from typing import Tuple
import torch
from labml import tracker
from labml.configs import BaseConfigs, option, meta_config
[docs]class OptimizerConfigs(BaseConfigs):
r"""
This creates a configurable optimizer.
Arguments:
learning_rate (float): Learning rate of the optimizer. Defaults to ``0.01``.
momentum (float): Momentum of the optimizer. Defaults to ``0.5``.
parameters: Model parameters to optimize.
d_model (int): Embedding size of the model (for Noam optimizer).
betas (Tuple[float, float]): Betas for Adam optimizer. Defaults to ``(0.9, 0.999)``.
eps (float): Epsilon for Adam/RMSProp optimizers. Defaults to ``1e-8``.
step_factor (int): Step factor for Noam optimizer. Defaults to ``1024``.
`Here's an example usage <https://github.com/labmlai/labml/blob/master/samples/pytorch/mnist/e_labml_helpers.py>`_.
Also there is a better (more options) implementation in ``labml_nn``.
`We recommend using that <https://nn.labml.ai/optimizers/configs.html>`_.
"""
optimizer: torch.optim.Adam
learning_rate: float = 0.01
momentum: float = 0.5
parameters: any
d_model: int
betas: Tuple[float, float] = (0.9, 0.999)
eps: float = 1e-8
step_factor: int = 1024
def __init__(self):
super().__init__(_primary='optimizer')
meta_config(OptimizerConfigs.parameters)
@option(OptimizerConfigs.optimizer, 'SGD')
def sgd_optimizer(c: OptimizerConfigs):
return torch.optim.SGD(c.parameters, c.learning_rate, c.momentum)
@option(OptimizerConfigs.optimizer, 'Adam')
def adam_optimizer(c: OptimizerConfigs):
return torch.optim.Adam(c.parameters, lr=c.learning_rate,
betas=c.betas, eps=c.eps)
class NoamOpt:
def __init__(self, model_size: int, learning_rate: float, warmup: int, step_factor: int, optimizer):
self.step_factor = step_factor
self.optimizer = optimizer
self.warmup = warmup
self.learning_rate = learning_rate
self.model_size = model_size
self._rate = 0
def step(self):
rate = self.rate(tracker.get_global_step() / self.step_factor)
for p in self.optimizer.param_groups:
p['lr'] = rate
self._rate = rate
self.optimizer.step()
def rate(self, step):
factor = self.model_size ** (-0.5) * min(step ** (-0.5), step * self.warmup ** (-1.5))
return self.learning_rate * factor
def zero_grad(self):
self.optimizer.zero_grad()
@option(OptimizerConfigs.optimizer, 'Noam')
def noam_optimizer(c: OptimizerConfigs):
optimizer = torch.optim.Adam(c.parameters, lr=0.0, betas=c.betas, eps=c.eps)
return NoamOpt(c.d_model, 1, 2000, c.step_factor, optimizer)
def _test_noam_optimizer():
import matplotlib.pyplot as plt
import numpy as np
opts = [NoamOpt(512, 1, 4000, None),
NoamOpt(512, 1, 8000, None),
NoamOpt(2048, 1, 2000, None)]
plt.plot(np.arange(1, 20000), [[opt.rate(i) for opt in opts] for i in range(1, 20000)])
plt.legend(["512:4000", "512:8000", "256:4000"])
plt.title("Optimizer")
plt.show()
if __name__ == '__main__':
_test_noam_optimizer()