Dassl.pytorch/dassl/optim/radam.py

"""
Imported from: https://github.com/LiyuanLucasLiu/RAdam

https://arxiv.org/abs/1908.03265

@article{liu2019radam,
  title={On the Variance of the Adaptive Learning Rate and Beyond},
  author={Liu, Liyuan and Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Han, Jiawei},
  journal={arXiv preprint arXiv:1908.03265},
  year={2019}
}
"""
import math
import torch
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
        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"])
                    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_(
                            -group["weight_decay"] * group["lr"], p_data_fp32
                        )
                    denom = exp_avg_sq.sqrt().add_(group["eps"])
                    p_data_fp32.addcdiv_(
                        -step_size * group["lr"], exp_avg, denom
                    )
                    p.data.copy_(p_data_fp32)
                elif step_size > 0:
                    if group["weight_decay"] != 0:
                        p_data_fp32.add_(
                            -group["weight_decay"] * group["lr"], p_data_fp32
                        )
                    p_data_fp32.add_(-step_size * group["lr"], exp_avg)
                    p.data.copy_(p_data_fp32)

        return loss


class PlainRAdam(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
        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)

                # more conservative since it's an approximated value
                if N_sma >= 5:
                    if group["weight_decay"] != 0:
                        p_data_fp32.add_(
                            -group["weight_decay"] * group["lr"], p_data_fp32
                        )
                    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)
                    p.data.copy_(p_data_fp32)
                elif self.degenerated_to_sgd:
                    if group["weight_decay"] != 0:
                        p_data_fp32.add_(
                            -group["weight_decay"] * group["lr"], p_data_fp32
                        )
                    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 AdamW(Optimizer):

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

        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 = (
                    scheduled_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