feature: GrokFast optimizer

pytorch_optimizer/optimizer/grokfast.py

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+import math
+from collections import deque
+from typing import Dict, Literal, Optional
+
+import torch
+from torch import nn
+from torch.optim.optimizer import Optimizer
+
+from pytorch_optimizer.base.exception import NoSparseGradientError
+from pytorch_optimizer.base.optimizer import BaseOptimizer
+from pytorch_optimizer.base.types import BETAS, CLOSURE, DEFAULTS, LOSS, PARAMETERS
+
+FILTER_TYPE = Literal['mean', 'sum']
+
+
+@torch.no_grad()
+def gradfilter_ma(
+ model: nn.Module,
+ grads: Optional[Dict[str, deque]] = None,
+ window_size: int = 100,
+ lamb: float = 5.0,
+ filter_type: FILTER_TYPE = 'mean',
+ warmup: bool = True,
+) -> Dict[str, deque]:
+ r"""Grokfast-MA.
+
+ :param model: nn.Module. model that contains every trainable parameters.
+ :param grads: Optional[Dict[str, deque]]. running memory (Queue for windowed moving average). initialize by setting
+ it to None. feed the output of the method recursively after on.
+ :param window_size: int. the width of the filter window. additional memory requirements increases linearly with
+ respect to the windows size.
+ :param lamb: float. amplifying factor hyperparameter of the filter.
+ :param filter_type: FILTER_TYPE. aggregation method for the running queue.
+ :param warmup: bool. if true, filter is not applied until the queue is filled.
+ """
+ if grads is None:
+ grads = {n: deque(maxlen=window_size) for n, p in model.named_parameters() if p.requires_grad}
+
+ for n, p in model.named_parameters():
+ if p.requires_grad:
+ grads[n].append(p.grad)
+
+ if not warmup or len(grads[n]) == window_size:
+ if filter_type == 'mean':
+ avg = sum(grads[n]) / len(grads[n])
+ elif filter_type == 'sum':
+ avg = sum(grads[n])
+ else:
+ raise ValueError(f'Unrecognized filter_type {filter_type}')
+
+ p.grad.add_(avg, alpha=lamb)
+
+ return grads
+
+
+@torch.no_grad()
+def gradfilter_ema(
+ model: nn.Module,
+ grads: Optional[Dict[str, torch.Tensor]] = None,
+ alpha: float = 0.98,
+ lamb: float = 2.0,
+) -> Dict[str, torch.Tensor]:
+ r"""Grokfast.
+
+ :param model: nn.Module. model that contains every trainable parameters.
+ :param grads: Optional[Dict[str, deque]]. running memory (EMA). Initialize by setting it to None. Feed the output
+ of the method recursively after on.
+ :param alpha: int. momentum hyperparameter of the EMA.
+ :param lamb: float. amplifying factor hyperparameter of the filter.
+ """
+ if grads is None:
+ grads = {n: p.grad for n, p in model.named_parameters() if p.requires_grad}
+
+ for n, p in model.named_parameters():
+ if p.requires_grad:
+ grads[n].mul_(alpha).add_(p.grad, alpha=1.0 - alpha)
+ p.grad.add_(grads[n], alpha=lamb)
+
+ return grads
+
+
+class GrokFastAdamW(Optimizer, BaseOptimizer):
+ r"""Accelerated Grokking by Amplifying Slow Gradients with AdamW.
+
+ :param params: PARAMETERS. iterable of parameters to optimize or dicts defining parameter groups.
+ :param lr: float. learning rate.
+ :param betas: BETAS. coefficients used for computing running averages of gradient and the squared hessian trace.
+ :param grokfast: bool. whether to use grokfast.
+ :param grokfast_alpha: float. momentum hyperparameter of the EMA.
+ :param grokfast_lamb: float. amplifying factor hyperparameter of the filter..
+ :param grokfast_after_step: int. warmup step for grokfast.
+ :param weight_decay: float. weight decay (L2 penalty).
+ :param weight_decouple: bool. the optimizer uses decoupled weight decay as in AdamW.
+ :param fixed_decay: bool. fix weight decay.
+ :param eps: float. term added to the denominator to improve numerical stability.
+ """
+
+ def __init__(
+ self,
+ params: PARAMETERS,
+ lr: float = 1e-4,
+ betas: BETAS = (0.9, 0.99),
+ grokfast: bool = True,
+ grokfast_alpha: float = 0.98,
+ grokfast_lamb: float = 2.0,
+ grokfast_after_step: int = 0,
+ weight_decay: float = 0.0,
+ weight_decouple: bool = True,
+ fixed_decay: bool = False,
+ normalize_lr: bool = True,
+ eps: float = 1e-8,
+ ):
+ self.validate_learning_rate(lr)
+ self.validate_betas(betas)
+ self.validate_non_negative(weight_decay, 'weight_decay')
+ self.validate_range(grokfast_alpha, 'grokfast_alpha', 0.0, 1.0)
+ self.validate_non_negative(eps, 'eps')
+
+ if grokfast and normalize_lr:
+ lr /= 1.0 + grokfast_lamb
+
+ defaults: DEFAULTS = {
+ 'lr': lr,
+ 'betas': betas,
+ 'weight_decay': weight_decay,
+ 'weight_decouple': weight_decouple,
+ 'fixed_decay': fixed_decay,
+ 'grokfast': grokfast,
+ 'grokfast_alpha': grokfast_alpha,
+ 'grokfast_lamb': grokfast_lamb,
+ 'grokfast_after_step': grokfast_after_step,
+ 'eps': eps,
+ }
+ super().__init__(params, defaults)
+
+ def __str__(self) -> str:
+ return 'GrokFastAdamW'
+
+ @torch.no_grad()
+ def reset(self):
+ for group in self.param_groups:
+ group['step'] = 0
+ for p in group['params']:
+ state = self.state[p]
+
+ state['exp_avg'] = torch.zeros_like(p)
+ state['exp_avg_sq'] = torch.zeros_like(p)
+
+ @torch.no_grad()
+ def step(self, closure: CLOSURE = None) -> LOSS:
+ loss: LOSS = None
+ if closure is not None:
+ with torch.enable_grad():
+ loss = closure()
+
+ for group in self.param_groups:
+ if 'step' in group:
+ group['step'] += 1
+ else:
+ group['step'] = 1
+
+ beta1, beta2 = group['betas']
+
+ bias_correction1: float = 1.0 - beta1 ** group['step']
+ bias_correction2_sq: float = math.sqrt(1.0 - beta2 ** group['step'])
+
+ should_grokfast: bool = (
+ group['grokfast'] and group['step'] > group['grokfast_after_step'] and group['grokfast_lamb'] > 0
+ )
+
+ for p in group['params']:
+ if p.grad is None:
+ continue
+
+ grad = p.grad
+ if grad.is_sparse:
+ raise NoSparseGradientError(str(self))
+
+ state = self.state[p]
+
+ if len(state) == 0:
+ state['exp_avg'] = torch.zeros_like(p)
+ state['exp_avg_sq'] = torch.zeros_like(p)
+ if should_grokfast:
+ state['grok_exp_avg'] = grad.clone()
+
+ self.apply_weight_decay(
+ p=p,
+ grad=grad,
+ lr=group['lr'],
+ weight_decay=group['weight_decay'],
+ weight_decouple=group['weight_decouple'],
+ fixed_decay=group['fixed_decay'],
+ )
+
+ if should_grokfast:
+ grok_exp_avg = state['grok_exp_avg']
+ grok_exp_avg.lerp_(grad, weight=1.0 - group['grokfast_alpha'])
+
+ grad.add_(grok_exp_avg, alpha=group['grokfast_lamb'])
+
+ exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+ exp_avg.mul_(beta1).add_(grad, alpha=1.0 - beta1)
+ exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
+
+ de_nom = exp_avg_sq.sqrt().div_(bias_correction2_sq).clamp_(min=group['eps'])
+
+ update = exp_avg.div(bias_correction1).div_(de_nom)
+
+ p.add_(update, alpha=-group['lr'])
+
+ return loss