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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# LARS optimizer, implementation from MoCo v3:
# https://github.com/facebookresearch/moco-v3
# --------------------------------------------------------
import torch
class LARS(torch.optim.Optimizer):
"""
LARS optimizer, no rate scaling or weight decay for parameters <= 1D.
"""
def __init__(
self, params, lr=0, weight_decay=0, momentum=0.9, trust_coefficient=0.001
):
defaults = dict(
lr=lr,
weight_decay=weight_decay,
momentum=momentum,
trust_coefficient=trust_coefficient,
)
super().__init__(params, defaults)
@torch.no_grad()
def step(self):
for g in self.param_groups:
for p in g["params"]:
dp = p.grad
if dp is None:
continue
if p.ndim > 1: # if not normalization gamma/beta or bias
dp = dp.add(p, alpha=g["weight_decay"])
param_norm = torch.norm(p)
update_norm = torch.norm(dp)
one = torch.ones_like(param_norm)
q = torch.where(
param_norm > 0.0,
torch.where(
update_norm > 0,
(g["trust_coefficient"] * param_norm / update_norm),
one,
),
one,
)
dp = dp.mul(q)
param_state = self.state[p]
if "mu" not in param_state:
param_state["mu"] = torch.zeros_like(p)
mu = param_state["mu"]
mu.mul_(g["momentum"]).add_(dp)
p.add_(mu, alpha=-g["lr"])
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