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parallel_main.py
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parallel_main.py
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import os
import pathlib
import random
import time
from torch.utils.tensorboard import SummaryWriter
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from utils.conv_type import FixedSubnetConv, SampleSubnetConv
from utils.logging import AverageMeter, ProgressMeter
from utils.net_utils import (
set_model_prune_rate,
freeze_model_weights,
save_checkpoint,
get_lr,
LabelSmoothing,
)
from utils.schedulers import get_policy
from args import args
import importlib
import data
import models
def main():
print(args)
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
args.distributed = True
# Simply call main_worker function
main_worker(args)
def main_worker(args):
# NEW: equivalent to MPI init.
print("world size ", os.environ['OMPI_COMM_WORLD_SIZE'])
print("rank ", os.environ['OMPI_COMM_WORLD_RANK'])
torch.distributed.init_process_group(backend="nccl", init_method="env://",
world_size=int(os.environ['OMPI_COMM_WORLD_SIZE']),
rank=int(os.environ['OMPI_COMM_WORLD_RANK']))
# NEW: lookup number of ranks in the job, and our rank
args.world_size = torch.distributed.get_world_size()
print("world size ", args.world_size)
args.rank = torch.distributed.get_rank()
print("rank ", args.rank)
ngpus_per_node = torch.cuda.device_count()
print("ngpus_per_node ", ngpus_per_node)
local_rank = args.rank % ngpus_per_node
print("local_rank ", local_rank)
# NEW: Globalize variables
global best_acc1
global best_acc5
global best_train_acc1
global best_train_acc5
#args.gpu = None
# NEW: Specify gpu
args.gpu = local_rank
train, validate, modifier = get_trainer(args)
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
# create model and optimizer
model = get_model(args)
# NEW: Distributed data
#if args.distributed:
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
#model = set_gpu(args, model)
# NEW: Modified function for loading gpus on multinode setups
model = lassen_set_gpu(args, model)
if args.pretrained:
pretrained(args, model)
optimizer = get_optimizer(args, model)
data = get_dataset(args)
lr_policy = get_policy(args.lr_policy)(optimizer, args)
if args.label_smoothing is None:
#criterion = nn.CrossEntropyLoss().cuda()
# NEW: Specify gpu
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
else:
criterion = LabelSmoothing(smoothing=args.label_smoothing)
# optionally resume from a checkpoint
best_acc1 = 0.0
best_acc5 = 0.0
best_train_acc1 = 0.0
best_train_acc5 = 0.0
if args.resume:
best_acc1 = resume(args, model, optimizer)
# Data loading code
if args.evaluate:
acc1, acc5 = validate(
data.val_loader, model, criterion, args, writer=None, epoch=args.start_epoch
)
return
# Set up directories
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
run_base_dir, ckpt_base_dir, log_base_dir = get_directories(args)
args.ckpt_base_dir = ckpt_base_dir
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
writer = SummaryWriter(log_dir=log_base_dir)
else:
writer = None
epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False)
validation_time = AverageMeter("validation_time", ":.4f", write_avg=False)
train_time = AverageMeter("train_time", ":.4f", write_avg=False)
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
progress_overall = ProgressMeter(
1, [epoch_time, validation_time, train_time], prefix="Overall Timing"
)
end_epoch = time.time()
args.start_epoch = args.start_epoch or 0
acc1 = None
# Save the initial state
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
save_checkpoint(
{
"epoch": 0,
"arch": args.arch,
"state_dict": model.state_dict(),
"best_acc1": best_acc1,
"best_acc5": best_acc5,
"best_train_acc1": best_train_acc1,
"best_train_acc5": best_train_acc5,
"optimizer": optimizer.state_dict(),
"curr_acc1": acc1 if acc1 else "Not evaluated",
},
False,
filename=ckpt_base_dir / f"initial.state",
save=False,
)
# Start training
for epoch in range(args.start_epoch, args.epochs):
# NEW: Distributed data
#if args.distributed:
data.train_sampler.set_epoch(epoch)
data.val_sampler.set_epoch(epoch)
lr_policy(epoch, iteration=None)
#modifier(args, epoch, model)
cur_lr = get_lr(optimizer)
# train for one epoch
start_train = time.time()
train_acc1, train_acc5 = train(
data.train_loader, model, criterion, optimizer, epoch, args, writer=writer
)
#train_acc1, train_acc5 = train(
# data.train_loader, model, criterion, optimizer, epoch, args, writer=None
#)
train_time.update((time.time() - start_train) / 60)
# evaluate on validation set
start_validation = time.time()
# NEW: Only write values to tensorboard for main processor (one with global rank 0)
if args.rank == 0:
acc1, acc5 = validate(data.val_loader, model, criterion, args, writer, epoch)
else:
acc1, acc5 = validate(data.val_loader, model, criterion, args, None, epoch)
validation_time.update((time.time() - start_validation) / 60)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
best_acc5 = max(acc5, best_acc5)
best_train_acc1 = max(train_acc1, best_train_acc1)
best_train_acc5 = max(train_acc5, best_train_acc5)
save = ((epoch % args.save_every) == 0) and args.save_every > 0
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
if is_best or save or epoch == args.epochs - 1:
if is_best:
print(f"==> New best, saving at {ckpt_base_dir / 'model_best.pth'}")
save_checkpoint(
{
"epoch": epoch + 1,
"arch": args.arch,
"state_dict": model.state_dict(),
"best_acc1": best_acc1,
"best_acc5": best_acc5,
"best_train_acc1": best_train_acc1,
"best_train_acc5": best_train_acc5,
"optimizer": optimizer.state_dict(),
"curr_acc1": acc1,
"curr_acc5": acc5,
},
is_best,
filename=ckpt_base_dir / f"epoch_most_recent.state",
save=save,
)
#filename=ckpt_base_dir / f"epoch_{epoch}.state",
epoch_time.update((time.time() - end_epoch) / 60)
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
progress_overall.display(epoch)
progress_overall.write_to_tensorboard(
writer, prefix="diagnostics", global_step=epoch
)
if args.conv_type == "SampleSubnetConv":
count = 0
sum_pr = 0.0
for n, m in model.named_modules():
if isinstance(m, SampleSubnetConv):
# avg pr across 10 samples
pr = 0.0
for _ in range(10):
pr += (
(torch.rand_like(m.clamped_scores) >= m.clamped_scores)
.float()
.mean()
.item()
)
pr /= 10.0
writer.add_scalar("pr/{}".format(n), pr, epoch)
sum_pr += pr
count += 1
args.prune_rate = sum_pr / count
writer.add_scalar("pr/average", args.prune_rate, epoch)
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
writer.add_scalar("test/lr", cur_lr, epoch)
end_epoch = time.time()
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
write_result_to_csv(
best_acc1=best_acc1,
best_acc5=best_acc5,
best_train_acc1=best_train_acc1,
best_train_acc5=best_train_acc5,
prune_rate=args.prune_rate,
curr_acc1=acc1,
curr_acc5=acc5,
base_config=args.config,
name=args.name,
)
def get_trainer(args):
print(f"=> Using trainer from trainers.{args.trainer}")
trainer = importlib.import_module(f"trainers.{args.trainer}")
return trainer.train, trainer.validate, trainer.modifier
def set_gpu(args, model):
assert torch.cuda.is_available(), "CPU-only experiments currently unsupported"
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
elif args.multigpu is None:
device = torch.device("cpu")
else:
# DataParallel will divide and allocate batch_size to all available GPUs
print(f"=> Parallelizing on {args.multigpu} gpus")
torch.cuda.set_device(args.multigpu[0])
args.gpu = args.multigpu[0]
model = torch.nn.DataParallel(model, device_ids=args.multigpu).cuda(
args.multigpu[0]
)
cudnn.benchmark = True
return model
def lassen_set_gpu(args, model):
assert torch.cuda.is_available(), "CPU-only experiments currently unsupported"
# DataParallel will divide and allocate batch_size to all available GPUs
print(f"=> Parallelizing: On gpu {args.gpu}")
device = torch.device('cuda')
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
model.to(device)
cudnn.benchmark = True
return model
def resume(args, model, optimizer):
if os.path.isfile(args.resume):
print(f"=> Loading checkpoint '{args.resume}'")
#checkpoint = torch.load(args.resume, map_location=f"cuda:{args.multigpu[0]}")
# NEW: Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
if args.start_epoch is None:
print(f"=> Setting new start epoch at {checkpoint['epoch']}")
args.start_epoch = checkpoint["epoch"]
best_acc1 = checkpoint["best_acc1"]
## NEW: best_acc1 may be from a checkpoint from a different GPU
#best_acc1 = best_acc1.to(args.gpu)
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
print(f"=> Loaded checkpoint '{args.resume}' (epoch {checkpoint['epoch']})")
return best_acc1
else:
print(f"=> No checkpoint found at '{args.resume}'")
def pretrained(args, model):
if os.path.isfile(args.pretrained):
print("=> loading pretrained weights from '{}'".format(args.pretrained))
pretrained = torch.load(
args.pretrained,
map_location=torch.device("cuda:{}".format(args.multigpu[0])),
)["state_dict"]
model_state_dict = model.state_dict()
for k, v in pretrained.items():
if k not in model_state_dict or v.size() != model_state_dict[k].size():
print("IGNORE:", k)
pretrained = {
k: v
for k, v in pretrained.items()
if (k in model_state_dict and v.size() == model_state_dict[k].size())
}
model_state_dict.update(pretrained)
model.load_state_dict(model_state_dict)
else:
print("=> no pretrained weights found at '{}'".format(args.pretrained))
for n, m in model.named_modules():
if isinstance(m, FixedSubnetConv):
m.set_subnet()
def get_dataset(args):
print(f"=> Getting {args.set} dataset")
dataset = getattr(data, args.set)(args)
return dataset
def get_model(args):
if args.first_layer_dense:
args.first_layer_type = "DenseConv"
print("=> Creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
# applying sparsity to the network
if (
args.conv_type != "DenseConv"
and args.conv_type != "SampleSubnetConv"
and args.conv_type != "ContinuousSparseConv"
):
if args.prune_rate < 0:
raise ValueError("Need to set a positive prune rate")
set_model_prune_rate(model, prune_rate=args.prune_rate)
print(
f"=> Rough estimate model params {sum(int(p.numel() * (1-args.prune_rate)) for n, p in model.named_parameters() if not n.endswith('scores'))}"
)
# freezing the weights if we are only doing subnet training
if args.freeze_weights:
freeze_model_weights(model)
return model
def get_optimizer(args, model):
for n, v in model.named_parameters():
if v.requires_grad:
print("<DEBUG> gradient to", n)
if not v.requires_grad:
print("<DEBUG> no gradient to", n)
if args.optimizer == "sgd":
parameters = list(model.named_parameters())
bn_params = [v for n, v in parameters if ("bn" in n) and v.requires_grad]
rest_params = [v for n, v in parameters if ("bn" not in n) and v.requires_grad]
optimizer = torch.optim.SGD(
[
{
"params": bn_params,
"weight_decay": 0 if args.no_bn_decay else args.weight_decay,
},
{"params": rest_params, "weight_decay": args.weight_decay},
],
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=args.nesterov,
)
elif args.optimizer == "adam":
optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr
)
return optimizer
def _run_dir_exists(run_base_dir):
log_base_dir = run_base_dir / "logs"
ckpt_base_dir = run_base_dir / "checkpoints"
return log_base_dir.exists() or ckpt_base_dir.exists()
def get_directories(args):
if args.config is None or args.name is None:
raise ValueError("Must have name and config")
config = pathlib.Path(args.config).stem
if args.log_dir is None:
run_base_dir = pathlib.Path(
f"runs/{config}/{args.name}/prune_rate={args.prune_rate}"
)
else:
run_base_dir = pathlib.Path(
f"{args.log_dir}/{config}/{args.name}/prune_rate={args.prune_rate}"
)
if args.width_mult != 1.0:
run_base_dir = run_base_dir / "width_mult={}".format(str(args.width_mult))
if _run_dir_exists(run_base_dir):
rep_count = 0
while _run_dir_exists(run_base_dir / str(rep_count)):
rep_count += 1
run_base_dir = run_base_dir / str(rep_count)
log_base_dir = run_base_dir / "logs"
ckpt_base_dir = run_base_dir / "checkpoints"
if not run_base_dir.exists():
os.makedirs(run_base_dir)
(run_base_dir / "settings.txt").write_text(str(args))
return run_base_dir, ckpt_base_dir, log_base_dir
def write_result_to_csv(**kwargs):
results = pathlib.Path("runs") / "results.csv"
if not results.exists():
results.write_text(
"Date Finished, "
"Base Config, "
"Name, "
"Prune Rate, "
"Current Val Top 1, "
"Current Val Top 5, "
"Best Val Top 1, "
"Best Val Top 5, "
"Best Train Top 1, "
"Best Train Top 5\n"
)
now = time.strftime("%m-%d-%y_%H:%M:%S")
with open(results, "a+") as f:
f.write(
(
"{now}, "
"{base_config}, "
"{name}, "
"{prune_rate}, "
"{curr_acc1:.02f}, "
"{curr_acc5:.02f}, "
"{best_acc1:.02f}, "
"{best_acc5:.02f}, "
"{best_train_acc1:.02f}, "
"{best_train_acc5:.02f}\n"
).format(now=now, **kwargs)
)
if __name__ == "__main__":
main()