optimization.py

import math

import torch
from collections import defaultdict
from torch.optim.optimizer import Optimizer


class RAdam(Optimizer):

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))

        self.degenerated_to_sgd = degenerated_to_sgd
        if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):
            for param in params:
                if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]):
                    param['buffer'] = [[None, None, None] for _ in range(10)]
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
                        buffer=[[None, None, None] for _ 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_(tensor1=grad, tensor2=grad, value=(1 - beta2))
                exp_avg.mul_(beta1).add_(grad, alpha=1-beta1)

                state['step'] += 1
                buffered = group['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

                    # more conservative since it's an approximated value
                    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'])
                    elif self.degenerated_to_sgd:
                        step_size = 1.0 / (1 - beta1 ** state['step'])
                    else:
                        step_size = -1
                    buffered[2] = step_size

                # more conservative since it's an approximated value
                if N_sma >= 5:
                    if group['weight_decay'] != 0:
                        p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr'])
                    denom = exp_avg_sq.sqrt().add_(group['eps'])
                    p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size * group['lr'])
                    p.data.copy_(p_data_fp32)
                elif step_size > 0:
                    if group['weight_decay'] != 0:
                        p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr'])
                    p_data_fp32.add_(exp_avg, alpha=-step_size * group['lr'])
                    p.data.copy_(p_data_fp32)

        return loss


class Lookahead(Optimizer):
    """
    PyTorch implementation of the lookahead wrapper.
    Lookahead Optimizer: https://arxiv.org/abs/1907.08610
    """
    def __init__(self, optimizer, alpha=0.5, k=6, pullback_momentum="none"):
        """

        Args:
            optimizer: inner optimizer
            alpha (float): number of lookahead steps
            k (int): linear interpolation factor. 1.0 recovers the inner optimizer.
            pullback_momentum (str): change to inner optimizer momentum on interpolation update
        """

        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 = optimizer
        self.param_groups = self.optimizer.param_groups
        self.alpha = alpha
        self.k = k
        self.step_counter = 0
        assert pullback_momentum in ["reset", "pullback", "none"]
        self.pullback_momentum = pullback_momentum
        self.state = defaultdict(dict)

        # Cache the current optimizer parameters
        for group in self.optimizer.param_groups:
            for p in group['params']:
                param_state = self.state[p]
                param_state['cached_params'] = torch.zeros_like(p.data)
                param_state['cached_params'].copy_(p.data)

    def __getstate__(self):
        return {
            'state': self.state,
            'optimizer': self.optimizer,
            'alpha': self.alpha,
            'step_counter': self.step_counter,
            'k':self.k,
            'pullback_momentum': self.pullback_momentum
        }

    def zero_grad(self):
        self.optimizer.zero_grad()

    def state_dict(self):
        return self.optimizer.state_dict()

    def load_state_dict(self, state_dict):
        self.optimizer.load_state_dict(state_dict)

    def _backup_and_load_cache(self):
        """Useful for performing evaluation on the slow weights (which typically generalize better)
        """
        for group in self.optimizer.param_groups:
            for p in group['params']:
                param_state = self.state[p]
                param_state['backup_params'] = torch.zeros_like(p.data)
                param_state['backup_params'].copy_(p.data)
                p.data.copy_(param_state['cached_params'])

    def _clear_and_load_backup(self):
        for group in self.optimizer.param_groups:
            for p in group['params']:
                param_state = self.state[p]
                p.data.copy_(param_state['backup_params'])
                del param_state['backup_params']

    def step(self, closure=None):
        """Performs a single Lookahead optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = self.optimizer.step(closure)
        self.step_counter += 1

        if self.step_counter >= self.k:
            self.step_counter = 0
            # Lookahead and cache the current optimizer parameters
            for group in self.optimizer.param_groups:
                for p in group['params']:
                    param_state = self.state[p]
                    # crucial line
                    p.data.mul_(self.alpha).add_(param_state['cached_params'], alpha=1.0-self.alpha)
                    param_state['cached_params'].copy_(p.data)
                    if self.pullback_momentum == "pullback":
                        internal_momentum = self.optimizer.state[p]["momentum_buffer"]
                        self.optimizer.state[p]["momentum_buffer"] = internal_momentum.mul_(self.alpha).add_(
                            param_state["cached_mom"], alpha=1.0-self.alpha)
                        param_state["cached_mom"] = self.optimizer.state[p]["momentum_buffer"]
                    elif self.pullback_momentum == "reset":
                        self.optimizer.state[p]["momentum_buffer"] = torch.zeros_like(p.data)

        return loss