r"""
optimizer 模块定义了 fastNLP 中所需的各种优化器，一般做为 :class:`~fastNLP.Trainer` 的参数使用。
"""
__all__ = [
"Optimizer",
"SGD",
"Adam",
"AdamW"
]
import math
import torch
from torch.optim.optimizer import Optimizer as TorchOptimizer
[文档]class Optimizer(object):
r"""
Optimizer
"""
[文档] def __init__(self, model_params, **kwargs):
r"""
:param model_params: a generator. E.g. ``model.parameters()`` for PyTorch models.
:param kwargs: additional parameters.
"""
if model_params is not None and not hasattr(model_params, "__next__"):
raise RuntimeError("model parameters should be a generator, rather than {}.".format(type(model_params)))
self.model_params = model_params
self.settings = kwargs
def construct_from_pytorch(self, model_params):
raise NotImplementedError
@staticmethod
def _get_require_grads_param(params):
r"""
将params中不需要gradient的删除
:param iterable params: parameters
:return: list(nn.Parameters)
"""
return [param for param in params if param.requires_grad]
class NullOptimizer(Optimizer):
r"""
当不希望Trainer更新optimizer时，传入本optimizer，但请确保通过callback的方式对参数进行了更新。
"""
def __init__(self):
super().__init__(None)
def construct_from_pytorch(self, model_params):
return self
def __getattr__(self, item):
def pass_func(*args, **kwargs):
pass
return pass_func
[文档]class SGD(Optimizer):
r"""
SGD
"""
[文档] def __init__(self, lr=0.001, momentum=0, model_params=None):
r"""
:param float lr: learning rate. Default: 0.01
:param float momentum: momentum. Default: 0
:param model_params: a generator. E.g. ``model.parameters()`` for PyTorch models.
"""
if not isinstance(lr, float):
raise TypeError("learning rate has to be float.")
super(SGD, self).__init__(model_params, lr=lr, momentum=momentum)
def construct_from_pytorch(self, model_params):
if self.model_params is None:
# careful! generator cannot be assigned.
return torch.optim.SGD(self._get_require_grads_param(model_params), **self.settings)
else:
return torch.optim.SGD(self._get_require_grads_param(self.model_params), **self.settings)
[文档]class Adam(Optimizer):
r"""
Adam
"""
[文档] def __init__(self, lr=0.001, weight_decay=0, betas=(0.9, 0.999), eps=1e-8, amsgrad=False, model_params=None):
r"""
:param float lr: learning rate
:param float weight_decay:
:param eps:
:param amsgrad:
:param model_params: a generator. E.g. ``model.parameters()`` for PyTorch models.
"""
if not isinstance(lr, float):
raise TypeError("learning rate has to be float.")
super(Adam, self).__init__(model_params, lr=lr, betas=betas, eps=eps, amsgrad=amsgrad,
weight_decay=weight_decay)
def construct_from_pytorch(self, model_params):
if self.model_params is None:
# careful! generator cannot be assigned.
return torch.optim.Adam(self._get_require_grads_param(model_params), **self.settings)
else:
return torch.optim.Adam(self._get_require_grads_param(self.model_params), **self.settings)
[文档]class AdamW(TorchOptimizer):
r"""
对AdamW的实现，该实现在pytorch 1.2.0版本中已经出现，https://github.com/pytorch/pytorch/pull/21250。
这里加入以适配低版本的pytorch
.. todo::
翻译成中文
The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
.. _Adam\: A Method for Stochastic Optimization: https://arxiv.org/abs/1412.6980
.. _Decoupled Weight Decay Regularization: https://arxiv.org/abs/1711.05101
.. _On the Convergence of Adam and Beyond: https://openreview.net/forum?id=ryQu7f-RZ
"""
[文档] def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=1e-2, amsgrad=False):
r"""
:param params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
:param lr (float, optional): learning rate (default: 1e-3)
:param betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.99))
:param eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
:param weight_decay (float, optional): weight decay coefficient (default: 1e-2)
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: 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)
[文档] def step(self, closure=None):
r"""Performs a single optimization step.
:param 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
# Perform stepweight decay
p.data.mul_(1 - group['lr'] * group['weight_decay'])
# Perform optimization step
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
p.data.addcdiv_(-step_size, exp_avg, denom)
return loss