#
# Cerebras implementation of RAdam optimizer. Adapted from the `torch.optim.RMSProp` implementation.
#
# Copyright 2016-2023 Cerebras Systems
# SPDX-License-Identifier: BSD-3-Clause
#
import torch
import cerebras.pytorch as cstorch
from .optimizer import Optimizer, ParamsT
[docs]class RMSprop(Optimizer):
"""
RMSprop optimizer implemented to perform the required
pre-initialization of the optimizer state.
"""
def __init__(
self,
params: ParamsT,
lr: float = 1e-2,
alpha: float = 0.99,
eps: float = 1e-8,
weight_decay: float = 0,
momentum: float = 0,
centered: bool = False,
):
if lr < 0.0:
raise ValueError(f"Invalid learning rate: {lr}")
if eps < 0.0:
raise ValueError(f"Invalid epsilon value: {eps}")
if momentum < 0.0:
raise ValueError(f"Invalid momentum value: {momentum}")
if weight_decay < 0.0:
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
if alpha < 0.0:
raise ValueError(f"Invalid alpha value: {alpha}")
defaults = dict(
lr=lr,
momentum=momentum,
alpha=alpha,
eps=eps,
centered=centered,
weight_decay=weight_decay,
)
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']:
self.state[p]["square_avg"] = cstorch.zeros_like(p)
if group['momentum'] > 0:
self.state[p]["momentum_buffer"] = cstorch.zeros_like(p)
if group['centered']:
self.state[p]["grad_avg"] = cstorch.zeros_like(p)
@torch.no_grad()
def step(self, closure=None):
"""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:
lr = group["lr"]
alpha = group["alpha"]
weight_decay = group["weight_decay"]
momentum = group["momentum"]
eps = group["eps"]
centered = group["centered"]
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad
if grad.is_sparse:
raise RuntimeError(
"RMSprop does not support sparse gradients."
)
state = self.state[p]
square_avg = state["square_avg"]
grad = grad + p * weight_decay
square_avg.mul_(alpha).addcmul_(grad, grad, value=1.0 - alpha)
if centered:
grad_avg = state["grad_avg"]
grad_avg.mul_(alpha).add_(grad, alpha=1 - alpha)
avg = (
square_avg.addcmul(grad_avg, grad_avg, value=-1.0)
.sqrt_()
.add_(eps)
)
else:
avg = square_avg.sqrt().add_(eps)
if momentum > 0.0:
momentum_buffer = state["momentum_buffer"]
momentum_buffer.mul_(momentum).addcdiv_(grad, avg)
p.add_(-lr * momentum_buffer)
else:
p.addcdiv_(-lr * grad, avg)
return loss