# This code is adapted from
# https://github.com/cybertronai/pytorch-lamb/blob/master/pytorch_lamb/lamb.py
#
# Copyright 2016-2023 Cerebras Systems
# SPDX-License-Identifier: MIT
#
# Copyright (c) 2019 cybertronai
#
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import torch
import cerebras.pytorch as cstorch
from .optimizer import Optimizer
[docs]class Lamb(Optimizer):
r"""Implements Lamb algorithm.
It has been proposed in `Large Batch Optimization for Deep Learning:
Training BERT in 76 minutes`_.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
adam (bool, optional): always use trust ratio = 1, which turns this into
Adam. Useful for comparison purposes.
.. _Large Batch Optimization for Deep Learning\: Training BERT in 76 minutes:
https://arxiv.org/abs/1904.00962
"""
def __init__(
self,
params,
lr=1e-3,
betas=(0.9, 0.999),
eps=1e-6,
weight_decay=0,
adam=False,
):
if lr < 0.0:
raise ValueError(f"Invalid learning rate: {lr}")
if eps < 0.0:
raise ValueError(f"Invalid epsilon value: {eps}")
if not 0.0 <= betas[0] < 1.0:
raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
if not 0.0 <= betas[1] < 1.0:
raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
defaults = dict(
lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, adam=adam
)
super().__init__(params, defaults)
[docs] def preinitialize(self):
"""
Allocates tensors for the optimizer state to allow direct compilation
of the model before the first step.
"""
for group in self.param_groups:
for p in group['params']:
state = self.state[p]
# Exponential moving average of gradient values
state['exp_avg'] = cstorch.zeros_like(p)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = cstorch.zeros_like(p)
@torch.no_grad()
def step(self, closure=None):
r"""Performs a single optimization step.
Args:
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
if grad.is_sparse:
raise RuntimeError(
'Lamb does not support sparse gradients, consider SparseAdam instad.'
)
state = self.state[p]
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
# Decay the first and second moment running average coefficient
# m_t
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
# v_t
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
# Paper v3 does not use debiasing.
# bias_correction1 = 1 - beta1 ** state['step']
# bias_correction2 = 1 - beta2 ** state['step']
# Apply bias to lr to avoid broadcast.
step_size = group[
'lr'
] # * math.sqrt(bias_correction2) / bias_correction1
weight_norm = p.pow(2).sum().sqrt().clamp(0, 10).to(torch.float)
adam_step = exp_avg / exp_avg_sq.sqrt().add(group['eps'])
adam_step = adam_step + p * group["weight_decay"]
adam_norm = adam_step.pow(2).sum().sqrt().to(torch.float)
# pytorch version for future reference (we don't support
# weight_norm == 0 or adam_norm == 0)
# if weight_norm == 0 or adam_norm == 0:
# trust_ratio = 1
# else:
# trust_ratio = weight_norm / adam_norm
zero = torch.tensor(
0.0, dtype=torch.float32, device=weight_norm.device
)
trust_ratio = torch.where(
torch.gt(weight_norm, zero),
torch.where(
torch.gt(adam_norm, zero),
weight_norm / adam_norm,
torch.tensor(
1.0, dtype=torch.float32, device=weight_norm.device
),
),
torch.tensor(
1.0, dtype=torch.float32, device=weight_norm.device
),
)
if group['adam']:
trust_ratio = 1
update_step = adam_step.mul(trust_ratio)
p.sub_(update_step * step_size)
return loss