optimization_pytorch from OpenAI with adapted HP - check if there is weights decay on bias

optimization_pytorch.py

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+import math
+import torch
+from torch.optim import Optimizer
+from torch.nn.utils import clip_grad_norm_
+
+def warmup_cosine(x, warmup=0.002):
+    s = 1 if x <= warmup else 0
+    return s*(x/warmup) + (1-s)*(0.5 * (1 + torch.cos(math.pi * x)))
+
+def warmup_constant(x, warmup=0.002):
+    s = 1 if x <= warmup else 0
+    return s*(x/warmup) + (1-s)*1
+
+def warmup_linear(x, warmup=0.002):
+    s = 1 if x <= warmup else 0
+    return (s*(x/warmup) + (1-s))*(1-x)
+
+SCHEDULES = {
+    'warmup_cosine':warmup_cosine,
+    'warmup_constant':warmup_constant,
+    'warmup_linear':warmup_linear,
+}
+
+
+class OpenAIAdam(Optimizer):
+    """Implements Open AI version of Adam algorithm with weight decay fix.
+    """
+    def __init__(self, params, lr, schedule, warmup, t_total,
+                 b1=0.9, b2=0.999, e=1e-6, l2=0,
+                 vector_l2=False, max_grad_norm=-1, **kwargs):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if schedule not in SCHEDULES:
+            raise ValueError("Invalid schedule parameter: {}".format(schedule))
+        if not 0 <= warmup:
+            raise ValueError("Invalid warmup: {}".format(warmup))
+        if not 0.0 <= b1 < 1.0:
+            raise ValueError("Invalid b1 parameter: {}".format(b1))
+        if not 0.0 <= b2 < 1.0:
+            raise ValueError("Invalid b2 parameter: {}".format(b2))
+        if not 0.0 <= e:
+            raise ValueError("Invalid epsilon value: {}".format(e))
+        defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total,
+                        b1=b1, b2=b2, e=e, l2=l2, vector_l2=vector_l2,
+                        max_grad_norm=max_grad_norm)
+        super(OpenAIAdam, self).__init__(params, defaults)
+
+    def get_lr(self):
+        lr = []
+        for group in self.param_groups:
+            for p in group['params']:
+                state = self.state[p]
+                if len(state) == 0:
+                    return [0]
+                schedule_fct = SCHEDULES[group['schedule']]
+                lr_scheduled = group['lr'] * schedule_fct(state['step']/group['t_total'], group['warmup'])
+                lr.append(lr_scheduled)
+        return lr
+
+    def to(self, device):
+        """ Move the optimizer state to a specified device"""
+        for state in self.state.values():
+            state['exp_avg'].to(device)
+            state['exp_avg_sq'].to(device)
+
+    def initialize_step(self, initial_step):
+        """Initialize state with a defined step (but we don't have stored averaged).
+        Arguments:
+            initial_step (int): Initial step number.
+        """
+        for group in self.param_groups:
+            for p in group['params']:
+                state = self.state[p]
+                # State initialization
+                state['step'] = initial_step
+                # 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)
+
+    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')
+
+                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['b1'], group['b2']
+
+                state['step'] += 1
+
+                # Add grad clipping
+                if group['max_grad_norm'] > 0:
+                    clip_grad_norm_(p, group['max_grad_norm'])
+
+                # 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['e'])
+
+                bias_correction1 = 1 - beta1 ** state['step']
+                bias_correction2 = 1 - beta2 ** state['step']
+
+                schedule_fct = SCHEDULES[group['schedule']]
+                lr_scheduled = group['lr'] * schedule_fct(state['step']/group['t_total'], group['warmup'])
+                step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1
+
+                p.data.addcdiv_(-step_size, exp_avg, denom)
+
+                # Just adding the square of the weights to the loss function is *not*
+                # the correct way of using L2 regularization/weight decay with Adam,
+                # since that will interact with the m and v parameters in strange ways.
+                #
+                # Instead we want ot decay the weights in a manner that doesn't interact
+                # with the m/v parameters. This is equivalent to adding the square
+                # of the weights to the loss with plain (non-momentum) SGD.
+                if (len(p.size()) > 1 or group['vector_l2']) and group['l2'] > 0:
+                    p.data.add_(-lr_scheduled * group['l2'], p.data)
+
+        return loss