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optim.py
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optim.py
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'''A wrapper class for scheduled optimizer '''
import numpy as np
import torch
import math
class ScheduledOptim:
'''A simple wrapper class for learning rate scheduling'''
def __init__(self, optimizer, lr, n_warmup_steps=4000, init_lr=0.):
self._optimizer = optimizer
self.init_lr = init_lr
self.n_warmup_steps = n_warmup_steps
self.n_steps = 0
self.lr = lr
self.lr_step = (lr - init_lr) / n_warmup_steps
self.decay_factor = lr * n_warmup_steps ** 0.5
def step(self, scaler=None):
"Step with the inner optimizer"
self._update_learning_rate()
if scaler is None:
self._optimizer.step()
else:
scaler.step(self._optimizer)
scaler.update()
def zero_grad(self):
"Zero out the gradients with the inner optimizer"
self._optimizer.zero_grad()
def _get_lr_scale(self):
if self.n_steps < self.n_warmup_steps:
self.lr = self.init_lr + self.n_steps * self.lr_step
else:
self.lr = self.decay_factor * self.n_warmup_steps ** -0.5
def _update_learning_rate(self):
''' Learning rate scheduling per step '''
self.n_steps += 1
self._get_lr_scale()
for param_group in self._optimizer.param_groups:
param_group['lr'] = self.lr
def state_dict(self):
return [self._optimizer.state_dict(),
self.init_lr,
self.n_warmup_steps,
self.n_steps,
self.lr,
self.lr_step,
self.decay_factor]
def load_state_dict(self, param):
self._optimizer.load_state_dict(param[0])
self.init_lr, self.n_warmup_steps, self.n_steps, self.lr, self.lr_step, self.decay_factor = param[1:]
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)
@property
def supports_memory_efficient_fp16(self):
return True
@property
def supports_flat_params(self):
return True
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.dtype in {torch.float16, torch.bfloat16}:
grad = grad.float()
if grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
amsgrad = group['amsgrad']
p_data_fp32 = p.data
if p.data.dtype in {torch.float16, torch.bfloat16}:
p_data_fp32 = p_data_fp32.float()
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_fp32)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p_data_fp32)
else:
state['exp_avg'] = state['exp_avg'].to(p_data_fp32)
state['exp_avg_sq'] = state['exp_avg_sq'].to(p_data_fp32)
if amsgrad:
state['max_exp_avg_sq'] = state['max_exp_avg_sq'].to(p_data_fp32)
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_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr'])
p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size)
if p.data.dtype in {torch.float16, torch.bfloat16}:
p.data.copy_(p_data_fp32)
return loss