# Copyright 2021 Facebook and Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Callable, Iterable, Tuple
import torch
from torch import nn
from torch.optim import Optimizer
[docs]class AdamW(Optimizer):
"""
Implements Adam algorithm with weight decay fix as introduced in `Decoupled Weight Decay Regularization
<https://arxiv.org/abs/1711.05101>`.
Parameters:
params (Iterable[nn.parameter.Parameter]):
Iterable of parameters to optimize or dictionaries defining parameter groups.
lr (float, optional):
The learning rate to use.
betas (Tuple[float,float], optional):
Adam's betas parameters (b1, b2).
eps (float, optional):
Adam's epsilon for numerical stability.
weight_decay (float, optional):
Decoupled weight decay to apply.
correct_bias (bool, optional):
Whether or not to correct bias in Adam.
"""
def __init__(
self,
params: Iterable[nn.parameter.Parameter],
lr: float = 1e-3,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-6,
weight_decay: float = 0.0,
correct_bias: bool = True,
):
if lr < 0.0:
raise ValueError(f"Invalid learning rate: {lr} - should be >= 0.0")
if not 0.0 <= betas[0] < 1.0:
raise ValueError(f"Invalid beta parameter: {betas[0]} - should be in [0.0, 1.0[")
if not 0.0 <= betas[1] < 1.0:
raise ValueError(f"Invalid beta parameter: {betas[1]} - should be in [0.0, 1.0[")
if 0.0 > eps:
raise ValueError(f"Invalid epsilon value: {eps} - should be >= 0.0")
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, correct_bias=correct_bias)
super().__init__(params, defaults)
[docs] def step(self, closure: Callable = 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.is_sparse:
raise RuntimeError("Adam does not support sparse gradients, please consider SparseAdam instead")
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)
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
# Decay the first and second moment running average coefficient
# In-place operations to update the averages at the same time
exp_avg.mul_(beta1).add_(grad, alpha=(1.0 - beta1))
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
denom = exp_avg_sq.sqrt().add_(group["eps"])
step_size = group["lr"]
if group["correct_bias"]: # No bias correction for Bert
bias_correction1 = 1.0 - beta1 ** state["step"]
bias_correction2 = 1.0 - beta2 ** state["step"]
step_size = step_size * math.sqrt(bias_correction2) / bias_correction1
p.data.addcdiv_(exp_avg, denom, value=-step_size)
# Just adding the square of the weights to the loss function is *not*
# the correct way of using L2 regularization/weight decay with Adam,
# since that will interact with the m and v parameters in strange ways.
#
# Instead we want to decay the weights in a manner that doesn't interact
# with the m/v parameters. This is equivalent to adding the square
# of the weights to the loss with plain (non-momentum) SGD.
# Add weight decay at the end (fixed version)
if group["weight_decay"] > 0.0:
p.data.add_(p.data, alpha=(-group["lr"] * group["weight_decay"]))
return loss
