WIP LAMB Optimizer

onmt/opts.py

@@ -429,7 +429,7 @@ def train_opts(parser):
               nargs="*", default=None,
               help='Criteria to use for early stopping.')
     group.add('--optim', '-optim', default='sgd',
-              choices=['sgd', 'adagrad', 'adadelta', 'adam',
+              choices=['sgd', 'adagrad', 'adadelta', 'adam', 'lamb',
                        'sparseadam', 'adafactor', 'fusedadam'],
               help="Optimization method.")
     group.add('--adagrad_accumulator_init', '-adagrad_accumulator_init',
@@ -466,6 +466,14 @@ def train_opts(parser):
                    'suggested a value of 0.98 for beta2, this parameter may '
                    'not work well for normal models / default '
                    'baselines.')
+    group.add('--lamb_beta1', '-lamb_beta1', type=float, default=0.9,
+              help="The beta1 parameter used by Lamb.")
+    group.add('--lamb_beta2', '-lamb_beta2', type=float, default=0.999,
+              help="The beta2 parameter used by Lamb.")
+    group.add('--lamb_eps', '-lamb_eps', type=float, default=1e-8,
+              help="The epsilon parameter used by Lamb.")
+    group.add('--lamb_wd', '-lamb_wd', type=float, default=0.0,
+              help="The weight decay parameter used by Lamb." )
     group.add('--label_smoothing', '-label_smoothing', type=float, default=0.0,
               help="Label smoothing value epsilon. "
                    "Probabilities of all non-true labels "

onmt/utils/optimizers.py

@@ -6,6 +6,7 @@
 import functools
 from copy import copy
 from math import sqrt
+import math
 
 from onmt.utils.misc import fn_args
 
@@ -82,6 +83,13 @@ def build_torch_optimizer(model, opt):
             params,
             lr=opt.learning_rate,
             betas=betas)
+    elif opt.optim == 'lamb':
+        optimizer = Lamb(
+            params,
+            lr=opt.learning_rate,
+            betas=(opt.lamb_beta1, opt.lamb_beta2),
+            eps=opt.lamb_eps,
+            weight_decay=opt.lamb_wd)
     else:
         raise ValueError('Invalid optimizer type: ' + opt.optim)
 
@@ -517,3 +525,101 @@ def step(self, closure=None):
                     p.data.add_(-group['weight_decay'] * lr_t, p.data)
 
         return loss
+
+
+# Code below is an implementation of https://arxiv.org/pdf/1904.00962.pdf
+# inspired but modified from https://github.com/cybertronai/pytorch-lamb
+
+class Lamb(torch.optim.Optimizer):
+    """Implements Lamb algorithm.
+    It has been proposed in `Reducing BERT Pre-Training Time from 3 Days to 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.
+    .. _Reducing BERT Pre-Training Time from 3 Days to 76 Minutes:
+        https://arxiv.org/abs/1904.00962
+    """
+
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
+                 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
+
+                if group['weight_decay'] != 0:
+                    grad.add_(group['weight_decay'], p.data)
+
+                # 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']
+                # Apply bias to lr to avoid broadcast.
+                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
+
+                adam_step = exp_avg / denom
+                # L2 norm uses sum, but here since we're dividing, use mean to avoid overflow.
+                r1 = p.data.pow(2).mean().sqrt()
+                r2 = adam_step.pow(2).mean().sqrt()
+                r = 1 if r1 == 0 or r2 == 0 else  min(r1/r2, 10)
+                state['r1'] = r1
+                state['r2'] = r2
+                state['r'] = r
+                if self.adam:
+                    r = 1
+
+                p.data.add_(-step_size * r, adam_step)
+
+        return loss