kv_proj_encoder(self) -> QKVParallelLinear:
        layer = self.proj["kv_proj_encoder"]
        for name, param in self.named_parameters():
            target_param = getattr(layer, name, None)
            if target_param is not None:
                self.sync_weight_attrs(param,
                                       target_param,
                                       mode="kv_proj_encoder")
        return layer

    def sync_weight_attrs(
        self,
        src_param: nn.Parameter,
        tgt_param: nn.Parameter,
        mode: Literal["q_proj_decoder", "kv_proj_encoder"],
    ):
        missing_attrs_dict = {
            k: getattr(src_param, k)
            for k in (set(vars(src_param).keys()) -
                      set(vars(tgt_param).keys()))
        }
        # TODO(Isotr0py): handle bitsandbytes 8bit
        use_bitsandbytes_4bit = getattr(src_param, "use_bitsandbytes_4bit",
                                        False)
        if (missing_attrs_dict and use_bitsandbytes_4bit):
            q_proj_attrs, kv_proj_attrs = left_shift_bitsandbytes_4bit_shard(
                missing_attrs_dict)
            if mode == "q_proj_decoder":
                set_weight_attrs(tgt_param, q_proj_attrs)
            elif mode == "kv_proj_encoder":
                set_weight_attrs(tgt_param, kv_proj_attrs)
        else:
            set_weight_attrs(tgt_param, missing_attrs_dict)

    def _is_same_param(
        self,
        src_param: torch.nn.Parameter,
        map_param: torch.nn.Parameter,
    ) -> bool:
        """Check if two parameters are exactly pointing to same things."""
        # ignore weight_loader because it's always different
        key_to_ignore = ["weight_loader", "_weight_loader"]
        has_same_type_name = type(src_param) is type(map_param)
        src_param_attrs = {
            k: v
            for k, v in src_param.__dict__.items() if k not in key_to_ignore
        }
        map_param_attrs = {
            k: v
            for k, v in map_param.__dict__.items() if k not in key_to_ignore
        }
        has_same_attrs = src_param_attrs == map_param_attrs
        return has_same_type_name and has_same_attrs

    def select_proj_params(
        self,
        layer: nn.Module,
        param: nn.Parameter,
    ) -> nn.Parameter:
        """
        Given the placeholder param, 
        return the corresponding param in the proj layers.
        """
        target_param_list = [
            v for _, v in layer.named_parameters()
            if self._is_same_param(param, v)
        ]
        assert len(target_param_list) == 1
        target_param = target_param_list[0]
        return target_param

    def forward(  # type: ignore[override]
        self,
        decoder_hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
    ) -> tuple[torch.Tensor, ...]:
        q, _ = self.q_proj_decoder(decoder_hidden_states)
        if encoder_hidden_states is None:
            # Encoder KV already cached.
            k = None
            v = None
        else:
            # Prefill phase, encoder KV cached here.
            kv_enc, _ = self.kv_proj_encoder(encoder_hidden_states)
            # Split kv in half
            k, v = kv_enc.split(self.kv_size, dim=-1)
        return q, k, v

    def weight_loader(self,
                      param: torch.nn.Parameter,
                      loaded_weight: torch.Tensor,
                      loaded_shard_id: Optional[str] = None):
        layer = (self.q_proj_decoder
                 if loaded_shard_id == "q" else self.kv_proj_encoder)
        target_param = self.select_proj_params(layer, param)
        shard_id_args = (loaded_shard_id, ) if loaded_shard_id != "q" else ()
        if self.quant_method.__class__.__name__ in WEIGHT_LOADER_V2_SUPPORTED:
            layer.weight_loader_v2(target_param, loaded_weight, *shard_id_args)
        else:
            layer.weight_loader(target_param, loaded_weight, *shard_id_args)

    def extra_repr(self) -> str:
        s = f"in_features={self.input_size}"
        s += f", q_size={self.q_size}"
        s += f", kv_size={self.kv_size}"
        s += f", bias={self.bias is not None}"
        s += f", tp_size={get_tensor_model_parallel_world_size()}"
        s += ", gather_output=False"
        return s
    
    
class RowParallelMoELinear(RowParallelLinear):

    def __init__(self,
                 num_experts: int,
                 input_size: int,
                 output_size: int,
                 params_dtype: Optional[torch.dtype] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = ""):
        torch.nn.Module.__init__(self)
        self.num_experts = num_experts
        self.input_size = input_size
        self.output_size = output_size
        self.reduce_results = False
        if params_dtype is None:
            params_dtype = torch.get_default_dtype()
        self.params_dtype = params_dtype
        if quant_config is None:
            self.quant_method: Optional[
                QuantizeMethodBase] = UnquantizedMoELinearMethod()
        else:
            self.quant_method = quant_config.get_quant_method(self,
                                                              prefix=prefix)
            # FIXME(ys): hack for moe
            if isinstance(self.quant_method, UnquantizedLinearMethod):
                self.quant_method = UnquantizedMoELinearMethod()

        self.tp_size = get_tensor_model_parallel_world_size()
        self.input_size_per_partition = divide(input_size, self.tp_size)
        assert self.quant_method is not None
        self.quant_method.create_weights(self,
                                         self.input_size_per_partition,
                                         [self.output_size],
                                         self.input_size,
                                         self.output_size,
                                         self.params_dtype,
                                         self.num_experts,
                                         weight_loader=self.weight_loader)
        self.register_parameter("bias", None)

    def forward(  # type: ignore[override]
            self,
            input_,
            residual=None,
            expert_idx: int = -1,
            output: Optional[torch.Tensor] = None):
        if isinstance(self.quant_method, UnquantizedMoELinearMethod):
            # use optimus moe_ffn outside
            return
        bias = None
        assert self.quant_method is not None
        output = self.quant_method.apply(self,
                                         input_,
                                         bias,
                                         expert_idx=expert_idx,
                                         output=output)
        return output