""" PyTorch MADGRAD optimizer
MADGRAD: https://arxiv.org/abs/2101.11075
Code from: https://github.com/facebookresearch/madgrad
"""
# 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
from typing import TYPE_CHECKING, Any, Callable, Optional

import torch
import torch.optim

if TYPE_CHECKING:
    from torch.optim.optimizer import _params_t
else:
    _params_t = Any


class MADGRAD(torch.optim.Optimizer):
    """
    MADGRAD_: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic
    Optimization.
    .. _MADGRAD: https://arxiv.org/abs/2101.11075
    MADGRAD is a general purpose optimizer that can be used in place of SGD or
    Adam may converge faster and generalize better. Currently GPU-only.
    Typically, the same learning rate schedule that is used for SGD or Adam may
    be used. The overall learning rate is not comparable to either method and
    should be determined by a hyper-parameter sweep.
    MADGRAD requires less weight decay than other methods, often as little as
    zero. Momentum values used for SGD or Adam's beta1 should work here also.
    On sparse problems both weight_decay and momentum should be set to 0.
    Arguments:
        params (iterable):
            Iterable of parameters to optimize or dicts defining parameter groups.
        lr (float):
            Learning rate (default: 1e-2).
        momentum (float):
            Momentum value in  the range [0,1) (default: 0.9).
        weight_decay (float):
            Weight decay, i.e. a L2 penalty (default: 0).
        eps (float):
            Term added to the denominator outside of the root operation to improve numerical stability. (default: 1e-6).
    """

    def __init__(
            self,
            params: _params_t,
            lr: float = 1e-2,
            momentum: float = 0.9,
            weight_decay: float = 0,
            eps: float = 1e-6,
            decoupled_decay: bool = False,
    ):
        if momentum < 0 or momentum >= 1:
            raise ValueError(f"Momentum {momentum} must be in the range [0,1]")
        if lr <= 0:
            raise ValueError(f"Learning rate {lr} must be positive")
        if weight_decay < 0:
            raise ValueError(f"Weight decay {weight_decay} must be non-negative")
        if eps < 0:
            raise ValueError(f"Eps must be non-negative")

        defaults = dict(
            lr=lr, eps=eps, momentum=momentum, weight_decay=weight_decay, decoupled_decay=decoupled_decay)
        super().__init__(params, defaults)

    @property
    def supports_memory_efficient_fp16(self) -> bool:
        return False

    @property
    def supports_flat_params(self) -> bool:
        return True

    @torch.no_grad()
    def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
        """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:
            with torch.enable_grad():
                loss = closure()

        for group in self.param_groups:
            eps = group['eps']
            lr = group['lr'] + eps
            weight_decay = group['weight_decay']
            momentum = group['momentum']
            ck = 1 - momentum

            for p in group["params"]:
                if p.grad is None:
                    continue
                grad = p.grad
                if momentum != 0.0 and grad.is_sparse:
                    raise RuntimeError("momentum != 0 is not compatible with sparse gradients")

                state = self.state[p]
                if len(state) == 0:
                    state['step'] = 0
                    state['grad_sum_sq'] = torch.zeros_like(p)
                    state['s'] = torch.zeros_like(p)
                    if momentum != 0:
                        state['x0'] = torch.clone(p).detach()

                state['step'] += 1
                grad_sum_sq = state['grad_sum_sq']
                s = state['s']
                lamb = lr * math.sqrt(state['step'])

                # Apply weight decay
                if weight_decay != 0:
                    if group['decoupled_decay']:
                        p.mul_(1.0 - group['lr'] * weight_decay)
                    else:
                        if grad.is_sparse:
                            raise RuntimeError("weight_decay option is not compatible with sparse gradients")
                        grad.add_(p, alpha=weight_decay)

                if grad.is_sparse:
                    grad = grad.coalesce()
                    grad_val = grad._values()

                    p_masked = p.sparse_mask(grad)
                    grad_sum_sq_masked = grad_sum_sq.sparse_mask(grad)
                    s_masked = s.sparse_mask(grad)

                    # Compute x_0 from other known quantities
                    rms_masked_vals = grad_sum_sq_masked._values().pow(1 / 3).add_(eps)
                    x0_masked_vals = p_masked._values().addcdiv(s_masked._values(), rms_masked_vals, value=1)

                    # Dense + sparse op
                    grad_sq = grad * grad
                    grad_sum_sq.add_(grad_sq, alpha=lamb)
                    grad_sum_sq_masked.add_(grad_sq, alpha=lamb)

                    rms_masked_vals = grad_sum_sq_masked._values().pow_(1 / 3).add_(eps)

                    s.add_(grad, alpha=lamb)
                    s_masked._values().add_(grad_val, alpha=lamb)

                    # update masked copy of p
                    p_kp1_masked_vals = x0_masked_vals.addcdiv(s_masked._values(), rms_masked_vals, value=-1)
                    # Copy updated masked p to dense p using an add operation
                    p_masked._values().add_(p_kp1_masked_vals, alpha=-1)
                    p.add_(p_masked, alpha=-1)
                else:
                    if momentum == 0:
                        # Compute x_0 from other known quantities
                        rms = grad_sum_sq.pow(1 / 3).add_(eps)
                        x0 = p.addcdiv(s, rms, value=1)
                    else:
                        x0 = state['x0']

                    # Accumulate second moments
                    grad_sum_sq.addcmul_(grad, grad, value=lamb)
                    rms = grad_sum_sq.pow(1 / 3).add_(eps)

                    # Update s
                    s.add_(grad, alpha=lamb)

                    # Step
                    if momentum == 0:
                        p.copy_(x0.addcdiv(s, rms, value=-1))
                    else:
                        z = x0.addcdiv(s, rms, value=-1)

                        # p is a moving average of z
                        p.mul_(1 - ck).add_(z, alpha=ck)

        return loss
torch.zeros_like torch.no_grad torch.clone torch.enable_grad