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NLG/model/optim.py

+import math
+import torch
+
+
+class Adam(torch.optim.Optimizer):
+    """Implements Adam algorithm.
+    This implementation is modified from torch.optim.Adam based on:
+    `Fixed Weight Decay Regularization in Adam`
+    (see https://arxiv.org/abs/1711.05101)
+    It has been proposed in `Adam: A Method for Stochastic Optimization`_.
+    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)
+        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
+            algorithm from the paper `On the Convergence of Adam and Beyond`_
+    .. _Adam\: A Method for Stochastic Optimization:
+        https://arxiv.org/abs/1412.6980
+    .. _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):
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad)
+        super(Adam, 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('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'] * group['lr'], p.data)
+
+                p.data.addcdiv_(-step_size, exp_avg, denom)
+
+        return loss
+
+
+class NoamOpt:
+    def __init__(self, embeddings_size, factor, warmup, optimizer):
+        self.embeddings_size = embeddings_size
+        self.factor = factor
+        self.warmup = warmup
+        self.optimizer = optimizer
+
+        self._step = 1
+        
+    def state_dict(self):
+        return {'step': self._step,
+                'optimizer': self.optimizer.state_dict()}
+
+    def load_state_dict(self, state_dict):
+        self._step = state_dict['step']
+        self.optimizer.load_state_dict(state_dict['optimizer'])
+
+    def zero_grad(self):
+        return self.optimizer.zero_grad()
+
+    @property
+    def param_groups(self):
+        return self.optimizer.param_groups
+
+    def step(self):
+        self._step += 1
+        rate = self.rate()
+        for p in self.optimizer.param_groups:
+            p['lr'] = rate
+        self.optimizer.step()
+
+    def curr_step(self):
+        return self._step
+
+    def rate(self, step=None):
+        if step is None:
+            step = self._step
+            
+        return self.factor * (self.embeddings_size ** (-0.5) * min(step ** (-0.5), step * self.warmup ** (-1.5)))
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