functional.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import ctypes as ct
import itertools
import operator
import random
import torch
import itertools
import math
from scipy.stats import norm
import numpy as np

from functools import reduce  # Required in Python 3
from typing import Tuple
from torch import Tensor

from .cextension import COMPILED_WITH_CUDA, lib


# math.prod not compatible with python < 3.8
def prod(iterable):
    return reduce(operator.mul, iterable, 1)

name2qmap = {}

if COMPILED_WITH_CUDA:
    """C FUNCTIONS FOR OPTIMIZERS"""
    str2optimizer32bit = {}
    str2optimizer32bit["adam"] = (lib.cadam32bit_grad_fp32, lib.cadam32bit_grad_fp16, lib.cadam32bit_grad_bf16)
    str2optimizer32bit["momentum"] = (
        lib.cmomentum32bit_grad_32,
        lib.cmomentum32bit_grad_16,
    )
    str2optimizer32bit["rmsprop"] = (
        lib.crmsprop32bit_grad_32,
        lib.crmsprop32bit_grad_16,
    )
    str2optimizer32bit["lion"] = (lib.clion32bit_grad_fp32, lib.clion32bit_grad_fp16, lib.clion32bit_grad_bf16)
    str2optimizer32bit["adagrad"] = (
        lib.cadagrad32bit_grad_32,
        lib.cadagrad32bit_grad_16,
    )

    str2optimizer8bit = {}
    str2optimizer8bit["adam"] = (
        lib.cadam_static_8bit_grad_32,
        lib.cadam_static_8bit_grad_16,
    )
    str2optimizer8bit["momentum"] = (
        lib.cmomentum_static_8bit_grad_32,
        lib.cmomentum_static_8bit_grad_16,
    )
    str2optimizer8bit["rmsprop"] = (
        lib.crmsprop_static_8bit_grad_32,
        lib.crmsprop_static_8bit_grad_16,
    )
    str2optimizer8bit["lion"] = (
        lib.clion_static_8bit_grad_32,
        lib.clion_static_8bit_grad_16,
    )
    str2optimizer8bit["lamb"] = (
        lib.cadam_static_8bit_grad_32,
        lib.cadam_static_8bit_grad_16,
    )
    str2optimizer8bit["lars"] = (
        lib.cmomentum_static_8bit_grad_32,
        lib.cmomentum_static_8bit_grad_16,
    )

    str2optimizer8bit_blockwise = {}
    str2optimizer8bit_blockwise["adam"] = (
        lib.cadam_8bit_blockwise_grad_fp32,
        lib.cadam_8bit_blockwise_grad_fp16,
        lib.cadam_8bit_blockwise_grad_bf16,
    )
    str2optimizer8bit_blockwise["momentum"] = (
        lib.cmomentum_8bit_blockwise_grad_fp32,
        lib.cmomentum_8bit_blockwise_grad_fp16,
    )
    str2optimizer8bit_blockwise["rmsprop"] = (
        lib.crmsprop_8bit_blockwise_grad_fp32,
        lib.crmsprop_8bit_blockwise_grad_fp16,
    )
    str2optimizer8bit_blockwise["lion"] = (
        lib.clion_8bit_blockwise_grad_fp32,
        lib.clion_8bit_blockwise_grad_fp16,
        lib.clion_8bit_blockwise_grad_bf16,
    )
    str2optimizer8bit_blockwise["adagrad"] = (
        lib.cadagrad_8bit_blockwise_grad_fp32,
        lib.cadagrad_8bit_blockwise_grad_fp16,
    )

class GlobalPageManager:
    _instance = None

    def __init__(self):
        raise RuntimeError("Call get_instance() instead")

    def initialize(self):
        self.paged_tensors = []

    @classmethod
    def get_instance(cls):
        if cls._instance is None:
            cls._instance = cls.__new__(cls)
            cls._instance.initialize()
        return cls._instance

    def prefetch_all(self, to_cpu=False):
        # assume the first added, will be hte
        # ones that are used first, so swap them in last
        # in the case they are evicted again
        for t in self.paged_tensors[::-1]:
            prefetch_tensor(t, to_cpu)



class CUBLAS_Context:
    _instance = None

    def __init__(self):
        raise RuntimeError("Call get_instance() instead")

    def initialize(self):
        self.context = {}

    @classmethod
    def get_instance(cls):
        if cls._instance is None:
            cls._instance = cls.__new__(cls)
            cls._instance.initialize()
        return cls._instance

    def get_context(self, device):
        if device.index not in self.context:
            prev_device = torch.cuda.current_device()
            torch.cuda.set_device(device)
            self.context[device.index] = ct.c_void_p(lib.get_context())
            torch.cuda.set_device(prev_device)
        return self.context[device.index]


class Cusparse_Context:
    _instance = None

    def __init__(self):
        raise RuntimeError("Call get_instance() instead")

    def initialize(self):
        self.context = ct.c_void_p(lib.get_cusparse())

    @classmethod
    def get_instance(cls):
        if cls._instance is None:
            cls._instance = cls.__new__(cls)
            cls._instance.initialize()
        return cls._instance

dtype2bytes = {}
dtype2bytes[torch.float32] = 4
dtype2bytes[torch.float16] = 2
dtype2bytes[torch.bfloat16] = 2
dtype2bytes[torch.uint8] = 1
dtype2bytes[torch.int8] = 1

def get_paged(*shape, dtype=torch.float32, device=torch.device('cuda', index=0)):
    num_bytes = dtype2bytes[dtype]*prod(shape)
    cuda_ptr = lib.cget_managed_ptr(ct.c_size_t(num_bytes))
    c_ptr = ct.cast(cuda_ptr, ct.POINTER(ct.c_int))
    new_array = np.ctypeslib.as_array(c_ptr, shape=shape)
    out = torch.frombuffer(new_array, dtype=dtype, count=prod(shape)).view(shape)
    out.is_paged = True
    out.page_deviceid = device.index
    return out

def prefetch_tensor(A, to_cpu=False):
    assert A.is_paged, 'Only paged tensors can be prefetched!'
    if to_cpu:
        deviceid = -1
    else:
        deviceid = A.page_deviceid

    num_bytes = dtype2bytes[A.dtype]*A.numel()
    lib.cprefetch(get_ptr(A), ct.c_size_t(num_bytes), ct.c_int32(deviceid))

def elementwise_func(func_name, A, B, value, prefetch=True):
    func = None
    if A.dtype == torch.float32:
        func = getattr(lib, f'c{func_name}_fp32', None)
        cvalue = ct.c_float(value)
    elif A.dtype == torch.uint8:
        func = getattr(lib, f'c{func_name}_uint8', None)
        cvalue = ct.c_uint8(value)

    if func is None: raise NotImplementedError(f'Function not implemented: {func_name}')

    is_managed = getattr(A, 'is_managed', False)
    if is_managed and prefetch:
        prefetch_tensor(A)
        if B is not None: prefetch_tensor(B)

    func(get_ptr(A), get_ptr(B), cvalue, ct.c_int64(A.numel()))
    if A.is_paged or B.is_paged:
        # paged function are fully asynchronous
        # if we return from this function, we want to the tensor
        # to be in the correct state, that is the final state after the
        # operation occured. So we synchronize.
        torch.cuda.synchronize()

def fill(A, value, device=None, prefetch=True): elementwise_func('fill', A, None, value)
def arange(A, device=None): elementwise_func('arange', A, None, 0)
def _mul(A, B, device=None): elementwise_func('_mul', A, B, 0)


def create_linear_map(signed=True, total_bits=8, add_zero=True):
    sign = (-1.0 if signed else 0.0)
    total_values = 2**total_bits
    if add_zero or total_bits < 8:
        # add a zero
        # since we simulate less bits by having zeros in the data type, we
        # we need to center the quantization around zero and as such lose
        # a single value
        total_values = (2**total_bits if not signed else 2**total_bits-1)

    values = torch.linspace(sign, 1.0, total_values)
    gap = 256 - values.numel()
    if gap == 0:
        return values
    else:
        l = values.numel()//2
        return torch.Tensor(values[:l].tolist() + [0]*gap + values[l:].tolist())

def create_normal_map(offset=0.9677083, use_extra_value=True):

    if use_extra_value:
        # one more positive value, this is an asymmetric type
        v1 = norm.ppf(torch.linspace(offset, 0.5, 9)[:-1]).tolist()
        v2 = [0]*(256-15) ## we have 15 non-zero values in this data type
        v3 = (-norm.ppf(torch.linspace(offset, 0.5, 8)[:-1])).tolist()
    else:
        v1 = norm.ppf(torch.linspace(offset, 0.5, 8)[:-1]).tolist()
        v2 = [0]*(256-14) ## we have 14 non-zero values in this data type
        v3 = (-norm.ppf(torch.linspace(offset, 0.5, 8)[:-1])).tolist()

    v = v1 + v2 + v3

    values = torch.Tensor(v)
    values = values.sort().values
    values /= values.max()

    assert values.numel() == 256

    return values

def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2, total_bits=8):
    e = exponent_bits
    p = precision_bits
    has_sign = 1 if signed else 0
    assert e+p == total_bits-has_sign
    # the exponent is biased to 2^(e-1) -1 == 0
    evalues = []
    pvalues = []
    for i, val in enumerate(range(-((2**(exponent_bits-has_sign))), 2**(exponent_bits-has_sign), 1)):
        evalues.append(2**val)


    values = []
    lst = list(itertools.product([0, 1], repeat=precision_bits))
    #for ev in evalues:
    bias = 2**(exponent_bits-1)
    for evalue in range(2**(exponent_bits)):
        for bit_pattern in lst:
            value = (1 if evalue != 0 else 0)
            for i, pval in enumerate(list(bit_pattern)):
                value += pval*(2**-(i+1))
            if evalue == 0:
                # subnormals
                value = value*2**-(bias)
            else:
                # normals
                value = value*2**-(evalue-bias-1)
            values.append(value)
            if signed:
                values.append(-value)


    assert len(values) == 2**total_bits
    values.sort()
    if total_bits < 8:
        gap = 256 - len(values)
        for i in range(gap):
            values.append(0)
    values.sort()
    code = torch.Tensor(values)
    code /= code.max()

    return code



def create_dynamic_map(signed=True, max_exponent_bits=7, total_bits=8):
    """
    Creates the dynamic quantiztion map.

    The dynamic data type is made up of a dynamic exponent and
    fraction. As the exponent increase from 0 to -7 the number
    of bits available for the fraction shrinks.

    This is a generalization of the dynamic type where a certain
    number of the bits and be reserved for the linear quantization
    region (the fraction). n determines the maximum number of
    exponent bits.

    For more details see
    (8-Bit Approximations for Parallelism in Deep Learning)[https://arxiv.org/abs/1511.04561]
    """

    data = []
    # these are additional items that come from the case
    # where all the exponent bits are zero and no
    # indicator bit is present
    non_sign_bits = total_bits - (1 if signed else 1)
    additional_items = 2 ** (non_sign_bits - max_exponent_bits) - 1
    for i in range(max_exponent_bits):
        fraction_items = int((2 ** (i + non_sign_bits - max_exponent_bits) + 1 if signed else 2 ** (i + non_sign_bits - max_exponent_bits + 1) + 1))
        boundaries = torch.linspace(0.1, 1, fraction_items)
        means = (boundaries[:-1] + boundaries[1:]) / 2.0
        data += ((10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()
        if signed:
            data += (-(10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()

    if additional_items > 0:
        boundaries = torch.linspace(0.1, 1, additional_items + 1)
        means = (boundaries[:-1] + boundaries[1:]) / 2.0
        data += ((10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()
        if signed:
            data += (-(10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()

    data.append(0)
    data.append(1.0)

    assert len(data) == 2**total_bits

    gap = 256 - len(data)
    for i in range(gap):
        data.append(0)

    data.sort()
    return Tensor(data)

def create_quantile_map(A, total_bits=8):
    q = estimate_quantiles(A, num_quantiles=2**total_bits-1)
    q = q.tolist()
    q.append(0)

    gap = 256 - len(q)
    for i in range(gap):
        q.append(0)

    q.sort()

    q = Tensor(q)
    q = q/q.abs().max()
    return q

def get_special_format_str():
    if not torch.cuda.is_available(): return 'col_turing'
    major, _minor = torch.cuda.get_device_capability()
    if major <= 7:
        return "col_turing"
    if major == 8:
        return "col_ampere"
    return "col_turing"



def is_on_gpu(tensors):
    on_gpu = True
    gpu_ids = set()
    for t in tensors:
        if t is None: continue # NULL pointers are fine
        is_paged = getattr(t, 'is_paged', False)
        on_gpu &= (t.device.type == 'cuda' or is_paged)
        if not is_paged:
            gpu_ids.add(t.device.index)
    if not on_gpu:
        raise TypeError(f'All input tensors need to be on the same GPU, but found some tensors to not be on a GPU:\n {[(t.shape, t.device) for t in tensors]}')
    if len(gpu_ids) > 1:
        raise TypeError(f'Input tensors need to be on the same GPU, but found the following tensor and device combinations:\n {[(t.shape, t.device) for t in tensors]}')
    return on_gpu

def get_ptr(A: Tensor) -> ct.c_void_p:
    """
    Get the ctypes pointer from a PyTorch Tensor.

    Parameters
    ----------
    A : torch.tensor
        The PyTorch tensor.

    Returns
    -------
    ctypes.c_void_p
    """
    if A is None:
        return None
    else:
        return ct.c_void_p(A.data.data_ptr())


def pre_call(device):
    prev_device = torch.cuda.current_device()
    torch.cuda.set_device(device)
    return prev_device


def post_call(prev_device):
    torch.cuda.set_device(prev_device)


def get_transform_func(dtype, orderA, orderOut, transpose=False):
    name = f'ctransform_{(8 if dtype == torch.int8 else 32)}_{orderA}_to_{orderOut}_{"t" if transpose else "n"}'
    if not hasattr(lib, name):
        print(name)
        raise ValueError(
            f"Transform function not supported: {orderA} to {orderOut} for data type {dtype} and transpose={transpose}"
        )
    else:
        return getattr(lib, name)


def get_transform_buffer(
    shape, dtype, device, to_order, from_order="row", transpose=False
):
    # init_func = torch.empty
    init_func = torch.zeros
    dims = len(shape)

    if dims == 2:
        rows = shape[0]
    elif dims == 3:
        rows = shape[0] * shape[1]
    cols = shape[-1]

    state = (shape, to_order)
    if transpose:
        # swap dims
        tmp = rows
        rows = cols
        cols = tmp
        state = (shape[::-1], to_order)

    if to_order == "row" or to_order == "col":
        return init_func(shape, dtype=dtype, device=device), state
    elif to_order == "col32":
        # blocks of 32 columns (padded)
        cols = 32 * ((cols + 31) // 32)
        return init_func((rows, cols), dtype=dtype, device=device), state
    elif to_order == "col_turing":
        # blocks of 32 columns and 8 rows
        cols = 32 * ((cols + 31) // 32)
        rows = 8 * ((rows + 7) // 8)
        return init_func((rows, cols), dtype=dtype, device=device), state
    elif to_order == "col_ampere":
        # blocks of 32 columns and 32 rows
        cols = 32 * ((cols + 31) // 32)
        rows = 32 * ((rows + 31) // 32)
        return init_func((rows, cols), dtype=dtype, device=device), state
    else:
        raise NotImplementedError(f"To_order not supported: {to_order}")


def nvidia_transform(
    A,
    to_order,
    from_order="row",
    out=None,
    transpose=False,
    state=None,
    ld=None,
):
    if state is None:
        state = (A.shape, from_order)
    else:
        from_order = state[1]
    if out is None:
        out, new_state = get_transform_buffer(
            state[0], A.dtype, A.device, to_order, state[1]
        )
    else:
        new_state = (state[1], to_order)
    func = get_transform_func(A.dtype, from_order, to_order, transpose)

    shape = state[0]
    if len(shape) == 2:
        dim1 = ct.c_int32(shape[0])
        dim2 = ct.c_int32(shape[1])
    elif ld is not None:
        n = prod(shape)
        dim1 = prod([shape[i] for i in ld])
        dim2 = ct.c_int32(n // dim1)
        dim1 = ct.c_int32(dim1)
    else:
        dim1 = ct.c_int32(shape[0] * shape[1])
        dim2 = ct.c_int32(shape[2])

    ptr = CUBLAS_Context.get_instance().get_context(A.device)
    func(ptr, get_ptr(A), get_ptr(out), dim1, dim2)

    return out, new_state


def estimate_quantiles(A: Tensor, out: Tensor = None, offset: float = 1 / 512, num_quantiles=256) -> Tensor:
    '''
    Estimates 256 equidistant quantiles on the input tensor eCDF.

    Uses SRAM-Quantiles algorithm to quickly estimate 256 equidistant quantiles
    via the eCDF of the input tensor `A`. This is a fast but approximate algorithm
    and the extreme quantiles close to 0 and 1 have high variance / large estimation
    errors. These large errors can be avoided by using the offset variable which trims
    the distribution. The default offset value of 1/512 ensures minimum entropy encoding -- it
    trims 1/512 = 0.2% from each side of the distrivution. An offset value of 0.01 to 0.02
    usually has a much lower error but is not a minimum entropy encoding. Given an offset
    of 0.02 equidistance points in the range [0.02, 0.98] are used for the quantiles.

    Parameters
    ----------
    A : torch.Tensor
        The input tensor. Any shape.
    out : torch.Tensor
        Tensor with the 256 estimated quantiles.
    offset : float
        The offset for the first and last quantile from 0 and 1. Default: 1/(2*num_quantiles)
    num_quantiles : int
        The number of equally spaced quantiles.

    Returns
    -------
    torch.Tensor:
        The 256 quantiles in float32 datatype.
    '''
    if A.numel() < 256: raise NotImplementedError(f'Quantile estimation needs at least 256 values in the Tensor, but Tensor had only {A.numel()} values.')
    if num_quantiles > 256: raise NotImplementedError(f"Currently only a maximum of 256 equally spaced quantiles are supported, but the argument num_quantiles={num_quantiles}")
    if num_quantiles < 256 and offset == 1/(512):
        # override default arguments
        offset = 1/(2*num_quantiles)

    if out is None: out = torch.zeros((256,), dtype=torch.float32, device=A.device)
    is_on_gpu([A, out])
    device = pre_call(A.device)
    if A.dtype == torch.float32:
        lib.cestimate_quantiles_fp32(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel()))
    elif A.dtype == torch.float16:
        lib.cestimate_quantiles_fp16(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel()))
    else:
        raise NotImplementedError(f"Not supported data type {A.dtype}")
    post_call(device)

    if num_quantiles < 256:
        step = round(256/num_quantiles)
        idx = torch.linspace(0, 255, num_quantiles).long().to(A.device)
        out = out[idx]

    return out

class QuantState:
    def __init__(self, absmax, shape=None, code=None, blocksize=None, quant_type=None, dtype=None, offset=None, state2=None):
        self.absmax = absmax
        self.shape = shape
        self.code = code
        self.dtype = dtype
        self.blocksize = blocksize
        self.quant_type = quant_type
        self.offset = offset
        self.state2 = state2
        self.nested = state2 is not None

    @classmethod
    def from_kwargs(cls, kwargs, device):

        tensor2str = lambda xx: ''.join([chr(x) for x in xx]).strip('.')

        kwargs = {k.split('.')[-1] :v for k, v in kwargs.items()}
        
        if 'nested_absmax' in kwargs:
            offset = kwargs['nested_offset']
            state2 = cls(
                absmax=kwargs['nested_absmax'].to(device),
                code=kwargs['nested_code'].to(device),
                blocksize=kwargs['nested_blocksize'].item(),
                dtype=getattr(torch, tensor2str(kwargs['nested_dtype'])),
            )
        else:
            offset, state2 = None, None

        quant_state = cls(
            absmax=kwargs['absmax'].to(device), 
            shape=torch.Size(kwargs['shape']),
            dtype=getattr(torch, tensor2str(kwargs['dtype'])),
            blocksize=kwargs['blocksize'].item(),
            offset=offset,
            state2=state2,
            quant_type=tensor2str(kwargs['quant_type']),
            code=kwargs['code'].to(device),
        )
        return quant_state

    def to(self, device):
        # make sure the quantization state is on the right device
        self.absmax = self.absmax.to(device)
        if self.nested:
            self.offset = self.offset.to(device)
            self.state2.absmax = self.state2.absmax.to(device)
            self.state2.code = self.state2.code.to(device)

def quantize_blockwise(A: Tensor, code: Tensor = None, absmax: Tensor = None, out: Tensor = None, blocksize=4096, nested=False) -> Tensor:
    """
    Quantize tensor A in blocks of size 4096 values.

    Quantizes tensor A by dividing it into blocks of 4096 values.
    Then the absolute maximum value within these blocks is calculated
    for the non-linear quantization.

    Parameters
    ----------
    A : torch.Tensor
        The input tensor.
    code : torch.Tensor
        The quantization map.
    absmax : torch.Tensor
        The absmax values.
    out : torch.Tensor
        The output tensor (8-bit).

    Returns
    -------
    torch.Tensor:
        The 8-bit tensor.
    tuple(torch.Tensor, torch.Tensor):
        The quantization state to undo the quantization.
    """


    if code is None:
        if "dynamic" not in name2qmap:
            name2qmap["dynamic"] = create_dynamic_map().to(A.device)
        code = name2qmap["dynamic"]

    if absmax is None:
        n = A.numel()
        blocks = n // blocksize
        blocks += 1 if n % blocksize > 0 else 0
        absmax = torch.zeros((blocks,), device=A.device, dtype=torch.float32)

    if out is None:
        out = torch.zeros_like(A, dtype=torch.uint8)

    if A.device.type != 'cpu':
        assert blocksize in [4096, 2048, 1024, 512, 256, 128, 64]
        cblocksize = ct.c_int32(blocksize)
        prev_device = pre_call(A.device)
        code = code.to(A.device)
        is_on_gpu([code, A, out, absmax])
        if A.dtype == torch.float32:
            lib.cquantize_blockwise_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), cblocksize, ct.c_int(A.numel()))
        elif A.dtype == torch.float16:
            lib.cquantize_blockwise_fp16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), cblocksize, ct.c_int(A.numel()))
        elif A.dtype == torch.bfloat16:
            lib.cquantize_blockwise_bf16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), cblocksize, ct.c_int(A.numel()))
        else:
            raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
        post_call(A.device)
    else:
        # cpu
        code = code.cpu()
        lib.cquantize_blockwise_cpu_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_longlong(blocksize), ct.c_longlong(A.numel()))

    if nested:
        offset = absmax.mean()
        absmax -= offset
        qabsmax, state2 = quantize_blockwise(absmax, blocksize=blocksize, nested=False)
        quant_state = QuantState(absmax=qabsmax, code=code, blocksize=blocksize, dtype=A.dtype, offset=offset, state2=state2)
    else:
        quant_state = QuantState(absmax=absmax, code=code, blocksize=blocksize, dtype=A.dtype)

    return out, quant_state


def dequantize_blockwise(
    A: Tensor,
    quant_state: QuantState = None,
    absmax: Tensor = None,
    code: Tensor = None,
    out: Tensor = None,
    blocksize: int = 4096,
    nested=False
) -> Tensor:
    """
    Dequantizes blockwise quantized values.

    Dequantizes the tensor A with maximum absolute values absmax in
    blocks of size 4096.

    Parameters
    ----------
    A : torch.Tensor
        The input 8-bit tensor.
    quant_state : QuantState
        Object with code, absmax and other quantization state components.
    absmax : torch.Tensor
        The absmax values.
    code : torch.Tensor
        The quantization map.
    out : torch.Tensor
        Dequantized output tensor (default: float32)


    Returns
    -------
    torch.Tensor:
        Dequantized tensor (default: float32)
    """
    assert quant_state is not None or absmax is not None
    if code is None and quant_state is None:
        if "dynamic" not in name2qmap:
            name2qmap["dynamic"] = create_dynamic_map().to(A.device)
        code = name2qmap["dynamic"]

    if quant_state is None:
       quant_state = QuantState(absmax=absmax, code=code, blocksize=blocksize, dtype=torch.float32)
    
    absmax = quant_state.absmax
    if quant_state.nested:
        absmax = dequantize_blockwise(quant_state.absmax, quant_state.state2)
        absmax += quant_state.offset
        if absmax.dtype != torch.float32: absmax = absmax.float()

    if out is None:
        out = torch.empty(A.shape, dtype=quant_state.dtype, device=A.device)

    if A.device.type != 'cpu':
        device = pre_call(A.device)
        code = quant_state.code.to(A.device)
        if quant_state.blocksize not in [2048, 4096, 1024, 512, 256, 128, 64]:
            raise ValueError(f"The blockwise of {quant_state.blocksize} is not supported. Supported values: [2048, 4096, 1024, 512, 256, 128, 64]")
        is_on_gpu([A, absmax, out])
        if out.dtype == torch.float32:
            lib.cdequantize_blockwise_fp32(get_ptr(quant_state.code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(A.numel()))
        elif out.dtype == torch.float16:
            lib.cdequantize_blockwise_fp16(get_ptr(quant_state.code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(A.numel()))
        elif out.dtype == torch.bfloat16:
            lib.cdequantize_blockwise_bf16(get_ptr(quant_state.code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(A.numel()))
        else:
            raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
        post_call(A.device)
    else:
        code = quant_state.code.cpu()
        lib.cdequantize_blockwise_cpu_fp32(get_ptr(code), get_ptr(A), get_ptr(quant_state.absmax), get_ptr(out), ct.c_longlong(quant_state.blocksize), ct.c_longlong(A.numel()))

    return out

def get_4bit_type(typename, device=None, blocksize=64):
    if device is None: device = 'cuda'
    data = None
    if typename == 'nf4':
        ''' Implements the NF4 data type.

            Constructs a quantization data type where each bin has equal area under a standard normal distribution N(0, 1) that
            is normalized into the range [-1, 1].

            For more information read the paper: QLoRA: Efficient Finetuning of Quantized LLMs (https://arxiv.org/abs/2305.14314)

            Implementation of the NF4 data type in bitsandbytes can be found in the `create_normal_map` function in
            the `functional.py` file: https://github.com/TimDettmers/bitsandbytes/blob/main/bitsandbytes/functional.py#L236.
        '''
        data = [-1.0, -0.6961928009986877, -0.5250730514526367, -0.39491748809814453, -0.28444138169288635,
                -0.18477343022823334, -0.09105003625154495, 0.0, 0.07958029955625534, 0.16093020141124725,
                0.24611230194568634, 0.33791524171829224, 0.44070982933044434, 0.5626170039176941,
                0.7229568362236023, 1.0]
    elif typename == 'fp4':
        # 0b000 = 0
        # 0b001 = 0.0625
        # 0b010 = 8
        # 0b011 = 12
        # 0b100 = 4
        # 0b101 = 6
        # 0b110 = 2
        # 0b111 = 3
        # can also be created with bnb.functional.create_fp8_map(signed=True, exponent_bits=2, precision_bits=1, total_bits=4)
        data = [0, 0.0625, 8.0, 12.0, 4.0, 6.0, 2.0, 3.0, -0, -0.0625, -8.0, -12.0, -4.0, -6.0, -2.0, -3.0]
    elif typename == 'int4':
        data = [7, 6, 5, 4, 3, 2, 1, 0, -0, -1, -2, -3, -4, -5, -6, -7]
    elif typename == 'af4':
        # Taken from: NF4 Isn't Information Theoretically Optimal (and that's Good)
        # https://arxiv.org/abs/2306.06965
        if blocksize == 64:
            data = [-1., -0.69441008, -0.51243739, -0.3736951, -0.25607552, -0.14982478,
                    -0.04934812,  0., 0.04273164, 0.12934483, 0.21961274, 0.31675666,
                    0.42563882,  0.55496234,  0.72424863,  1.][::-1]
        else:
            raise NotImplementedError(f'4-bit AbnormalFloats currently only support blocksize 64.')

    if data is None:
        raise NotImplementedError(f'Typename {typename} not supported')

    data = Tensor(data)
    data /= data.abs().max()
    assert data.numel() == 16

    return data.to(device)



def quantize_fp4(A: Tensor, absmax: Tensor = None, out: Tensor = None, blocksize=64, compress_statistics=False):
    return quantize_4bit(A, absmax, out, blocksize, compress_statistics, 'fp4')

def quantize_nf4(A: Tensor, absmax: Tensor = None, out: Tensor = None, blocksize=64, compress_statistics=False):
    return quantize_4bit(A, absmax, out, blocksize, compress_statistics, 'nf4')

def quantize_4bit(A: Tensor, absmax: Tensor = None, out: Tensor = None, blocksize=64, compress_statistics=False, quant_type='fp4') -> Tensor:
    """
    Quantize tensor A in blocks of 4-bit values.

    Quantizes tensor A by dividing it into blocks which are independently quantized to FP4.

    Parameters
    ----------
    A : torch.Tensor
        The input tensor.
    absmax : torch.Tensor
        The absmax values.
    out : torch.Tensor
        The output tensor (8-bit).
    blocksize : int
        The blocksize used in quantization.
    quant_type : str
        The 4-bit quantization data type {fp4, nf4}

    Returns
    -------
    torch.Tensor:
        The 8-bit tensor with packed 4-bit values.
    tuple(torch.Tensor, torch.Size, torch.dtype, int):
        The quantization state to undo the quantization.
    """
    if A.device.type != 'cuda':
        raise NotImplementedError(f'Device type not supported for FP4 quantization: {A.device.type}')
    if quant_type not in ['fp4', 'nf4']:
        raise NotImplementedError(f'4-bit quantization data type {quant_type} is not implemented.')

    n = A.numel()
    input_shape = A.shape

    if absmax is None:
        blocks = n // blocksize
        blocks += 1 if n % blocksize > 0 else 0
        absmax = torch.zeros((blocks,), device=A.device, dtype=torch.float32)


    if out is None:
        out = torch.zeros(((n+1)//2, 1), dtype=torch.uint8, device=A.device)

    assert blocksize in [4096, 2048, 1024, 512, 256, 128, 64]

    prev_device = pre_call(A.device)
    is_on_gpu([A, out, absmax])

    if A.dtype == torch.float32:
        if quant_type == 'fp4':
            lib.cquantize_blockwise_fp32_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n))
        else:
            lib.cquantize_blockwise_fp32_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n))
    elif A.dtype == torch.float16:
        if quant_type == 'fp4':
            lib.cquantize_blockwise_fp16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n))
        else:
            lib.cquantize_blockwise_fp16_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n))
    elif A.dtype == torch.bfloat16:
        if quant_type == 'fp4':
            lib.cquantize_blockwise_bf16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n))
        else:
            lib.cquantize_blockwise_bf16_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n))
    else:
        raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
    post_call(A.device)

    code = get_4bit_type(quant_type, device=A.device)

    if compress_statistics:
        offset = absmax.mean()
        absmax -= offset
        qabsmax, state2 = quantize_blockwise(absmax, blocksize=256)
        del absmax
        state = QuantState(absmax=qabsmax, shape=input_shape, dtype=A.dtype, blocksize=blocksize, code=code, quant_type=quant_type, offset=offset, state2=state2)
    else:
        state = QuantState(absmax=absmax, shape=input_shape, dtype=A.dtype, blocksize=blocksize, code=code, quant_type=quant_type, )

    return out, state

def dequantize_fp4(A: Tensor, quant_state: QuantState = None, absmax: Tensor = None, out: Tensor = None, blocksize: int = 64) -> Tensor:
    return dequantize_4bit(A, quant_state, absmax, out, blocksize, 'fp4')

def dequantize_nf4(A: Tensor, quant_state: QuantState = None, absmax: Tensor = None, out: Tensor = None, blocksize: int = 64) -> Tensor:
    return dequantize_4bit(A, quant_state, absmax, out, blocksize, 'nf4')

def dequantize_4bit(A: Tensor, quant_state: QuantState = None, absmax: Tensor = None, out: Tensor = None, blocksize: int = 64, quant_type='fp4') -> Tensor:
    """
    Dequantizes FP4 blockwise quantized values.

    Dequantizes the tensor A with maximum absolute values absmax in blocks of size blocksize.

    Parameters
    ----------
    A : torch.Tensor
        The input 8-bit tensor (packed 4-bit values).
    quant_state : QuantState
        object with quantisation stats, incl. absmax values, original tensor shape and original dtype.
    absmax : torch.Tensor
        The absmax values.
    out : torch.Tensor
        Dequantized output tensor.
    blocksize : int
        The blocksize used in quantization.
    quant_type : str
        The 4-bit quantization data type {fp4, nf4}


    Returns
    -------
    torch.Tensor:
        Dequantized tensor.
    """
    if blocksize not in [2048, 4096, 1024, 512, 256, 128, 64]:
        raise ValueError(f"The blockwise of {blocksize} is not supported. Supported values: [2048, 4096, 1024, 512, 256, 128, 64]")
    if quant_type not in ['fp4', 'nf4']:
        raise NotImplementedError(f'4-bit quantization data type {quant_type} is not implemented.')

    if quant_state is None:
        assert absmax is not None and out is not None

        quant_state = QuantState(absmax=absmax, shape=out.shape, dtype=out.dtype, blocksize=blocksize, quant_type=quant_type)

    else:
        absmax = quant_state.absmax


    if quant_state.nested:
        absmax = dequantize_blockwise(quant_state.absmax, quant_state.state2)
        absmax += quant_state.offset
        if absmax.dtype != torch.float32: absmax = absmax.float()

    if out is None:
        out = torch.empty(quant_state.shape, dtype=quant_state.dtype, device=A.device)

    n = out.numel()

    device = pre_call(A.device)
    is_on_gpu([A, absmax, out])
    if out.dtype == torch.float32:
        if quant_state.quant_type == 'fp4':
            lib.cdequantize_blockwise_fp32_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n))
        else:
            lib.cdequantize_blockwise_fp32_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n))
    elif out.dtype == torch.float16:
        if quant_state.quant_type == 'fp4':
            lib.cdequantize_blockwise_fp16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n))
        else:
            lib.cdequantize_blockwise_fp16_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n))
    elif out.dtype == torch.bfloat16:
        if quant_state.quant_type == 'fp4':
            lib.cdequantize_blockwise_bf16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n))
        else:
            lib.cdequantize_blockwise_bf16_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n))
    else:
        raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
    post_call(A.device)

    is_transposed = (True if A.shape[0] == 1 else False)
    if is_transposed: return out.t()
    else: return out


def quantize(A: Tensor, code: Tensor = None, out: Tensor = None) -> Tensor:
    if code is None:
        if "dynamic" not in name2qmap:
            name2qmap["dynamic"] = create_dynamic_map().to(A.device)
        code = name2qmap["dynamic"]
        code = code.to(A.device)

    absmax = torch.abs(A).max()
    if absmax.dtype != torch.float32: absmax = absmax.float()
    inp = A / absmax
    out = quantize_no_absmax(inp, code, out)
    return out, (absmax, code)


def dequantize(
    A: Tensor,
    state: Tuple[Tensor, Tensor] = None,
    absmax: Tensor = None,
    code: Tensor = None,
    out: Tensor = None,
) -> Tensor:
    assert state is not None or absmax is not None
    if code is None and state is None:
        if "dynamic" not in name2qmap:
            name2qmap["dynamic"] = create_dynamic_map().to(A.device)
        code = name2qmap["dynamic"]
        code = code.to(A.device)

    if state is None:
        state = (absmax, code)
    out = dequantize_no_absmax(A, state[1], out)
    return out * state[0]


def quantize_no_absmax(A: Tensor, code: Tensor, out: Tensor = None) -> Tensor:
    '''
    Quantizes input tensor to 8-bit.

    Quantizes the 32-bit input tensor `A` to the 8-bit output tensor
    `out` using the quantization map `code`.

    Parameters
    ----------
    A : torch.Tensor
        The input tensor.
    code : torch.Tensor
        The quantization map.
    out : torch.Tensor, optional
        The output tensor. Needs to be of type byte.

    Returns
    -------
    torch.Tensor:
        Quantized 8-bit tensor.
    '''
    prev_device = pre_call(A.device)
    if out is None: out = torch.zeros_like(A, dtype=torch.uint8)
    is_on_gpu([A, out])
    lib.cquantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel()))
    post_call(prev_device)
    return out


def dequantize_no_absmax(A: Tensor, code: Tensor, out: Tensor = None) -> Tensor:
    '''
    Dequantizes the 8-bit tensor to 32-bit.

    Dequantizes the 8-bit tensor `A` to the 32-bit tensor `out` via
    the quantization map `code`.

    Parameters
    ----------
    A : torch.Tensor
        The 8-bit input tensor.
    code : torch.Tensor
        The quantization map.
    out : torch.Tensor
        The 32-bit output tensor.

    Returns
    -------
    torch.Tensor:
        32-bit output tensor.
    '''
    prev_device = pre_call(A.device)
    if out is None: out = torch.zeros_like(A, dtype=torch.float32)
    is_on_gpu([code, A, out])
    lib.cdequantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel()))
    post_call(prev_device)
    return out


def optimizer_update_32bit(
    optimizer_name: str,
    g: Tensor,
    p: Tensor,
    state1: Tensor,
    beta1: float,
    eps: float,
    step: int,
    lr: float,
    state2: Tensor = None,
    beta2: float = 0.0,
    weight_decay: float = 0.0,
    gnorm_scale: float = 1.0,
    unorm_vec: Tensor = None,
    max_unorm: float = 0.0,
    skip_zeros=False,
) -> None:
    """
    Performs an inplace optimizer update with one or two optimizer states.

    Universal optimizer update for 32-bit state and 32/16-bit gradients/weights.

    Parameters
    ----------
    optimizer_name : str
        The name of the optimizer: {adam}.
    g : torch.Tensor
        Gradient tensor.
    p : torch.Tensor
        Parameter tensor.
    state1 : torch.Tensor
        Optimizer state 1.
    beta1 : float
        Optimizer beta1.
    eps : float
        Optimizer epsilon.
    weight_decay : float
        Weight decay.
    step : int
        Current optimizer step.
    lr : float
        The learning rate.
    state2 : torch.Tensor
        Optimizer state 2.
    beta2 : float
        Optimizer beta2.
    gnorm_scale : float
        The factor to rescale the gradient to the max clip value.
    unorm_vec : torch.Tensor
        The tensor for the update norm.
    max_unorm : float
        The maximum update norm relative to the weight norm.
    skip_zeros : bool
        Whether to skip zero-valued gradients or not (default: False).
    """

    param_norm = 0.0
    if max_unorm > 0.0:
        param_norm = torch.norm(p.data.float())


    optim_func = None
    if g.dtype == torch.float32:
        optim_func = str2optimizer32bit[optimizer_name][0]
    elif g.dtype == torch.float16:
        optim_func = str2optimizer32bit[optimizer_name][1]
    elif (g.dtype == torch.bfloat16 and len(str2optimizer32bit[optimizer_name])==3):
        optim_func = str2optimizer32bit[optimizer_name][2]
    else:
        raise ValueError(f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}")

    is_on_gpu([g, p, state1, state2, unorm_vec])
    prev_device = pre_call(g.device)
    optim_func(
        get_ptr(g),
        get_ptr(p),
        get_ptr(state1),
        get_ptr(state2),
        get_ptr(unorm_vec),
        ct.c_float(max_unorm),
        ct.c_float(param_norm),
        ct.c_float(beta1),
        ct.c_float(beta2),
        ct.c_float(eps),
        ct.c_float(weight_decay),
        ct.c_int32(step),
        ct.c_float(lr),
        ct.c_float(gnorm_scale),
        ct.c_bool(skip_zeros),
        ct.c_int32(g.numel()))
    post_call(prev_device)


def optimizer_update_8bit(
    optimizer_name: str,
    g: Tensor,
    p: Tensor,
    state1: Tensor,
    state2: Tensor,
    beta1: float,
    beta2: float,
    eps: float,
    step: int,
    lr: float,
    qmap1: Tensor,
    qmap2: Tensor,
    max1: Tensor,
    max2: Tensor,
    new_max1: Tensor,
    new_max2: Tensor,
    weight_decay: float = 0.0,
    gnorm_scale: float = 1.0,
    unorm_vec: Tensor = None,
    max_unorm: float = 0.0,
) -> None:
    """
    Performs an inplace Adam update.

    Universal Adam update for 32/8-bit state and 32/16-bit gradients/weights.
    Uses AdamW formulation if weight decay > 0.0.

    Parameters
    ----------
    optimizer_name : str
        The name of the optimizer. Choices {adam, momentum}
    g : torch.Tensor
        Gradient tensor.
    p : torch.Tensor
        Parameter tensor.
    state1 : torch.Tensor
        Adam state 1.
    state2 : torch.Tensor
        Adam state 2.
    beta1 : float
        Adam beta1.
    beta2 : float
        Adam beta2.
    eps : float
        Adam epsilon.
    weight_decay : float
        Weight decay.
    step : int
        Current optimizer step.
    lr : float
        The learning rate.
    qmap1 : torch.Tensor
        Quantization map for first Adam state.
    qmap2 : torch.Tensor
        Quantization map for second Adam state.
    max1 : torch.Tensor
        Max value for first Adam state update.
    max2 : torch.Tensor
        Max value for second Adam state update.
    new_max1 : torch.Tensor
        Max value for the next Adam update of the first state.
    new_max2 : torch.Tensor
        Max value for the next Adam update of the second state.
    gnorm_scale : float
        The factor to rescale the gradient to the max clip value.
    unorm_vec : torch.Tensor
        The tensor for the update norm.
    max_unorm : float
        The maximum update norm relative to the weight norm.
    """

    param_norm = 0.0
    if max_unorm > 0.0:
        param_norm = torch.norm(p.data.float())

    prev_device = pre_call(g.device)
    is_on_gpu([g, p, state1, state2, unorm_vec, qmap1, qmap2, max1, max2, new_max1, new_max2])
    if g.dtype == torch.float32 and state1.dtype == torch.uint8:
        str2optimizer8bit[optimizer_name][0](
            get_ptr(p),
            get_ptr(g),
            get_ptr(state1),
            get_ptr(state2),
            get_ptr(unorm_vec),
            ct.c_float(max_unorm),
            ct.c_float(param_norm),
            ct.c_float(beta1),
            ct.c_float(beta2),
            ct.c_float(eps),
            ct.c_int32(step),
            ct.c_float(lr),
            get_ptr(qmap1),
            get_ptr(qmap2),
            get_ptr(max1),
            get_ptr(max2),
            get_ptr(new_max1),
            get_ptr(new_max2),
            ct.c_float(weight_decay),
            ct.c_float(gnorm_scale),
            ct.c_int32(g.numel()),
        )
    elif g.dtype == torch.float16 and state1.dtype == torch.uint8:
        str2optimizer8bit[optimizer_name][1](
            get_ptr(p),
            get_ptr(g),
            get_ptr(state1),
            get_ptr(state2),
            get_ptr(unorm_vec),
            ct.c_float(max_unorm),
            ct.c_float(param_norm),
            ct.c_float(beta1),
            ct.c_float(beta2),
            ct.c_float(eps),
            ct.c_int32(step),
            ct.c_float(lr),
            get_ptr(qmap1),
            get_ptr(qmap2),
            get_ptr(max1),
            get_ptr(max2),
            get_ptr(new_max1),
            get_ptr(new_max2),
            ct.c_float(weight_decay),
            ct.c_float(gnorm_scale),
            ct.c_int32(g.numel()),
        )
    else:
        raise ValueError(
            f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}"
        )
    post_call(prev_device)


def optimizer_update_8bit_blockwise(
    optimizer_name: str,
    g: Tensor,
    p: Tensor,
    state1: Tensor,
    state2: Tensor,
    beta1: float,
    beta2: float,
    eps: float,
    step: int,
    lr: float,
    qmap1: Tensor,
    qmap2: Tensor,
    absmax1: Tensor,
    absmax2: Tensor,
    weight_decay: float = 0.0,
    gnorm_scale: float = 1.0,
    skip_zeros=False,
) -> None:

    optim_func = None
    prev_device = pre_call(g.device)
    is_on_gpu([g, p, state1, state2, qmap1, qmap2, absmax1, absmax2])
    if g.dtype == torch.float32 and state1.dtype == torch.uint8:
        optim_func = str2optimizer8bit_blockwise[optimizer_name][0]
    elif g.dtype == torch.float16 and state1.dtype == torch.uint8:
        optim_func = str2optimizer8bit_blockwise[optimizer_name][1]
    elif (g.dtype == torch.bfloat16 and state1.dtype == torch.uint8 and
          len(str2optimizer8bit_blockwise[optimizer_name])==3):
        optim_func = str2optimizer8bit_blockwise[optimizer_name][2]
    else:
        raise ValueError(
            f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}"
        )
    post_call(prev_device)

    is_on_gpu([p, g, state1, state2, qmap1, qmap2, absmax1, absmax2])

    prev_device = pre_call(g.device)
    optim_func(
        get_ptr(p),
        get_ptr(g),
        get_ptr(state1),
        get_ptr(state2),
        ct.c_float(beta1),
        ct.c_float(beta2),
        ct.c_float(eps),
        ct.c_int32(step),
        ct.c_float(lr),
        get_ptr(qmap1),
        get_ptr(qmap2),
        get_ptr(absmax1),
        get_ptr(absmax2),
        ct.c_float(weight_decay),
        ct.c_float(gnorm_scale),
        ct.c_bool(skip_zeros),
        ct.c_int32(g.numel()),
    )
    post_call(prev_device)

def percentile_clipping(
    grad: Tensor, gnorm_vec: Tensor, step: int, percentile: int = 5
):
    """Applies percentile clipping

    grad: torch.Tensor
        The gradient tensor.
    gnorm_vec: torch.Tensor
        Vector of gradient norms. 100 elements expected.
    step: int
        The current optimiation steps (number of past gradient norms).

    """
    prev_device = pre_call(grad.device)
    is_on_gpu([grad, gnorm_vec])
    if grad.dtype == torch.float32:
        lib.cpercentile_clipping_g32(
            get_ptr(grad),
            get_ptr(gnorm_vec),
            ct.c_int32(step),
            ct.c_int32(grad.numel()),
        )
    elif grad.dtype == torch.float16:
        lib.cpercentile_clipping_g16(
            get_ptr(grad),
            get_ptr(gnorm_vec),
            ct.c_int32(step),
            ct.c_int32(grad.numel()),
        )
    else:
        raise ValueError(f"Gradient type {grad.dtype} not supported!")
    post_call(prev_device)

    current_gnorm = torch.sqrt(gnorm_vec[step % 100])
    vals, idx = torch.sort(gnorm_vec)
    clip_value = torch.sqrt(vals[percentile])
    gnorm_scale = 1.0

    if current_gnorm > clip_value:
        gnorm_scale = clip_value / current_gnorm

    return current_gnorm, clip_value, gnorm_scale


def histogram_scatter_add_2d(
    histogram: Tensor, index1: Tensor, index2: Tensor, source: Tensor
):
    assert len(histogram.shape) == 2
    assert histogram.dtype == torch.float32
    assert source.dtype == torch.float32
    assert index1.dtype == torch.int32
    assert index2.dtype == torch.int32

    assert histogram.device.type == "cuda"
    assert index1.device.type == "cuda"
    assert index2.device.type == "cuda"
    assert source.device.type == "cuda"

    maxdim1 = ct.c_int32(histogram.shape[0])
    n = ct.c_int32(index1.numel())
    is_on_gpu([histogram, index1, index2, source])
    lib.chistogram_scatter_add_2d(get_ptr(histogram), get_ptr(index1), get_ptr(index2), get_ptr(source), maxdim1, n)

def check_matmul(A, B, out, transposed_A, transposed_B, expected_type=torch.int8):
    if not torch.cuda.is_initialized(): torch.cuda.init()
    if A.dtype != expected_type or B.dtype != expected_type:
        raise TypeError(
            f"Expected torch.int8 input tensors A and B, but got {A.dtype} and {B.dtype}"
        )

    sA = A.shape
    sB = B.shape
    tA = transposed_A
    tB = transposed_B

    correct = True

    if len(sA) == 2 and len(sB) == 2:
        if not tA and not tB and A.shape[1] != B.shape[0]:
            correct = False
        elif tA and not tB and A.shape[0] != B.shape[0]:
            correct = False
        elif tA and tB and A.shape[0] != B.shape[1]:
            correct = False
        elif not tA and tB and A.shape[1] != B.shape[1]:
            correct = False
    elif len(sA) == 3 and len(sB) == 2:
        if not tA and not tB and A.shape[2] != B.shape[0]:
            correct = False
        elif tA and not tB and A.shape[1] != B.shape[0]:
            correct = False
        elif tA and tB and A.shape[1] != B.shape[1]:
            correct = False
        elif not tA and tB and A.shape[2] != B.shape[1]:
            correct = False
    elif len(sA) == 3 and len(sB) == 3:
        if not tA and not tB and A.shape[2] != B.shape[1]:
            correct = False
        elif tA and not tB and A.shape[1] != B.shape[1]:
            correct = False
        elif tA and tB and A.shape[1] != B.shape[2]:
            correct = False
        elif not tA and tB and A.shape[2] != B.shape[2]:
            correct = False

    if out is not None:
        sout = out.shape
        # special case common in backprop
        if not correct and len(sA) == 3 and len(sB) == 3:
            if (
                sout[0] == sA[2]
                and sout[1] == sB[2]
                and sA[0] == sB[0]
                and sA[1] == sB[1]
            ):
                correct = True
    else:
        if len(sA) == 2 and len(sB) == 2:
            if not tA and not tB:
                sout = (sA[0], sB[1])
            elif tA and tB:
                sout = (sA[1], sB[0])
            elif tA and not tB:
                sout = (sA[1], sB[1])
            elif not tA and tB:
                sout = (sA[0], sB[0])
        elif len(sA) == 3 and len(sB) == 2:
            if not tA and not tB:
                sout = (sA[0], sA[1], sB[1])
            elif tA and tB:
                sout = (sA[0], sA[2], sB[0])
            elif tA and not tB:
                sout = (sA[0], sA[2], sB[1])
            elif not tA and tB:
                sout = (sA[0], sA[1], sB[0])
        elif len(sA) == 3 and len(sB) == 3:
            if not tA and not tB:
                sout = (sA[0], sA[1], sB[2])
            elif tA and tB:
                sout = (sA[0], sA[2], sB[1])
            elif tA and not tB:
                sout = (sA[0], sA[2], sB[2])
            elif not tA and tB:
                sout = (sA[0], sA[1], sB[1])

    if not correct:
        raise ValueError(
            f"Tensor dimensions incorrect for matrix mulitiplication: A x B: {sA} x {sB} with transpose for A x B: {tA} x {tB}."
        )

    return sout

def gemv_4bit(
    A: Tensor,
    B: Tensor,
    out: Tensor = None,
    transposed_A=False,
    transposed_B=False,
    quant_state=None
):
    prev_device = pre_call(A.device)
    #sout = check_matmul(A, B, out, transposed_A, transposed_B, expected_type=A.dtype)
    if quant_state is None:
        raise ValueError(f'state cannot None. gem_4bit( ) requires the state from quantize_4bit( )')

    if A.numel() != A.shape[-1]:
        raise ValueError(f'Dimensions of A are invalid. Must be a vector with the leading dimensions of "1", e.g. [1, 1, 2048]')

    Bshape = quant_state.shape
    bout = Bshape[0]
    absmax = quant_state.absmax
    if quant_state.nested:
        absmax = dequantize_blockwise(quant_state.absmax, quant_state.state2)
        absmax += quant_state.offset

    if out is None:
        if len(A.shape) == 3:
            out = torch.empty(size=(A.shape[0], A.shape[1], bout), dtype=A.dtype, device=A.device)
        else:
            out = torch.empty(size=(A.shape[0], bout), dtype=A.dtype, device=A.device)

    n = 1
    m = Bshape[0]
    k = Bshape[1]
    lda = Bshape[0]
    ldc = Bshape[0]
    ldb = (A.shape[-1]+1)//2
    is_on_gpu([B, A, out, absmax, quant_state.code])
    m = ct.c_int32(m)
    n = ct.c_int32(n)
    k = ct.c_int32(k)
    lda = ct.c_int32(lda)
    ldb = ct.c_int32(ldb)
    ldc = ct.c_int32(ldc)

    if B.dtype == torch.uint8:
        if A.dtype == torch.float16:
            lib.cgemm_4bit_inference_naive_fp16(m, n, k, get_ptr(A), get_ptr(B), get_ptr(absmax), get_ptr(quant_state.code), get_ptr(out), lda, ldb, ldc, ct.c_int32(quant_state.blocksize))
        elif A.dtype == torch.bfloat16:
            lib.cgemm_4bit_inference_naive_bf16(m, n, k, get_ptr(A), get_ptr(B), get_ptr(absmax), get_ptr(quant_state.code), get_ptr(out), lda, ldb, ldc, ct.c_int32(quant_state.blocksize))
        elif A.dtype == torch.float32:
            lib.cgemm_4bit_inference_naive_fp32(m, n, k, get_ptr(A), get_ptr(B), get_ptr(absmax), get_ptr(quant_state.code), get_ptr(out), lda, ldb, ldc, ct.c_int32(quant_state.blocksize))
        else:
            raise NotImplementedError(f'Matmul not implemented for data type {A.dtype}')

    else:
        raise NotImplementedError(f'Matmul not implemented for data type {A.dtype}')

    post_call(prev_device)

    return out

def igemm(
    A: Tensor,
    B: Tensor,
    out: Tensor = None,
    transposed_A=False,
    transposed_B=False,
):
    sout = check_matmul(A, B, out, transposed_A, transposed_B)
    if out is None:
        out = torch.zeros(size=sout, dtype=torch.int32, device=A.device)
    if len(A.shape) == 3 and len(B.shape) == 3:
        if A.shape[0] == B.shape[0] and A.shape[2] == B.shape[1]:
            return batched_igemm(A, B, out)

    sA = A.shape
    sB = B.shape
    if transposed_A and len(sA) == 2:
        sA = (sA[1], sA[0])
    elif transposed_A and len(sA) == 3:
        sA = (sA[0], sA[2], sA[0])
    if transposed_B and len(sB) == 2:
        sB = (sB[1], sB[0])
    elif transposed_B and len(sB) == 3:
        sB = (sB[0], sB[2], sB[0])
    # this is a mess: cuBLAS expect column major, but PyTorch is row major.
    # So to perform the matrix multiplication, we have to treat A, B, and C matrices
    # (transpose of row major is column major)
    # This means we compute B^T A^T = C^T and we explicitly switch the dimensions of each of these

    # matrices in the input arguments for cuBLAS
    # column major: A @ B = C: [m, k] @ [k, n] = [m, n]
    # row major: B^T @ A^T = C^T: [m, k] @ [k, n] = [m, n]
    # column major with row major layout: B^T @ A^T = C^T: [k, m] @ [n, k] = [n, m]
    if len(sB) == 2:
        if B.stride()[0] == B.shape[1]:
            transposed_B = False
        elif B.stride()[1] == B.shape[0]:
            transposed_B = True
        if len(A.shape) == 2:
            if A.stride()[0] == A.shape[1]:
                transposed_A = False
            elif A.stride()[1] == A.shape[0]:
                transposed_A = True
        else:
            if A.stride()[1] == A.shape[2]:
                transposed_A = False
            elif A.stride()[2] == A.shape[1]:
                transposed_A = True

        if len(sA) == 2:
            n = sA[0]
            ldb = A.stride()[1 if transposed_A else 0]
        elif len(sA) == 3 and len(sB) == 2:
            n = sA[0] * sA[1]
            ldb = sA[2]

        m = sB[1]
        k = sB[0]
        lda = B.stride()[(1 if transposed_B else 0)]
        ldc = sB[1]
    elif len(sB) == 3:
        # special case
        assert len(sA) == 3
        if not (sA[0] == sB[0] and sA[1] == sB[1]):
            raise ValueError(
                f"Only bsi,bso->io supported for tensor contractions, but dims for A x B were: {sA} x {sB}"
            )

        transposed_A = True
        transposed_B = False

        m = sB[2]
        n = sA[2]
        k = sB[0] * sB[1]

        lda = m
        ldb = sA[2]
        ldc = m

    ptr = CUBLAS_Context.get_instance().get_context(A.device)

    # B^T @ A^T = C^T
    # [km, nk -> mn]
    is_on_gpu([B, A, out])
    lib.cigemm(ptr, ct.c_bool(transposed_B), ct.c_bool(transposed_A), ct.c_int32(m), ct.c_int32(n), ct.c_int32(k),
               get_ptr(B), get_ptr(A), get_ptr(out), ct.c_int32(lda), ct.c_int32(ldb), ct.c_int32(ldc))
    return out


def batched_igemm(
    A: Tensor,
    B: Tensor,
    out: Tensor = None,
    transposed_A=False,
    transposed_B=False,
):
    if not len(A.shape) == 3 or not len(B.shape) == 3:
        raise ValueError(
            f"Expected 3-dimensional tensors for bmm, but got shapes A and B: {A.shape} and {B.shape}"
        )
    sout = check_matmul(A, B, out, transposed_A, transposed_B)
    if out is None:
        out = torch.zeros(size=sout, dtype=torch.int32, device=A.device)

    if B.is_contiguous():
        lda = B.stride()[1]
        transposed_A = False
    else:
        s = B.stride()
        if s[0] != B.shape[0]:
            B = B.contiguous()
            lda = B.stride()[1]
        elif s[2] == B.shape[1]:
            transposed_A = True
            lda = B.stride()[2]
        else:
            if s[2] == 1:
                B = B.contiguous()
                lda = B.stride()[1]
            elif s[1] == 1:
                B = B.contiguous()
                lda = B.stride()[1]
            else:
                B = B.contiguous()
                lda = B.stride()[1]

    if A.is_contiguous():
        ldb = A.stride()[1]
        transposed_B = False
    else:
        s = A.stride()
        if s[0] != A.shape[0]:
            A = A.contiguous()
            ldb = A.stride()[1]
            transposed_B = False
        elif s[2] == A.shape[1]:
            ldb = A.stride()[2]
            transposed_B = True
        else:
            A = A.contiguous()
            ldb = A.stride()[1]
            transposed_B = False

    # this is a mess: cuBLAS expect column major, but PyTorch is row major.
    # So to perform the matrix multiplication, we have to treat A, B, and C matrices
    # (transpose of row major is column major)
    # This means we compute B^T A^T = C^T and we explicitly switch the dimensions of each of these
    # matrices in the input arguments for cuBLAS

    # column major: A @ B = C: [batch, m, k] @ [batch, k, n] = [batch, m, n]
    # row major: B^T @ A^T = C^T: [batch, m, k] @ [batch, k, n] = [batch, m, n]
    # column major with row major layout: B^T @ A^T = C^T: [batch, k, m] @ [batch, n, k] = [batch, n, m]
    num_batch = A.shape[0]
    n = A.shape[1]
    m = B.shape[2]
    k = B.shape[1]

    ldc = m

    strideA = B.shape[1] * B.shape[2]
    strideB = A.shape[1] * A.shape[2]
    strideC = A.shape[1] * B.shape[2]

    ptr = CUBLAS_Context.get_instance().get_context(A.device)

    is_on_gpu([B, A, out])
    lib.cbatched_igemm(ptr, ct.c_bool(transposed_B), ct.c_bool(transposed_A), ct.c_int32(m), ct.c_int32(n), ct.c_int32(k),
               get_ptr(B), get_ptr(A), get_ptr(out), ct.c_int32(lda), ct.c_int32(ldb), ct.c_int32(ldc),
               ct.c_long(strideA), ct.c_long(strideB), ct.c_long(strideC), ct.c_uint32(num_batch))
    return out


def igemmlt(A, B, SA, SB, out=None, Sout=None, dtype=torch.int32):
    shapeA = SA[0]
    shapeB = SB[0]
    dimsA = len(shapeA)
    dimsB = len(shapeB)
    assert dimsB == 2, 'Only two dimensional matrices are supported for argument B'
    if dimsA == 2:
        m = shapeA[0]
    elif dimsA == 3:
        m = shapeA[0] * shapeA[1]

    rows = n = shapeB[0]
    assert prod(list(shapeA)) > 0, f'Input tensor dimensions need to be > 0: {shapeA}'

    # if the tensor is empty, return a transformed empty tensor with the right dimensions
    if shapeA[0] == 0 and dimsA == 2:
        return torch.empty((0, shapeB[0]), device=A.device, dtype=torch.float16)
    elif shapeA[1] == 0 and dimsA == 3:
        return torch.empty(tuple(shapeA[:2] + [shapeB[0]]), device=A.device, dtype=torch.float16)

    if dimsA == 2 and out is None:
        out, Sout = get_transform_buffer(
            (shapeA[0], shapeB[0]), dtype, A.device, "col32", "row"
        )
    elif dimsA == 3 and out is None:
        out, Sout = get_transform_buffer(
            (shapeA[0], shapeA[1], shapeB[0]), dtype, A.device, "col32", "row"
        )

    assert dimsB != 3, "len(B.shape)==3 not supported"
    assert A.device.type == "cuda"
    assert B.device.type == "cuda"
    assert A.dtype == torch.int8
    assert B.dtype == torch.int8
    assert out.dtype == dtype
    assert SA[1] == "col32"
    assert SB[1] in ["col_turing", "col_ampere"]
    assert Sout[1] == "col32"
    assert (
        shapeA[-1] == shapeB[-1]
    ), f"Matmullt only supports A @ B^T. Inner matrix dimensions do not match: A @ B = {shapeA} @ {shapeB}"
    formatB = SB[1]
    prev_device = A.device
    torch.cuda.set_device(A.device)

    ptr = CUBLAS_Context.get_instance().get_context(A.device)
    ptrA = get_ptr(A)
    ptrB = get_ptr(B)
    ptrC = get_ptr(out)

    k = shapeA[-1]
    lda = ct.c_int32(m * 32)
    if formatB == "col_turing":
        # turing: tiles with rows filled up to multiple of 8 rows by 32 columns
        # n = rows
        ldb = ct.c_int32(((rows + 7) // 8) * 8 * 32)
    else:
        # ampere: tiles with rows filled up to multiple of 32 rows by 32 columns
        # n = rows
        ldb = ct.c_int32(((rows + 31) // 32) * 32 * 32)

    ldc = ct.c_int32(m * 32)
    m = ct.c_int32(m)
    n = ct.c_int32(n)
    k = ct.c_int32(k)

    has_error = 0
    ptrRowScale = get_ptr(None)
    is_on_gpu([A, B, out])
    if formatB == 'col_turing':
        if dtype == torch.int32:
            has_error = lib.cigemmlt_turing_32(
                ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc
            )
        else:
            has_error = lib.cigemmlt_turing_8(
                ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc
            )
    elif formatB == "col_ampere":
        if dtype == torch.int32:
            has_error = lib.cigemmlt_ampere_32(
                ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc
            )
        else:
            has_error = lib.cigemmlt_ampere_8(
                ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc
            )

    if has_error == 1:
        print(f'A: {shapeA}, B: {shapeB}, C: {Sout[0]}; (lda, ldb, ldc): {(lda, ldb, ldc)}; (m, n, k): {(m, n, k)}')
        raise Exception('cublasLt ran into an error!')

    torch.cuda.set_device(prev_device)

    return out, Sout


def mm_dequant(
    A,
    state,
    row_stats,
    col_stats,
    out=None,
    new_row_stats=None,
    new_col_stats=None,
    bias=None
):
    assert A.dtype == torch.int32
    if bias is not None: assert bias.dtype == torch.float16
    out_shape = state[0]
    if len(out_shape) == 3:
        out_shape = (out_shape[0] * out_shape[1], out_shape[2])

    if out is None:
        out = torch.empty(out_shape, dtype=torch.float16, device=A.device)
    if new_row_stats is None:
        new_row_stats = torch.empty(
            out_shape[0], dtype=torch.float32, device=A.device
        )
    if new_col_stats is None:
        new_col_stats = torch.empty(
            out_shape[1], dtype=torch.float32, device=A.device
        )
    assert (
        new_row_stats.shape[0] == row_stats.shape[0]
    ), f"{new_row_stats.shape} vs {row_stats.shape}"
    assert (
        new_col_stats.shape[0] == col_stats.shape[0]
    ), f"{new_col_stats.shape} vs {col_stats.shape}"

    prev_device = pre_call(A.device)
    ptrA = get_ptr(A)
    ptrOut = get_ptr(out)
    ptrRowStats = get_ptr(row_stats)
    ptrColStats = get_ptr(col_stats)
    ptrNewRowStats = get_ptr(new_row_stats)
    ptrNewColStats = get_ptr(new_col_stats)
    ptrBias = get_ptr(bias)
    numRows = ct.c_int32(out_shape[0])
    numCols = ct.c_int32(out_shape[1])

    is_on_gpu([A, row_stats, col_stats, out, new_row_stats, new_col_stats, bias])
    lib.cdequant_mm_int32_fp16(ptrA, ptrRowStats, ptrColStats, ptrOut, ptrNewRowStats, ptrNewColStats, ptrBias, numRows, numCols)
    post_call(prev_device)

    return out


def get_colrow_absmax(
    A, row_stats=None, col_stats=None, nnz_block_ptr=None, threshold=0.0
):
    assert A.dtype == torch.float16
    device = A.device

    cols = A.shape[-1]
    if len(A.shape) == 3:
        rows = A.shape[0] * A.shape[1]
    else:
        rows = A.shape[0]

    col_tiles = (cols + 255) // 256
    tiled_rows = ((rows + 15) // 16) * 16
    if row_stats is None:
        row_stats = torch.empty(
            (rows,), dtype=torch.float32, device=device
        ).fill_(-50000.0)
    if col_stats is None:
        col_stats = torch.empty(
            (cols,), dtype=torch.float32, device=device
        ).fill_(-50000.0)

    if nnz_block_ptr is None and threshold > 0.0:
        nnz_block_ptr = torch.zeros(
            ((tiled_rows * col_tiles) + 1,), dtype=torch.int32, device=device
        )

    ptrA = get_ptr(A)
    ptrRowStats = get_ptr(row_stats)
    ptrColStats = get_ptr(col_stats)
    ptrNnzrows = get_ptr(nnz_block_ptr)
    rows = ct.c_int32(rows)
    cols = ct.c_int32(cols)

    prev_device = pre_call(A.device)
    is_on_gpu([A, row_stats, col_stats, nnz_block_ptr])
    lib.cget_col_row_stats(ptrA, ptrRowStats, ptrColStats, ptrNnzrows, ct.c_float(threshold), rows, cols)
    post_call(prev_device)

    if threshold > 0.0:
        nnz_block_ptr.cumsum_(0)

    return row_stats, col_stats, nnz_block_ptr


class COOSparseTensor:
    def __init__(self, rows, cols, nnz, rowidx, colidx, values):
        assert rowidx.dtype == torch.int32
        assert colidx.dtype == torch.int32
        assert values.dtype == torch.float16
        assert values.numel() == nnz
        assert rowidx.numel() == nnz
        assert colidx.numel() == nnz

        self.rows = rows
        self.cols = cols
        self.nnz = nnz
        self.rowidx = rowidx
        self.colidx = colidx
        self.values = values


class CSRSparseTensor:
    def __init__(self, rows, cols, nnz, rowptr, colidx, values):
        assert rowptr.dtype == torch.int32
        assert colidx.dtype == torch.int32
        assert values.dtype == torch.float16
        assert values.numel() == nnz
        assert colidx.numel() == nnz
        assert rowptr.numel() == rows + 1

        self.rows = rows
        self.cols = cols
        self.nnz = nnz
        self.rowptr = rowptr
        self.colidx = colidx
        self.values = values


class CSCSparseTensor:
    def __init__(self, rows, cols, nnz, colptr, rowidx, values):
        assert colptr.dtype == torch.int32
        assert rowidx.dtype == torch.int32
        assert values.dtype == torch.float16
        assert values.numel() == nnz
        assert rowidx.numel() == nnz
        assert colptr.numel() == cols + 1

        self.rows = rows
        self.cols = cols
        self.nnz = nnz
        self.colptr = colptr
        self.rowidx = rowidx
        self.values = values


def coo2csr(cooA):
    values, counts = torch.unique(cooA.rowidx, return_counts=True)
    values.add_(1)
    rowptr = torch.zeros(
        (cooA.rows + 1,), dtype=torch.int32, device=cooA.rowidx.device
    )
    rowptr.scatter_(index=values.long(), src=counts.int(), dim=0)
    rowptr.cumsum_(0)
    return CSRSparseTensor(
        cooA.rows, cooA.cols, cooA.nnz, rowptr, cooA.colidx, cooA.values
    )


def coo2csc(cooA):
    val, col2rowidx = torch.sort(cooA.colidx)
    rowidx = cooA.rowidx[col2rowidx]
    values = cooA.values[col2rowidx]
    colvalues, counts = torch.unique(val, return_counts=True)
    colvalues.add_(1)
    colptr = torch.zeros(
        (cooA.cols + 1,), dtype=torch.int32, device=cooA.colidx.device
    )
    colptr.scatter_(index=colvalues.long(), src=counts.int(), dim=0)
    colptr.cumsum_(0)
    return CSCSparseTensor(
        cooA.rows, cooA.cols, cooA.nnz, colptr, rowidx, values
    )


def coo_zeros(rows, cols, nnz, device, dtype=torch.half):
    rowidx = torch.zeros((nnz,), dtype=torch.int32, device=device)
    colidx = torch.zeros((nnz,), dtype=torch.int32, device=device)
    values = torch.zeros((nnz,), dtype=dtype, device=device)
    return COOSparseTensor(rows, cols, nnz, rowidx, colidx, values)


def double_quant(
    A, col_stats=None, row_stats=None, out_col=None, out_row=None, threshold=0.0
):
    device = A.device
    assert A.dtype == torch.half
    assert device.type == "cuda"
    prev_device = pre_call(A.device)

    cols = A.shape[-1]
    if len(A.shape) == 3:
        rows = A.shape[0] * A.shape[1]
    else:
        rows = A.shape[0]

    if row_stats is None or col_stats is None:
        row_stats, col_stats, nnz_row_ptr = get_colrow_absmax(
            A, threshold=threshold
        )

    if out_col is None:
        out_col = torch.zeros(A.shape, device=device, dtype=torch.int8)
    if out_row is None:
        out_row = torch.zeros(A.shape, device=device, dtype=torch.int8)

    coo_tensor = None
    ptrA = get_ptr(A)
    ptrColStats = get_ptr(col_stats)
    ptrRowStats = get_ptr(row_stats)
    ptrOutCol = get_ptr(out_col)
    ptrOutRow = get_ptr(out_row)

    is_on_gpu([A, col_stats, row_stats, out_col, out_row])
    if threshold > 0.0:
        nnz = nnz_row_ptr[-1].item()
        if nnz > 0:
            coo_tensor = coo_zeros(
                A.shape[0], A.shape[1], nnz_row_ptr[-1].item(), device
            )
            ptrRowIdx = get_ptr(coo_tensor.rowidx)
            ptrColIdx = get_ptr(coo_tensor.colidx)
            ptrVal = get_ptr(coo_tensor.values)
            ptrRowPtr = get_ptr(nnz_row_ptr)

            lib.cdouble_rowcol_quant(
                ptrA,
                ptrRowStats,
                ptrColStats,
                ptrOutCol,
                ptrOutRow,
                ptrRowIdx,
                ptrColIdx,
                ptrVal,
                ptrRowPtr,
                ct.c_float(threshold),
                ct.c_int32(rows),
                ct.c_int32(cols),
            )
            val, idx = torch.sort(coo_tensor.rowidx)
            coo_tensor.rowidx = val
            coo_tensor.colidx = coo_tensor.colidx[idx]
            coo_tensor.values = coo_tensor.values[idx]
        else:
            lib.cdouble_rowcol_quant(
                ptrA,
                ptrRowStats,
                ptrColStats,
                ptrOutCol,
                ptrOutRow,
                None,
                None,
                None,
                None,
                ct.c_float(0.0),
                ct.c_int32(rows),
                ct.c_int32(cols),
            )
    else:
        lib.cdouble_rowcol_quant(
            ptrA,
            ptrRowStats,
            ptrColStats,
            ptrOutCol,
            ptrOutRow,
            None,
            None,
            None,
            None,
            ct.c_float(threshold),
            ct.c_int32(rows),
            ct.c_int32(cols),
        )
    post_call(prev_device)

    return out_row, out_col, row_stats, col_stats, coo_tensor


def transform(A, to_order, from_order='row', out=None, transpose=False, state=None, ld=None):
    prev_device = pre_call(A.device)
    if state is None: state = (A.shape, from_order)
    else: from_order = state[1]
    if out is None: out, new_state = get_transform_buffer(state[0], A.dtype, A.device, to_order, state[1], transpose)
    else: new_state = (state[0], to_order) # (shape, order)

    shape = state[0]
    if len(shape) == 2:
        dim1 = ct.c_int32(shape[0])
        dim2 = ct.c_int32(shape[1])
    else:
        dim1 = ct.c_int32(shape[0] * shape[1])
        dim2 = ct.c_int32(shape[2])

    is_on_gpu([A, out])
    if to_order == 'col32':
        if transpose:
            lib.ctransform_row2col32T(get_ptr(A), get_ptr(out), dim1, dim2)
        else:
            lib.ctransform_row2col32(get_ptr(A), get_ptr(out), dim1, dim2)
    elif to_order == "col_turing":
        if transpose:
            lib.ctransform_row2turingT(get_ptr(A), get_ptr(out), dim1, dim2)
        else:
            lib.ctransform_row2turing(get_ptr(A), get_ptr(out), dim1, dim2)
    elif to_order == "col_ampere":
        if transpose:
            lib.ctransform_row2ampereT(get_ptr(A), get_ptr(out), dim1, dim2)
        else:
            lib.ctransform_row2ampere(get_ptr(A), get_ptr(out), dim1, dim2)
    elif to_order == "row":
        if from_order == "col_turing":
            lib.ctransform_turing2row(get_ptr(A), get_ptr(out), dim1, dim2)
        elif from_order == "col_ampere":
            lib.ctransform_ampere2row(get_ptr(A), get_ptr(out), dim1, dim2)
    else:
        raise NotImplementedError(f'Transform function not implemented: From {from_order} to {to_order}')

    post_call(prev_device)

    return out, new_state


def spmm_coo(cooA, B, out=None):
    if out is None:
        out = torch.empty(
            (cooA.rows, B.shape[1]), device=B.device, dtype=B.dtype
        )
    nnz = cooA.nnz
    assert cooA.rowidx.numel() == nnz
    assert cooA.colidx.numel() == nnz
    assert cooA.values.numel() == nnz
    assert cooA.cols == B.shape[0]

    transposed_B = False if B.is_contiguous() else True

    ldb = B.stride()[(1 if transposed_B else 0)]
    ldc = B.shape[1]

    ptr = Cusparse_Context.get_instance().context

    ptrRowidx = get_ptr(cooA.rowidx)
    ptrColidx = get_ptr(cooA.colidx)
    ptrValues = get_ptr(cooA.values)
    ptrB = get_ptr(B)
    ptrC = get_ptr(out)
    cnnz = ct.c_int32(cooA.nnz)
    crowsA = ct.c_int32(cooA.rows)
    ccolsA = ct.c_int32(cooA.cols)
    ccolsB = ct.c_int32(B.shape[1])
    cldb = ct.c_int32(ldb)
    cldc = ct.c_int32(ldc)

    is_on_gpu([cooA.rowidx, cooA.colidx, cooA.values, B, out])
    lib.cspmm_coo(ptr, ptrRowidx, ptrColidx, ptrValues, cnnz, crowsA, ccolsA, ccolsB, cldb, ptrB, cldc, ptrC, ct.c_bool(transposed_B))

    return out


def spmm_coo_very_sparse(cooA, B, dequant_stats=None, out=None):
    if out is None:
        out = torch.zeros(
            (cooA.rows, B.shape[1]), device=B.device, dtype=cooA.values.dtype
        )
    nnz = cooA.nnz
    prev_device = pre_call(B.device)
    assert cooA.rowidx.numel() == nnz
    assert cooA.colidx.numel() == nnz
    assert cooA.values.numel() == nnz
    assert cooA.cols == B.shape[0], f"{cooA.cols} vs {B.shape}"

    transposed_B = False if B.is_contiguous() else True

    ldb = B.stride()[(1 if transposed_B else 0)]
    ldc = B.shape[1]

    values, counts = torch.unique(cooA.rowidx, return_counts=True)
    offset = counts.cumsum(0).int()
    max_count, max_idx = torch.sort(counts, descending=True)
    max_idx = max_idx.int()
    max_count = max_count.int()
    assert (
        max_count[0] <= 32
    ), f"Current max count per row is 8 but found {max_count[0]}."
    assert B.dtype in [torch.float16, torch.int8]
    ptrOffset = get_ptr(offset)
    ptrMaxCount = get_ptr(max_count)
    ptrMaxIdx = get_ptr(max_idx)

    ptrRowidx = get_ptr(cooA.rowidx)
    ptrColidx = get_ptr(cooA.colidx)
    ptrValues = get_ptr(cooA.values)
    ptrB = get_ptr(B)
    ptrC = get_ptr(out)
    ptrDequantStats = get_ptr(dequant_stats)
    cnnz_rows = ct.c_int32(counts.numel())
    cnnz = ct.c_int32(cooA.nnz)
    crowsA = ct.c_int32(cooA.rows)
    ccolsA = ct.c_int32(cooA.cols)
    crowsB = ct.c_int32(B.shape[1])
    ccolsB = ct.c_int32(B.shape[1])
    cldb = ct.c_int32(ldb)
    cldc = ct.c_int32(ldc)

    is_on_gpu([cooA.rowidx, cooA.colidx, cooA.values, B, out, dequant_stats])
    if B.dtype == torch.float16:
        lib.cspmm_coo_very_sparse_naive_fp16(
            ptrMaxCount,
            ptrMaxIdx,
            ptrOffset,
            ptrRowidx,
            ptrColidx,
            ptrValues,
            ptrB,
            ptrC,
            ptrDequantStats,
            cnnz_rows,
            cnnz,
            crowsA,
            crowsB,
            ccolsB,
        )
    elif B.dtype == torch.int8:
        lib.cspmm_coo_very_sparse_naive_int8(
            ptrMaxCount,
            ptrMaxIdx,
            ptrOffset,
            ptrRowidx,
            ptrColidx,
            ptrValues,
            ptrB,
            ptrC,
            ptrDequantStats,
            cnnz_rows,
            cnnz,
            crowsA,
            crowsB,
            ccolsB,
        )
    # else: assertion error
    post_call(prev_device)

    return out


C = 127.0


def vectorwise_quant(x, dim=1, quant_type="vector"):
    if quant_type == "linear":
        max1 = torch.abs(x).max().float()
        xq = torch.round(x / max1 * 127).to(torch.int8)
        return xq, max1
    elif quant_type in ["vector", "row"]:
        max1 = torch.amax(torch.abs(x), dim=dim, keepdim=True)
        xq = torch.round(x * (C / max1)).to(torch.int8)
        return xq, max1
    elif quant_type == "zeropoint":
        dtype = x.dtype
        x = x.float()
        dyna = x.max() - x.min()
        if dyna == 0:
            dyna = 1
        qx = 255.0 / dyna
        minx = x.min()
        zpx = torch.round(minx * qx)
        x = torch.round(qx * x - zpx) + zpx
        return x, qx
    elif quant_type in ["vector-zeropoint", "row-zeropoint"]:
        dtype = x.dtype
        x = x.float()
        dyna = torch.amax(x, dim=dim, keepdim=True) - torch.amin(
            x, dim=dim, keepdim=True
        )
        dyna[dyna == 0] = 1
        qx = 255.0 / dyna
        minx = torch.amin(x, dim=dim, keepdim=True)
        zpx = torch.round(minx * qx)
        x = torch.round(qx * x - zpx) + zpx
        return x, qx
    elif quant_type == "truncated-vector":
        with torch.no_grad():
            absx = torch.abs(x)
            max1 = torch.amax(absx, dim=dim, keepdim=True)
            max1 = max1 * 0.7
            idx = absx > max1.expand_as(absx)
            sign = torch.sign(x[idx])
            x[idx] = max1.expand_as(absx)[idx] * sign
            xq = torch.round(x / max1 * C).to(torch.int8)
        return xq, max1
    else:
        return None


def vectorwise_dequant(xq, max1, quant_type="vector"):
    if quant_type == "vector":
        x = (xq / C * max1).to(torch.float32)
        return x
    else:
        return None


def vectorwise_mm_dequant(xq, S1, S2, dtype=torch.half, quant_type="vector"):
    if quant_type == "linear":
        norm = S1 * S2 / (C * C)
        # double cast needed to prevent overflows
        return (xq.float() * norm).to(dtype)
    elif quant_type == "zeropoint":
        norm = 1.0 / (S1 * S2)
        return (xq.float() * norm).to(dtype)
    elif quant_type == "row-zeropoint":
        norm = 1.0 / (S1 * S2)
        x = xq.float()
        if len(S1.shape) == 3 and len(x.shape) == 2:
            S1 = S1.squeeze(0)
        if len(S2.shape) == 3 and len(x.shape) == 2:
            S2 = S2.squeeze(0)
        if len(S1.shape) == 2:
            x *= norm
        else:
            x *= norm
        return x.to(dtype)
    elif quant_type == "vector-zeropoint":
        x = xq.float()
        if len(S1.shape) == 3 and len(x.shape) == 2:
            S1 = S1.squeeze(0)
        if len(S2.shape) == 3 and len(x.shape) == 2:
            S2 = S2.squeeze(0)
        if len(S1.shape) == 2:
            x *= 1.0 / S1
        else:
            x *= 1.0 / S1
        x *= 1.0 / S2.t()
        return x.to(dtype)
    elif quant_type == "row":
        x = xq.float()
        if len(S1.shape) == 3 and len(x.shape) == 2:
            S1 = S1.squeeze(0)
        if len(S2.shape) == 3 and len(x.shape) == 2:
            S2 = S2.squeeze(0)
        if len(S1.shape) == 2:
            x *= S1 * S2 / (C * C)
        else:
            x *= S1 * S2 / (C * C)
        return x.to(dtype)
    elif quant_type in ["truncated-vector", "vector"]:
        x = xq.float()
        if len(S1.shape) == 3 and len(x.shape) == 2:
            S1 = S1.squeeze(0)
        if len(S2.shape) == 3 and len(x.shape) == 2:
            S2 = S2.squeeze(0)
        if len(S1.shape) == 2:
            x *= S1 / C
        else:
            x *= S1 / C
        x *= S2 / C
        return x.to(dtype)
    else:
        return None


def dequant_min_max(xq, A, B, SA, SB, dtype=torch.half):
    offset = B.float().t().sum(0) * (SA[0] + SA[1])
    x = xq.float()
    if len(xq.shape) == 2 and len(SB.shape) == 3:
        SB = SB.squeeze(0)
    if len(SB.shape) == 2:
        x *= SB.t() / 127
    else:
        x *= SB / 127
    x *= SA[1] / 127
    x += offset
    return x.to(dtype)


def extract_outliers(A, SA, idx):
    shapeA = SA[0]
    formatA = SA[1]
    assert formatA in ["col_turing", "col_ampere"]
    assert A.device.type == "cuda"

    out = torch.zeros(
        (shapeA[0], idx.numel()), dtype=torch.int8, device=A.device
    )

    idx_size = ct.c_int32(idx.numel())
    rows = ct.c_int32(shapeA[0])
    cols = ct.c_int32(shapeA[1])
    ptrA = get_ptr(A)
    ptrIdx = get_ptr(idx)
    ptrOut = get_ptr(out)

    prev_device = pre_call(A.device)
    if formatA == 'col_turing':
        lib.cextractOutliers_turing(ptrA, ptrIdx, ptrOut, idx_size, rows, cols)
    elif formatA == "col_ampere":
        lib.cextractOutliers_ampere(ptrA, ptrIdx, ptrOut, idx_size, rows, cols)
    post_call(prev_device)

    return out

def pipeline_test(A, batch_size):
    out = torch.zeros_like(A)
    lib.cpipeline_test(get_ptr(A), get_ptr(out), ct.c_size_t(A.numel()), ct.c_size_t(batch_size))
    return out