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from collections import defaultdict
from torch.optim.optimizer import Optimizer
class Lookahead(Optimizer):
"""
Implements the Lookahead optimization algorithm.
"""
def __init__(self, optimizer, k=5, alpha=0.5):
"""
Constructor.
Args:
optimizer (torch.optim.Optimizer): The base optimizer.
k (int, optional): Number of steps for lookahead. Defaults to 5.
alpha (float, optional): Coef for blending the fast & slow weights. Defaults to 0.5.
"""
self.optimizer = optimizer
self.k = k
self.alpha = alpha
self.param_groups = self.optimizer.param_groups
self.state = defaultdict(dict)
self.fast_state = self.optimizer.state
for group in self.param_groups:
group["counter"] = 0
def update(self, group):
"""
Updates parameters in a group.
Group represents a parameter group in the optimizer.
It typically contains the following keys:
- "params" (a list of parameters),
- "lr" (learning rate),
- "momentum",
- "dampening",
- "weight_decay",
and other optimizer-specific parameters.
Args:
group (dict): Parameter group.
"""
for fast in group["params"]:
param_state = self.state[fast]
if "slow_param" not in param_state:
param_state["slow_param"] = torch.zeros_like(fast.data)
param_state["slow_param"].copy_(fast.data)
slow = param_state["slow_param"]
slow += (fast.data - slow) * self.alpha
fast.data.copy_(slow)
def update_lookahead(self):
"""
Updates all parameters.
"""
for group in self.param_groups:
self.update(group)
def step(self, closure=None):
"""
Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model and returns the loss.
Returns:
float: The loss value after the optimization step.
"""
loss = self.optimizer.step(closure)
for group in self.param_groups:
if group["counter"] == 0:
self.update(group)
group["counter"] += 1
if group["counter"] >= self.k:
group["counter"] = 0
return loss
def state_dict(self):
"""
Returns the state of the optimizer as a dictionary.
Returns:
dict: The optimizer state.
"""
fast_state_dict = self.optimizer.state_dict()
slow_state = {
(id(k) if isinstance(k, torch.Tensor) else k): v
for k, v in self.state.items()
}
fast_state = fast_state_dict["state"]
param_groups = fast_state_dict["param_groups"]
return {
"fast_state": fast_state,
"slow_state": slow_state,
"param_groups": param_groups,
}
def load_state_dict(self, state_dict):
"""
Loads the optimizer state.
Args:
state_dict (dict): The optimizer state dictionary.
"""
slow_state_dict = {
"state": state_dict["slow_state"],
"param_groups": state_dict["param_groups"],
}
fast_state_dict = {
"state": state_dict["fast_state"],
"param_groups": state_dict["param_groups"],
}
super(Lookahead, self).load_state_dict(slow_state_dict)
self.optimizer.load_state_dict(fast_state_dict)
self.fast_state = self.optimizer.state
def add_param_group(self, param_group):
"""
Adds a parameter group to the optimizer.
Args:
param_group (dict): Parameter group.
"""
param_group["counter"] = 0
self.optimizer.add_param_group(param_group)
def define_optimizer(
model, name, lr=1e-3, lr_encoder=1e-3, weight_decay=0, betas=(0.9, 0.999)
):
"""
Defines an optimizer for the given model based on the specified name.
Supports discriminative lr between the encoder and other layers.
Args:
model (torch.nn.Module): The model for which to define the optimizer.
name (str): The name of the optimizer.
lr (float, optional): The learning rate. Defaults to 1e-3.
lr_encoder (float, optional): The learning rate for encoder layers. Defaults to 1e-3.
weight_decay (float, optional): The weight decay. Defaults to 0.
betas (tuple, optional): Optimizer betas. Defaults to (0.9, 0.999).
Raises:
NotImplementedError: If the specified optimizer name is not supported.
Returns:
torch.optim.Optimizer: The defined optimizer.
"""
if weight_decay or lr != lr_encoder:
no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
opt_params = []
for n, p in model.named_parameters():
wd = 0 if any(nd in n for nd in no_decay) else weight_decay
lr_ = lr_encoder if "encoder" in n else lr
opt_params.append(
{
"params": [p],
"weight_decay": wd,
"lr": lr_,
}
)
else:
opt_params = model.parameters()
if name.lower() == "ranger":
radam = getattr(torch.optim, "RAdam")(opt_params, lr=lr, betas=betas)
return Lookahead(radam, alpha=0.5, k=5)
try:
optimizer = getattr(torch.optim, name)(opt_params, lr=lr, betas=betas)
except AttributeError:
raise NotImplementedError(name)
return optimizer
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