ranger.py

import math
import torch
from torch.optim.optimizer import Optimizer, required
import itertools as it


class Ranger(Optimizer):
    def __init__(
        self,
        params,
        lr=1e-3,
        alpha=0.5,
        k=6,
        betas=(0.9, 0.999),
        eps=1e-8,
        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}")

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

        # now we can get to work...
        for group in self.param_groups:
            group["step_counter"] = 0
            # print("group step counter init")

        # look ahead params
        self.alpha = alpha
        self.k = k

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

        # lookahead weights
        self.slow_weights = [
            [p.clone().detach() for p in group["params"]] for group in self.param_groups
        ]

        # don't use grad for lookahead weights
        for w in it.chain(*self.slow_weights):
            w.requires_grad = False

    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()

        # ------------ radam
        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.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 > 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"])
                    else:
                        step_size = group["lr"] / (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 > 5:
                    denom = exp_avg_sq.sqrt().add_(group["eps"])
                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
                else:
                    p_data_fp32.add_(-step_size, exp_avg)

                p.data.copy_(p_data_fp32)

        # ---------------- end radam step

        # look ahead tracking and updating if latest batch = k
        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