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train_LocalSGD.py
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train_LocalSGD.py
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import os
import numpy as np
import time
import argparse
import sys
from math import ceil
from random import Random
import torch
import torch.distributed as dist
import torch.utils.data.distributed
import torch.nn as nn
import torch.nn.functional as F
# import torch.optim as optim
from torch.multiprocessing import Process
import torchvision
from torchvision import datasets, transforms
import torch.backends.cudnn as cudnn
import torchvision.models as models
from distoptim import LocalSGD, OverlapLocalSGD
import util_v4 as util
from comm_helpers import SyncAllreduce
parser = argparse.ArgumentParser(description='CIFAR-10 baseline')
parser.add_argument('--name','-n',
default="default",
type=str,
help='experiment name, used for saving results')
parser.add_argument('--backend',
default="nccl",
type=str,
help='experiment name, used for saving results')
parser.add_argument('--dataset',
default="cifar10",
type=str,
help='dataset name')
parser.add_argument('--model',
default="res",
type=str,
help='neural network model')
parser.add_argument('--alpha',
default=0.2,
type=float,
help='alpha')
parser.add_argument('--gmf',
default=0,
type=float,
help='global momentum factor')
parser.add_argument('--lr',
default=0.1,
type=float,
help='learning rate')
parser.add_argument('--bs',
default=512,
type=int,
help='batch size on each worker')
parser.add_argument('--epoch',
default=200,
type=int,
help='total epoch')
parser.add_argument('--cp',
default=98,
type=int,
help='communication period / work per clock')
parser.add_argument('--print_freq',
default=100,
type=int,
help='print info frequency')
parser.add_argument('--rank',
default=0,
type=int,
help='the rank of worker')
parser.add_argument('--size',
default=8,
type=int,
help='number of workers')
parser.add_argument('--seed',
default=1,
type=int,
help='random seed')
parser.add_argument('--save', '-s',
action='store_true',
help='whether save the training results')
parser.add_argument('--all_reduce',
action='store_true',
help='whether use AR-SGD')
parser.add_argument('--schedule', nargs='+', default=None,
type=float, help='learning rate schedule')
parser.add_argument('--warmup', default='False', type=str,
help='whether to warmup learning rate for first 5 epochs')
parser.add_argument('--p', '-p',
action='store_true',
help='whether the dataset is partitioned or not')
parser.add_argument('--NIID',
action='store_true',
help='whether the dataset is partitioned or not')
args = parser.parse_args()
args.lr_schedule = {}
if args.schedule is None:
args.schedule = [30, 0.1, 60, 0.1, 80, 0.1]
i, epoch = 0, None
for v in args.schedule:
if i == 0:
epoch = v
elif i == 1:
args.lr_schedule[epoch] = v
i = (i + 1) % 2
del args.schedule
print(args)
def run(rank, size):
# initiate experiments folder
save_path = '/users/jianyuw1/SGD_non_iid/results/'
folder_name = save_path+args.name
if rank == 0 and os.path.isdir(folder_name)==False and args.save:
os.mkdir(folder_name)
dist.barrier()
# initiate log files
tag = '{}/lr{:.3f}_bs{:d}_cp{:d}_a{:.2f}_b{:.2f}_e{}_r{}_n{}.csv'
saveFileName = tag.format(folder_name, args.lr, args.bs, args.cp,
args.alpha, args.gmf, args.seed, rank, size)
args.out_fname = saveFileName
with open(args.out_fname, 'w+') as f:
print(
'BEGIN-TRAINING\n'
'World-Size,{ws}\n'
'Batch-Size,{bs}\n'
'Epoch,itr,BT(s),avg:BT(s),std:BT(s),'
'CT(s),avg:CT(s),std:CT(s),'
'Loss,avg:Loss,Prec@1,avg:Prec@1,val'.format(
ws=args.size,
bs=args.bs),
file=f)
# seed for reproducibility
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
# load datasets
train_loader, test_loader = util.partition_dataset(rank, size, args)
# define neural nets model, criterion, and optimizer
model = util.select_model(10, args).cuda()
criterion = nn.CrossEntropyLoss().cuda()
optimizer = LocalSGD(model.parameters(),
lr=args.lr,
gmf=args.gmf,
tau=args.cp,
size=size,
momentum=0.9,
nesterov = True,
weight_decay=1e-4)
# optimizer = OverlapLocalSGD(model.parameters(),
# lr=args.lr,
# alpha=args.alpha,
# gmf=args.gmf,
# tau = args.cp,
# size=size,
# momentum=0.9,
# nesterov = True,
# weight_decay=1e-4)
batch_meter = util.Meter(ptag='Time')
comm_meter = util.Meter(ptag='Time')
best_test_accuracy = 0
req = None
for epoch in range(args.epoch):
train(model, criterion, optimizer, batch_meter, comm_meter,
train_loader, epoch)
test_acc = evaluate(model, test_loader)
if test_acc > best_test_accuracy:
best_test_accuracy = test_acc
with open(args.out_fname, '+a') as f:
print('{ep},{itr},{bt:.4f},{filler},{filler},'
'{ct:.4f},{filler},{filler},'
'{filler},{filler},'
'{filler},{filler},'
'{val:.4f}'
.format(ep=epoch, itr=-1,
bt=batch_meter.sum,
ct=comm_meter.sum,
filler=-1, val=test_acc),
file=f)
def evaluate(model, test_loader):
model.eval()
top1 = util.AverageMeter()
with torch.no_grad():
for batch_idx, (data, target) in enumerate(test_loader):
data = data.cuda(non_blocking = True)
target = target.cuda(non_blocking = True)
outputs = model(data)
acc1 = util.comp_accuracy(outputs, target)
top1.update(acc1[0].item(), data.size(0))
return top1.avg
def train(model, criterion, optimizer, batch_meter, comm_meter,
loader, epoch):
model.train()
losses = util.Meter(ptag='Loss')
top1 = util.Meter(ptag='Prec@1')
weights = [1/args.size for i in range(args.size)]
iter_time = time.time()
for batch_idx, (data, target) in enumerate(loader):
# data loading
data = data.cuda(non_blocking = True)
target = target.cuda(non_blocking = True)
# forward pass
output = model(data)
loss = criterion(output, target)
# backward pass
loss.backward()
update_learning_rate(optimizer, epoch, itr=batch_idx,
itr_per_epoch=len(loader))
# gradient step
optimizer.step()
optimizer.zero_grad()
torch.cuda.synchronize()
comm_start = time.time()
# Communication step: average local models
optimizer.average()
if not (epoch == 0 and batch_idx == 0):
torch.cuda.synchronize()
comm_meter.update(time.time() - comm_start)
batch_meter.update(time.time() - iter_time)
# write log files
train_acc = util.comp_accuracy(output, target)
losses.update(loss.item(), data.size(0))
top1.update(train_acc[0].item(), data.size(0))
if batch_idx % args.print_freq == 0 and args.save:
print('epoch {} itr {}, '
'rank {}, loss value {:.4f}, train accuracy {:.3f}'.format(
epoch, batch_idx, rank, losses.avg, top1.avg))
with open(args.out_fname, '+a') as f:
print('{ep},{itr},{bt},{ct},'
'{loss.val:.4f},{loss.avg:.4f},'
'{top1.val:.3f},{top1.avg:.3f},-1'
.format(ep=epoch, itr=batch_idx,
bt=batch_meter, ct=comm_meter,
loss=losses, top1=top1), file=f)
torch.cuda.synchronize()
iter_time = time.time()
with open(args.out_fname, '+a') as f:
print('{ep},{itr},{bt},{ct},'
'{loss.val:.4f},{loss.avg:.4f},'
'{top1.val:.3f},{top1.avg:.3f},-1'
.format(ep=epoch, itr=batch_idx,
bt=batch_meter, ct=comm_meter,
loss=losses, top1=top1), file=f)
def update_learning_rate(optimizer, epoch, itr=None, itr_per_epoch=None,
scale=1):
"""
1) Linearly warmup to reference learning rate (5 epochs)
2) Decay learning rate exponentially (epochs 30, 60, 80)
** note: args.lr is the reference learning rate from which to scale up
** note: minimum global batch-size is 256
"""
target_lr = args.lr * args.bs * scale * args.size / 128
lr = None
if args.warmup and epoch < 5: # warmup to scaled lr
if target_lr <= args.lr:
lr = target_lr
else:
assert itr is not None and itr_per_epoch is not None
count = epoch * itr_per_epoch + itr + 1
incr = (target_lr - args.lr) * (count / (5 * itr_per_epoch))
lr = args.lr + incr
else:
lr = target_lr
for e in args.lr_schedule:
if epoch >= e:
lr *= args.lr_schedule[e]
if lr is not None:
# print('Updating learning rate to {}'.format(lr))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def init_processes(rank, size, fn):
""" Initialize the distributed environment. """
dist.init_process_group(backend=args.backend,
init_method='tcp://h0:22000',
rank=rank,
world_size=size)
fn(rank, size)
if __name__ == "__main__":
rank = args.rank
size = args.size
print(rank)
init_processes(rank, size, run)