changed the optimize over9000 implementation

optimizers/over9000/__init__.py

@@ -0,0 +1,14 @@
+from .radam import PlainRAdam,RAdam
+from .novograd import Novograd
+from .ranger import Ranger
+from .ralamb import Ralamb
+from .rangerlars import RangerLars
+from .lookahead import Lookahead,LookaheadAdam
+#from .lamb import Lamb
+from .diffgrad import DiffGrad
+from .adamod import AdaMod
+from .madam import Madam
+from .apollo import Apollo
+from .adabelief import AdaBelief
+from .madgrad import MADGRAD
+from .adan import Adan

optimizers/over9000/adabelief.py

@@ -0,0 +1,222 @@
+# from https://github.com/raw/juntang-zhuang/Adabelief-Optimizer/master/PyTorch_Experiments/AdaBelief.py
+import math
+import torch
+from torch.optim.optimizer import Optimizer
+from tabulate import tabulate
+from colorama import Fore, Back, Style
+
+version_higher = ( torch.__version__ >= "1.5.0" )
+
+class AdaBelief(Optimizer):
+    r"""Implements AdaBelief algorithm. Modified from Adam in PyTorch
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, optional): learning rate (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-16)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
+            algorithm from the paper `On the Convergence of Adam and Beyond`_
+            (default: False)
+        weight_decouple (boolean, optional): ( default: True) If set as True, then
+            the optimizer uses decoupled weight decay as in AdamW
+        fixed_decay (boolean, optional): (default: False) This is used when weight_decouple
+            is set as True.
+            When fixed_decay == True, the weight decay is performed as
+            $W_{new} = W_{old} - W_{old} \times decay$.
+            When fixed_decay == False, the weight decay is performed as
+            $W_{new} = W_{old} - W_{old} \times decay \times lr$. Note that in this case, the
+            weight decay ratio decreases with learning rate (lr).
+        rectify (boolean, optional): (default: True) If set as True, then perform the rectified
+            update similar to RAdam
+        degenerated_to_sgd (boolean, optional) (default:True) If set as True, then perform SGD update
+            when variance of gradient is high
+    reference: AdaBelief Optimizer, adapting stepsizes by the belief in observed gradients, NeurIPS 2020
+    """
+
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-16,
+                 weight_decay=0, amsgrad=False, weight_decouple=True, fixed_decay=False, rectify=True,
+                 degenerated_to_sgd=True):
+
+        # ------------------------------------------------------------------------------
+        # Print modifications to default arguments
+        print(Fore.RED + 'Please check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.')
+        print(Fore.RED + 'Modifications to default arguments:')
+        default_table = tabulate([
+                ['adabelief-pytorch=0.0.5','1e-8','False','False'],
+                ['Current version (0.1.0)','1e-16','True','True']],
+                headers=['eps','weight_decouple','rectify'])
+        print(Fore.RED + default_table)
+
+        print(Fore.RED +'For a complete table of recommended hyperparameters, see')
+        print(Fore.RED + 'https://github.com/juntang-zhuang/Adabelief-Optimizer')
+
+        print(Style.RESET_ALL)
+        # ------------------------------------------------------------------------------
+
+        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
+        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, amsgrad=amsgrad, buffer=[[None, None, None] for _ in range(10)])
+        super(AdaBelief, self).__init__(params, defaults)
+
+        self.degenerated_to_sgd = degenerated_to_sgd
+        self.weight_decouple = weight_decouple
+        self.rectify = rectify
+        self.fixed_decay = fixed_decay
+        if self.weight_decouple:
+            print('Weight decoupling enabled in AdaBelief')
+            if self.fixed_decay:
+                print('Weight decay fixed')
+        if self.rectify:
+            print('Rectification enabled in AdaBelief')
+        if amsgrad:
+            print('AMSGrad enabled in AdaBelief')
+
+    def __setstate__(self, state):
+        super(AdaBelief, self).__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('amsgrad', False)
+
+    def reset(self):
+        for group in self.param_groups:
+            for p in group['params']:
+                state = self.state[p]
+                amsgrad = group['amsgrad']
+
+                # State initialization
+                state['step'] = 0
+                # Exponential moving average of gradient values
+                state['exp_avg'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
+                    if version_higher else torch.zeros_like(p.data)
+
+                # Exponential moving average of squared gradient values
+                state['exp_avg_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
+                    if version_higher else torch.zeros_like(p.data)
+
+                if amsgrad:
+                    # Maintains max of all exp. moving avg. of sq. grad. values
+                    state['max_exp_avg_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
+                        if version_higher else torch.zeros_like(p.data)
+
+    def step(self, closure=None):
+        """Performs a single optimization step.
+        Arguments:
+            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
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        'AdaBelief does not support sparse gradients, please consider SparseAdam instead')
+                amsgrad = group['amsgrad']
+
+                state = self.state[p]
+
+                beta1, beta2 = group['betas']
+
+                # State initialization
+                if len(state) == 0:
+                    state['step'] = 0
+                    # Exponential moving average of gradient values
+                    state['exp_avg'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
+                        if version_higher else torch.zeros_like(p.data)
+                    # Exponential moving average of squared gradient values
+                    state['exp_avg_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
+                        if version_higher else torch.zeros_like(p.data)
+                    if amsgrad:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state['max_exp_avg_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
+                            if version_higher else torch.zeros_like(p.data)
+
+                # get current state variable
+                exp_avg, exp_avg_var = state['exp_avg'], state['exp_avg_var']
+
+                state['step'] += 1
+                bias_correction1 = 1 - beta1 ** state['step']
+                bias_correction2 = 1 - beta2 ** state['step']
+
+                # Update first and second moment running average
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                grad_residual = grad - exp_avg
+                exp_avg_var.mul_(beta2).addcmul_( grad_residual, grad_residual, value=1 - beta2)
+
+                if amsgrad:
+                    max_exp_avg_var = state['max_exp_avg_var']
+                    # Maintains the maximum of all 2nd moment running avg. till now
+                    torch.max(max_exp_avg_var, exp_avg_var, out=max_exp_avg_var)
+
+                    # Use the max. for normalizing running avg. of gradient
+                    denom = (max_exp_avg_var.add_(group['eps']).sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
+                else:
+                    denom = (exp_avg_var.add_(group['eps']).sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
+
+                # perform weight decay, check if decoupled weight decay
+                if self.weight_decouple:
+                    if not self.fixed_decay:
+                        p.data.mul_(1.0 - group['lr'] * group['weight_decay'])
+                    else:
+                        p.data.mul_(1.0 - group['weight_decay'])
+                else:
+                    if group['weight_decay'] != 0:
+                        grad.add_(p.data, alpha=group['weight_decay'])
+
+                # update
+                if not self.rectify:
+                    # Default update
+                    step_size = group['lr'] / bias_correction1
+                    p.data.addcdiv_( exp_avg, denom, value=-step_size)
+
+                else:  # Rectified update, forked from RAdam
+                    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 = 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
+
+                    if N_sma >= 5:
+                        denom = exp_avg_var.sqrt().add_(group['eps'])
+                        p.data.addcdiv_(exp_avg, denom, value=-step_size * group['lr'])
+                    elif step_size > 0:
+                        p.data.add_( exp_avg, alpha=-step_size * group['lr'])
+
+        return loss

optimizers/over9000/adamod.py

@@ -0,0 +1,98 @@
+# original repo https://github.com/lancopku/AdaMod/blob/master/adamod/adamod.py
+import math
+import torch
+from torch.optim import Optimizer
+
+class AdaMod(Optimizer):
+    """Implements AdaMod algorithm with Decoupled Weight Decay (arxiv.org/abs/1711.05101)
+    It has been proposed in `Adaptive and Momental Bounds for Adaptive Learning Rate Methods`_.
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, optional): learning rate (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        beta3 (float, optional): smoothing coefficient for adaptive learning rates (default: 0.9999)
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+    """
+
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), beta3=0.999,
+                 eps=1e-8, weight_decay=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]))
+        if not 0.0 <= beta3 < 1.0:
+            raise ValueError("Invalid beta3 parameter: {}".format(beta3))
+        defaults = dict(lr=lr, betas=betas, beta3=beta3, eps=eps,
+                        weight_decay=weight_decay)
+        super(AdaMod, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(AdaMod, self).__setstate__(state)
+
+    def step(self, closure=None):
+        """Performs a single optimization step.
+        Arguments:
+            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
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        'AdaMod does not support sparse gradients')
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state['step'] = 0
+                    # Exponential moving average of gradient values
+                    state['exp_avg'] = torch.zeros_like(p.data)
+                    # Exponential moving average of squared gradient values
+                    state['exp_avg_sq'] = torch.zeros_like(p.data)
+                    # Exponential moving average of actual learning rates
+                    state['exp_avg_lr'] = torch.zeros_like(p.data)
+
+                exp_avg, exp_avg_sq, exp_avg_lr = state['exp_avg'], state['exp_avg_sq'], state['exp_avg_lr']
+                beta1, beta2 = group['betas']
+
+                state['step'] += 1
+
+                # 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']
+                bias_correction2 = 1 - beta2 ** state['step']
+                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
+
+                if group['weight_decay'] != 0:
+                    p.data.add_(-group['weight_decay'] * group['lr'], p.data)
+
+                # Applies momental bounds on actual learning rates
+                step_size = torch.full_like(denom, step_size)
+                step_size.div_(denom)
+                exp_avg_lr.mul_(group['beta3']).add_(1 - group['beta3'], step_size)
+                step_size = torch.min(step_size,  exp_avg_lr)
+                step_size.mul_(exp_avg)
+
+                p.data.add_(-step_size)
+
+        return loss