fast-reid/fastreid/solver/optim/ranger.py

# Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer.

# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
# and/or
# https://github.com/lessw2020/Best-Deep-Learning-Optimizers

# Ranger has now been used to capture 12 records on the FastAI leaderboard.

# This version = 20.4.11

# Credits:
# Gradient Centralization --> https://arxiv.org/abs/2004.01461v2 (a new optimization technique for DNNs), github:  https://github.com/Yonghongwei/Gradient-Centralization
# RAdam -->  https://github.com/LiyuanLucasLiu/RAdam
# Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.
# Lookahead paper --> MZhang,G Hinton  https://arxiv.org/abs/1907.08610

# summary of changes:
# 4/11/20 - add gradient centralization option.  Set new testing benchmark for accuracy with it, toggle with use_gc flag at init.
# full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights),
# supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.
# changes 8/31/19 - fix references to *self*.N_sma_threshold;
# changed eps to 1e-5 as better default than 1e-8.

import math

import torch
from torch.optim.optimizer import Optimizer


class Ranger(Optimizer):

    def __init__(self, params, lr=1e-3,  # lr
                 alpha=0.5, k=6, N_sma_threshhold=5,  # Ranger options
                 betas=(.95, 0.999), eps=1e-5, weight_decay=0,  # Adam options
                 use_gc=True, gc_conv_only=False
                 # Gradient centralization on or off, applied to conv layers only or conv + fc layers
                 ):

        # 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}')

        # parameter comments:
        # beta1 (momentum) of .95 seems to work better than .90...
        # N_sma_threshold of 5 seems better in testing than 4.
        # In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.

        # prep defaults and init torch.optim base
        defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold,
                        eps=eps, weight_decay=weight_decay)
        super().__init__(params, defaults)

        # adjustable threshold
        self.N_sma_threshhold = N_sma_threshhold

        # look ahead params

        self.alpha = alpha
        self.k = k

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

        # gc on or off
        self.use_gc = use_gc

        # level of gradient centralization
        self.gc_gradient_threshold = 3 if gc_conv_only else 1

        print(f"Ranger optimizer loaded. \nGradient Centralization usage = {self.use_gc}")
        if (self.use_gc and self.gc_gradient_threshold == 1):
            print(f"GC applied to both conv and fc layers")
        elif (self.use_gc and self.gc_gradient_threshold == 3):
            print(f"GC applied to conv layers only")

    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()

        # Evaluate averages and grad, update param tensors
        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('Ranger optimizer does not support sparse gradients')

                p_data_fp32 = p.data.float()

                state = self.state[p]  # get state dict for this param

                if len(state) == 0:  # if first time to run...init dictionary with our desired entries
                    # if self.first_run_check==0:
                    # self.first_run_check=1
                    # print("Initializing slow buffer...should not see this at load from saved model!")
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)

                    # look ahead weight storage now in state dict
                    state['slow_buffer'] = torch.empty_like(p.data)
                    state['slow_buffer'].copy_(p.data)

                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)

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

                # GC operation for Conv layers and FC layers
                if grad.dim() > self.gc_gradient_threshold:
                    grad.add_(-grad.mean(dim=tuple(range(1, grad.dim())), keepdim=True))

                state['step'] += 1

                # compute variance mov avg
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                # compute mean moving avg
                exp_avg.mul_(beta1).add_(1 - beta1, grad)

                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 > self.N_sma_threshhold:
                        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)

                # apply lr
                if N_sma > self.N_sma_threshhold:
                    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)

                # integrated look ahead...
                # we do it at the param level instead of group level
                if state['step'] % group['k'] == 0:
                    slow_p = state['slow_buffer']  # get access to slow param tensor
                    slow_p.add_(self.alpha, p.data - slow_p)  # (fast weights - slow weights) * alpha
                    p.data.copy_(slow_p)  # copy interpolated weights to RAdam param tensor

        return loss