# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# 
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#!/usr/bin/env python
# -*- coding: utf-8 -*-

import torch
import torch.optim
import math

class AdamWScheduleFree(torch.optim.Optimizer):
    r"""
    Schedule-Free AdamW
    As the name suggests, no scheduler is needed with this optimizer. 
    To add warmup, rather than using a learning rate schedule you can just
    set the warmup_steps parameter.
    
    This optimizer requires that .train() and .eval() be called before the
    beginning of training and evaluation respectively. The optimizer should
    also be placed in eval mode when saving checkpoints.
    """
    def __init__(self,
                 params, 
                 lr=0.0025, 
                 betas=(0.9, 0.999), 
                 eps=1e-8,
                 weight_decay=0,
                 warmup_steps=0,
                 r=0.0,
                 weight_lr_power=2.0,
                 foreach=hasattr(torch, "_foreach_mul_")
                 ):

        defaults = dict(lr=lr, 
                        betas=betas, 
                        eps=eps,
                        r=r,
                        k=0,
                        warmup_steps=warmup_steps,
                        train_mode=True,
                        weight_sum=0.0,
                        lr_max=-1.0,
                        weight_lr_power=weight_lr_power,
                        weight_decay=weight_decay,
                        foreach=foreach)
        super().__init__(params, defaults)
    
    def eval(self):
        for group in self.param_groups:
            train_mode = group['train_mode']
            beta1, _ = group['betas']
            if train_mode:
                for p in group['params']:
                    state = self.state[p]
                    if 'z' in state:
                        # Set p.data to x
                        p.data.lerp_(end=state['z'], weight=1-1/beta1)
                group['train_mode'] = False

    def train(self):
        for group in self.param_groups:
            train_mode = group['train_mode']
            beta1, _ = group['betas']
            if not train_mode:
                for p in group['params']:
                    state = self.state[p]
                    if 'z' in state:
                        # Set p.data to y
                        p.data.lerp_(end=state['z'], weight=1-beta1)
                group['train_mode'] = 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:
            eps = group['eps']
            beta1, beta2 = group['betas']
            decay = group['weight_decay']
            k = group['k']
            r = group['r']
            warmup_steps = group['warmup_steps']
            weight_lr_power = group['weight_lr_power']
            
            if k < warmup_steps:
              sched = (k+1) / warmup_steps
            else:
              sched = 1.0
            
            bias_correction2 = 1 - beta2 ** (k+1)
            lr = group['lr']*sched*math.sqrt(bias_correction2)
            
            lr_max = group['lr_max'] = max(lr, group['lr_max'])
            
            weight = ((k+1)**r) * (lr_max**weight_lr_power)
            weight_sum = group['weight_sum'] = group['weight_sum'] + weight

            try:
                ckp1 = weight/weight_sum
            except ZeroDivisionError:
                ckp1 = 0

            if not group['train_mode']:
                raise Exception("Not in train mode!")

            active_p = [p for p in group['params'] if p.grad is not None]
            
            for p in active_p:
                if 'z' not in self.state[p]:
                    self.state[p]['z'] = torch.clone(p.data)
                    self.state[p]['exp_avg_sq'] = torch.zeros_like(p.data)

            if group['foreach'] and len(active_p) > 0:
                y, grad, exp_avg_sq, z = zip(*[(p.data, 
                                                p.grad, 
                                                self.state[p]['exp_avg_sq'], 
                                                self.state[p]['z']) 
                                                for p in active_p])

                # Decay the first and second moment running average coefficient
                torch._foreach_mul_(exp_avg_sq, beta2)
                torch._foreach_addcmul_(exp_avg_sq, grad, grad, value=1-beta2)
                denom = torch._foreach_sqrt(exp_avg_sq)
                torch._foreach_add_(denom, eps)

                # Normalize grad in-place for memory efficiency
                torch._foreach_div_(grad, denom)

                # Weight decay calculated at y
                if decay != 0:
                    torch._foreach_add_(grad, y, alpha=decay)

                # These operations update y in-place,
                # without computing x explicitly.
                torch._foreach_lerp_(y, z, weight=ckp1)
                torch._foreach_add_(y, grad, alpha=lr*(beta1*(1-ckp1)-1))

                # z step
                torch._foreach_sub_(z, grad, alpha=lr)
            else:
                for p in active_p:
                    y = p.data # Notation to match theory
                    grad = p.grad.data

                    state = self.state[p]

                    z = state['z']
                    exp_avg_sq = state['exp_avg_sq']

                    exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1-beta2)
                    denom = exp_avg_sq.sqrt().add_(eps)

                    # Reuse grad buffer for memory efficiency
                    grad_normalized = grad.div_(denom)

                    # Weight decay calculated at y
                    if decay != 0:
                        grad_normalized.add_(y, alpha=decay)

                    # These operations update y in-place,
                    # without computing x explicitly.
                    y.lerp_(end=z, weight=ckp1)
                    y.add_(grad_normalized, alpha=lr*(beta1*(1-ckp1)-1))

                    # z step
                    z.sub_(grad_normalized, alpha=lr)

            group['k'] = k+1
        return loss