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trainer_gatherSI_resnet.py
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trainer_gatherSI_resnet.py
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import argparse
import os
import shutil
import time
import numpy as np
import copy
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
from torch.nn.functional import relu, avg_pool2d
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.nn.functional as F
from math import ceil
from random import Random
# Importing modules related to distributed processing
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.multiprocessing import Process
from torch.autograd import Variable
from torch.multiprocessing import spawn
#from torch.utils.tensorboard import SummaryWriter
###########
from gossip_choco import GossipDataParallel
from gossip_choco import RingGraph, GridGraph
from gossip_choco import UniformMixing
from gossip_choco import *
from models import *
import parser as file_parser
from collections import OrderedDict
from copy import deepcopy
import notmnist_setup
import miniimagenet_setup
import medmnist
from medmnist import INFO, Evaluator
parser = argparse.ArgumentParser(description='Propert AlexNet for CIFAR10/CIFAR100 in pytorch')
parser.add_argument('--devices', default=4, type=int, help='number of available GPU cards')
parser.add_argument('--dataset', dest='dataset', help='available datasets: 5datasets, miniimagenet, medmnist', default='5datasets', type=str)
parser.add_argument('--classes', default=100, type=int, help='number of classes in the dataset')
parser.add_argument('-b', '--batch-size', default=128, type=int, metavar='N', help='mini-batch size (default: 128)')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float, metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0, type=float, metavar='M', help='momentum')
parser.add_argument('--weight-decay', '--wd', default=0, type=float, metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('-world_size', '--world_size', default=4, type=int, help='total number of nodes')
parser.add_argument('-neighbors', '--neighbors', default=1, type=int, help='total number of neighbors of any node, added keeping in mind ring topology')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=100, type=int, metavar='N', help='number of total epochs to run')
parser.add_argument('--seed', default=1234, type=int, help='set seed')
parser.add_argument('--run_no', default=1, type=str, help='parallel run number, models saved as model_{rank}_{run_no}.th')
parser.add_argument('--print-freq', '-p', default=50, type=int, metavar='N', help='print frequency (default: 50)')
parser.add_argument('--save-dir', dest='save_dir', help='The directory used to save the trained models', default='save_temp', type=str)
parser.add_argument('--port', dest='port', help='between 3000 to 65000',default='29500' , type=str)
parser.add_argument('--save-every', dest='save_every', help='Saves checkpoints at every specified number of epochs', type=int, default=5)
parser.add_argument('--biased', dest='biased', action='store_true', help='biased compression')
parser.add_argument('--unbiased', dest='biased', action='store_false', help='biased compression')
parser.add_argument('--level', default=32, type=int, metavar='k', help='quantization level 1-32')
parser.add_argument('--eta', default=1.0, type=float, metavar='AR', help='averaging rate') # default=1.0, and 0.0 means no sharing
parser.add_argument('--compress', default=False, type=bool, metavar='COMP', help='True: compress by sending coefficients associated with the orthogonal basis space')
parser.add_argument('--skew', default=0.0, type=float, help='belongs to [0,1] where 0= completely iid and 1=completely non-iid')
parser.add_argument('--num_tasks', default=5, type=int, help='number of tasks (over time)') #CIFAR-100 split into 10 tasks
parser.add_argument('--graph', default='ring', type=str, help='graph structure')
parser.add_argument('--si_epsilon', default=1e-3, type=float, help='SI damping factor')
parser.add_argument('--ewc_lamb', default=0.1, type=float, help='regularization strength')
parser.add_argument('--glances', default=1, type=int, help='glances (kind of ignored)')
parser.add_argument('--grad_clip_norm', default=500, type=float, help='grad clip')
parser.add_argument('--cuda', default=True, type=bool, help='cuda')
parser.add_argument('--backend', default='nccl', type=str, help='backend for distributed setup')
# ewc_lamb is the 'c' or regularization factor in SI.
args = parser.parse_args()
## Define ResNet18 model
def compute_conv_output_size(Lin,kernel_size,stride=1,padding=0,dilation=1):
return int(np.floor((Lin+2*padding-dilation*(kernel_size-1)-1)/float(stride)+1))
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv7x7(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=7, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_planes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes, track_running_stats=False)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes, track_running_stats=False)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1,
stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes, track_running_stats=False)
)
self.act = OrderedDict()
def forward(self, x):
self.act['conv_0'] = x
out = relu(self.bn1(self.conv1(x)))
self.act['conv_1'] = out
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, nf, dataset, ntasks=2):
super(ResNet, self).__init__()
self.in_planes = nf
if(dataset=='5datasets' or dataset=='medmnist'):
self.conv1 = conv3x3(3, nf * 1, 1)
factor = 4
else:
self.conv1 = conv3x3(3, nf * 1, 2)
factor = 9
self.bn1 = nn.BatchNorm2d(nf * 1, track_running_stats=False)
self.layer1 = self._make_layer(block, nf * 1, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, nf * 2, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, nf * 4, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, nf * 8, num_blocks[3], stride=2)
self.ntasks = ntasks
self.linear=torch.nn.ModuleList()
for t, n in self.ntasks:
self.linear.append(nn.Linear(nf * 8 * block.expansion * factor, n, bias=False))
self.act = OrderedDict()
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
self.act['conv_in'] = x
out = relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = avg_pool2d(out, 2)
out = out.view(out.size(0), -1)
y=[]
for t,i in self.ntasks:
y.append(self.linear[t](out))
return y
def ResNet18(dataset, ntasks, nf=32):
return ResNet(BasicBlock, [2, 2, 2, 2], nf, dataset, ntasks)
class Partition(object):
def __init__(self, data, index):
self.data = data
self.index = index
def __len__(self):
return len(self.index)
def __getitem__(self, index):
data_idx = self.index[index]
return self.data[data_idx]
def skew_sort(indices, skew, classes, class_size, seed):
# skew belongs to [0,1]
rng = Random()
rng.seed(seed)
class_indices = {}
for i in range(0, classes):
class_indices[i]=indices[0:class_size[i]]
indices = indices[class_size[i]:]
random_indices = []
sorted_indices = []
for i in range(0, classes):
sorted_size = int(skew*class_size[i])
sorted_indices = sorted_indices + class_indices[i][0:sorted_size]
random_indices = random_indices + class_indices[i][sorted_size:]
rng.shuffle(random_indices)
return random_indices, sorted_indices
class DataPartitioner(object):
""" Partitions a dataset into different chunks"""
def __init__(self, data, sizes, skew, classes, class_size, seed, device, tasks=2):
assert classes%tasks==0
self.data = data
self.partitions = {}
data_len = len(data)
dataset = torch.utils.data.DataLoader(data, batch_size=512, shuffle=False, num_workers=2)
labels = []
for batch_idx, (inputs, targets) in enumerate(dataset):
labels = labels+targets.tolist()
sort_index = np.argsort(np.array(labels))
indices_full = sort_index.tolist()
task_data_len = int(data_len/tasks)
for n in range(tasks):
ind_per_task = indices_full[n*task_data_len: (n+1)*task_data_len]
indices_rand, indices = skew_sort(ind_per_task, skew=skew, classes=int(classes/tasks), class_size=class_size, seed=seed)
self.partitions[n] = []
for frac in sizes:
if skew==1:
part_len = int(frac*task_data_len)
self.partitions[n].append(indices[0:part_len])
indices = indices[part_len:]
elif skew==0:
part_len = int(frac*task_data_len)
self.partitions[n].append(indices_rand[0:part_len])
if(args.eta!=0.0):
indices_rand = indices_rand[part_len:] #remove to use full data at each node for experiment
else:
part_len = int(frac*task_data_len*skew);
part_len_rand = int(frac*task_data_len*(1-skew))
part_ind = indices[0:part_len]+indices_rand[0:part_len_rand]
self.partitions[n].append(part_ind)
indices = indices[part_len:]
indices_rand = indices_rand[part_len_rand:]
def use(self, partition, task):
return Partition(self.data, self.partitions[task][partition])
class DataPartition_5set(object):
""" Partitions 5-datasets across different nodes, not setup for non-IID data yet, works only for SKEW=0"""
def __init__(self, data_type, data, sizes, skew, classes, class_size, seed, device, tasks=2):
self.data = data
self.partitions = {}
indices_full = []
data_len= []
for i in range(len(data)):
dataset = torch.utils.data.DataLoader(data[i], batch_size=512, shuffle=False, num_workers=2)
data_len.append(len(data[i]))
labels= []
if(data_type=='5datasets'):
for batch_idx, (inputs, targets) in enumerate(dataset):
labels = labels+targets.tolist()
else:
for batch_idx, (inputs, targets) in enumerate(dataset):
t = np.array(targets.tolist()).reshape(-1)
labels = labels+t.tolist()
sort_index = np.argsort(np.array(labels))
indices_full.append(sort_index.tolist())
for n in range(tasks):
task_data_len = int(data_len[n])
ind_per_task = indices_full[n]
rng = Random()
rng.seed(seed)
rng.shuffle(ind_per_task)
self.partitions[n] = []
for frac in sizes:
part_len = int(frac*task_data_len)
self.partitions[n].append(ind_per_task[0:part_len])
if(args.eta!=0.0):
ind_per_task = ind_per_task[part_len:] #remove to use full data at each node for experiment
def use(self, partition, task):
return Partition(self.data[task], self.partitions[task][partition])
def partition_trainDataset(device,tasks=2):
"""Partitioning dataset"""
if args.dataset == '5datasets':
dataset= []
classes= 10 #each task has 10 classes
c= int(classes)
mean=[x/255 for x in [125.3,123.0,113.9]]
std=[x/255 for x in [63.0,62.1,66.7]]
dataset_1= datasets.CIFAR10(root=f'Five_data/',train=True,download=True,transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
mean=(0.1,)
std=(0.2752,)
dataset_2= datasets.MNIST(root=f'Five_data/',train=True,download=True,transform=transforms.Compose([transforms.Pad(padding=2,fill=0),transforms.ToTensor(), transforms.Normalize(mean,std)]))
mean=[0.4377,0.4438,0.4728]
std=[0.198,0.201,0.197]
dataset_3= datasets.SVHN(root=f'Five_data/SVHN',split='train',download=True,transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
mean=(0.2190,)
std=(0.3318,)
dataset_4= datasets.FashionMNIST(root=f'Five_data/', train=True, download=True, transform=transforms.Compose([
transforms.Pad(padding=2, fill=0), transforms.ToTensor(),transforms.Normalize(mean, std)]))
mean=(0.4254,)
std=(0.4501,)
dataset_5= notmnist_setup.notMNIST(root=f'Five_data/notmnist', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
dataset= [dataset_1, dataset_2, dataset_3, dataset_4, dataset_5]
elif args.dataset == 'medmnist':
#5-tasks: tissuemnist, organamnist, octmnist, pathmnist, bloodmnist
classes= 11
c= int(classes)
# preprocessing
data_transform = transforms.Compose([
transforms.Pad(padding=2,fill=0),
transforms.ToTensor(),
transforms.Normalize(mean=[.5], std=[.5])
])
info = INFO['tissuemnist']
DataClass = getattr(medmnist, info['python_class'])
# load the data
dataset_1 = DataClass(split='train', transform=data_transform, download=True)
info = INFO['organamnist']
DataClass = getattr(medmnist, info['python_class'])
dataset_2 = DataClass(split='train', transform=data_transform, download=True)
info = INFO['octmnist']
DataClass = getattr(medmnist, info['python_class'])
dataset_3 = DataClass(split='train', transform=data_transform, download=True)
info = INFO['pathmnist']
DataClass = getattr(medmnist, info['python_class'])
dataset_4 = DataClass(split='train', transform=data_transform, download=True)
info = INFO['bloodmnist']
DataClass = getattr(medmnist, info['python_class'])
dataset_5 = DataClass(split='train', transform=data_transform, download=True)
dataset= [dataset_1, dataset_2, dataset_3, dataset_4, dataset_5]
elif args.dataset == 'miniimagenet':
dataset= []
classes= 100 #each task has 5 classes
c= int(classes/tasks)
class_size = {x:500 for x in range(100)}
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
dataset= miniimagenet_setup.MiniImageNet(root='data_minii', train=True, transform=transforms.Compose([transforms.Resize((84,84)),transforms.ToTensor(),transforms.Normalize(mean,std)]))
size = dist.get_world_size()
train_set={}
if(args.eta==0.0):
bsz = int((args.batch_size)) #exp for single agent setting in this setup (communication turned off)
partition_sizes = [1.0 for _ in range(size)]
else:
bsz = int((args.batch_size) / float(size))
partition_sizes = [1.0/size for _ in range(size)]
if(dist.get_rank()==0):
print("partition_sizes:", partition_sizes)
if(args.dataset=='5datasets' or args.dataset=='medmnist'):
partition= DataPartition_5set(args.dataset, dataset, partition_sizes, skew=args.skew, classes=classes, class_size=0, seed=args.seed, device=device, tasks=tasks)
else:
partition = DataPartitioner(dataset, partition_sizes, skew=args.skew, classes=classes, class_size=class_size, seed=args.seed, device=device, tasks=tasks)
for n in range(tasks):
task_partition = partition.use(dist.get_rank(), n)
train_set[n] = torch.utils.data.DataLoader(task_partition, batch_size=bsz, shuffle=True, num_workers=1)
return train_set, bsz, c
def test_Dataset_split(tasks):
if args.dataset == '5datasets':
mean=[x/255 for x in [125.3,123.0,113.9]]
std=[x/255 for x in [63.0,62.1,66.7]]
dataset_1= datasets.CIFAR10(root=f'Five_data/',train=False,download=True,transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
mean=(0.1,)
std=(0.2752,)
dataset_2= datasets.MNIST(root=f'Five_data/',train=False,download=True,transform=transforms.Compose([transforms.Pad(padding=2,fill=0),transforms.ToTensor(),transforms.Normalize(mean,std)]))
loader = torch.utils.data.DataLoader(dataset_2, batch_size=1, shuffle=False)
for image, target in loader:
image=image.expand(1,3,image.size(2),image.size(3))
mean=[0.4377,0.4438,0.4728]
std=[0.198,0.201,0.197]
dataset_3= datasets.SVHN(root=f'Five_data/SVHN',split='test',download=True,transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
mean=(0.2190,)
std=(0.3318,)
dataset_4= datasets.FashionMNIST(root=f'Five_data/', train=False, download=True, transform=transforms.Compose([
transforms.Pad(padding=2, fill=0), transforms.ToTensor(),transforms.Normalize(mean, std)]))
mean=(0.4254,)
std=(0.4501,)
dataset_5= notmnist_setup.notMNIST(root=f'Five_data/notmnist', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
dataset= [dataset_1, dataset_2, dataset_3, dataset_4, dataset_5]
val_set={}
val_bsz = 64
for n in range(tasks):
task_data = dataset[n]
val_set[n] = torch.utils.data.DataLoader(task_data, batch_size=val_bsz, shuffle=True, num_workers=5) #shuffle=False gives low test acc for bn with track_run_stats=False
elif args.dataset == 'medmnist':
# preprocessing
data_transform = transforms.Compose([
transforms.Pad(padding=2,fill=0),
transforms.ToTensor(),
transforms.Normalize(mean=[.5], std=[.5])
])
info = INFO['tissuemnist']
DataClass = getattr(medmnist, info['python_class'])
dataset_1 = DataClass(split='test', transform=data_transform, download=True)
info = INFO['organamnist']
DataClass = getattr(medmnist, info['python_class'])
dataset_2 = DataClass(split='test', transform=data_transform, download=True)
info = INFO['octmnist']
DataClass = getattr(medmnist, info['python_class'])
dataset_3 = DataClass(split='test', transform=data_transform, download=True)
info = INFO['pathmnist']
DataClass = getattr(medmnist, info['python_class'])
dataset_4 = DataClass(split='test', transform=data_transform, download=True)
info = INFO['bloodmnist']
DataClass = getattr(medmnist, info['python_class'])
dataset_5 = DataClass(split='test', transform=data_transform, download=True)
dataset= [dataset_1, dataset_2, dataset_3, dataset_4, dataset_5]
val_set={}
val_bsz = 64
for n in range(tasks):
task_data = dataset[n]
val_set[n] = torch.utils.data.DataLoader(task_data, batch_size=val_bsz, shuffle=True, num_workers=5) #shuffle=False gives low test acc for bn with track_run_stats=False
elif args.dataset == 'miniimagenet':
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
dataset= miniimagenet_setup.MiniImageNet(root='data_minii', train=False, transform=transforms.Compose([transforms.Resize((84,84)),transforms.ToTensor(),transforms.Normalize(mean,std)]))
data_len = len(dataset)
d = torch.utils.data.DataLoader(dataset, batch_size=512, shuffle=False, num_workers=1)
labels = []
for batch_idx, (inputs, targets) in enumerate(d):
labels = labels+targets.tolist()
sort_index = np.argsort(np.array(labels))
indices = sort_index.tolist()
task_data_len = int(data_len/tasks)
val_bsz=10
for n in range(tasks):
ind_per_task = indices[n*task_data_len: (n+1)*task_data_len]
task_data = Partition(dataset, ind_per_task)
val_set[n] = torch.utils.data.DataLoader(task_data, batch_size=val_bsz, shuffle=True, num_workers=2) #shuffle=False gives low test acc for bn with track_run_stats=False
return val_set, val_bsz
def run(rank, size, q1, q2):
global args, best_prec1
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
device = torch.device("cuda:{}".format(rank%args.devices))
if(args.dataset == '5datasets'):
task_details= [(0,10), (1,10), (2,10), (3,10), (4,10)]
elif(args.dataset == 'medmnist'):
task_details=[(0,8), (1,11), (2,4), (3,9), (4,8)]
else:
task_details = [(task,int(args.classes/args.num_tasks)) for task in range(args.num_tasks)] # ex: [(0,5), (1,5)] for 2 tasks
acc_matrix=np.zeros((args.num_tasks,args.num_tasks))
prec_list = []
best_prec1 = 0
n_outputs=100
n_tasks=10
result_val_a = []
result_test_a = []
result_val_t = []
result_test_t = []
##############
data_transferred = []
if(args.dataset == 'medmnist'):
network= ResNet18(args.dataset, task_details, nf=32).to(device)
else:
network= ResNet18(args.dataset, task_details, nf=20).to(device)
no_layers= 20
model= alexnet_scaled_si(network, n_outputs, n_tasks, args)
if rank==0:
print(args)
print ('Model parameters ---')
for k_t, (m, param) in enumerate(model.named_parameters()):
print (k_t,m,param.shape)
print ('-'*40)
if(args.dataset=='medmnist'):
print("*********5-tasks: tissuemnist, organamnist, octmnist, pathmnist, bloodmnist**********")
if(args.graph.lower()=='torus'):
graph = GridGraph(rank, size, args.devices, peers_per_itr= args.neighbors) #Torus structure
else:
graph = RingGraph(rank, size, args.devices, peers_per_itr= args.neighbors) #undirected/directed ring structure based on neighbors
if(rank==0):
print(graph.get_peers())
feature_list = []
orth_basis= []
mixing = UniformMixing(graph, device)
model = GossipDataParallel(model,
device_ids=[rank%args.devices],
rank=rank,
world_size=size,
graph=graph,
mixing=mixing,
comm_device=device,
level = args.level,
biased = args.biased,
eta = args.eta,
compress = args.compress,
no_layers = no_layers,
momentum=args.momentum,
weight_decay = args.weight_decay,
lr = args.lr,
qgm = 0)
model.to(device)
cudnn.benchmark = True
train_loader, bsz_train, c = partition_trainDataset(device, args.num_tasks)
val_loader, bsz_val = test_Dataset_split(args.num_tasks)
## SI - initialization
model.module.register_starting_param_values()
for task_id in range(0, args.num_tasks):
## initialize/reset optimizer after each task
if(rank==0):
print("************TASK*************:", task_id)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().to(device)
if(args.dataset == '5datasets'):
if(task_id==0):
optimizer = optim.SGD(model.parameters(), args.lr, weight_decay=args.weight_decay, momentum = args.momentum, nesterov=False)
else:
optimizer = optim.SGD(model.parameters(), args.lr*0.1, weight_decay=args.weight_decay, momentum = args.momentum, nesterov=False)
elif(args.dataset == 'medmnist'):
if(task_id in [0, 1, 2]):
optimizer = optim.SGD(model.parameters(), args.lr, weight_decay=args.weight_decay, momentum = args.momentum, nesterov=False)
else:
optimizer = optim.SGD(model.parameters(), args.lr*0.1, weight_decay=args.weight_decay, momentum = args.momentum, nesterov=False)
else:
optimizer = optim.SGD(model.parameters(), args.lr, weight_decay=args.weight_decay, momentum = args.momentum, nesterov=False)
if rank==0 and task_id==0: print(optimizer)
gamma= 0.1
step1= int(args.epochs/2)
step2= int(3/4*args.epochs)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, gamma = gamma, milestones=[step1, step2])
if(args.eta!=0):
omega={}
dist.barrier()
if(dist.get_rank()!=0 and task_id>0):
o_recv= q2.get()
o= {}
for o_key in o_recv.keys():
o.update({o_key: torch.from_numpy(o_recv[o_key]).to(device)})
omega= o.copy()
model.module.store_omega(omega)
## SI - per task running parameter initialization
W, p_old = model.module.prepare_importance_estimates_dicts(dist.get_rank(), task_id)
for epoch in range(0, args.epochs):
losses = AverageMeter()
top1 = AverageMeter()
print('current lr {:.5e}'.format(optimizer.param_groups[0]['lr']))
model.block()
prog_bar= train_loader[task_id]
model.train()
for (i, (x, y)) in enumerate(prog_bar):
v_x = Variable(x).to(device)
v_y = Variable(y%c).to(device)
if(args.dataset=='medmnist'):
v_y = v_y.squeeze(1).to(dtype=torch.long)
else:
v_y = v_y.to(dtype=torch.long)
if(v_x.size(dim=1)==1):
v_x= v_x.repeat(1, 3, 1, 1)
logits = model(v_x)[task_id]
loss_ce = criterion(logits, v_y)
# SI - Regularization loss
loss_reg=0
if task_id>0:
loss_reg = model.module.surrogate_loss()
# total Loss
loss = loss_ce + args.ewc_lamb * loss_reg
loss.backward()
optimizer.step()
W, p_old= model.module.update_importance_estimates(W, p_old, dist.get_rank())
res = model.transfer_params(epoch=epoch+(1e-3*i), lr=args.lr)
optimizer.zero_grad()
logits = logits.float()
loss = loss.float()
# measure accuracy and record loss
prec1 = accuracy(logits.data, v_y)[0]
losses.update(loss.item(), v_x.size(0))
top1.update(prec1.item(), v_x.size(0))
if i % args.print_freq == 0:
print('Rank: {0}\t'
'Epoch: [{1}][{2}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
dist.get_rank(), epoch, i, loss=losses, top1=top1))
lr_scheduler.step()
prec1= validate(args.dataset,val_loader[task_id], model, criterion, bsz_val, device, task_id, epoch, c)
if(args.eta!=0.0):
orth_basis=[]
dt= gossip_avg(args.dataset, train_loader[task_id], model, criterion, optimizer, epoch, bsz_train, optimizer.param_groups[0]['lr'], device, rank, task_id, c, orth_basis, args.compress)
else:
print("no gossip averaging in case of turned off communication")
# test validation accuracy for all tasks
jj = 0
prec= []
for tn in range(task_id+1):
acc_matrix[task_id,jj] = validate(args.dataset,val_loader[tn], model, criterion, bsz_val, device, tn, epoch, c)
prec.append(acc_matrix[task_id,jj])
jj +=1
prec_list.append(prec)
print('Accuracies for node ', rank, '=')
for i_a in range(task_id+1):
print('\t',end='')
for j_a in range(acc_matrix.shape[1]):
print('{:5.1f}% '.format(acc_matrix[i_a,j_a]),end='')
print()
### SI - importance (Omega) computation and old model (p_old) update
o, p= model.module.update_omega(W,args.si_epsilon, dist.get_rank())
if(args.eta!=0):
if(dist.get_rank()!=0):
o_send= {}
#p_send= {}
for o_key in o.keys():
o_send.update({o_key: o[o_key].cpu().numpy()})
q1.put(o_send)
dist.barrier()
if(dist.get_rank()==0):
o_avg= {}
for n,p in model.named_parameters():
if p.requires_grad:
n = n.replace('module.net.','')
n = n.replace('.', '__')
o_avg.update({n:0*p.data})
o_recv= []
for i in range(args.world_size-1):
o_recv.append(q1.get()) #other nodes omega matrix
for o_key in o.keys():
for i in range(args.world_size-1):
o_avg[o_key]+=torch.from_numpy(o_recv[i][o_key]).to(device)
o_avg[o_key]+=o[o_key]
o_avg[o_key]=o_avg[o_key]/args.world_size #new updated average omega matrix
model.module.store_omega(o_avg)
o_avg_send={}
for o_key in o_avg.keys():
o_avg_send.update({o_key: o_avg[o_key].cpu().numpy()})
for i in range(args.world_size-1):
q2.put(o_avg_send) #send the final updated omega to all the other nodes!
print ('Final Avg Accuracy: {:5.2f}%'.format(acc_matrix[-1].mean()))
bwt=np.mean((acc_matrix[-1]-np.diag(acc_matrix))[:-1])
print ('Backward transfer: {:5.2f}%'.format(bwt))
def gossip_avg(dataset,train_loader, model, criterion, optimizer, epoch, batch_size, lr, device, rank, task_id, c, orth_basis, compress):
"""
This function runs only gossip averaging for 50 iterations without local sgd updates - used to obtain the average model
"""
data_transferred = 0
n = 50
# switch to train mode
model.train()
for i, (input, target) in enumerate(train_loader):
input_var, target_var = Variable(input).to(device), Variable(target%c).to(device)
if(dataset=='medmnist'):
target_var = target_var.squeeze(1).to(dtype=torch.long)
else:
target_var = target_var.to(dtype=torch.long)
if(input_var.size(dim=1)==1):
input_var= input_var.repeat(1, 3, 1, 1)
# compute output
output = model(input_var)
loss = criterion(output[task_id], target_var)
loss.backward()
optimizer.zero_grad()
if(task_id==0):
_, amt_data_transfer, _= model.transfer_params(epoch=epoch+(1e-3*i), lr=lr, orth_basis=orth_basis, compress=False)
else:
_, amt_data_transfer, _ = model.transfer_params(epoch=epoch+(1e-3*i), lr=lr, orth_basis=orth_basis, compress=compress)
data_transferred += amt_data_transfer
if i==n: break
return data_transferred
def validate(dataset, val_loader, model, criterion, batch_size, device, task_id, epoch, c):
"""
Run evaluation
"""
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
# switch to evaluate mode
model.eval()
step = len(val_loader)*batch_size*epoch
end = time.time()
with torch.no_grad():
for i, (input, target) in enumerate(val_loader):
input_var, target_var = Variable(input).to(device), Variable(target%c).to(device)
if(dataset=='medmnist'):
target_var = target_var.squeeze(1).to(dtype=torch.long)
else:
target_var = target_var.to(dtype=torch.long)
if(input_var.size(dim=1)==1):
input_var= input_var.repeat(1, 3, 1, 1)
output = model.module.forward(input_var)[task_id]
loss = criterion(output, target_var)
output = output.float()
loss = loss.float()
# measure accuracy and record loss
prec1 = accuracy(output.data, target_var)[0]
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Rank: {0}\t'
'Test: [{1}/{2}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
dist.get_rank(),i, len(val_loader),
loss=losses,
top1=top1))
step += batch_size
print(' * Prec@1 {top1.avg:.3f}' .format(top1=top1))
return top1.avg
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
"""
Save the training model
"""
torch.save(state, filename)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def init_process(rank, size, fn, q1, q2,backend=args.backend):
"""Initialize distributed enviornment"""
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = args.port
dist.init_process_group(backend, rank=rank, world_size=size)
fn(rank,size,q1,q2)
if __name__ == '__main__':
size = args.world_size
print(torch.cuda.device_count())
manager= mp.Manager()
q1= manager.Queue()
q2= manager.Queue()
spawn(init_process, args=(size,run,q1,q2), nprocs=size,join=True)