optimizers.py

import torch
import warnings
from torch.optim.optimizer import Optimizer
import math
import itertools as it
import torch.optim as optim
warnings.filterwarnings("once")


class Ranger(Optimizer):

    # https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer/blob/master/ranger.py

    def __init__(self, params, lr=1e-3, alpha=0.5, k=6,
                 N_sma_threshhold=5, betas=(.95, 0.999), eps=1e-5, weight_decay=0):
        # 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)]

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

    def step(self, closure=None):
        loss = None

        # 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']

                #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)

                state['step'] += 1


                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)

                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


class RAdam(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(RAdam, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(RAdam, 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('RAdam 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']

                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                exp_avg.mul_(beta1).add_(1 - beta1, grad)

                state['step'] += 1
                buffered = self.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 >= 5:
                        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)

                # more conservative since it's an approximated value
                if N_sma >= 5:
                    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)

        return loss

# https://github.com/lonePatient/lookahead_pytorch/blob/master/optimizer.py


class Lookahead(Optimizer):
    def __init__(self, base_optimizer,alpha=0.5, k=6):
        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}')
        self.optimizer = base_optimizer
        self.param_groups = self.optimizer.param_groups
        self.alpha = alpha
        self.k = k
        for group in self.param_groups:
            group["step_counter"] = 0
        self.slow_weights = [[p.clone().detach() for p in group['params']]
                                for group in self.param_groups]

        for w in it.chain(*self.slow_weights):
            w.requires_grad = False

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()
        loss = self.optimizer.step()
        for group,slow_weights in zip(self.param_groups,self.slow_weights):
            group['step_counter'] += 1
            if group['step_counter'] % self.k != 0:
                continue
            for p,q in zip(group['params'],slow_weights):
                if p.grad is None:
                    continue
                q.data.add_(self.alpha,p.data - q.data)
                p.data.copy_(q.data)
        return loss


class Ralamb(Optimizer):
    '''
    Ralamb optimizer (RAdam + LARS trick)
    '''
    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 = 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 = 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 = group['lr'] / (1 - beta1 ** state['step'])
                    buffered[2] = radam_step

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

                weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10)
                radam_norm = p_data_fp32.pow(2).sum().sqrt()
                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

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

                p.data.copy_(p_data_fp32)

        return loss


def get_optimizer(optimizer: str = 'Adam',
                  lookahead: bool = False,
                  model=None,
                  separate_decoder: bool = True,
                  lr: float = 1e-3,
                  lr_e: float = 1e-3):
    """
    # https://github.com/lonePatient/lookahead_pytorch/blob/master/run.py

    :param optimizer:
    :param lookahead:
    :param model:
    :param separate_decoder:
    :param lr:
    :param lr_e:
    :return:
    """

    if separate_decoder:
        params = [
                    {'params': model.decoder.parameters(), 'lr': lr
                     },
                    {'params': model.encoder.parameters(), 'lr': lr_e},
                ]
    else:
        params = [{'params': model.parameters(), 'lr': lr}]

    if optimizer == 'Adam':
        optimizer = optim.Adam(params, lr=lr)
    elif optimizer == 'RAdam':
        optimizer = RAdam(params, lr=lr)
    elif optimizer == 'Ralamb':
        optimizer = Ralamb(params, lr=lr)
    else:
        raise ValueError('unknown base optimizer type')

    if lookahead:
        optimizer = Lookahead(base_optimizer=optimizer, k=5, alpha=0.5)

    return optimizer