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# 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 math
import os

import torch
import torch.distributed as dist

import bitsandbytes.functional as F
from bitsandbytes.optim.optimizer import Optimizer2State


class Adam(Optimizer2State):
    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
        is_paged=False,
    ):
        """
        Base Adam optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-3):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            eps (`float`, defaults to 1e-8):
                The epsilon value prevents division by zero in the optimizer.
            weight_decay (`float`, defaults to 0.0):
                The weight decay value for the optimizer.
            amsgrad (`bool`, defaults to `False`):
                Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
            optim_bits (`int`, defaults to 32):
                The number of bits of the optimizer state.
            args (`object`, defaults to `None`):
                An object with additional arguments.
            min_8bit_size (`int`, defaults to 4096):
                The minimum number of elements of the parameter tensors for 8-bit optimization.
            percentile_clipping (`int`, defaults to 100):
                Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
            block_wise (`bool`, defaults to `True`):
                Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        super().__init__(
            "adam",
            params,
            lr,
            betas,
            eps,
            weight_decay,
            optim_bits,
            args,
            min_8bit_size,
            percentile_clipping,
            block_wise,
            is_paged=is_paged,
        )


class Adam8bit(Optimizer2State):
    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
        is_paged=False,
    ):
        """
        8-bit Adam optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-3):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            eps (`float`, defaults to 1e-8):
                The epsilon value prevents division by zero in the optimizer.
            weight_decay (`float`, defaults to 0.0):
                The weight decay value for the optimizer.
            amsgrad (`bool`, defaults to `False`):
                Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
            optim_bits (`int`, defaults to 32):
                The number of bits of the optimizer state.
            args (`object`, defaults to `None`):
                An object with additional arguments.
            min_8bit_size (`int`, defaults to 4096):
                The minimum number of elements of the parameter tensors for 8-bit optimization.
            percentile_clipping (`int`, defaults to 100):
                Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
            block_wise (`bool`, defaults to `True`):
                Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        super().__init__(
            "adam",
            params,
            lr,
            betas,
            eps,
            weight_decay,
            8,
            args,
            min_8bit_size,
            percentile_clipping,
            block_wise,
            is_paged=is_paged,
        )


class Adam32bit(Optimizer2State):
    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
        is_paged=False,
    ):
        """
        32-bit Adam optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-3):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            eps (`float`, defaults to 1e-8):
                The epsilon value prevents division by zero in the optimizer.
            weight_decay (`float`, defaults to 0.0):
                The weight decay value for the optimizer.
            amsgrad (`bool`, defaults to `False`):
                Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
            optim_bits (`int`, defaults to 32):
                The number of bits of the optimizer state.
            args (`object`, defaults to `None`):
                An object with additional arguments.
            min_8bit_size (`int`, defaults to 4096):
                The minimum number of elements of the parameter tensors for 8-bit optimization.
            percentile_clipping (`int`, defaults to 100):
                Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
            block_wise (`bool`, defaults to `True`):
                Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        super().__init__(
            "adam",
            params,
            lr,
            betas,
            eps,
            weight_decay,
            32,
            args,
            min_8bit_size,
            percentile_clipping,
            block_wise,
            is_paged=is_paged,
        )


class PagedAdam(Optimizer2State):
    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
        is_paged=False,
    ):
        """
        Paged Adam optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-3):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            eps (`float`, defaults to 1e-8):
                The epsilon value prevents division by zero in the optimizer.
            weight_decay (`float`, defaults to 0.0):
                The weight decay value for the optimizer.
            amsgrad (`bool`, defaults to `False`):
                Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
            optim_bits (`int`, defaults to 32):
                The number of bits of the optimizer state.
            args (`object`, defaults to `None`):
                An object with additional arguments.
            min_8bit_size (`int`, defaults to 4096):
                The minimum number of elements of the parameter tensors for 8-bit optimization.
            percentile_clipping (`int`, defaults to 100):
                Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
            block_wise (`bool`, defaults to `True`):
                Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        super().__init__(
            "adam",
            params,
            lr,
            betas,
            eps,
            weight_decay,
            optim_bits,
            args,
            min_8bit_size,
            percentile_clipping,
            block_wise,
            is_paged=True,
        )


class PagedAdam8bit(Optimizer2State):
    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
        is_paged=False,
    ):
        """
        8-bit paged Adam optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-3):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            eps (`float`, defaults to 1e-8):
                The epsilon value prevents division by zero in the optimizer.
            weight_decay (`float`, defaults to 0.0):
                The weight decay value for the optimizer.
            amsgrad (`bool`, defaults to `False`):
                Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
            optim_bits (`int`, defaults to 32):
                The number of bits of the optimizer state.
            args (`object`, defaults to `None`):
                An object with additional arguments.
            min_8bit_size (`int`, defaults to 4096):
                The minimum number of elements of the parameter tensors for 8-bit optimization.
            percentile_clipping (`int`, defaults to 100):
                Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
            block_wise (`bool`, defaults to `True`):
                Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        super().__init__(
            "adam",
            params,
            lr,
            betas,
            eps,
            weight_decay,
            8,
            args,
            min_8bit_size,
            percentile_clipping,
            block_wise,
            is_paged=True,
        )


class PagedAdam32bit(Optimizer2State):
    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
        is_paged=False,
    ):
        """
        Paged 32-bit Adam optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-3):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            eps (`float`, defaults to 1e-8):
                The epsilon value prevents division by zero in the optimizer.
            weight_decay (`float`, defaults to 0.0):
                The weight decay value for the optimizer.
            amsgrad (`bool`, defaults to `False`):
                Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
            optim_bits (`int`, defaults to 32):
                The number of bits of the optimizer state.
            args (`object`, defaults to `None`):
                An object with additional arguments.
            min_8bit_size (`int`, defaults to 4096):
                The minimum number of elements of the parameter tensors for 8-bit optimization.
            percentile_clipping (`int`, defaults to 100):
                Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
            block_wise (`bool`, defaults to `True`):
                Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        super().__init__(
            "adam",
            params,
            lr,
            betas,
            eps,
            weight_decay,
            32,
            args,
            min_8bit_size,
            percentile_clipping,
            block_wise,
            is_paged=True,
        )


class AnalysisAdam(torch.optim.Optimizer):
    """Adam that performs 8-bit vs 32-bit error analysis.
    This implementation is modified from torch.optim.Adam based on:
    `Fixed Weight Decay Regularization in Adam`
    (see https://arxiv.org/abs/1711.05101)
    It has been proposed in `Adam: A Method for Stochastic Optimization`_.
    Arguments:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        lr (float, optional): learning rate (default: 1e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square (default: (0.9, 0.999))
        eps (float, optional): term added to the denominator to improve
            numerical stability (default: 1e-8)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
            algorithm from the paper `On the Convergence of Adam and Beyond`_
    .. _Adam: A Method for Stochastic Optimization:
        https://arxiv.org/abs/1412.6980
    .. _On the Convergence of Adam and Beyond:
        https://openreview.net/forum?id=ryQu7f-RZ
    """

    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
        bnb_analysis="dynamic-blockwise",
        savedir=None,
    ):
        defaults = dict(
            lr=lr,
            betas=betas,
            eps=eps,
            weight_decay=weight_decay,
            amsgrad=amsgrad,
        )
        super().__init__(params, defaults)
        self.analysis = bnb_analysis
        self.savedir = savedir

    @property
    def supports_memory_efficient_fp16(self):
        return True

    @property
    def supports_flat_params(self):
        return True

    def step(self, closure=None):
        """Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p_id, p in enumerate(group["params"]):
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.dtype in {torch.float16, torch.bfloat16}:
                    grad = grad.float()
                if grad.is_sparse:
                    raise RuntimeError("Adam does not support sparse gradients, please consider SparseAdam instead")
                amsgrad = group.get("amsgrad", False)
                assert not amsgrad

                p_data_fp32 = p.data
                if p.data.dtype in {torch.float16, torch.bfloat16}:
                    p_data_fp32 = p_data_fp32.float()

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state["step"] = 0
                    # Exponential moving average of gradient values
                    state["exp_avg"] = torch.zeros_like(p_data_fp32)
                    # Exponential moving average of squared gradient values
                    state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
                    state["abserrors"] = torch.zeros((256, 256), device=p_data_fp32.device)
                    state["relerrors"] = torch.zeros((256, 256), device=p_data_fp32.device)
                    state["counts"] = torch.zeros((256, 256), device=p_data_fp32.device)
                    if amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32)
                else:
                    state["exp_avg"] = state["exp_avg"].to(p_data_fp32)
                    state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32)
                    if amsgrad:
                        state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to(p_data_fp32)

                state["step"] += 1
                beta1, beta2 = group["betas"]
                bias_correction1 = 1 - beta1 ** state["step"]
                bias_correction2 = 1 - beta2 ** state["step"]
                step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1
                e = state["abserrors"]
                rele = state["relerrors"]
                counts = state["counts"]

                if group["weight_decay"] != 0:
                    p_data_fp32.add_(p_data_fp32, alpha=-group["weight_decay"] * group["lr"])

                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
                if amsgrad:
                    max_exp_avg_sq = state["max_exp_avg_sq"]

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)

                denom = exp_avg_sq.sqrt().add_(group["eps"])
                update_fp32 = exp_avg / denom

                if p_data_fp32.numel() <= 8192 or p_data_fp32.numel() > 50000 * 1000:
                    # embedding layer or too small
                    p_data_fp32 += -step_size * update_fp32
                else:
                    if self.analysis == "dynamic-blockwise":
                        code1 = F.create_dynamic_map(signed=True).to(p.device)
                        code2 = F.create_dynamic_map(signed=False).to(p.device)
                        C1, S1 = F.quantize_blockwise(exp_avg, code=code1)
                        state1 = F.dequantize_blockwise(C1, S1)
                        C2, S2 = F.quantize_blockwise(exp_avg_sq, code=code2)
                        state2 = F.dequantize_blockwise(C2, S2)
                    elif self.analysis == "dynamic":
                        code1 = F.create_dynamic_map(signed=True).to(p.device)
                        code2 = F.create_dynamic_map(signed=False).to(p.device)
                        C1, S1 = F.quantize(exp_avg, code=code1)
                        state1 = F.dequantize(C1, S1)
                        C2, S2 = F.quantize(exp_avg_sq, code=code2)
                        state2 = F.dequantize(C2, S2)
                    elif self.analysis == "linear":
                        code1 = F.create_linear_map(signed=True).to(p.device)
                        code2 = F.create_linear_map(signed=False).to(p.device)
                        C1, S1 = F.quantize(exp_avg, code=code1)
                        state1 = F.dequantize(C1, S1)
                        C2, S2 = F.quantize(exp_avg_sq, code=code2)
                        state2 = F.dequantize(C2, S2)
                    elif self.analysis == "quantile":
                        code1 = F.estimate_quantiles(exp_avg)
                        code2 = F.estimate_quantiles(exp_avg_sq)
                        C1 = F.quantize_no_absmax(exp_avg, code=code1)
                        state1 = F.dequantize_no_absmax(C1, code1)
                        C2 = F.quantize_no_absmax(exp_avg_sq, code=code2)
                        state2 = F.dequantize_no_absmax(C2, code2)
                    elif self.analysis == "my-quantization-routine":
                        pass
                        # 1. get code
                        # 2. quantize
                        # 3. dequantize
                        # Error will be calculated automatically!
                    else:
                        raise ValueError(f"Invalid analysis value: {self.analysis}!")

                    denom = state2.sqrt().add_(group["eps"])
                    update_8bit = state1 / denom

                    abserr = torch.abs(update_8bit - update_fp32)
                    relerr = abserr / torch.abs(update_fp32 + 1e-6)

                    C1, C2 = C1.int(), C2.int()

                    F.histogram_scatter_add_2d(e, C1.int(), C2.int(), abserr)
                    F.histogram_scatter_add_2d(rele, C1.int(), C2.int(), relerr)
                    F.histogram_scatter_add_2d(counts, C1.int(), C2.int(), torch.ones_like(abserr))

                    p_data_fp32 += -step_size * update_fp32

                    if not dist.is_initialized() or dist.get_rank() == 0:
                        if self.savedir != "" and state["step"] % 100 == 0:
                            if not os.path.exists(self.savedir):
                                os.makedirs(self.savedir)
                            shapestr = "_".join([str(dim) for dim in p_data_fp32.shape])
                            pathe = os.path.join(self.savedir, f"{p_id}_{shapestr}_abserr.pkl")
                            pathrele = os.path.join(self.savedir, f"{p_id}_{shapestr}_relerr.pkl")
                            pathcounts = os.path.join(self.savedir, f"{p_id}_{shapestr}_counts.pkl")
                            torch.save(e, pathe)
                            torch.save(rele, pathrele)
                            torch.save(counts, pathcounts)

                if p.data.dtype in {torch.float16, torch.bfloat16}:
                    p.data.copy_(p_data_fp32)

        return loss

	# 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 math
	import os

	import torch
	import torch.distributed as dist

	import bitsandbytes.functional as F
	from bitsandbytes.optim.optimizer import Optimizer2State


	class Adam(Optimizer2State):
	def __init__(
	self,
	params,
	lr=1e-3,
	betas=(0.9, 0.999),
	eps=1e-8,
	weight_decay=0,
	amsgrad=False,
	optim_bits=32,
	args=None,
	min_8bit_size=4096,
	percentile_clipping=100,
	block_wise=True,
	is_paged=False,
	):
	"""
	Base Adam optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-3):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	eps (`float`, defaults to 1e-8):
	The epsilon value prevents division by zero in the optimizer.
	weight_decay (`float`, defaults to 0.0):
	The weight decay value for the optimizer.
	amsgrad (`bool`, defaults to `False`):
	Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
	optim_bits (`int`, defaults to 32):
	The number of bits of the optimizer state.
	args (`object`, defaults to `None`):
	An object with additional arguments.
	min_8bit_size (`int`, defaults to 4096):
	The minimum number of elements of the parameter tensors for 8-bit optimization.
	percentile_clipping (`int`, defaults to 100):
	Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
	block_wise (`bool`, defaults to `True`):
	Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
	is_paged (`bool`, defaults to `False`):
	Whether the optimizer is a paged optimizer or not.
	"""
	super().__init__(
	"adam",
	params,
	lr,
	betas,
	eps,
	weight_decay,
	optim_bits,
	args,
	min_8bit_size,
	percentile_clipping,
	block_wise,
	is_paged=is_paged,
	)


	class Adam8bit(Optimizer2State):
	def __init__(
	self,
	params,
	lr=1e-3,
	betas=(0.9, 0.999),
	eps=1e-8,
	weight_decay=0,
	amsgrad=False,
	optim_bits=32,
	args=None,
	min_8bit_size=4096,
	percentile_clipping=100,
	block_wise=True,
	is_paged=False,
	):
	"""
	8-bit Adam optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-3):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	eps (`float`, defaults to 1e-8):
	The epsilon value prevents division by zero in the optimizer.
	weight_decay (`float`, defaults to 0.0):
	The weight decay value for the optimizer.
	amsgrad (`bool`, defaults to `False`):
	Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
	optim_bits (`int`, defaults to 32):
	The number of bits of the optimizer state.
	args (`object`, defaults to `None`):
	An object with additional arguments.
	min_8bit_size (`int`, defaults to 4096):
	The minimum number of elements of the parameter tensors for 8-bit optimization.
	percentile_clipping (`int`, defaults to 100):
	Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
	block_wise (`bool`, defaults to `True`):
	Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
	is_paged (`bool`, defaults to `False`):
	Whether the optimizer is a paged optimizer or not.
	"""
	super().__init__(
	"adam",
	params,
	lr,
	betas,
	eps,
	weight_decay,
	8,
	args,
	min_8bit_size,
	percentile_clipping,
	block_wise,
	is_paged=is_paged,
	)


	class Adam32bit(Optimizer2State):
	def __init__(
	self,
	params,
	lr=1e-3,
	betas=(0.9, 0.999),
	eps=1e-8,
	weight_decay=0,
	amsgrad=False,
	optim_bits=32,
	args=None,
	min_8bit_size=4096,
	percentile_clipping=100,
	block_wise=True,
	is_paged=False,
	):
	"""
	32-bit Adam optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-3):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	eps (`float`, defaults to 1e-8):
	The epsilon value prevents division by zero in the optimizer.
	weight_decay (`float`, defaults to 0.0):
	The weight decay value for the optimizer.
	amsgrad (`bool`, defaults to `False`):
	Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
	optim_bits (`int`, defaults to 32):
	The number of bits of the optimizer state.
	args (`object`, defaults to `None`):
	An object with additional arguments.
	min_8bit_size (`int`, defaults to 4096):
	The minimum number of elements of the parameter tensors for 8-bit optimization.
	percentile_clipping (`int`, defaults to 100):
	Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
	block_wise (`bool`, defaults to `True`):
	Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
	is_paged (`bool`, defaults to `False`):
	Whether the optimizer is a paged optimizer or not.
	"""
	super().__init__(
	"adam",
	params,
	lr,
	betas,
	eps,
	weight_decay,
	32,
	args,
	min_8bit_size,
	percentile_clipping,
	block_wise,
	is_paged=is_paged,
	)


	class PagedAdam(Optimizer2State):
	def __init__(
	self,
	params,
	lr=1e-3,
	betas=(0.9, 0.999),
	eps=1e-8,
	weight_decay=0,
	amsgrad=False,
	optim_bits=32,
	args=None,
	min_8bit_size=4096,
	percentile_clipping=100,
	block_wise=True,
	is_paged=False,
	):
	"""
	Paged Adam optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-3):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	eps (`float`, defaults to 1e-8):
	The epsilon value prevents division by zero in the optimizer.
	weight_decay (`float`, defaults to 0.0):
	The weight decay value for the optimizer.
	amsgrad (`bool`, defaults to `False`):
	Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
	optim_bits (`int`, defaults to 32):
	The number of bits of the optimizer state.
	args (`object`, defaults to `None`):
	An object with additional arguments.
	min_8bit_size (`int`, defaults to 4096):
	The minimum number of elements of the parameter tensors for 8-bit optimization.
	percentile_clipping (`int`, defaults to 100):
	Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
	block_wise (`bool`, defaults to `True`):
	Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
	is_paged (`bool`, defaults to `False`):
	Whether the optimizer is a paged optimizer or not.
	"""
	super().__init__(
	"adam",
	params,
	lr,
	betas,
	eps,
	weight_decay,
	optim_bits,
	args,
	min_8bit_size,
	percentile_clipping,
	block_wise,
	is_paged=True,
	)


	class PagedAdam8bit(Optimizer2State):
	def __init__(
	self,
	params,
	lr=1e-3,
	betas=(0.9, 0.999),
	eps=1e-8,
	weight_decay=0,
	amsgrad=False,
	optim_bits=32,
	args=None,
	min_8bit_size=4096,
	percentile_clipping=100,
	block_wise=True,
	is_paged=False,
	):
	"""
	8-bit paged Adam optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-3):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	eps (`float`, defaults to 1e-8):
	The epsilon value prevents division by zero in the optimizer.
	weight_decay (`float`, defaults to 0.0):
	The weight decay value for the optimizer.
	amsgrad (`bool`, defaults to `False`):
	Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
	optim_bits (`int`, defaults to 32):
	The number of bits of the optimizer state.
	args (`object`, defaults to `None`):
	An object with additional arguments.
	min_8bit_size (`int`, defaults to 4096):
	The minimum number of elements of the parameter tensors for 8-bit optimization.
	percentile_clipping (`int`, defaults to 100):
	Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
	block_wise (`bool`, defaults to `True`):
	Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
	is_paged (`bool`, defaults to `False`):
	Whether the optimizer is a paged optimizer or not.
	"""
	super().__init__(
	"adam",
	params,
	lr,
	betas,
	eps,
	weight_decay,
	8,
	args,
	min_8bit_size,
	percentile_clipping,
	block_wise,
	is_paged=True,
	)


	class PagedAdam32bit(Optimizer2State):
	def __init__(
	self,
	params,
	lr=1e-3,
	betas=(0.9, 0.999),
	eps=1e-8,
	weight_decay=0,
	amsgrad=False,
	optim_bits=32,
	args=None,
	min_8bit_size=4096,
	percentile_clipping=100,
	block_wise=True,
	is_paged=False,
	):
	"""
	Paged 32-bit Adam optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-3):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	eps (`float`, defaults to 1e-8):
	The epsilon value prevents division by zero in the optimizer.
	weight_decay (`float`, defaults to 0.0):
	The weight decay value for the optimizer.
	amsgrad (`bool`, defaults to `False`):
	Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead.
	optim_bits (`int`, defaults to 32):
	The number of bits of the optimizer state.
	args (`object`, defaults to `None`):
	An object with additional arguments.
	min_8bit_size (`int`, defaults to 4096):
	The minimum number of elements of the parameter tensors for 8-bit optimization.
	percentile_clipping (`int`, defaults to 100):
	Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
	block_wise (`bool`, defaults to `True`):
	Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
	is_paged (`bool`, defaults to `False`):
	Whether the optimizer is a paged optimizer or not.
	"""
	super().__init__(
	"adam",
	params,
	lr,
	betas,
	eps,
	weight_decay,
	32,
	args,
	min_8bit_size,
	percentile_clipping,
	block_wise,
	is_paged=True,
	)


	class AnalysisAdam(torch.optim.Optimizer):
	"""Adam that performs 8-bit vs 32-bit error analysis.

	This implementation is modified from torch.optim.Adam based on:
	`Fixed Weight Decay Regularization in Adam`
	(see https://arxiv.org/abs/1711.05101)

	It has been proposed in `Adam: A Method for Stochastic Optimization`_.

	Arguments:
	params (iterable): iterable of parameters to optimize or dicts defining
	parameter groups
	lr (float, optional): learning rate (default: 1e-3)
	betas (Tuple[float, float], optional): coefficients used for computing
	running averages of gradient and its square (default: (0.9, 0.999))
	eps (float, optional): term added to the denominator to improve
	numerical stability (default: 1e-8)
	weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
	amsgrad (boolean, optional): whether to use the AMSGrad variant of this
	algorithm from the paper `On the Convergence of Adam and Beyond`_

	.. _Adam: A Method for Stochastic Optimization:
	https://arxiv.org/abs/1412.6980
	.. _On the Convergence of Adam and Beyond:
	https://openreview.net/forum?id=ryQu7f-RZ
	"""

	def __init__(
	self,
	params,
	lr=1e-3,
	betas=(0.9, 0.999),
	eps=1e-8,
	weight_decay=0,
	amsgrad=False,
	bnb_analysis="dynamic-blockwise",
	savedir=None,
	):
	defaults = dict(
	lr=lr,
	betas=betas,
	eps=eps,
	weight_decay=weight_decay,
	amsgrad=amsgrad,
	)
	super().__init__(params, defaults)
	self.analysis = bnb_analysis
	self.savedir = savedir

	@property
	def supports_memory_efficient_fp16(self):
	return True

	@property
	def supports_flat_params(self):
	return True

	def step(self, closure=None):
	"""Performs a single optimization step.

	Arguments:
	closure (callable, optional): A closure that reevaluates the model
	and returns the loss.
	"""
	loss = None
	if closure is not None:
	loss = closure()

	for group in self.param_groups:
	for p_id, p in enumerate(group["params"]):
	if p.grad is None:
	continue
	grad = p.grad.data
	if grad.dtype in {torch.float16, torch.bfloat16}:
	grad = grad.float()
	if grad.is_sparse:
	raise RuntimeError("Adam does not support sparse gradients, please consider SparseAdam instead")
	amsgrad = group.get("amsgrad", False)
	assert not amsgrad

	p_data_fp32 = p.data
	if p.data.dtype in {torch.float16, torch.bfloat16}:
	p_data_fp32 = p_data_fp32.float()

	state = self.state[p]

	# State initialization
	if len(state) == 0:
	state["step"] = 0
	# Exponential moving average of gradient values
	state["exp_avg"] = torch.zeros_like(p_data_fp32)
	# Exponential moving average of squared gradient values
	state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
	state["abserrors"] = torch.zeros((256, 256), device=p_data_fp32.device)
	state["relerrors"] = torch.zeros((256, 256), device=p_data_fp32.device)
	state["counts"] = torch.zeros((256, 256), device=p_data_fp32.device)
	if amsgrad:
	# Maintains max of all exp. moving avg. of sq. grad. values
	state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32)
	else:
	state["exp_avg"] = state["exp_avg"].to(p_data_fp32)
	state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32)
	if amsgrad:
	state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to(p_data_fp32)

	state["step"] += 1
	beta1, beta2 = group["betas"]
	bias_correction1 = 1 - beta1 ** state["step"]
	bias_correction2 = 1 - beta2 ** state["step"]
	step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1
	e = state["abserrors"]
	rele = state["relerrors"]
	counts = state["counts"]

	if group["weight_decay"] != 0:
	p_data_fp32.add_(p_data_fp32, alpha=-group["weight_decay"] * group["lr"])

	exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
	if amsgrad:
	max_exp_avg_sq = state["max_exp_avg_sq"]

	# Decay the first and second moment running average coefficient
	exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
	exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)

	denom = exp_avg_sq.sqrt().add_(group["eps"])
	update_fp32 = exp_avg / denom

	if p_data_fp32.numel() <= 8192 or p_data_fp32.numel() > 50000 * 1000:
	# embedding layer or too small
	p_data_fp32 += -step_size * update_fp32
	else:
	if self.analysis == "dynamic-blockwise":
	code1 = F.create_dynamic_map(signed=True).to(p.device)
	code2 = F.create_dynamic_map(signed=False).to(p.device)
	C1, S1 = F.quantize_blockwise(exp_avg, code=code1)
	state1 = F.dequantize_blockwise(C1, S1)
	C2, S2 = F.quantize_blockwise(exp_avg_sq, code=code2)
	state2 = F.dequantize_blockwise(C2, S2)
	elif self.analysis == "dynamic":
	code1 = F.create_dynamic_map(signed=True).to(p.device)
	code2 = F.create_dynamic_map(signed=False).to(p.device)
	C1, S1 = F.quantize(exp_avg, code=code1)
	state1 = F.dequantize(C1, S1)
	C2, S2 = F.quantize(exp_avg_sq, code=code2)
	state2 = F.dequantize(C2, S2)
	elif self.analysis == "linear":
	code1 = F.create_linear_map(signed=True).to(p.device)
	code2 = F.create_linear_map(signed=False).to(p.device)
	C1, S1 = F.quantize(exp_avg, code=code1)
	state1 = F.dequantize(C1, S1)
	C2, S2 = F.quantize(exp_avg_sq, code=code2)
	state2 = F.dequantize(C2, S2)
	elif self.analysis == "quantile":
	code1 = F.estimate_quantiles(exp_avg)
	code2 = F.estimate_quantiles(exp_avg_sq)
	C1 = F.quantize_no_absmax(exp_avg, code=code1)
	state1 = F.dequantize_no_absmax(C1, code1)
	C2 = F.quantize_no_absmax(exp_avg_sq, code=code2)
	state2 = F.dequantize_no_absmax(C2, code2)
	elif self.analysis == "my-quantization-routine":
	pass
	# 1. get code
	# 2. quantize
	# 3. dequantize
	# Error will be calculated automatically!
	else:
	raise ValueError(f"Invalid analysis value: {self.analysis}!")

	denom = state2.sqrt().add_(group["eps"])
	update_8bit = state1 / denom

	abserr = torch.abs(update_8bit - update_fp32)
	relerr = abserr / torch.abs(update_fp32 + 1e-6)

	C1, C2 = C1.int(), C2.int()

	F.histogram_scatter_add_2d(e, C1.int(), C2.int(), abserr)
	F.histogram_scatter_add_2d(rele, C1.int(), C2.int(), relerr)
	F.histogram_scatter_add_2d(counts, C1.int(), C2.int(), torch.ones_like(abserr))

	p_data_fp32 += -step_size * update_fp32

	if not dist.is_initialized() or dist.get_rank() == 0:
	if self.savedir != "" and state["step"] % 100 == 0:
	if not os.path.exists(self.savedir):
	os.makedirs(self.savedir)
	shapestr = "_".join([str(dim) for dim in p_data_fp32.shape])
	pathe = os.path.join(self.savedir, f"{p_id}_{shapestr}_abserr.pkl")
	pathrele = os.path.join(self.savedir, f"{p_id}_{shapestr}_relerr.pkl")
	pathcounts = os.path.join(self.savedir, f"{p_id}_{shapestr}_counts.pkl")
	torch.save(e, pathe)
	torch.save(rele, pathrele)
	torch.save(counts, pathcounts)

	if p.data.dtype in {torch.float16, torch.bfloat16}:
	p.data.copy_(p_data_fp32)

	return loss