# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/009_optimizers.ipynb (unless otherwise specified).

__all__ = ['RALAMB', 'Over9000', 'RangerLars', 'Rover', 'Ranger']

# Cell
from .imports import *

# Cell
import torch, math
from torch.optim.optimizer import Optimizer

# RAdam + LARS
class RALAMB(Optimizer):

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        self.buffer = [[None, None, None] for ind in range(10)]
        super(RALAMB, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(RALAMB, self).__setstate__(state)

    def step(self, closure=None):

        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data.float()
                if grad.is_sparse:
                    raise RuntimeError('Ralamb does not support sparse gradients')

                p_data_fp32 = p.data.float()

                state = self.state[p]

                if len(state) == 0:
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
                else:
                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']

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

                state['step'] += 1
                buffered = self.buffer[int(state['step'] % 10)]

                if state['step'] == buffered[0]:
                    N_sma, radam_step_size = buffered[1], buffered[2]
                else:
                    buffered[0] = state['step']
                    beta2_t = beta2 ** state['step']
                    N_sma_max = 2 / (1 - beta2) - 1
                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
                    buffered[1] = N_sma

                    # more conservative since it's an approximated value
                    if N_sma >= 5:
                        radam_step_size = group['lr'] * math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])
                    else:
                        radam_step_size = group['lr'] / (1 - beta1 ** state['step'])
                    buffered[2] = radam_step_size

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

                # more conservative since it's an approximated value
                radam_step = p_data_fp32.clone()
                if N_sma >= 5:
                    denom = exp_avg_sq.sqrt().add_(group['eps'])
                    radam_step.addcdiv_(-radam_step_size, exp_avg, denom)
                else:
                    radam_step.add_(-radam_step_size, exp_avg)

                radam_norm = radam_step.pow(2).sum().sqrt()
                weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10)
                if weight_norm == 0 or radam_norm == 0:
                    trust_ratio = 1
                else:
                    trust_ratio = weight_norm / radam_norm

                state['weight_norm'] = weight_norm
                state['adam_norm'] = radam_norm
                state['trust_ratio'] = trust_ratio

                if N_sma >= 5:
                    p_data_fp32.addcdiv_(-radam_step_size * trust_ratio, exp_avg, denom)
                else:
                    p_data_fp32.add_(-radam_step_size * trust_ratio, exp_avg)

                p.data.copy_(p_data_fp32)

        return loss

# Cell
def Over9000(params, alpha=0.5, k=5, *args, **kwargs):
    ralamb = RALAMB(params, *args, **kwargs)
    return Lookahead(ralamb, alpha, k)

RangerLars = Over9000
Rover = Over9000

# Cell
# Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer.

# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
# and/or
# https://github.com/lessw2020/Best-Deep-Learning-Optimizers

# Ranger has now been used to capture 12 records on the FastAI leaderboard.

# This version = 20.4.11

# Credits:
# Gradient Centralization --> https://arxiv.org/abs/2004.01461v2 (a new optimization technique for DNNs), github:  https://github.com/Yonghongwei/Gradient-Centralization
# RAdam -->  https://github.com/LiyuanLucasLiu/RAdam
# Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.
# Lookahead paper --> MZhang,G Hinton  https://arxiv.org/abs/1907.08610

# summary of changes:
# 4/11/20 - add gradient centralization option.  Set new testing benchmark for accuracy with it, toggle with use_gc flag at init.
# full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights),
# supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.
# changes 8/31/19 - fix references to *self*.N_sma_threshold;
# changed eps to 1e-5 as better default than 1e-8.

import math
import torch
from torch.optim.optimizer import Optimizer, required


class Ranger(Optimizer):

    def __init__(self, params, lr=1e-3,                       # lr
                alpha=0.5, k=6, N_sma_threshhold=5,           # Ranger options
                betas=(.95,0.999), eps=1e-5, weight_decay=0,  # Adam options
                use_gc=True, gc_conv_only=False               # Gradient centralization on or off, applied to conv layers only or conv + fc layers
                ):

        #parameter checks
        if not 0.0 <= alpha <= 1.0:
            raise ValueError(f'Invalid slow update rate: {alpha}')
        if not 1 <= k:
            raise ValueError(f'Invalid lookahead steps: {k}')
        if not lr > 0:
            raise ValueError(f'Invalid Learning Rate: {lr}')
        if not eps > 0:
            raise ValueError(f'Invalid eps: {eps}')

        #parameter comments:
        # beta1 (momentum) of .95 seems to work better than .90...
        #N_sma_threshold of 5 seems better in testing than 4.
        #In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.

        #prep defaults and init torch.optim base
        defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold, eps=eps, weight_decay=weight_decay)
        super().__init__(params,defaults)

        #adjustable threshold
        self.N_sma_threshhold = N_sma_threshhold


        #look ahead params

        self.alpha = alpha
        self.k = k

        #radam buffer for state
        self.radam_buffer = [[None,None,None] for ind in range(10)]

        #gc on or off
        self.use_gc=use_gc

        #level of gradient centralization
        self.gc_gradient_threshold = 3 if gc_conv_only else 1


        print(f"Ranger optimizer loaded. \nGradient Centralization usage = {self.use_gc}")
        if (self.use_gc and self.gc_gradient_threshold==1):
            print(f"GC applied to both conv and fc layers")
        elif (self.use_gc and self.gc_gradient_threshold==3):
            print(f"GC applied to conv layers only")

    def __setstate__(self, state):
        print("set state called")
        super(Ranger, self).__setstate__(state)


    def step(self, closure=None):
        loss = None
        #note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.
        #Uncomment if you need to use the actual closure...

        #if closure is not None:
            #loss = closure()

        #Evaluate averages and grad, update param tensors
        for group in self.param_groups:

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data.float()

                if grad.is_sparse:
                    raise RuntimeError('Ranger optimizer does not support sparse gradients')

                p_data_fp32 = p.data.float()

                state = self.state[p]  #get state dict for this param

                if len(state) == 0:   #if first time to run...init dictionary with our desired entries
                    #if self.first_run_check==0:
                        #self.first_run_check=1
                        #print("Initializing slow buffer...should not see this at load from saved model!")
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)

                    #look ahead weight storage now in state dict
                    state['slow_buffer'] = torch.empty_like(p.data)
                    state['slow_buffer'].copy_(p.data)

                else:
                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)

                #begin computations
                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']

                #GC operation for Conv layers and FC layers
                if grad.dim() > self.gc_gradient_threshold: grad.add_(-grad.mean(dim = tuple(range(1,grad.dim())), keepdim = True))

                state['step'] += 1

                #compute variance mov avg
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                #compute mean moving avg
                exp_avg.mul_(beta1).add_(1 - beta1, grad)

                buffered = self.radam_buffer[int(state['step'] % 10)]

                if state['step'] == buffered[0]:
                    N_sma, step_size = buffered[1], buffered[2]
                else:
                    buffered[0] = state['step']
                    beta2_t = beta2 ** state['step']
                    N_sma_max = 2 / (1 - beta2) - 1
                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
                    buffered[1] = N_sma
                    if N_sma > self.N_sma_threshhold:
                        step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])
                    else:
                        step_size = 1.0 / (1 - beta1 ** state['step'])
                    buffered[2] = step_size


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

                # apply lr
                if N_sma > self.N_sma_threshhold:
                    denom = exp_avg_sq.sqrt().add_(group['eps'])
                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
                else:
                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)

                p.data.copy_(p_data_fp32)

                #integrated look ahead...
                #we do it at the param level instead of group level
                if state['step'] % group['k'] == 0:
                    slow_p = state['slow_buffer'] #get access to slow param tensor
                    slow_p.add_(self.alpha, p.data - slow_p)  #(fast weights - slow weights) * alpha
                    p.data.copy_(slow_p)  #copy interpolated weights to RAdam param tensor

        return loss