#
# Cerebras implementation of Rprop optimizer. Adapted from the `torch.optim.Rprop` implementation.
#
# Copyright 2016-2023 Cerebras Systems
# SPDX-License-Identifier: BSD-3-Clause
#
from typing import Iterable, Tuple
import torch
import cerebras_pytorch as cstorch
from .optimizer import Optimizer
[docs]class Rprop(Optimizer):
r"""Rprop optimizer implemented to conform to execution within the constraints
of the Cerebras WSE, including pre-initializing optimizer state
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
etas (Tuple[float, float], optional): step size multipliers
step_size (Tuple[float, float], optional): Tuple of min, max step size
values. Step size is clamped to be between these values.
"""
[docs] def __init__(
self,
params: Iterable[torch.nn.Parameter],
lr: float = 1e-3,
etas: Tuple[float, float] = (0.5, 1.2),
step_sizes: Tuple[float, float] = (1e-6, 50.0),
):
if lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
defaults = dict(lr=lr, etas=etas, step_sizes=step_sizes,)
super().__init__(params, defaults)
[docs] def state_names_to_sparsify(self):
return ["prev", "step_size"]
[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]
state["prev"] = cstorch.zeros_like(p)
state["step_size"] = cstorch.full_like(p, group['lr'])
@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:
step_size_min, step_size_max = [
ss if isinstance(ss, torch.Tensor) else torch.tensor(ss)
for ss in group['step_sizes']
]
etaminus, etaplus = [
eta if isinstance(eta, torch.Tensor) else torch.tensor(eta)
for eta in group['etas']
]
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad
if grad.is_sparse:
raise RuntimeError(
"Rprop does not support sparse gradients."
)
state = self.state[p]
prev = state["prev"]
prev_step_size = state["step_size"]
zero = torch.tensor(0.0).to(p.device)
one = torch.tensor(1.0).to(p.device)
neg_one = torch.tensor(-1.0).to(p.device)
# check gradient for sign change - set step multiplier to etaplus, etaminus, or 1
step_multiplier = grad.mul(prev)
step_multiplier = torch.where(
torch.gt(step_multiplier, zero),
etaplus.to(p.device),
step_multiplier,
)
step_multiplier = torch.where(
torch.lt(step_multiplier, zero),
etaminus.to(p.device),
step_multiplier,
)
step_multiplier = torch.where(
torch.eq(step_multiplier, zero), one, step_multiplier,
)
# calculate new step size and clamp between step_size_min and step_size_max
step_size = prev_step_size.mul_(step_multiplier).to(p.device)
step_size = torch.clamp(
step_size, min=step_size_min, max=step_size_max,
)
# Zero out the gradient wherever there was a sign change
mask = torch.where(
torch.eq(step_multiplier, etaminus.to(p.device)), zero, one,
)
grad.mul_(mask.to(p.device))
# Get the sign of the gradient
sign_grad = torch.zeros_like(grad)
sign_grad = torch.where(torch.gt(grad, zero), one, sign_grad)
sign_grad = torch.where(
torch.lt(grad, zero), neg_one, sign_grad
)
update = torch.mul(sign_grad, step_size).to(p.device)
# update parameters
p.sub_(update)
prev.copy_(grad)
prev_step_size.copy_(step_size)
return loss