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optimiser.py
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optimiser.py
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import numpy as np
import torch
from collections import namedtuple
from util import PiecewiseLinear
from torch.optim.optimizer import Optimizer, required
import torch.distributed as dist
class TorchOptimiser():
def __init__(self, weights, optimizer, step_number=0, **opt_params):
self.weights = weights
self.step_number = step_number
self.opt_params = opt_params
self._opt = optimizer(weights, **self.param_values())
def param_values(self):
return {k: v(self.step_number) if callable(v) else v for k, v in self.opt_params.items()}
def step(self):
self.step_number += 1
self._opt.param_groups[0].update(**self.param_values())
self._opt.step()
def __repr__(self):
return repr(self._opt)
def SGD(weights, lr=0, momentum=0, weight_decay=0, dampening=0, nesterov=False):
return TorchOptimiser(weights, torch.optim.SGD, lr=lr, momentum=momentum,
weight_decay=weight_decay, dampening=dampening,
nesterov=nesterov)
class FedProx(Optimizer):
def __init__(self, params, ratio, gmf, lr=required, momentum=0, dampening=0,
weight_decay=0, nesterov=False, variance=0, mu=0):
self.gmf = gmf
self.ratio = ratio
self.itr = 0
self.a_sum = 0
self.mu = mu
if lr is not required and 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))
defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
weight_decay=weight_decay, nesterov=nesterov, variance=variance)
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError("Nesterov momentum requires a momentum and zero dampening")
super(FedProx, self).__init__(params, defaults)
def __setstate__(self, state):
super(FedProx, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('nesterov', False)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
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:
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
for p in group['params']:
if p.grad is None:
continue
d_p = p.grad.data
if weight_decay != 0:
d_p.add_(weight_decay, p.data)
param_state = self.state[p]
if 'old_init' not in param_state:
param_state['old_init'] = torch.clone(p.data).detach()
if momentum != 0:
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = torch.clone(d_p).detach()
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(1 - dampening, d_p)
if nesterov:
d_p = d_p.add(momentum, buf)
else:
d_p = buf
# apply proximal update
d_p.add_(self.mu, p.data - param_state['old_init'])
p.data.add_(-group['lr'], d_p)
return loss
def average(self):
param_list = []
for group in self.param_groups:
for p in group['params']:
p.data.mul_(self.ratio)
param_list.append(p.data)
communicate(param_list, dist.all_reduce)
for group in self.param_groups:
for p in group['params']:
param_state = self.state[p]
param_state['old_init'] = torch.clone(p.data).detach()
# Reinitialize momentum buffer
if 'momentum_buffer' in param_state:
param_state['momentum_buffer'].zero_()
# helper functions for fedprox
def communicate(tensors, communication_op):
"""
Reference: https://github.com/facebookresearch/stochastic_gradient_push
Communicate a list of tensors.
Arguments:
tensors (Iterable[Tensor]): list of tensors.
communication_op: a method or partial object which takes a tensor as
input and communicates it. It can be a partial object around
something like torch.distributed.all_reduce.
"""
flat_tensor = flatten_tensors(tensors)
communication_op(tensor=flat_tensor)
for f, t in zip(unflatten_tensors(flat_tensor, tensors), tensors):
t.set_(f)
def flatten_tensors(tensors):
"""
Reference: https://github.com/facebookresearch/stochastic_gradient_push
Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
same dense type.
Since inputs are dense, the resulting tensor will be a concatenated 1D
buffer. Element-wise operation on this buffer will be equivalent to
operating individually.
Arguments:
tensors (Iterable[Tensor]): dense tensors to flatten.
Returns:
A 1D buffer containing input tensors.
"""
if len(tensors) == 1:
return tensors[0].view(-1).clone()
flat = torch.cat([t.view(-1) for t in tensors], dim=0)
return flat
def unflatten_tensors(flat, tensors):
"""
Reference: https://github.com/facebookresearch/stochastic_gradient_push
View a flat buffer using the sizes of tensors. Assume that tensors are of
same dense type, and that flat is given by flatten_dense_tensors.
Arguments:
flat (Tensor): flattened dense tensors to unflatten.
tensors (Iterable[Tensor]): dense tensors whose sizes will be used to
unflatten flat.
Returns:
Unflattened dense tensors with sizes same as tensors and values from
flat.
"""
outputs = []
offset = 0
for tensor in tensors:
numel = tensor.numel()
outputs.append(flat.narrow(0, offset, numel).view_as(tensor))
offset += numel
return tuple(outputs)