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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

import ctypes
import operator
from functools import reduce
from logging import getLogger

import torch
import torch.nn as nn

logger = getLogger(__name__)

from typing import List, Union, Optional

from bitblas.cache import global_operator_cache, get_database_path
from bitblas import Matmul, MatmulConfig
from bitblas.quantization.utils import general_compress
from bitblas import auto_detect_nvidia_target

BITBLAS_DATABASE_PATH = get_database_path()


def unpack_qzeros(qzeros, bits):
    qzeros = qzeros.view(torch.int32)
    elems_per_int32 = 32 // bits
    unpacked_zeros = torch.zeros(
        (qzeros.shape[0], qzeros.shape[1] * elems_per_int32),
        dtype=torch.int8,
        device=qzeros.device,
        requires_grad=False,
    )
    for col in range(unpacked_zeros.shape[1]):
        i = col % elems_per_int32
        unpacked_zeros[:, col] = (qzeros[:, col // elems_per_int32] >> (bits * i))

    # Follow the instruction in AutoGPTQ qlinear_cuda_old.py line 303
    # NOTE: It appears that casting after the `unpacked_zeros  + 1` is important.
    return torch.bitwise_and(unpacked_zeros + 1, 2**bits - 1)


# For gptqv2 from gptqmodel
def unpack_qzeros_v2(qzeros, bits):
    qzeros = qzeros.view(torch.int32)
    elems_per_int32 = 32 // bits
    unpacked_zeros = torch.zeros(
        (qzeros.shape[0], qzeros.shape[1] * elems_per_int32),
        dtype=torch.int8,
        device=qzeros.device,
        requires_grad=False,
    )
    for col in range(unpacked_zeros.shape[1]):
        i = col % elems_per_int32
        unpacked_zeros[:, col] = (qzeros[:, col // elems_per_int32] >> (bits * i))

    # Follow the instruction in AutoGPTQ qlinear_cuda_old.py line 303
    # NOTE: It appears that casting after the `unpacked_zeros  + 1` is important.
    return torch.bitwise_and(unpacked_zeros, 2**bits - 1)


def unpack_qweight(qweight, bits):
    qweight = qweight.view(torch.int8)
    elems_per_int8 = 8 // bits
    unpacked_weight = torch.zeros(
        (qweight.shape[0], qweight.shape[1] * elems_per_int8),
        dtype=torch.int8,
        device=qweight.device,
        requires_grad=False,
    )
    for col in range(unpacked_weight.shape[1]):
        i = col % elems_per_int8
        unpacked_weight[:, col] = (qweight[:, col // elems_per_int8] >> (bits * i))

    return torch.bitwise_and(unpacked_weight, 2**bits - 1)


class Linear(nn.Module):
    opt_M = [16, 32, 64, 128, 256, 512]
    STORAGE_DTYPE = "int8"  # assume int8 storage
    TORCH_STORAGE_DTYPE = getattr(torch, STORAGE_DTYPE)
    BITBLAS_DTYPES = {
        torch.float32: "float32",
        torch.float16: "float16",
        torch.half: "float16",
        torch.int8: "int8",
    }

    def __init__(
        self,
        in_features: int,
        out_features: int,
        bias: bool = False,
        A_dtype: str = "float16",
        W_dtype: str = "float16",
        accum_dtype: str = "float16",
        out_dtype: str = "float16",
        # configs for weight only quantization
        group_size: int = -1,
        with_scaling: bool = None,
        with_zeros: bool = False,
        zeros_mode: str = None,
        opt_M: Union[int, List[int]] = opt_M,
        # performance related configs
        enable_tuning: bool = True,
        fast_decoding: Optional[bool] = None,
        propagate_b: bool = False,
    ):
        """
        @opt_M: optimize range of the input shape for dynamic symbolic
        if the input shape is a range, we will optimize the matmul with dynamic symbolic.
        if the input shape is int, we will optimize the matmul with static symbolic.
        """
        super().__init__()

        self.in_features = in_features
        self.out_features = out_features
        self.opt_M = opt_M
        self.group_size = self._set_group_size(group_size, in_features)
        self.torch_dtype = getattr(torch, A_dtype)
        self.is_consitent = A_dtype == W_dtype
        self.zeros_mode = zeros_mode
        self._validate_parameters(self.group_size, in_features, out_features)
        self._configure_bitblas_matmul(
            A_dtype,
            W_dtype,
            accum_dtype,
            out_dtype,
            with_scaling,
            with_zeros,
            zeros_mode,
            enable_tuning,
            fast_decoding,
            bias,
            propagate_b,
        )
        self._initialize_buffers(in_features, out_features, bias)

    def init_params(self):
        # eliminate runtime overhead like exllama state
        if self.is_consitent:
            param_list = [self.weight]
            if self.bitblas_matmul.config.with_bias:
                param_list.append(self.bias)
            self.q_params = [ctypes.c_void_p(arr.data_ptr()) for arr in param_list]
        else:
            param_list = [self.qweight]
            if self.bitblas_matmul.config.with_scaling:
                param_list.append(self.scales)
            if self.bitblas_matmul.config.with_zeros:
                param_list.append(self.zeros)
            if self.bitblas_matmul.config.with_bias:
                param_list.append(self.bias)
            self.q_params = [ctypes.c_void_p(arr.data_ptr()) for arr in param_list]

    def _validate_parameters(self, group_size, in_features, out_features):
        if in_features % 16 != 0 or out_features % 16 != 0:
            raise ValueError("`in_features` and `out_features` must be divisible by 16.")
        if in_features % group_size != 0:
            raise ValueError("`in_features` must be divisible by `group_size`.")

    def _set_group_size(self, group_size, in_features):
        return in_features if (group_size == -1 or group_size is None) else group_size

    def _initialize_buffers(self, in_features, out_features, bias):
        if self.consistent:
            self.register_buffer(
                "weight",
                torch.zeros((out_features, in_features // self.group_size), dtype=self.torch_dtype),
            )
        else:
            self.register_buffer(
                "qweight",
                torch.zeros(
                    self.bitblas_matmul.retrieve_weight_shape(),
                    dtype=self.TORCH_STORAGE_DTYPE,
                ),
            )
            self.register_buffer(
                "scales",
                torch.zeros((out_features, in_features // self.group_size), dtype=self.torch_dtype),
            )
            if self.zeros_mode == "quantized":
                storage_nbit = int("".join(c for c in self.STORAGE_DTYPE if c.isdigit()))
                self.register_buffer(
                    "zeros",
                    torch.zeros(
                        (
                            in_features // self.group_size,
                            out_features // storage_nbit * self.bits,
                        ),
                        dtype=self.TORCH_STORAGE_DTYPE,
                    ),
                )
            else:
                self.register_buffer(
                    "zeros",
                    torch.zeros(
                        (out_features, in_features // self.group_size),
                        dtype=self.torch_dtype,
                    ),
                )
        if bias:
            self.register_buffer("bias", torch.zeros((out_features), dtype=self.torch_dtype))
        else:
            self.bias = None

    def _configure_bitblas_matmul(
        self,
        A_dtype,
        W_dtype,
        accum_dtype,
        out_dtype,
        with_scaling,
        with_zeros,
        zeros_mode,
        enable_tuning,
        fast_decoding,
        bias,
        propagate_b,
    ):
        matmul_config = MatmulConfig(
            M=self.opt_M,
            N=self.out_features,
            K=self.in_features,
            A_dtype=A_dtype,
            W_dtype=W_dtype,
            accum_dtype=accum_dtype,
            out_dtype=out_dtype,
            storage_dtype=self.STORAGE_DTYPE,
            with_scaling=with_scaling,
            with_zeros=with_zeros,
            group_size=self.group_size,
            fast_decoding=fast_decoding,
            with_bias=bias,
            propagate_b=propagate_b,
            zeros_mode=zeros_mode,
        )
        self.bitblas_matmul = self._get_or_create_bitblas_operator(matmul_config, enable_tuning)
        self.bits = self.bitblas_matmul.bit
        self.source_format = self.bitblas_matmul.source_format

    def _get_or_create_bitblas_operator(self, config, enable_tuning):
        BITBLAS_TARGET = auto_detect_nvidia_target()

        if global_operator_cache.size() == 0:
            global_operator_cache.load_from_database(BITBLAS_DATABASE_PATH, BITBLAS_TARGET)
            logger.info(f"Loaded {global_operator_cache.size()} operators from database.")

        bitblas_matmul = global_operator_cache.get(config)
        if bitblas_matmul is None:
            # should disable tuning for the first time because we may require loading bitblas operator from database.
            bitblas_matmul = Matmul(config, target=BITBLAS_TARGET, enable_tuning=False)
            if enable_tuning:
                bitblas_matmul.hardware_aware_finetune(topk=20)
                global_operator_cache.add(config, bitblas_matmul)
                global_operator_cache.save_into_database(BITBLAS_DATABASE_PATH, BITBLAS_TARGET)
                print("BitBLAS Tuning done, appended operator to global_operator_cache.")
            else:
                print("BitBLAS Operator created.")
        else:
            print("BitBLAS Operator found in global_operator_cache.")
        return bitblas_matmul

    def warmup(self, topk=20):
        self.bitblas_matmul.hardware_aware_finetune(topk=topk)

    def forward(self, A, output=None):
        A = self.bitblas_matmul.transform_input(A)
        stream = torch.cuda.current_stream()

        A_void = ctypes.c_void_p(A.data_ptr())
        stream_handle = ctypes.c_void_p(stream.cuda_stream)
        # can be lifted to post init.
        self.init_params()
        args = [A_void, *self.q_params]
        if output is None:
            output = torch.zeros(
                A.shape[:-1] + (self.out_features,),
                dtype=getattr(torch, self.bitblas_matmul.out_dtype),
                device=A.device)
        args.append(ctypes.c_void_p(output.data_ptr()))
        if self.bitblas_matmul.dynamic_range is not None:
            m = reduce(operator.mul, A.shape[:-1], 1)
            args.append(m)
        args.append(stream_handle)
        # m is the product of the last n - 1 dimensions of A
        self.bitblas_matmul.lib.call(*args)

        return output

    def load_and_transform_weight(
        self,
        weight: torch.Tensor,
        scales: torch.Tensor = None,
        zeros: torch.Tensor = None,
        bias: torch.Tensor = None,
    ):
        if self.consistent:
            assert scales is None, "scales should be None for consistent mode."
            assert zeros is None, "zeros should be None for consistent mode."
            weight = self.bitblas_matmul.transform_weight(weight)
            self.weight = nn.Parameter(weight)
            if bias is not None:
                self.bias = bias
        else:
            weight = self.bitblas_matmul.transform_weight(weight)
            self.qweight = weight
            if scales is not None:
                self.scales = scales
            if zeros is not None:
                self.zeros = zeros
            if bias is not None:
                self.bias = bias

    def repack_from_gptq(self, gptq_module, device="cuda"):
        # qweight in gptq old quant linear stored with (out_features, in_features), should be transposed.
        qweight = gptq_module.qweight.T.contiguous().view(self.TORCH_STORAGE_DTYPE)
        intweight = unpack_qweight(qweight, self.bits).contiguous()
        if self.bitblas_matmul.weight_transform is not None:
            qweight = self.bitblas_matmul.weight_transform(intweight.cpu()).to(device)
        self.qweight = qweight
        # scales in gptq old quant linear stored with (in_features // group_size, out_features), should be transposed.
        scales = gptq_module.scales.T.contiguous().view(self.torch_dtype)
        self.scales = scales
        # qzeros should be dequantized to int zeros.
        intzeros = unpack_qzeros(gptq_module.qzeros, self.bits).T.contiguous()
        if self.bitblas_matmul.config.zeros_mode == "original":
            self.zeros = intzeros.to(torch.float16).contiguous()
        elif self.bitblas_matmul.config.zeros_mode == "rescale":
            self.zeros[:, :] = intzeros.to(torch.float16)[:, :] * self.scales[:, :]
        elif self.bitblas_matmul.config.zeros_mode == "quantized":
            self.zeros = (
                torch.Tensor(general_compress(intzeros.T.contiguous().cpu().numpy(), self.bits)).to(
                    self.qweight.device).to(self.zeros.dtype).contiguous())
        else:
            raise ValueError(f"Unsupported zeros type: {self.bitblas_matmul.config.zeros_mode}")
        if self.bias is not None:
            self.bias = gptq_module.bias.data.to(torch.float16).contiguous()

    def repack_from_gptq_v2(self, gptq_module):
        # qweight in gptq old quant linear stored with (out_features, in_features), should be transposed.
        qweight = gptq_module.qweight.T.contiguous().view(self.TORCH_STORAGE_DTYPE)
        intweight = unpack_qweight(qweight, self.bits).contiguous()
        if self.bitblas_matmul.weight_transform is not None:
            qweight = self.bitblas_matmul.weight_transform(intweight.cpu()).cuda()
        self.qweight = qweight
        # scales in gptq old quant linear stored with (in_features // group_size, out_features), should be transposed.
        scales = gptq_module.scales.T.contiguous().view(self.torch_dtype)
        self.scales = scales
        # qzeros should be dequantized to int zeros.
        intzeros = unpack_qzeros_v2(gptq_module.qzeros, self.bits).T.contiguous()
        if self.bitblas_matmul.config.zeros_mode == "original":
            self.zeros = intzeros.to(torch.float16).contiguous()
        elif self.bitblas_matmul.config.zeros_mode == "rescale":
            self.zeros[:, :] = intzeros.to(torch.float16)[:, :] * self.scales[:, :]
        elif self.bitblas_matmul.config.zeros_mode == "quantized":
            self.zeros = (
                torch.Tensor(general_compress(intzeros.T.contiguous().cpu().numpy(), self.bits)).to(
                    self.qweight.device).to(self.zeros.dtype).contiguous())
        else:
            raise ValueError(f"Unsupported zeros type: {self.bitblas_matmul.config.zeros_mode}")
        if self.bias is not None:
            self.bias = gptq_module.bias.data.to(torch.float16).contiguous()

    @property
    def consistent(self):
        return self.is_consitent


__all__ = ["Linear"]

	# Copyright (c) Microsoft Corporation.
	# Licensed under the MIT License.

	import ctypes
	import operator
	from functools import reduce
	from logging import getLogger

	import torch
	import torch.nn as nn

	logger = getLogger(__name__)

	from typing import List, Union, Optional

	from bitblas.cache import global_operator_cache, get_database_path
	from bitblas import Matmul, MatmulConfig
	from bitblas.quantization.utils import general_compress
	from bitblas import auto_detect_nvidia_target

	BITBLAS_DATABASE_PATH = get_database_path()


	def unpack_qzeros(qzeros, bits):
	qzeros = qzeros.view(torch.int32)
	elems_per_int32 = 32 // bits
	unpacked_zeros = torch.zeros(
	(qzeros.shape[0], qzeros.shape[1] * elems_per_int32),
	dtype=torch.int8,
	device=qzeros.device,
	requires_grad=False,
	)
	for col in range(unpacked_zeros.shape[1]):
	i = col % elems_per_int32
	unpacked_zeros[:, col] = (qzeros[:, col // elems_per_int32] >> (bits * i))

	# Follow the instruction in AutoGPTQ qlinear_cuda_old.py line 303
	# NOTE: It appears that casting after the `unpacked_zeros + 1` is important.
	return torch.bitwise_and(unpacked_zeros + 1, 2**bits - 1)


	# For gptqv2 from gptqmodel
	def unpack_qzeros_v2(qzeros, bits):
	qzeros = qzeros.view(torch.int32)
	elems_per_int32 = 32 // bits
	unpacked_zeros = torch.zeros(
	(qzeros.shape[0], qzeros.shape[1] * elems_per_int32),
	dtype=torch.int8,
	device=qzeros.device,
	requires_grad=False,
	)
	for col in range(unpacked_zeros.shape[1]):
	i = col % elems_per_int32
	unpacked_zeros[:, col] = (qzeros[:, col // elems_per_int32] >> (bits * i))

	# Follow the instruction in AutoGPTQ qlinear_cuda_old.py line 303
	# NOTE: It appears that casting after the `unpacked_zeros + 1` is important.
	return torch.bitwise_and(unpacked_zeros, 2**bits - 1)


	def unpack_qweight(qweight, bits):
	qweight = qweight.view(torch.int8)
	elems_per_int8 = 8 // bits
	unpacked_weight = torch.zeros(
	(qweight.shape[0], qweight.shape[1] * elems_per_int8),
	dtype=torch.int8,
	device=qweight.device,
	requires_grad=False,
	)
	for col in range(unpacked_weight.shape[1]):
	i = col % elems_per_int8
	unpacked_weight[:, col] = (qweight[:, col // elems_per_int8] >> (bits * i))

	return torch.bitwise_and(unpacked_weight, 2**bits - 1)


	class Linear(nn.Module):
	opt_M = [16, 32, 64, 128, 256, 512]
	STORAGE_DTYPE = "int8" # assume int8 storage
	TORCH_STORAGE_DTYPE = getattr(torch, STORAGE_DTYPE)
	BITBLAS_DTYPES = {
	torch.float32: "float32",
	torch.float16: "float16",
	torch.half: "float16",
	torch.int8: "int8",
	}

	def __init__(
	self,
	in_features: int,
	out_features: int,
	bias: bool = False,
	A_dtype: str = "float16",
	W_dtype: str = "float16",
	accum_dtype: str = "float16",
	out_dtype: str = "float16",
	# configs for weight only quantization
	group_size: int = -1,
	with_scaling: bool = None,
	with_zeros: bool = False,
	zeros_mode: str = None,
	opt_M: Union[int, List[int]] = opt_M,
	# performance related configs
	enable_tuning: bool = True,
	fast_decoding: Optional[bool] = None,
	propagate_b: bool = False,
	):
	"""
	@opt_M: optimize range of the input shape for dynamic symbolic
	if the input shape is a range, we will optimize the matmul with dynamic symbolic.
	if the input shape is int, we will optimize the matmul with static symbolic.
	"""
	super().__init__()

	self.in_features = in_features
	self.out_features = out_features
	self.opt_M = opt_M
	self.group_size = self._set_group_size(group_size, in_features)
	self.torch_dtype = getattr(torch, A_dtype)
	self.is_consitent = A_dtype == W_dtype
	self.zeros_mode = zeros_mode
	self._validate_parameters(self.group_size, in_features, out_features)
	self._configure_bitblas_matmul(
	A_dtype,
	W_dtype,
	accum_dtype,
	out_dtype,
	with_scaling,
	with_zeros,
	zeros_mode,
	enable_tuning,
	fast_decoding,
	bias,
	propagate_b,
	)
	self._initialize_buffers(in_features, out_features, bias)

	def init_params(self):
	# eliminate runtime overhead like exllama state
	if self.is_consitent:
	param_list = [self.weight]
	if self.bitblas_matmul.config.with_bias:
	param_list.append(self.bias)
	self.q_params = [ctypes.c_void_p(arr.data_ptr()) for arr in param_list]
	else:
	param_list = [self.qweight]
	if self.bitblas_matmul.config.with_scaling:
	param_list.append(self.scales)
	if self.bitblas_matmul.config.with_zeros:
	param_list.append(self.zeros)
	if self.bitblas_matmul.config.with_bias:
	param_list.append(self.bias)
	self.q_params = [ctypes.c_void_p(arr.data_ptr()) for arr in param_list]

	def _validate_parameters(self, group_size, in_features, out_features):
	if in_features % 16 != 0 or out_features % 16 != 0:
	raise ValueError("`in_features` and `out_features` must be divisible by 16.")
	if in_features % group_size != 0:
	raise ValueError("`in_features` must be divisible by `group_size`.")

	def _set_group_size(self, group_size, in_features):
	return in_features if (group_size == -1 or group_size is None) else group_size

	def _initialize_buffers(self, in_features, out_features, bias):
	if self.consistent:
	self.register_buffer(
	"weight",
	torch.zeros((out_features, in_features // self.group_size), dtype=self.torch_dtype),
	)
	else:
	self.register_buffer(
	"qweight",
	torch.zeros(
	self.bitblas_matmul.retrieve_weight_shape(),
	dtype=self.TORCH_STORAGE_DTYPE,
	),
	)
	self.register_buffer(
	"scales",
	torch.zeros((out_features, in_features // self.group_size), dtype=self.torch_dtype),
	)
	if self.zeros_mode == "quantized":
	storage_nbit = int("".join(c for c in self.STORAGE_DTYPE if c.isdigit()))
	self.register_buffer(
	"zeros",
	torch.zeros(
	(
	in_features // self.group_size,
	out_features // storage_nbit * self.bits,
	),
	dtype=self.TORCH_STORAGE_DTYPE,
	),
	)
	else:
	self.register_buffer(
	"zeros",
	torch.zeros(
	(out_features, in_features // self.group_size),
	dtype=self.torch_dtype,
	),
	)
	if bias:
	self.register_buffer("bias", torch.zeros((out_features), dtype=self.torch_dtype))
	else:
	self.bias = None

	def _configure_bitblas_matmul(
	self,
	A_dtype,
	W_dtype,
	accum_dtype,
	out_dtype,
	with_scaling,
	with_zeros,
	zeros_mode,
	enable_tuning,
	fast_decoding,
	bias,
	propagate_b,
	):
	matmul_config = MatmulConfig(
	M=self.opt_M,
	N=self.out_features,
	K=self.in_features,
	A_dtype=A_dtype,
	W_dtype=W_dtype,
	accum_dtype=accum_dtype,
	out_dtype=out_dtype,
	storage_dtype=self.STORAGE_DTYPE,
	with_scaling=with_scaling,
	with_zeros=with_zeros,
	group_size=self.group_size,
	fast_decoding=fast_decoding,
	with_bias=bias,
	propagate_b=propagate_b,
	zeros_mode=zeros_mode,
	)
	self.bitblas_matmul = self._get_or_create_bitblas_operator(matmul_config, enable_tuning)
	self.bits = self.bitblas_matmul.bit
	self.source_format = self.bitblas_matmul.source_format

	def _get_or_create_bitblas_operator(self, config, enable_tuning):
	BITBLAS_TARGET = auto_detect_nvidia_target()

	if global_operator_cache.size() == 0:
	global_operator_cache.load_from_database(BITBLAS_DATABASE_PATH, BITBLAS_TARGET)
	logger.info(f"Loaded {global_operator_cache.size()} operators from database.")

	bitblas_matmul = global_operator_cache.get(config)
	if bitblas_matmul is None:
	# should disable tuning for the first time because we may require loading bitblas operator from database.
	bitblas_matmul = Matmul(config, target=BITBLAS_TARGET, enable_tuning=False)
	if enable_tuning:
	bitblas_matmul.hardware_aware_finetune(topk=20)
	global_operator_cache.add(config, bitblas_matmul)
	global_operator_cache.save_into_database(BITBLAS_DATABASE_PATH, BITBLAS_TARGET)
	print("BitBLAS Tuning done, appended operator to global_operator_cache.")
	else:
	print("BitBLAS Operator created.")
	else:
	print("BitBLAS Operator found in global_operator_cache.")
	return bitblas_matmul

	def warmup(self, topk=20):
	self.bitblas_matmul.hardware_aware_finetune(topk=topk)

	def forward(self, A, output=None):
	A = self.bitblas_matmul.transform_input(A)
	stream = torch.cuda.current_stream()

	A_void = ctypes.c_void_p(A.data_ptr())
	stream_handle = ctypes.c_void_p(stream.cuda_stream)
	# can be lifted to post init.
	self.init_params()
	args = [A_void, *self.q_params]
	if output is None:
	output = torch.zeros(
	A.shape[:-1] + (self.out_features,),
	dtype=getattr(torch, self.bitblas_matmul.out_dtype),
	device=A.device)
	args.append(ctypes.c_void_p(output.data_ptr()))
	if self.bitblas_matmul.dynamic_range is not None:
	m = reduce(operator.mul, A.shape[:-1], 1)
	args.append(m)
	args.append(stream_handle)
	# m is the product of the last n - 1 dimensions of A
	self.bitblas_matmul.lib.call(*args)

	return output

	def load_and_transform_weight(
	self,
	weight: torch.Tensor,
	scales: torch.Tensor = None,
	zeros: torch.Tensor = None,
	bias: torch.Tensor = None,
	):
	if self.consistent:
	assert scales is None, "scales should be None for consistent mode."
	assert zeros is None, "zeros should be None for consistent mode."
	weight = self.bitblas_matmul.transform_weight(weight)
	self.weight = nn.Parameter(weight)
	if bias is not None:
	self.bias = bias
	else:
	weight = self.bitblas_matmul.transform_weight(weight)
	self.qweight = weight
	if scales is not None:
	self.scales = scales
	if zeros is not None:
	self.zeros = zeros
	if bias is not None:
	self.bias = bias

	def repack_from_gptq(self, gptq_module, device="cuda"):
	# qweight in gptq old quant linear stored with (out_features, in_features), should be transposed.
	qweight = gptq_module.qweight.T.contiguous().view(self.TORCH_STORAGE_DTYPE)
	intweight = unpack_qweight(qweight, self.bits).contiguous()
	if self.bitblas_matmul.weight_transform is not None:
	qweight = self.bitblas_matmul.weight_transform(intweight.cpu()).to(device)
	self.qweight = qweight
	# scales in gptq old quant linear stored with (in_features // group_size, out_features), should be transposed.
	scales = gptq_module.scales.T.contiguous().view(self.torch_dtype)
	self.scales = scales
	# qzeros should be dequantized to int zeros.
	intzeros = unpack_qzeros(gptq_module.qzeros, self.bits).T.contiguous()
	if self.bitblas_matmul.config.zeros_mode == "original":
	self.zeros = intzeros.to(torch.float16).contiguous()
	elif self.bitblas_matmul.config.zeros_mode == "rescale":
	self.zeros[:, :] = intzeros.to(torch.float16)[:, :] * self.scales[:, :]
	elif self.bitblas_matmul.config.zeros_mode == "quantized":
	self.zeros = (
	torch.Tensor(general_compress(intzeros.T.contiguous().cpu().numpy(), self.bits)).to(
	self.qweight.device).to(self.zeros.dtype).contiguous())
	else:
	raise ValueError(f"Unsupported zeros type: {self.bitblas_matmul.config.zeros_mode}")
	if self.bias is not None:
	self.bias = gptq_module.bias.data.to(torch.float16).contiguous()

	def repack_from_gptq_v2(self, gptq_module):
	# qweight in gptq old quant linear stored with (out_features, in_features), should be transposed.
	qweight = gptq_module.qweight.T.contiguous().view(self.TORCH_STORAGE_DTYPE)
	intweight = unpack_qweight(qweight, self.bits).contiguous()
	if self.bitblas_matmul.weight_transform is not None:
	qweight = self.bitblas_matmul.weight_transform(intweight.cpu()).cuda()
	self.qweight = qweight
	# scales in gptq old quant linear stored with (in_features // group_size, out_features), should be transposed.
	scales = gptq_module.scales.T.contiguous().view(self.torch_dtype)
	self.scales = scales
	# qzeros should be dequantized to int zeros.
	intzeros = unpack_qzeros_v2(gptq_module.qzeros, self.bits).T.contiguous()
	if self.bitblas_matmul.config.zeros_mode == "original":
	self.zeros = intzeros.to(torch.float16).contiguous()
	elif self.bitblas_matmul.config.zeros_mode == "rescale":
	self.zeros[:, :] = intzeros.to(torch.float16)[:, :] * self.scales[:, :]
	elif self.bitblas_matmul.config.zeros_mode == "quantized":
	self.zeros = (
	torch.Tensor(general_compress(intzeros.T.contiguous().cpu().numpy(), self.bits)).to(
	self.qweight.device).to(self.zeros.dtype).contiguous())
	else:
	raise ValueError(f"Unsupported zeros type: {self.bitblas_matmul.config.zeros_mode}")
	if self.bias is not None:
	self.bias = gptq_module.bias.data.to(torch.float16).contiguous()

	@property
	def consistent(self):
	return self.is_consitent


	__all__ = ["Linear"]