Add RAdam, NovoGrad, Lookahead, and AdamW optimizers, a few ResNet tweaks and scheduler factory tweak.
* Add some of the trendy new optimizers. Decent results but not clearly better than the standards. * Can create a None scheduler for constant LR * ResNet defaults to zero_init of last BN in residual * add resnet50d configpull/31/head
Ross Wightman
5 years ago
parent
f37e633e9b
commit
fac58f609a
@ -1,3 +1,7 @@
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from .nadam import Nadam
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from .rmsprop_tf import RMSpropTF
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from .adamw import AdamW
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from .radam import RAdam
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from .novograd import NovoGrad
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from .lookahead import Lookahead
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from .optim_factory import create_optimizer
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@ -0,0 +1,117 @@
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""" AdamW Optimizer
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Impl copied from PyTorch master
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"""
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import math
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import torch
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from torch.optim.optimizer import Optimizer
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class AdamW(Optimizer):
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r"""Implements AdamW algorithm.
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The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
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The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
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Arguments:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 1e-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square (default: (0.9, 0.999))
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-8)
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weight_decay (float, optional): weight decay coefficient (default: 1e-2)
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amsgrad (boolean, optional): whether to use the AMSGrad variant of this
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algorithm from the paper `On the Convergence of Adam and Beyond`_
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(default: False)
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.. _Adam\: A Method for Stochastic Optimization:
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https://arxiv.org/abs/1412.6980
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.. _Decoupled Weight Decay Regularization:
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https://arxiv.org/abs/1711.05101
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.. _On the Convergence of Adam and Beyond:
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https://openreview.net/forum?id=ryQu7f-RZ
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"""
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
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weight_decay=1e-2, amsgrad=False):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay, amsgrad=amsgrad)
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super(AdamW, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(AdamW, self).__setstate__(state)
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for group in self.param_groups:
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group.setdefault('amsgrad', False)
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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loss = closure()
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is None:
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continue
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# Perform stepweight decay
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p.data.mul_(1 - group['lr'] * group['weight_decay'])
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# Perform optimization step
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grad = p.grad.data
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if grad.is_sparse:
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raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
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amsgrad = group['amsgrad']
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p.data)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p.data)
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if amsgrad:
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# Maintains max of all exp. moving avg. of sq. grad. values
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state['max_exp_avg_sq'] = torch.zeros_like(p.data)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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if amsgrad:
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max_exp_avg_sq = state['max_exp_avg_sq']
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beta1, beta2 = group['betas']
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state['step'] += 1
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bias_correction1 = 1 - beta1 ** state['step']
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bias_correction2 = 1 - beta2 ** state['step']
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# Decay the first and second moment running average coefficient
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exp_avg.mul_(beta1).add_(1 - beta1, grad)
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exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
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if amsgrad:
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# Maintains the maximum of all 2nd moment running avg. till now
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torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
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# Use the max. for normalizing running avg. of gradient
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denom = (max_exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
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else:
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denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
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step_size = group['lr'] / bias_correction1
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p.data.addcdiv_(-step_size, exp_avg, denom)
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return loss
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@ -0,0 +1,88 @@
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""" Lookahead Optimizer Wrapper.
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Implementation modified from: https://github.com/alphadl/lookahead.pytorch
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Paper: `Lookahead Optimizer: k steps forward, 1 step back` - https://arxiv.org/abs/1907.08610
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"""
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import torch
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from torch.optim.optimizer import Optimizer
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from collections import defaultdict
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class Lookahead(Optimizer):
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def __init__(self, base_optimizer, alpha=0.5, k=6):
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if not 0.0 <= alpha <= 1.0:
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raise ValueError(f'Invalid slow update rate: {alpha}')
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if not 1 <= k:
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raise ValueError(f'Invalid lookahead steps: {k}')
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self.alpha = alpha
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self.k = k
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self.base_optimizer = base_optimizer
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self.param_groups = self.base_optimizer.param_groups
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self.defaults = base_optimizer.defaults
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self.state = defaultdict(dict)
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for group in self.param_groups:
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group["step_counter"] = 0
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def update_slow_weights(self, group):
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for fast_p in group["params"]:
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if fast_p.grad is None:
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continue
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param_state = self.state[fast_p]
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if "slow_buffer" not in param_state:
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param_state["slow_buffer"] = torch.empty_like(fast_p.data)
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param_state["slow_buffer"].copy_(fast_p.data)
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slow = param_state["slow_buffer"]
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slow.add_(self.alpha, fast_p.data - slow)
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fast_p.data.copy_(slow)
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def sync_lookahead(self):
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for group in self.param_groups:
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self.update_slow_weights(group)
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def step(self, closure=None):
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loss = self.base_optimizer.step(closure)
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for group in self.param_groups:
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group['step_counter'] += 1
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if group['step_counter'] % self.k == 0:
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self.update_slow_weights(group)
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return loss
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def state_dict(self):
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fast_state_dict = self.base_optimizer.state_dict()
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slow_state = {
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(id(k) if isinstance(k, torch.Tensor) else k): v
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for k, v in self.state.items()
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}
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fast_state = fast_state_dict["state"]
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param_groups = fast_state_dict["param_groups"]
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return {
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"state": fast_state,
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"slow_state": slow_state,
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"param_groups": param_groups,
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}
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def load_state_dict(self, state_dict):
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if 'slow_state' not in state_dict:
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print('Loading state_dict from optimizer without Lookahead applied')
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state_dict['slow_state'] = defaultdict(dict)
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slow_state_dict = {
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"state": state_dict["slow_state"],
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"param_groups": state_dict["param_groups"],
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}
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fast_state_dict = {
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"state": state_dict["state"],
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"param_groups": state_dict["param_groups"],
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}
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super(Lookahead, self).load_state_dict(slow_state_dict)
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self.base_optimizer.load_state_dict(fast_state_dict)
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def add_param_group(self, param_group):
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r"""Add a param group to the :class:`Optimizer` s `param_groups`.
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This can be useful when fine tuning a pre-trained network as frozen
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layers can be made trainable and added to the :class:`Optimizer` as
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training progresses.
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Args:
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param_group (dict): Specifies what Tensors should be optimized along
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with group specific optimization options.
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"""
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param_group['step_counter'] = 0
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self.base_optimizer.add_param_group(param_group)
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@ -0,0 +1,77 @@
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"""NovoGrad Optimizer.
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Original impl by Masashi Kimura (Convergence Lab): https://github.com/convergence-lab/novograd
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Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
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- https://arxiv.org/abs/1905.11286
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"""
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import torch
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from torch.optim.optimizer import Optimizer
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import math
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class NovoGrad(Optimizer):
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def __init__(self, params, grad_averaging=False, lr=0.1, betas=(0.95, 0.98), eps=1e-8, weight_decay=0):
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
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super(NovoGrad, self).__init__(params, defaults)
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self._lr = lr
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self._beta1 = betas[0]
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self._beta2 = betas[1]
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self._eps = eps
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self._wd = weight_decay
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self._grad_averaging = grad_averaging
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self._momentum_initialized = False
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def step(self, closure=None):
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loss = None
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if closure is not None:
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loss = closure()
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if not self._momentum_initialized:
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is None:
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continue
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state = self.state[p]
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grad = p.grad.data
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if grad.is_sparse:
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raise RuntimeError('NovoGrad does not support sparse gradients')
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v = torch.norm(grad)**2
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m = grad/(torch.sqrt(v) + self._eps) + self._wd * p.data
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state['step'] = 0
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state['v'] = v
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state['m'] = m
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state['grad_ema'] = None
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self._momentum_initialized = True
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is None:
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continue
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state = self.state[p]
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state['step'] += 1
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step, v, m = state['step'], state['v'], state['m']
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grad_ema = state['grad_ema']
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grad = p.grad.data
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g2 = torch.norm(grad)**2
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grad_ema = g2 if grad_ema is None else grad_ema * \
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self._beta2 + g2 * (1. - self._beta2)
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grad *= 1.0 / (torch.sqrt(grad_ema) + self._eps)
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if self._grad_averaging:
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grad *= (1. - self._beta1)
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g2 = torch.norm(grad)**2
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v = self._beta2*v + (1. - self._beta2)*g2
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m = self._beta1*m + (grad / (torch.sqrt(v) + self._eps) + self._wd * p.data)
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bias_correction1 = 1 - self._beta1 ** step
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bias_correction2 = 1 - self._beta2 ** step
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step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
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state['v'], state['m'] = v, m
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state['grad_ema'] = grad_ema
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p.data.add_(-step_size, m)
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return loss
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@ -0,0 +1,152 @@
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"""RAdam Optimizer.
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Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam
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Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265
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"""
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import math
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import torch
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from torch.optim.optimizer import Optimizer, required
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class RAdam(Optimizer):
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
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self.buffer = [[None, None, None] for ind in range(10)]
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super(RAdam, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(RAdam, self).__setstate__(state)
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def step(self, closure=None):
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loss = None
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if closure is not None:
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loss = closure()
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad.data.float()
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if grad.is_sparse:
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raise RuntimeError('RAdam does not support sparse gradients')
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p_data_fp32 = p.data.float()
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state = self.state[p]
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if len(state) == 0:
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state['step'] = 0
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state['exp_avg'] = torch.zeros_like(p_data_fp32)
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state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
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else:
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state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
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state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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beta1, beta2 = group['betas']
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exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
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exp_avg.mul_(beta1).add_(1 - beta1, grad)
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state['step'] += 1
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buffered = self.buffer[int(state['step'] % 10)]
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if state['step'] == buffered[0]:
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N_sma, step_size = buffered[1], buffered[2]
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else:
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buffered[0] = state['step']
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beta2_t = beta2 ** state['step']
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N_sma_max = 2 / (1 - beta2) - 1
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N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
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buffered[1] = N_sma
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# more conservative since it's an approximated value
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if N_sma >= 5:
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step_size = group['lr'] * math.sqrt(
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(1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
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N_sma_max - 2)) / (1 - beta1 ** state['step'])
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else:
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step_size = group['lr'] / (1 - beta1 ** state['step'])
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buffered[2] = step_size
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if group['weight_decay'] != 0:
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p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
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# more conservative since it's an approximated value
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if N_sma >= 5:
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denom = exp_avg_sq.sqrt().add_(group['eps'])
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p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
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else:
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p_data_fp32.add_(-step_size, exp_avg)
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p.data.copy_(p_data_fp32)
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return loss
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class PlainRAdam(Optimizer):
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
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super(PlainRAdam, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(PlainRAdam, self).__setstate__(state)
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def step(self, closure=None):
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loss = None
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if closure is not None:
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loss = closure()
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad.data.float()
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if grad.is_sparse:
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raise RuntimeError('RAdam does not support sparse gradients')
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p_data_fp32 = p.data.float()
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state = self.state[p]
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if len(state) == 0:
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state['step'] = 0
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state['exp_avg'] = torch.zeros_like(p_data_fp32)
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state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
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else:
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state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
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state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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beta1, beta2 = group['betas']
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exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
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exp_avg.mul_(beta1).add_(1 - beta1, grad)
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state['step'] += 1
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beta2_t = beta2 ** state['step']
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N_sma_max = 2 / (1 - beta2) - 1
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N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
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if group['weight_decay'] != 0:
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p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
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# more conservative since it's an approximated value
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if N_sma >= 5:
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step_size = group['lr'] * math.sqrt(
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(1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
|
||||
N_sma_max - 2)) / (1 - beta1 ** state['step'])
|
||||
denom = exp_avg_sq.sqrt().add_(group['eps'])
|
||||
p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
|
||||
else:
|
||||
step_size = group['lr'] / (1 - beta1 ** state['step'])
|
||||
p_data_fp32.add_(-step_size, exp_avg)
|
||||
|
||||
p.data.copy_(p_data_fp32)
|
||||
|
||||
return loss
|
||||
Loading…
Reference in new issue