adam

examples/albert/optim.py

@@ -0,0 +1,151 @@
+import math
+
+import torch
+from torch.optim.optimizer import Optimizer
+
+from .types import Betas2, OptFloat, OptLossClosure, Params
+
+__all__ = ('Lamb',)
+
+
+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 of parameters to optimize or dicts defining
+            parameter groups
+        lr: learning rate (default: 1e-3)
+        betas: coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps: term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay: weight decay (L2 penalty) (default: 0)
+        clamp_value: clamp weight_norm in (0,clamp_value) (default: 10)
+            set to a high value to avoid it (e.g 10e3)
+        adam: always use trust ratio = 1, which turns this
+            into Adam. Useful for comparison purposes. (default: False)
+        debias: debias adam by (1 - beta**step) (default: False)
+
+    Example:
+        >>> import torch_optimizer as optim
+        >>> optimizer = optim.Lamb(model.parameters(), lr=0.1)
+        >>> optimizer.zero_grad()
+        >>> loss_fn(model(input), target).backward()
+        >>> optimizer.step()
+
+    __ https://arxiv.org/abs/1904.00962
+
+    Note:
+        Reference code: https://github.com/cybertronai/pytorch-lamb
+    """
+
+    def __init__(
+        self,
+        params: Params,
+        lr: float = 1e-3,
+        betas: Betas2 = (0.9, 0.999),
+        eps: float = 1e-6,
+        weight_decay: float = 0,
+        clamp_value: float = 10,
+        adam: bool = False,
+        debias: bool = False,
+    ) -> None:
+        if lr <= 0.0:
+            raise ValueError('Invalid learning rate: {}'.format(lr))
+        if eps < 0.0:
+            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 weight_decay < 0:
+            raise ValueError(
+                'Invalid weight_decay value: {}'.format(weight_decay)
+            )
+        if clamp_value < 0.0:
+            raise ValueError('Invalid clamp value: {}'.format(clamp_value))
+
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+        self.clamp_value = clamp_value
+        self.adam = adam
+        self.debias = debias
+
+        super(Lamb, self).__init__(params, defaults)
+
+    def step(self, closure: OptLossClosure = None) -> OptFloat:
+        r"""Performs a single optimization step.
+
+        Arguments:
+            closure: 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:
+                    msg = (
+                        'Lamb does not support sparse gradients, '
+                        'please consider SparseAdam instead'
+                    )
+                    raise RuntimeError(msg)
+
+                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, )
+                    # 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']
+                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.
+                if self.debias:
+                    bias_correction = math.sqrt(1 - beta2 ** state['step'])
+                    bias_correction /= 1 - beta1 ** state['step']
+                else:
+                    bias_correction = 1
+
+                # Apply bias to lr to avoid broadcast.
+                step_size = group['lr'] * bias_correction
+
+                weight_norm = torch.norm(p.data).clamp(0, self.clamp_value)
+
+                adam_step = exp_avg / exp_avg_sq.sqrt().add(group['eps'])
+                adam_step.add_(p.data, alpha=group['weight_decay'])
+
+                adam_norm = torch.norm(adam_step).clamp_min(0.001)
+                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