optimizers.py

"""This module contains shared optimizers.
"""

import math

import torch
import torch.optim as optim


class SharedAdam(optim.Adam):
    """Implements Adam algorithm with shared states, taken from
    https://github.com/ikostrikov/pytorch-a3c/
    """

    def __init__(self,
                 params,
                 lr=1e-3,
                 betas=(0.9, 0.999),
                 eps=1e-8,
                 weight_decay=0):
        super(SharedAdam, self).__init__(params, lr, betas, eps, weight_decay)

        # Is this really necessary?
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                state['step'] = torch.zeros(1)
                state['exp_avg'] = p.data.new().resize_as_(p.data).zero_()
                state['exp_avg_sq'] = p.data.new().resize_as_(p.data).zero_()

    def share_memory(self):
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                state['step'].share_memory_()
                state['exp_avg'].share_memory_()
                state['exp_avg_sq'].share_memory_()

    def step(self, closure=None):
        """Performs a single optimization step.
        Args:
            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:
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                state = self.state[p]

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

                state['step'] += 1

                if group['weight_decay'] != 0:
                    grad = grad.add(group['weight_decay'], p.data)

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

                denom = exp_avg_sq.sqrt().add_(group['eps'])

                bias_correction1 = 1 - beta1 ** state['step'].item()
                bias_correction2 = 1 - beta2 ** state['step'].item()
                step_size = group['lr'] * math.sqrt(
                    bias_correction2) / bias_correction1

                p.data.addcdiv_(-step_size, exp_avg, denom)

        return loss


class SharedRMSprop(optim.RMSprop):
    """Implementation of a shared RMSprop, taken from
    https://github.com/Kaixhin/ACER/blob/master/optim.py
    """

    def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0):
        super(SharedRMSprop, self).__init__(params, lr=lr, alpha=alpha,
                                            eps=eps, weight_decay=weight_decay,
                                            momentum=0, centered=False)
        # State initialisation (must be done before step, else will not be shared between threads)
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                state['step'] = p.data.new().resize_(1).zero_()
                state['square_avg'] = p.data.new().resize_as_(p.data).zero_()

    def share_memory(self):
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                state['step'].share_memory_()
                state['square_avg'].share_memory_()

    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
                state = self.state[p]

                square_avg = state['square_avg']
                alpha = group['alpha']

                state['step'] += 1

                if group['weight_decay'] != 0:
                    grad = grad.add(group['weight_decay'], p.data)

                # g = αg + (1 - α)Δθ^2
                square_avg.mul_(alpha).addcmul_(1 - alpha, grad, grad)
                # θ ← θ - ηΔθ/√(g + ε)
                avg = square_avg.sqrt().add_(group['eps'])
                p.data.addcdiv_(-group['lr'], grad, avg)
        return loss