optimizer.py

import math
import torch
from torch.optim.optimizer import Optimizer, required
from collections import defaultdict
from itertools import *


class LookAheadOptimizer(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}')
        defaults = dict(lookahead_alpha=alpha, lookahead_k=k, lookahead_step=0)
        self.base_optimizer = base_optimizer
        self.param_groups = self.base_optimizer.param_groups
        self.defaults = base_optimizer.defaults
        self.defaults.update(defaults)
        self.state = defaultdict(dict)
        # manually add our defaults to the param groups
        for name, default in defaults.items():
            for group in self.param_groups:
                group.setdefault(name, default)


    def update_slow(self, group):
        for fast_p in group["params"]:
            if fast_p.grad is None:
                continue
            param_state = self.state[fast_p]
            if 'slow_buffer' not in param_state:
                param_state['slow_buffer'] = torch.empty_like(fast_p.data)
                param_state['slow_buffer'].copy_(fast_p.data)
            slow = param_state['slow_buffer']
            slow.add_(group['lookahead_alpha'], fast_p.data - slow)
            fast_p.data.copy_(slow)


    def sync_lookahead(self):
        for group in self.param_groups:
            self.update_slow(group)


    def step(self, closure=None):
        loss = self.base_optimizer.step(closure)
        for group in self.param_groups:
            group['lookahead_step'] += 1
            if group['lookahead_step'] % group['lookahead_k'] == 0:
                self.update_slow(group)
        return loss


    def state_dict(self):
        fast_state_dict = self.base_optimizer.state_dict()
        slow_state = {
            (id(k) if isinstance(k, torch.Tensor) else k): v
            for k, v in self.state.items()
        }
        fast_state = fast_state_dict['state']
        param_groups = fast_state_dict['param_groups']
        return {
            'state': fast_state,
            'slow_state': slow_state,
            'param_groups': param_groups,
        }


    def load_state_dict(self, state_dict):
        fast_state_dict = {
            'state': state_dict['state'],
            'param_groups': state_dict['param_groups'],
        }
        self.base_optimizer.load_state_dict(fast_state_dict)

        # We want to restore the slow state, but share param_groups reference
        # with base_optimizer. This is a bit redundant but least code
        slow_state_new = False
        if 'slow_state' not in state_dict:
            print('Loading state_dict from optimizer without Lookahead applied.')
            state_dict['slow_state'] = defaultdict(dict)
            slow_state_new = True
        slow_state_dict = {
            'state': state_dict['slow_state'],
            'param_groups': state_dict['param_groups'],  # this is pointless but saves code
        }
        super(Lookahead, self).load_state_dict(slow_state_dict)
        self.param_groups = self.base_optimizer.param_groups  # make both ref same container
        if slow_state_new:
            # reapply defaults to catch missing lookahead specific ones
            for name, default in self.defaults.items():
                for group in self.param_groups:
                    group.setdefault(name, default)


class RAdamOptimizer(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

                    # 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'])
                    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


class PlainRAdamOptimizer(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)

        super(PlainRAdam, self).__init__(params, defaults)


    def __setstate__(self, state):
        super(PlainRAdam, 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
                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)

                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:                    
                    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'])
                    denom = exp_avg_sq.sqrt().add_(group['eps'])
                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
                else:
                    step_size = group['lr'] / (1 - beta1 ** state['step'])
                    p_data_fp32.add_(-step_size, exp_avg)

                p.data.copy_(p_data_fp32)

        return loss


class AdamWOptimizer(Optimizer):
    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, warmup = 0):
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay, warmup = warmup)
        super(AdamW, self).__init__(params, defaults)


    def __setstate__(self, state):
        super(AdamW, 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('Adam does not support sparse gradients, please consider SparseAdam instead')

                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']

                state['step'] += 1

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

                denom = exp_avg_sq.sqrt().add_(group['eps'])
                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']
                
                if group['warmup'] > state['step']:
                    scheduled_lr = 1e-8 + state['step'] * group['lr'] / group['warmup']
                else:
                    scheduled_lr = group['lr']

                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
                
                if group['weight_decay'] != 0:
                    p_data_fp32.add_(-group['weight_decay'] * scheduled_lr, p_data_fp32)

                p_data_fp32.addcdiv_(-step_size, exp_avg, denom)

                p.data.copy_(p_data_fp32)

        return loss


# RAdam + LARS
class RaLambOptimizer(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 = 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 = 1.0 / (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 * group['lr'], exp_avg, denom)
                else:
                    radam_step.add_(-radam_step_size * group['lr'], 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 * group['lr'] * trust_ratio, exp_avg, denom)
                else:
                    p_data_fp32.add_(-radam_step_size * group['lr'] * trust_ratio, exp_avg)

                p.data.copy_(p_data_fp32)

        return loss


# Adam + LookAhead
def LookaheadAdam(params, alpha=0.5, k=6, *args, **kwargs):
     adam = Adam(params, *args, **kwargs)
     return LookAheadOptimizer(adam, alpha, k)


# RAdam + LookAhead
def Ranger(params, alpha=0.5, k=6, *args, **kwargs):
    radam = RAdam(params, *args, **kwargs)
    return LookAheadOptimizer(radam, alpha, k)


# RaLamb
def RangerLars(params, alpha=0.5, k=6, *args, **kwargs):
    ralamb = RaLambOptimizer(params, *args, **kwargs)
    return LookAheadOptimizer(ralamb, alpha, k)