Source code for trainer.optimization.adamw

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 <>`. 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 = 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( # Exponential moving average of squared gradient values state["exp_avg_sq"] = torch.zeros_like( 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, 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:, alpha=(-group["lr"] * group["weight_decay"])) return loss