import math from typing import Tuple import torch # from deepspeed.ops import lamb from torch.optim.optimizer import Optimizer class LAMBtimm(Optimizer): """Implements a pure pytorch variant of FuseLAMB (NvLamb variant) optimizer from apex.optimizers.FusedLAMB reference: https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/Transformer-XL/pytorch/lamb.py LAMB was proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_. 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 norm. (default: (0.9, 0.999)) 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) grad_averaging (bool, optional): whether apply (1-beta2) to grad when calculating running averages of gradient. (default: True) max_grad_norm (float, optional): value used to clip global grad norm (default: 1.0) trust_clip (bool): enable LAMBC trust ratio clipping (default: False) always_adapt (boolean, optional): Apply adaptive learning rate to 0.0 weight decay parameter (default: False) .. _Large Batch Optimization for Deep Learning - Training BERT in 76 minutes: https://arxiv.org/abs/1904.00962 .. _On the Convergence of Adam and Beyond: https://openreview.net/forum?id=ryQu7f-RZ """ def __init__( self, params, lr=1e-3, bias_correction=True, betas=(0.9, 0.999), eps=1e-7, weight_decay=0.01, grad_averaging=True, max_grad_norm=2.0, trust_clip=False, always_adapt=False): defaults = dict( lr=lr, bias_correction=bias_correction, betas=betas, eps=eps, weight_decay=weight_decay, grad_averaging=grad_averaging, max_grad_norm=max_grad_norm, trust_clip=trust_clip, always_adapt=always_adapt) super().__init__(params, defaults) self.global_grad_norm = torch.tensor(0.1) @torch.no_grad() 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: with torch.enable_grad(): loss = closure() device = self.param_groups[0]['params'][0].device one_tensor = torch.tensor(1.0, dtype=torch.float32, device=device) # because torch.where doesn't handle scalars correctly global_grad_norm = torch.full(size=(1,), fill_value=1e-12, dtype=torch.float32, device=device) for group in self.param_groups: for p in group['params']: if p.grad is None: continue grad = p.grad if grad.is_sparse: raise RuntimeError('Lamb does not support sparse gradients, consider SparseAdam instad.') global_grad_norm.add_(grad.pow(2).sum()) global_grad_norm = torch.sqrt(global_grad_norm) self.global_grad_norm = global_grad_norm max_grad_norm = torch.tensor(self.defaults['max_grad_norm'], dtype=torch.float32, device=device) clip_global_grad_norm = 1 / torch.where( global_grad_norm > max_grad_norm, global_grad_norm / max_grad_norm, one_tensor) for group in self.param_groups: bias_correction = 1 if group['bias_correction'] else 0 beta1, beta2 = group['betas'] grad_averaging = 1 if group['grad_averaging'] else 0 beta3 = 1 - beta1 if grad_averaging else 1.0 # assume same step across group now to simplify things # per parameter step can be easily support by making it tensor, or pass list into kernel if 'step' in group: group['step'] += 1 else: group['step'] = 1 if bias_correction: bias_correction1 = 1 - beta1 ** group['step'] bias_correction2 = 1 - beta2 ** group['step'] else: bias_correction1, bias_correction2 = 1.0, 1.0 for p in group['params']: if p.grad is None: continue grad = p.grad.mul_(clip_global_grad_norm) state = self.state[p] # State initialization if len(state) == 0: # Exponential moving average of gradient valuesa state['exp_avg'] = torch.zeros_like(p) # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like(p) exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] # Decay the first and second moment running average coefficient exp_avg.mul_(beta1).add_(grad, alpha=beta3) # m_t exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) # v_t denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) update = (exp_avg / bias_correction1).div_(denom) weight_decay = group['weight_decay'] if weight_decay != 0: update.add_(p, alpha=weight_decay) if weight_decay != 0 or group['always_adapt']: # Layer-wise LR adaptation. By default, skip adaptation on parameters that are # excluded from weight decay, unless always_adapt == True, then always enabled. w_norm = p.norm(2.0) g_norm = update.norm(2.0) trust_ratio = torch.where( w_norm > 0, torch.where(g_norm > 0, w_norm / g_norm, one_tensor), one_tensor, ) if group['trust_clip']: # LAMBC trust clipping, upper bound fixed at one trust_ratio = torch.minimum(trust_ratio, one_tensor) update.mul_(trust_ratio) p.add_(update, alpha=-group['lr']) return loss class Lion(Optimizer): def __init__( self, params, lr: float = 1e-4, betas: Tuple[float, float] = (0.9, 0.99), weight_decay: float = 0.0, use_triton: bool = False ): assert lr > 0. assert all([0. <= beta <= 1. for beta in betas]) defaults = dict( lr=lr, betas=betas, weight_decay=weight_decay ) super().__init__(params, defaults) def update_fn(self, p, grad, exp_avg, lr, wd, beta1, beta2): # stepweight decay p.data.mul_(1 - lr * wd) # weight update update = exp_avg.clone().mul_(beta1).add(grad, alpha=1 - beta1).sign_() p.add_(update, alpha=-lr) # decay the momentum running average coefficient exp_avg.mul_(beta2).add_(grad, alpha=1 - beta2) @torch.no_grad() def step( self, closure=None ): loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: for p in filter(lambda p: p.grad is not None, group['params']): grad, lr, wd, beta1, beta2, state = p.grad, group['lr'], group['weight_decay'], *group['betas'], self.state[p] # init state - exponential moving average of gradient values if len(state) == 0: state['exp_avg'] = torch.zeros_like(p) exp_avg = state['exp_avg'] self.update_fn( p, grad, exp_avg, lr, wd, beta1, beta2 ) return loss