Source code for cerebras.pytorch.optim.SGD

# Copyright 2016-2023 Cerebras Systems
# SPDX-License-Identifier: BSD-3-Clause

"""contains the Cerebras SGD implementation"""
import torch

import cerebras.pytorch as cstorch

from .optimizer import Optimizer

[docs]class SGD(Optimizer): """ SGD optimizer implemented to conform to execution within the constraints of the Cerebras WSE, including pre-initializing optimizer state """
[docs] def __init__( self, params, lr, momentum=0, dampening=0, weight_decay=0, nesterov=False, maximize=False, ): if lr < 0.0: raise ValueError("Invalid learning rate: {}".format(lr)) if momentum < 0.0: raise ValueError("Invalid momentum value: {}".format(momentum)) if weight_decay < 0.0: raise ValueError( "Invalid weight_decay value: {}".format(weight_decay) ) if nesterov and (momentum <= 0 or dampening != 0): raise ValueError( f"Nesterov momentum requires a `momentum` and zero `dampening`. " f"`momentum` was {momentum} and `dampening` was {dampening}." ) defaults = dict( lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov, maximize=maximize, ) 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']: if group['momentum'] != 0: self.state[p]["momentum_buffer"] = 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"] weight_decay = group["weight_decay"] momentum = group['momentum'] dampening = group["dampening"] nesterov = group["nesterov"] maximize = group["maximize"] for p in group["params"]: if p.grad is None: continue grad = p.grad if grad.is_sparse: raise RuntimeError("SGD does not support sparse gradients.") grad = grad if not maximize else -grad if weight_decay != 0: grad = grad.add(p, alpha=weight_decay) if momentum != 0: buf = self.state[p]["momentum_buffer"] buf.mul_(momentum).add_(grad, alpha=1.0 - dampening) if nesterov: grad.add_(buf, alpha=momentum) else: grad = buf p.add_(-lr * grad) return loss