bitsandbytes-rocm/bitsandbytes/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 operator
import random
import torch
import itertools

from typing import Tuple
from torch import Tensor

from .cextension import COMPILED_WITH_CUDA, lib
from functools import reduce  # Required in Python 3

# 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_g32, lib.cadam32bit_g16)
    str2optimizer32bit["momentum"] = (
        lib.cmomentum32bit_g32,
        lib.cmomentum32bit_g16,
    )
    str2optimizer32bit["rmsprop"] = (
        lib.crmsprop32bit_g32,
        lib.crmsprop32bit_g16,
    )
    str2optimizer32bit["adagrad"] = (
        lib.cadagrad32bit_g32,
        lib.cadagrad32bit_g16,
    )
    str2optimizer32bit["lars"] = (
        lib.cmomentum32bit_g32,
        lib.cmomentum32bit_g16,
    )
    str2optimizer32bit["lamb"] = (lib.cadam32bit_g32, lib.cadam32bit_g16)

    str2optimizer8bit = {}
    str2optimizer8bit["adam"] = (
        lib.cadam_static_8bit_g32,
        lib.cadam_static_8bit_g16,
    )
    str2optimizer8bit["momentum"] = (
        lib.cmomentum_static_8bit_g32,
        lib.cmomentum_static_8bit_g16,
    )
    str2optimizer8bit["rmsprop"] = (
        lib.crmsprop_static_8bit_g32,
        lib.crmsprop_static_8bit_g16,
    )
    str2optimizer8bit["lamb"] = (
        lib.cadam_static_8bit_g32,
        lib.cadam_static_8bit_g16,
    )
    str2optimizer8bit["lars"] = (
        lib.cmomentum_static_8bit_g32,
        lib.cmomentum_static_8bit_g16,
    )

    str2optimizer8bit_blockwise = {}
    str2optimizer8bit_blockwise["adam"] = (
        lib.cadam_8bit_blockwise_fp32,
        lib.cadam_8bit_blockwise_fp16,
    )
    str2optimizer8bit_blockwise["momentum"] = (
        lib.cmomentum_8bit_blockwise_fp32,
        lib.cmomentum_8bit_blockwise_fp16,
    )
    str2optimizer8bit_blockwise["rmsprop"] = (
        lib.crmsprop_8bit_blockwise_fp32,
        lib.crmsprop_8bit_blockwise_fp16,
    )
    str2optimizer8bit_blockwise["adagrad"] = (
        lib.cadagrad_8bit_blockwise_fp32,
        lib.cadagrad_8bit_blockwise_fp16,
    )


class CUBLAS_Context(object):
    _instance = None

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

    def initialize(self):
        self.context = {}
        # prev_device = torch.cuda.current_device()
        # for i in range(torch.cuda.device_count()):
        #    torch.cuda.set_device(torch.device('cuda', i))
        #    self.context.append(ct.c_void_p(lib.get_context()))
        # torch.cuda.set_device(prev_device)

    @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(object):
    _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


def create_linear_map(signed=True):
    if signed:
        return torch.linspace(-1.0, 1.0, 256)
    else:
        return torch.linspace(0.0, 1.0, 256)


def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2):
    e = exponent_bits
    p = precision_bits
    assert e+p == 7
    # the exponent is biased to 2^(e-1) -1 == 0
    evalues = []
    pvalues = []
    for i, val in enumerate(range(-((2**(exponent_bits-1))), 2**(exponent_bits-1), 1)):
        evalues.append(2**val)


    lst = list(itertools.product([0, 1], repeat=precision_bits))
    for bit_pattern in lst:
        value = 1
        for i, pval in enumerate(list(bit_pattern)):
            value += pval*(2**-(i+1))
        pvalues.append(value)

    assert len(evalues)*len(pvalues) == 128
    values = []
    for ev in evalues:
        for pv in pvalues:
            values.append(-ev*pv)
            values.append(ev*pv)
    values.sort()
    code = torch.Tensor(values)
    code /= code.max()
    code[127] = 0

    return code



def create_dynamic_map(signed=True, n=7):
    """
    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
    additional_items = 2 ** (7 - n) - 1
    if not signed:
        additional_items = 2 * additional_items
    for i in range(n):
        fraction_items = (
            2 ** (i + 7 - n) + 1 if signed else 2 ** (i + 7 - n + 1) + 1
        )
        boundaries = torch.linspace(0.1, 1, fraction_items)
        means = (boundaries[:-1] + boundaries[1:]) / 2.0
        data += ((10 ** (-(n - 1) + i)) * means).tolist()
        if signed:
            data += (-(10 ** (-(n - 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 ** (-(n - 1) + i)) * means).tolist()
        if signed:
            data += (-(10 ** (-(n - 1) + i)) * means).tolist()

    data.append(0)
    data.append(1.0)
    data.sort()
    return Tensor(data)


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"
    elif major == 8:
        return "col_ampere"
    else:
        return "col_turing"



def is_on_gpu(tensors):
    on_gpu = True
    for t in tensors:
        if t is None: continue # NULL pointers are fine
        on_gpu &= t.device.type == 'cuda'
    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)
    ptrA = get_ptr(A)
    ptrOut = get_ptr(out)
    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
) -> 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: 1/512

    Returns
    -------
    torch.Tensor:
        The 256 quantiles in float32 datatype.
    '''
    if out is None: out = torch.zeros((256,), dtype=torch.float32, device=A.device)
    is_on_gpu([A, out])
    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}")
    return out


def quantize_blockwise(A: Tensor, code: Tensor = None, absmax: Tensor = None, rand=None, out: Tensor = None, blocksize=4096) -> 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.
    rand : torch.Tensor
        The tensor for stochastic rounding.
    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"]
        code = code.to(A.device)

    if absmax is None:
        n = A.numel()
        blocksize = (blocksize if A.device.type == 'cpu' else 4096)
        blocks = n // blocksize
        blocks += 1 if n % blocksize > 0 else 0
        absmax = torch.zeros((blocks,), device=A.device)

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

    if A.device.type != 'cpu':
        is_on_gpu([code, A, absmax, out, rand])
        if rand is not None:
            assert rand.numel() >= 1024
            rand_offset = random.randint(0, 1023)
            if A.dtype == torch.float32:
                lib.cquantize_blockwise_stochastic_fp32(get_ptr(code), get_ptr(A),get_ptr(absmax), get_ptr(out), get_ptr(rand), ct.c_int32(rand_offset), ct.c_int(A.numel()))
            elif A.dtype == torch.float16:
                lib.cquantize_blockwise_stochastic_fp16(get_ptr(code), get_ptr(A),get_ptr(absmax), get_ptr(out), get_ptr(rand), ct.c_int32(rand_offset), ct.c_int(A.numel()))
            else:
                raise ValueError(
                    f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}"
                )
        else:
            if A.dtype == torch.float32:
                lib.cquantize_blockwise_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out),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),ct.c_int(A.numel()))
            else:
                raise ValueError(
                    f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}"
                )
    else:
        # cpu
        assert rand is None
        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()))

    return out, (absmax, code)


def dequantize_blockwise(
    A: Tensor,
    quant_state: Tuple[Tensor, Tensor] = None,
    absmax: Tensor = None,
    code: Tensor = None,
    out: Tensor = None,
    blocksize: int = 4096,
) -> 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 : tuple(torch.Tensor, torch.Tensor)
        Tuple of code and absmax values.
    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"]
        code = code.to(A.device)

    if out is None:
        out = torch.zeros_like(A, dtype=torch.float32)
    if quant_state is None:
        quant_state = (absmax, code)


    if A.device.type != 'cpu':
        if blocksize not in [2048, 4096]:
            raise ValueError(f"The blockwise of {blocksize} is not supported. Supported values: [2048 4096]")
        is_on_gpu([A, out])
        if out.dtype == torch.float32:
            lib.cdequantize_blockwise_fp32(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel()))
        elif out.dtype == torch.float16:
            lib.cdequantize_blockwise_fp16(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel()))
        else:
            raise ValueError(
                f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}"
            )
    else:
        lib.cdequantize_blockwise_cpu_fp32(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_longlong(blocksize), ct.c_longlong(A.numel()))

    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()
    inp = A / absmax
    out = quantize_no_absmax(inp, code, out)
    return out, (absmax, code)


def dequantize(
    A: Tensor,
    quant_state: Tuple[Tensor, Tensor] = None,
    absmax: Tensor = None,
    code: Tensor = None,
    out: Tensor = None,
) -> Tensor:
    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"]
        code = code.to(A.device)

    if quant_state is None:
        quant_state = (absmax, code)
    out = dequantize_no_absmax(A, quant_state[1], out)
    return out * quant_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.
    '''
    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()))
    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.
    '''
    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()))
    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())

    if optimizer_name not in str2optimizer32bit:
        raise NotImplementedError(
            f'Optimizer not implemented: {optimizer_name}. Choices: {",".join(str2optimizer32bit.keys())}'
        )

    if g.dtype == torch.float32 and state1.dtype == torch.float32:
        str2optimizer32bit[optimizer_name][0](
            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()),
        )
    elif g.dtype == torch.float16 and state1.dtype == torch.float32:
        str2optimizer32bit[optimizer_name][1](
            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()),
        )
    else:
        raise ValueError(
            f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}"
        )


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())

    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}"
        )


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:

    if g.dtype == torch.float32 and state1.dtype == torch.uint8:
        str2optimizer8bit_blockwise[optimizer_name][0](
            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()),
        )
    elif g.dtype == torch.float16 and state1.dtype == torch.uint8:
        str2optimizer8bit_blockwise[optimizer_name][1](
            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()),
        )
    else:
        raise ValueError(
            f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}"
        )


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).

    """
    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!")

    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, index2d, 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 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,
    quant_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 = quant_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(object):
    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(object):
    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(object):
    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])

    ptrA = get_ptr(A)
    ptrOut = get_ptr(out)
    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
    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)
    # print(cooA.rowidx[:64])
    # print(cooA.colidx[:64].sort()[0])

    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

    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