LAMB optimizer

codes/trainer/optimizers/lamb.py

@@ -0,0 +1,131 @@
+"""
+Lamb optimizer.
+
+Adapted from original source: https://github.com/cybertronai/pytorch-lamb/blob/master/pytorch_lamb/lamb.py
+"""
+
+import collections
+import math
+
+import torch
+from torch.optim import Optimizer
+
+
+class Lamb(Optimizer):
+    r"""Implements Lamb algorithm.
+
+    It has been 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 square (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)
+        adam (bool, optional): always use trust ratio = 1, which turns this into
+            Adam. Useful for comparison purposes.
+
+    .. _Large Batch Optimization for Deep Learning: Training BERT in 76 minutes:
+        https://arxiv.org/abs/1904.00962
+    """
+
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6,
+                 weight_decay=0, adam=False):
+        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]))
+        defaults = dict(lr=lr, betas=betas, eps=eps,
+                        weight_decay=weight_decay)
+        self.adam = adam
+        super(Lamb, self).__init__(params, defaults)
+
+    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('Lamb does not support sparse gradients, consider SparseAdam instad.')
+
+                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)
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                state['step'] += 1
+
+                # Decay the first and second moment running average coefficient
+                # m_t
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                # v_t
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+                # Paper v3 does not use debiasing.
+                # bias_correction1 = 1 - beta1 ** state['step']
+                # bias_correction2 = 1 - beta2 ** state['step']
+                # Apply bias to lr to avoid broadcast.
+                step_size = group['lr'] # * math.sqrt(bias_correction2) / bias_correction1
+
+                weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10)
+
+                adam_step = exp_avg / exp_avg_sq.sqrt().add(group['eps'])
+                if group['weight_decay'] != 0:
+                    adam_step.add_(p.data, alpha=group['weight_decay'])
+
+                adam_norm = adam_step.pow(2).sum().sqrt()
+                if weight_norm == 0 or adam_norm == 0:
+                    trust_ratio = 1
+                else:
+                    trust_ratio = weight_norm / adam_norm
+                state['weight_norm'] = weight_norm
+                state['adam_norm'] = adam_norm
+                state['trust_ratio'] = trust_ratio
+                if self.adam:
+                    trust_ratio = 1
+
+                p.data.add_(adam_step, alpha=-step_size * trust_ratio)
+
+        return loss
+
+
+    def debug(self):
+        """Returns a histogram dict for recording various norms and the trust ratio."""
+        results = collections.defaultdict(list)
+        for group in self.param_groups:
+            for p in group['params']:
+                state = self.state[p]
+                for i in ('weight_norm', 'adam_norm', 'trust_ratio'):
+                    if i in state:
+                        results[i].append(state[i])
+
+        res = {}
+        for k, v in results.items():
+            res[f'histogram_lamb_{k}'] = torch.tensor(v)
+        return res

codes/trainer/steps.py

@@ -154,6 +154,18 @@ class ConfigurableStep(Module):
                                            weight_decay=opt_config['weight_decay'])
                 opt = LARC(optSGD, trust_coefficient=opt_config['lars_coefficient'])
                 opt._group_names = sorted(list(all_param_names))
+            elif self.step_opt['optimizer'] == 'lamb':
+                from trainer.optimizers.lamb import Lamb
+                opt_unweighted = torch.optim.AdamW(params_notweights, lr=opt_config['lr'], weight_decay=0,
+                                       betas=(opt_get(opt_config, ['beta1'], .9), opt_get(opt_config, ['beta2'], .999)))
+                opt_unweighted._config = opt_config
+                opt_unweighted._config['network'] = net_name
+                self.optimizers.append(opt_unweighted)
+
+                opt = Lamb(params_weights, lr=opt_config['lr'],
+                                   weight_decay=opt_get(opt_config, ['weight_decay'], 1e-2),
+                                   betas=(opt_get(opt_config, ['beta1'], .9), opt_get(opt_config, ['beta2'], .999)))
+                opt._group_names = [params_names_weights, params_names_notweights]
             elif self.step_opt['optimizer'] == 'sgd':
                 from torch.optim import SGD
                 opt = SGD(list(optim_params.values()), lr=opt_config['lr'], momentum=opt_config['momentum'], weight_decay=opt_config['weight_decay'])