added AdamW and SGDW optimizer and their LR schedular

for issue flairNLP#220

flair/optim.py

@@ -0,0 +1,318 @@
+import math
+from functools import partial
+
+import torch
+from torch.optim import Optimizer
+from torch.optim.optimizer import required
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+
+class SGDW(Optimizer):
+    r"""Implements stochastic gradient descent (optionally with momentum) with
+    weight decay from the paper `Fixing Weight Decay Regularization in Adam`_.
+
+    Nesterov momentum is based on the formula from
+    `On the importance of initialization and momentum in deep learning`__.
+
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float): learning rate
+        momentum (float, optional): momentum factor (default: 0)
+        weight_decay (float, optional): weight decay factor (default: 0)
+        dampening (float, optional): dampening for momentum (default: 0)
+        nesterov (bool, optional): enables Nesterov momentum (default: False)
+
+    .. _Fixing Weight Decay Regularization in Adam:
+        https://arxiv.org/abs/1711.05101
+
+    Example:
+        >>> optimizer = torch.optim.SGDW(model.parameters(), lr=0.1, momentum=0.9,
+                                         weight_decay=1e-5)
+        >>> optimizer.zero_grad()
+        >>> loss_fn(model(input), target).backward()
+        >>> optimizer.step()
+
+    __ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf
+
+    .. note::
+        The implementation of SGD with Momentum/Nesterov subtly differs from
+        Sutskever et. al. and implementations in some other frameworks.
+
+        Considering the specific case of Momentum, the update can be written as
+
+        .. math::
+                  v = \rho * v + g \\
+                  p = p - lr * v
+
+        where p, g, v and :math:`\rho` denote the parameters, gradient,
+        velocity, and momentum respectively.
+
+        This is in contrast to Sutskever et. al. and
+        other frameworks which employ an update of the form
+
+        .. math::
+             v = \rho * v + lr * g \\
+             p = p - v
+
+        The Nesterov version is analogously modified.
+    """
+
+    def __init__(self, params, lr=required, momentum=0, dampening=0,
+                 weight_decay=0, nesterov=False):
+        if lr is not required and lr < 0.0:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if momentum < 0.0:
+            raise ValueError("Invalid momentum value: {}".format(momentum))
+        if weight_decay < 0.0:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+
+        defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
+                        weight_decay=weight_decay, nesterov=nesterov)
+        if nesterov and (momentum <= 0 or dampening != 0):
+            raise ValueError("Nesterov momentum requires a momentum and zero dampening")
+        super(SGDW, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(SGDW, self).__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('nesterov', False)
+
+    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:
+            weight_decay = group['weight_decay']
+            momentum = group['momentum']
+            dampening = group['dampening']
+            nesterov = group['nesterov']
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                d_p = p.grad.data
+
+                if momentum != 0:
+                    param_state = self.state[p]
+                    if 'momentum_buffer' not in param_state:
+                        buf = param_state['momentum_buffer'] = torch.zeros_like(p.data)
+                        buf.mul_(momentum).add_(d_p)
+                    else:
+                        buf = param_state['momentum_buffer']
+                        buf.mul_(momentum).add_(1 - dampening, d_p)
+                    if nesterov:
+                        d_p = d_p.add(momentum, buf)
+                    else:
+                        d_p = buf
+
+                if weight_decay != 0:
+                    p.data.add_(-weight_decay, p.data)
+
+                p.data.add_(-group['lr'], d_p)
+
+        return loss
+
+
+class AdamW(Optimizer):
+    r"""Implements AdamW optimizer.
+
+    Adam has been proposed in `Adam\: A Method for Stochastic Optimization`_.
+    AdamW uses the weight decay method from the paper
+    `Fixing Weight Decay Regularization in Adam`_.
+
+    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 factor (default: 0)
+        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
+            algorithm from the paper `On the Convergence of Adam and Beyond`_
+            (default: False)
+
+    .. _Adam\: A Method for Stochastic Optimization:
+        https://arxiv.org/abs/1412.6980
+    .. _Fixing Weight Decay Regularization in Adam:
+        https://arxiv.org/abs/1711.05101
+    .. _On the Convergence of Adam and Beyond:
+        https://openreview.net/forum?id=ryQu7f-RZ
+    """
+
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
+                 weight_decay=0, amsgrad=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, amsgrad=amsgrad)
+        super(AdamW, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(AdamW, self).__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('amsgrad', False)
+
+    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('Adam does not support sparse gradients, please consider SparseAdam instead')
+                amsgrad = group['amsgrad']
+
+                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)
+                    if amsgrad:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state['max_exp_avg_sq'] = torch.zeros_like(p.data)
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                if amsgrad:
+                    max_exp_avg_sq = state['max_exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                state['step'] += 1
+
+                # 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)
+                if amsgrad:
+                    # Maintains the maximum of all 2nd moment running avg. till now
+                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
+                    # Use the max. for normalizing running avg. of gradient
+                    denom = max_exp_avg_sq.sqrt().add_(group['eps'])
+                else:
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+
+                bias_correction1 = 1 - beta1 ** state['step']
+                bias_correction2 = 1 - beta2 ** state['step']
+                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
+
+                if group['weight_decay'] != 0:
+                    p.data.add_(-group['weight_decay'], p.data)
+
+                p.data.addcdiv_(-step_size, exp_avg, denom)
+
+        return loss
+
+
+class ReduceLRWDOnPlateau(ReduceLROnPlateau):
+    """Reduce learning rate and weight decay when a metric has stopped
+    improving. Models often benefit from reducing the learning rate by
+    a factor of 2-10 once learning stagnates. This scheduler reads a metric
+    quantity and if no improvement is seen for a 'patience' number
+    of epochs, the learning rate and weight decay factor is reduced for
+    optimizers that implement the the weight decay method from the paper
+    `Fixing Weight Decay Regularization in Adam`_.
+
+    .. _Fixing Weight Decay Regularization in Adam:
+        https://arxiv.org/abs/1711.05101
+
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        mode (str): One of `min`, `max`. In `min` mode, lr will
+            be reduced when the quantity monitored has stopped
+            decreasing; in `max` mode it will be reduced when the
+            quantity monitored has stopped increasing. Default: 'min'.
+        factor (float): Factor by which the learning rate will be
+            reduced. new_lr = lr * factor. Default: 0.1.
+        patience (int): Number of epochs with no improvement after
+            which learning rate will be reduced. For example, if
+            `patience = 2`, then we will ignore the first 2 epochs
+            with no improvement, and will only decrease the LR after the
+            3rd epoch if the loss still hasn't improved then.
+            Default: 10.
+        verbose (bool): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+        threshold (float): Threshold for measuring the new optimum,
+            to only focus on significant changes. Default: 1e-4.
+        threshold_mode (str): One of `rel`, `abs`. In `rel` mode,
+            dynamic_threshold = best * ( 1 + threshold ) in 'max'
+            mode or best * ( 1 - threshold ) in `min` mode.
+            In `abs` mode, dynamic_threshold = best + threshold in
+            `max` mode or best - threshold in `min` mode. Default: 'rel'.
+        cooldown (int): Number of epochs to wait before resuming
+            normal operation after lr has been reduced. Default: 0.
+        min_lr (float or list): A scalar or a list of scalars. A
+            lower bound on the learning rate of all param groups
+            or each group respectively. Default: 0.
+        eps (float): Minimal decay applied to lr. If the difference
+            between new and old lr is smaller than eps, the update is
+            ignored. Default: 1e-8.
+
+    Example:
+        >>> optimizer = AdamW(model.parameters(), lr=0.1, weight_decay=1e-3)
+        >>> scheduler = ReduceLRWDOnPlateau(optimizer, 'min')
+        >>> for epoch in range(10):
+        >>>     train(...)
+        >>>     val_loss = validate(...)
+        >>>     # Note that step should be called after validate()
+        >>>     scheduler.step(val_loss)
+    """
+    def step(self, metrics, epoch=None):
+        current = metrics
+        if epoch is None:
+            epoch = self.last_epoch = self.last_epoch + 1
+        self.last_epoch = epoch
+
+        if self.is_better(current, self.best):
+            self.best = current
+            self.num_bad_epochs = 0
+        else:
+            self.num_bad_epochs += 1
+
+        if self.in_cooldown:
+            self.cooldown_counter -= 1
+            self.num_bad_epochs = 0  # ignore any bad epochs in cooldown
+
+        if self.num_bad_epochs > self.patience:
+            self._reduce_lr(epoch)
+            self._reduce_weight_decay(epoch)
+            self.cooldown_counter = self.cooldown
+            self.num_bad_epochs = 0
+
+    def _reduce_weight_decay(self, epoch):
+        for i, param_group in enumerate(self.optimizer.param_groups):
+            if param_group['weight_decay'] != 0:
+                old_weight_decay = float(param_group['weight_decay'])
+                new_weight_decay = max(old_weight_decay * self.factor, self.min_lrs[i])
+                if old_weight_decay - new_weight_decay > self.eps:
+                    param_group['weight_decay'] = new_weight_decay
+                    if self.verbose:
+                        print('Epoch {:5d}: reducing weight decay factor'
+                              ' of group {} to {:.4e}.'.format(epoch, i, new_weight_decay))
+