Source code for lmflow.optim.radam

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import math
import warnings
import torch
from torch.optim.optimizer import Optimizer



class RAdam(Optimizer):
    r"""Implements RAdam optimization algorithm.

    Note:
        Deprecated, please use version provided by PyTorch_.

    It has been proposed in `On the Variance of the Adaptive Learning
    Rate and Beyond`.
    https://arxiv.org/abs/1908.03265

    Note:
        Reference code: https://github.com/LiyuanLucasLiu/RAdam
    """

    def __init__(
        self,
        params,
        lr: float = 1e-3,
        betas = (0.9, 0.999),
        eps: float = 1e-8,
        weight_decay: float = 0,
    ) -> None:
        warnings.warn(
            "RAdam optimizer is deprecated, since it is included "
            "in pytorch natively.",
            DeprecationWarning,
            stacklevel=2,
        )
        if lr <= 0.0:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if eps < 0.0:
            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])
            )
        if weight_decay < 0:
            raise ValueError(
                "Invalid weight_decay value: {}".format(weight_decay)
            )

        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):
        r"""Performs a single optimization step.

        Arguments:
            closure: 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:
            lr = group["lr"]
            weight_decay = group["weight_decay"]
            beta1, beta2 = group["betas"]
            eps = group["eps"]

            for p in group["params"]:
                if p.grad is None:
                    continue
                grad = p.grad.data.float()
                if grad.is_sparse:
                    msg = (
                        "RAdam does not support sparse gradients, "
                        "please consider SparseAdam instead"
                    )
                    raise RuntimeError(msg)

                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, memory_format=torch.preserve_format
                    )
                    state["exp_avg_sq"] = torch.zeros_like(
                        p_data_fp32, memory_format=torch.preserve_format
                    )
                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"]

                exp_avg_sq.mul_(beta2).addcmul_(grad, 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 = (
                            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 = lr / (1 - beta1 ** state["step"])
                    buffered[2] = step_size

                if weight_decay != 0:
                    p_data_fp32.add_(p_data_fp32, alpha=-weight_decay * lr)

                # more conservative since it's an approximated value
                if N_sma >= 5:
                    denom = exp_avg_sq.sqrt().add_(eps)
                    p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size)
                else:
                    p_data_fp32.add_(exp_avg, alpha=-step_size)

                p.data.copy_(p_data_fp32)

        return loss