model.train()
for itr in range(1, args.niters + 1):
optimizer.zero_grad()
if args.spectral_norm: spectral_norm_power_iteration(model, 1)
loss = compute_loss(args, model)
loss_meter.update(loss.item())
if len(regularization_coeffs) > 0:
reg_states = get_regularization(model, regularization_coeffs)
reg_loss = sum(
reg_state * coeff for reg_state, coeff in zip(reg_states, regularization_coeffs) if coeff != 0
)
loss = loss + reg_loss
total_time = count_total_time(model)
nfe_forward = count_nfe(model)
loss.backward()
optimizer.step()
nfe_total = count_nfe(model)
nfe_backward = nfe_total - nfe_forward
nfef_meter.update(nfe_forward)
nfeb_meter.update(nfe_backward)
time_meter.update(time.time() - end)
tt_meter.update(total_time)
log_message = (
'Iter {:04d} | Time {:.4f}({:.4f}) | Loss {:.6f}({:.6f}) | NFE Forward {:.0f}({:.1f})'
def train():
model = build_model_tabular(args, 1).to(device)
set_cnf_options(args, model)
logger.info(model)
logger.info("Number of trainable parameters: {}".format(
count_parameters(model)))
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
time_meter = utils.RunningAverageMeter(0.93)
loss_meter = utils.RunningAverageMeter(0.93)
nfef_meter = utils.RunningAverageMeter(0.93)
nfeb_meter = utils.RunningAverageMeter(0.93)
tt_meter = utils.RunningAverageMeter(0.93)
end = time.time()
best_loss = float('inf')
model.train()
for itr in range(1, args.niters + 1):
optimizer.zero_grad()
loss = compute_loss(args, model)
loss_meter.update(loss.item())
total_time = count_total_time(model)
nfe_forward = count_nfe(model)
loss.backward()
optimizer.step()
nfe_total = count_nfe(model)
nfe_backward = nfe_total - nfe_forward
nfef_meter.update(nfe_forward)
nfeb_meter.update(nfe_backward)
time_meter.update(time.time() - end)
tt_meter.update(total_time)
log_message = (
'Iter {:04d} | Time {:.4f}({:.4f}) | Loss {:.6f}({:.6f}) | NFE Forward {:.0f}({:.1f})'
' | NFE Backward {:.0f}({:.1f}) | CNF Time {:.4f}({:.4f})'.format(
itr, time_meter.val, time_meter.avg, loss_meter.val,
loss_meter.avg, nfef_meter.val, nfef_meter.avg, nfeb_meter.val,
nfeb_meter.avg, tt_meter.val, tt_meter.avg))
logger.info(log_message)
if itr % args.val_freq == 0 or itr == args.niters:
with torch.no_grad():
model.eval()
test_loss = compute_loss(args,
model,
batch_size=args.test_batch_size)
test_nfe = count_nfe(model)
log_message = '[TEST] Iter {:04d} | Test Loss {:.6f} | NFE {:.0f}'.format(
itr, test_loss, test_nfe)
logger.info(log_message)
if test_loss.item() < best_loss:
best_loss = test_loss.item()
utils.makedirs(args.save)
torch.save(
{
'args': args,
'state_dict': model.state_dict(),
}, os.path.join(args.save, 'checkpt.pth'))
model.train()
if itr % args.viz_freq == 0:
with torch.no_grad():
model.eval()
xx = torch.linspace(-10, 10, 10000).view(-1, 1)
true_p = data_density(xx)
plt.plot(xx.view(-1).cpu().numpy(),
true_p.view(-1).exp().cpu().numpy(),
label='True')
true_p = model_density(xx, model)
plt.plot(xx.view(-1).cpu().numpy(),
true_p.view(-1).exp().cpu().numpy(),
label='Model')
utils.makedirs(os.path.join(args.save, 'figs'))
plt.savefig(
os.path.join(args.save, 'figs', '{:06d}.jpg'.format(itr)))
plt.close()
model.train()
end = time.time()
logger.info('Training has finished.')
def main():
#os.system('shutdown -c') # cancel previous shutdown command
if write_log:
utils.makedirs(args.save)
logger = utils.get_logger(logpath=os.path.join(args.save, 'logs'),
filepath=os.path.abspath(__file__))
logger.info(args)
args_file_path = os.path.join(args.save, 'args.yaml')
with open(args_file_path, 'w') as f:
yaml.dump(vars(args), f, default_flow_style=False)
if args.distributed:
if write_log: logger.info('Distributed initializing process group')
torch.cuda.set_device(args.local_rank)
distributed.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=dist_utils.env_world_size(),
rank=env_rank())
assert (dist_utils.env_world_size() == distributed.get_world_size())
if write_log:
logger.info("Distributed: success (%d/%d)" %
(args.local_rank, distributed.get_world_size()))
# get deivce
# device = torch.device("cuda:%d"%torch.cuda.current_device() if torch.cuda.is_available() else "cpu")
device = "cpu"
cvt = lambda x: x.type(torch.float32).to(device, non_blocking=True)
# load dataset
train_loader, test_loader, data_shape = get_dataset(args)
trainlog = os.path.join(args.save, 'training.csv')
testlog = os.path.join(args.save, 'test.csv')
traincolumns = [
'itr', 'wall', 'itr_time', 'loss', 'bpd', 'fe', 'total_time',
'grad_norm'
]
testcolumns = [
'wall', 'epoch', 'eval_time', 'bpd', 'fe', 'total_time',
'transport_cost'
]
# build model
regularization_fns, regularization_coeffs = create_regularization_fns(args)
model = create_model(args, data_shape, regularization_fns)
# model = model.cuda()
if args.distributed:
model = dist_utils.DDP(model,
device_ids=[args.local_rank],
output_device=args.local_rank)
traincolumns = append_regularization_keys_header(traincolumns,
regularization_fns)
if not args.resume and write_log:
with open(trainlog, 'w') as f:
csvlogger = csv.DictWriter(f, traincolumns)
csvlogger.writeheader()
with open(testlog, 'w') as f:
csvlogger = csv.DictWriter(f, testcolumns)
csvlogger.writeheader()
set_cnf_options(args, model)
if write_log: logger.info(model)
if write_log:
logger.info("Number of trainable parameters: {}".format(
count_parameters(model)))
if write_log:
logger.info('Iters per train epoch: {}'.format(len(train_loader)))
if write_log: logger.info('Iters per test: {}'.format(len(test_loader)))
# optimizer
if args.optimizer == 'adam':
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
momentum=0.9,
nesterov=False)
# restore parameters
if args.resume is not None:
checkpt = torch.load(
args.resume,
map_location=lambda storage, loc: storage.cuda(args.local_rank))
model.load_state_dict(checkpt["state_dict"])
if "optim_state_dict" in checkpt.keys():
optimizer.load_state_dict(checkpt["optim_state_dict"])
# Manually move optimizer state to device.
for state in optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = cvt(v)
# For visualization.
if write_log:
fixed_z = cvt(torch.randn(min(args.test_batch_size, 100), *data_shape))
if write_log:
time_meter = utils.RunningAverageMeter(0.97)
bpd_meter = utils.RunningAverageMeter(0.97)
loss_meter = utils.RunningAverageMeter(0.97)
steps_meter = utils.RunningAverageMeter(0.97)
grad_meter = utils.RunningAverageMeter(0.97)
tt_meter = utils.RunningAverageMeter(0.97)
if not args.resume:
best_loss = float("inf")
itr = 0
wall_clock = 0.
begin_epoch = 1
else:
chkdir = os.path.dirname(args.resume)
tedf = pd.read_csv(os.path.join(chkdir, 'test.csv'))
trdf = pd.read_csv(os.path.join(chkdir, 'training.csv'))
wall_clock = trdf['wall'].to_numpy()[-1]
itr = trdf['itr'].to_numpy()[-1]
best_loss = tedf['bpd'].min()
begin_epoch = int(tedf['epoch'].to_numpy()[-1] +
1) # not exactly correct
if args.distributed:
if write_log: logger.info('Syncing machines before training')
dist_utils.sum_tensor(torch.tensor([1.0]).float().cuda())
for epoch in range(begin_epoch, args.num_epochs + 1):
if not args.validate:
model.train()
with open(trainlog, 'a') as f:
if write_log: csvlogger = csv.DictWriter(f, traincolumns)
for _, (x, y) in enumerate(train_loader):
start = time.time()
update_lr(optimizer, itr)
optimizer.zero_grad()
# cast data and move to device
x = add_noise(cvt(x), nbits=args.nbits)
#x = x.clamp_(min=0, max=1)
# compute loss
bpd, (x, z), reg_states = compute_bits_per_dim(x, model)
if np.isnan(bpd.data.item()):
raise ValueError('model returned nan during training')
elif np.isinf(bpd.data.item()):
raise ValueError('model returned inf during training')
loss = bpd
if regularization_coeffs:
reg_loss = sum(reg_state * coeff
for reg_state, coeff in zip(
reg_states, regularization_coeffs)
if coeff != 0)
loss = loss + reg_loss
total_time = count_total_time(model)
loss.backward()
nfe_opt = count_nfe(model)
if write_log: steps_meter.update(nfe_opt)
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), args.max_grad_norm)
optimizer.step()
itr_time = time.time() - start
wall_clock += itr_time
batch_size = x.size(0)
metrics = torch.tensor([
1., batch_size,
loss.item(),
bpd.item(), nfe_opt, grad_norm, *reg_states
]).float()
rv = tuple(torch.tensor(0.) for r in reg_states)
total_gpus, batch_total, r_loss, r_bpd, r_nfe, r_grad_norm, *rv = dist_utils.sum_tensor(
metrics).cpu().numpy()
if write_log:
time_meter.update(itr_time)
bpd_meter.update(r_bpd / total_gpus)
loss_meter.update(r_loss / total_gpus)
grad_meter.update(r_grad_norm / total_gpus)
tt_meter.update(total_time)
fmt = '{:.4f}'
logdict = {
'itr': itr,
'wall': fmt.format(wall_clock),
'itr_time': fmt.format(itr_time),
'loss': fmt.format(r_loss / total_gpus),
'bpd': fmt.format(r_bpd / total_gpus),
'total_time': fmt.format(total_time),
'fe': r_nfe / total_gpus,
'grad_norm': fmt.format(r_grad_norm / total_gpus),
}
if regularization_coeffs:
rv = tuple(v_ / total_gpus for v_ in rv)
logdict = append_regularization_csv_dict(
logdict, regularization_fns, rv)
csvlogger.writerow(logdict)
if itr % args.log_freq == 0:
log_message = (
"Itr {:06d} | Wall {:.3e}({:.2f}) | "
"Time/Itr {:.2f}({:.2f}) | BPD {:.2f}({:.2f}) | "
"Loss {:.2f}({:.2f}) | "
"FE {:.0f}({:.0f}) | Grad Norm {:.3e}({:.3e}) | "
"TT {:.2f}({:.2f})".format(
itr, wall_clock, wall_clock / (itr + 1),
time_meter.val, time_meter.avg,
bpd_meter.val, bpd_meter.avg,
loss_meter.val, loss_meter.avg,
steps_meter.val, steps_meter.avg,
grad_meter.val, grad_meter.avg,
tt_meter.val, tt_meter.avg))
if regularization_coeffs:
log_message = append_regularization_to_log(
log_message, regularization_fns, rv)
logger.info(log_message)
itr += 1
# compute test loss
model.eval()
if args.local_rank == 0:
utils.makedirs(args.save)
torch.save(
{
"args":
args,
"state_dict":
model.module.state_dict()
if torch.cuda.is_available() else model.state_dict(),
"optim_state_dict":
optimizer.state_dict(),
"fixed_z":
fixed_z.cpu()
}, os.path.join(args.save, "checkpt.pth"))
if epoch % args.val_freq == 0 or args.validate:
with open(testlog, 'a') as f:
if write_log: csvlogger = csv.DictWriter(f, testcolumns)
with torch.no_grad():
start = time.time()
if write_log: logger.info("validating...")
lossmean = 0.
meandist = 0.
steps = 0
tt = 0.
for i, (x, y) in enumerate(test_loader):
sh = x.shape
x = shift(cvt(x), nbits=args.nbits)
loss, (x, z), _ = compute_bits_per_dim(x, model)
dist = (x.view(x.size(0), -1) -
z).pow(2).mean(dim=-1).mean()
meandist = i / (i + 1) * dist + meandist / (i + 1)
lossmean = i / (i + 1) * lossmean + loss / (i + 1)
tt = i / (i + 1) * tt + count_total_time(model) / (i +
1)
steps = i / (i + 1) * steps + count_nfe(model) / (i +
1)
loss = lossmean.item()
metrics = torch.tensor([1., loss, meandist, steps]).float()
total_gpus, r_bpd, r_mdist, r_steps = dist_utils.sum_tensor(
metrics).cpu().numpy()
eval_time = time.time() - start
if write_log:
fmt = '{:.4f}'
logdict = {
'epoch': epoch,
'eval_time': fmt.format(eval_time),
'bpd': fmt.format(r_bpd / total_gpus),
'wall': fmt.format(wall_clock),
'total_time': fmt.format(tt),
'transport_cost': fmt.format(r_mdist / total_gpus),
'fe': '{:.2f}'.format(r_steps / total_gpus)
}
csvlogger.writerow(logdict)
logger.info(
"Epoch {:04d} | Time {:.4f}, Bit/dim {:.4f}, Steps {:.4f}, TT {:.2f}, Transport Cost {:.2e}"
.format(epoch, eval_time, r_bpd / total_gpus,
r_steps / total_gpus, tt,
r_mdist / total_gpus))
loss = r_bpd / total_gpus
if loss < best_loss and args.local_rank == 0:
best_loss = loss
shutil.copyfile(os.path.join(args.save, "checkpt.pth"),
os.path.join(args.save, "best.pth"))
# visualize samples and density
if write_log:
with torch.no_grad():
fig_filename = os.path.join(args.save, "figs",
"{:04d}.jpg".format(epoch))
utils.makedirs(os.path.dirname(fig_filename))
generated_samples, _, _ = model(fixed_z, reverse=True)
generated_samples = generated_samples.view(-1, *data_shape)
nb = int(np.ceil(np.sqrt(float(fixed_z.size(0)))))
save_image(unshift(generated_samples, nbits=args.nbits),
fig_filename,
nrow=nb)
if args.validate:
break
best_loss = float("inf")
for epoch in range(args.begin_epoch, args.num_epochs + 1):
aug_model.train()
for temp_idx, x in enumerate(train_loader):
## x is a tuple of (values, times, stdv, masks)
start = time.time()
optimizer.zero_grad()
# cast data and move to device
x = map(cvt, x)
values, times, vars, masks = x
# compute loss
loss = run_model(args, aug_model, values, times, vars, masks)
total_time = count_total_time(aug_model)
## Assume the base distribution be Brownian motion
if regularization_coeffs:
reg_states = get_regularization(aug_model,
regularization_coeffs)
reg_loss = sum(reg_state * coeff for reg_state, coeff in zip(
reg_states, regularization_coeffs) if coeff != 0)
loss = loss + reg_loss
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(aug_model.parameters(),
args.max_grad_norm)
optimizer.step()
time_meter.update(time.time() - start)
def train(args, model, growth_model):
logger.info(model)
logger.info("Number of trainable parameters: {}".format(count_parameters(model)))
#optimizer = optim.Adam(set(model.parameters()) | set(growth_model.parameters()),
optimizer = optim.Adam(model.parameters(),
lr=args.lr, weight_decay=args.weight_decay)
#growth_optimizer = optim.Adam(growth_model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
time_meter = utils.RunningAverageMeter(0.93)
loss_meter = utils.RunningAverageMeter(0.93)
nfef_meter = utils.RunningAverageMeter(0.93)
nfeb_meter = utils.RunningAverageMeter(0.93)
tt_meter = utils.RunningAverageMeter(0.93)
end = time.time()
best_loss = float('inf')
model.train()
growth_model.eval()
for itr in range(1, args.niters + 1):
optimizer.zero_grad()
#growth_optimizer.zero_grad()
### Train
if args.spectral_norm: spectral_norm_power_iteration(model, 1)
#if args.spectral_norm: spectral_norm_power_iteration(growth_model, 1)
loss = compute_loss(args, model, growth_model)
loss_meter.update(loss.item())
if len(regularization_coeffs) > 0:
# Only regularize on the last timepoint
reg_states = get_regularization(model, regularization_coeffs)
reg_loss = sum(
reg_state * coeff for reg_state, coeff in zip(reg_states, regularization_coeffs) if coeff != 0
)
loss = loss + reg_loss
#if len(growth_regularization_coeffs) > 0:
# growth_reg_states = get_regularization(growth_model, growth_regularization_coeffs)
# reg_loss = sum(
# reg_state * coeff for reg_state, coeff in zip(growth_reg_states, growth_regularization_coeffs) if coeff != 0
# )
# loss2 = loss2 + reg_loss
total_time = count_total_time(model)
nfe_forward = count_nfe(model)
loss.backward()
#loss2.backward()
optimizer.step()
#growth_optimizer.step()
### Eval
nfe_total = count_nfe(model)
nfe_backward = nfe_total - nfe_forward
nfef_meter.update(nfe_forward)
nfeb_meter.update(nfe_backward)
time_meter.update(time.time() - end)
tt_meter.update(total_time)
log_message = (
'Iter {:04d} | Time {:.4f}({:.4f}) | Loss {:.6f}({:.6f}) | NFE Forward {:.0f}({:.1f})'
' | NFE Backward {:.0f}({:.1f}) | CNF Time {:.4f}({:.4f})'.format(
itr, time_meter.val, time_meter.avg, loss_meter.val, loss_meter.avg, nfef_meter.val, nfef_meter.avg,
nfeb_meter.val, nfeb_meter.avg, tt_meter.val, tt_meter.avg
)
)
if len(regularization_coeffs) > 0:
log_message = append_regularization_to_log(log_message, regularization_fns, reg_states)
logger.info(log_message)
if itr % args.val_freq == 0 or itr == args.niters:
with torch.no_grad():
model.eval()
growth_model.eval()
test_loss = compute_loss(args, model, growth_model)
test_nfe = count_nfe(model)
log_message = '[TEST] Iter {:04d} | Test Loss {:.6f} | NFE {:.0f}'.format(itr, test_loss, test_nfe)
logger.info(log_message)
if test_loss.item() < best_loss:
best_loss = test_loss.item()
utils.makedirs(args.save)
torch.save({
'args': args,
'state_dict': model.state_dict(),
'growth_state_dict': growth_model.state_dict(),
}, os.path.join(args.save, 'checkpt.pth'))
model.train()
if itr % args.viz_freq == 0:
with torch.no_grad():
model.eval()
for i, tp in enumerate(timepoints):
p_samples = viz_sampler(tp)
sample_fn, density_fn = get_transforms(model, int_tps[:i+1])
#growth_sample_fn, growth_density_fn = get_transforms(growth_model, int_tps[:i+1])
plt.figure(figsize=(9, 3))
visualize_transform(
p_samples, torch.randn, standard_normal_logprob, transform=sample_fn, inverse_transform=density_fn,
samples=True, npts=100, device=device
)
fig_filename = os.path.join(args.save, 'figs', '{:04d}_{:01d}.jpg'.format(itr, i))
utils.makedirs(os.path.dirname(fig_filename))
plt.savefig(fig_filename)
plt.close()
#visualize_transform(
# p_samples, torch.rand, uniform_logprob, transform=growth_sample_fn,
# inverse_transform=growth_density_fn,
# samples=True, npts=800, device=device
#)
#fig_filename = os.path.join(args.save, 'growth_figs', '{:04d}_{:01d}.jpg'.format(itr, i))
#utils.makedirs(os.path.dirname(fig_filename))
#plt.savefig(fig_filename)
#plt.close()
model.train()
"""
if itr % args.viz_freq_growth == 0:
with torch.no_grad():
growth_model.eval()
# Visualize growth transform
growth_filename = os.path.join(args.save, 'growth', '{:04d}.jpg'.format(itr))
utils.makedirs(os.path.dirname(growth_filename))
visualize_growth(growth_model, data, labels, npts=200, device=device)
plt.savefig(growth_filename)
plt.close()
growth_model.train()
"""
end = time.time()
logger.info('Training has finished.')
def train(
device, args, model, growth_model, regularization_coeffs, regularization_fns, logger
):
optimizer = optim.Adam(
model.parameters(), lr=args.lr, weight_decay=args.weight_decay
)
time_meter = utils.RunningAverageMeter(0.93)
loss_meter = utils.RunningAverageMeter(0.93)
nfef_meter = utils.RunningAverageMeter(0.93)
nfeb_meter = utils.RunningAverageMeter(0.93)
tt_meter = utils.RunningAverageMeter(0.93)
full_data = (
torch.from_numpy(
args.data.get_data()[args.data.get_times() != args.leaveout_timepoint]
)
.type(torch.float32)
.to(device)
)
best_loss = float("inf")
growth_model.eval()
end = time.time()
for itr in range(1, args.niters + 1):
model.train()
optimizer.zero_grad()
# Train
if args.spectral_norm:
spectral_norm_power_iteration(model, 1)
loss = compute_loss(device, args, model, growth_model, logger, full_data)
loss_meter.update(loss.item())
if len(regularization_coeffs) > 0:
# Only regularize on the last timepoint
reg_states = get_regularization(model, regularization_coeffs)
reg_loss = sum(
reg_state * coeff
for reg_state, coeff in zip(reg_states, regularization_coeffs)
if coeff != 0
)
loss = loss + reg_loss
total_time = count_total_time(model)
nfe_forward = count_nfe(model)
loss.backward()
optimizer.step()
# Eval
nfe_total = count_nfe(model)
nfe_backward = nfe_total - nfe_forward
nfef_meter.update(nfe_forward)
nfeb_meter.update(nfe_backward)
time_meter.update(time.time() - end)
tt_meter.update(total_time)
log_message = (
"Iter {:04d} | Time {:.4f}({:.4f}) | Loss {:.6f}({:.6f}) |"
" NFE Forward {:.0f}({:.1f})"
" | NFE Backward {:.0f}({:.1f})".format(
itr,
time_meter.val,
time_meter.avg,
loss_meter.val,
loss_meter.avg,
nfef_meter.val,
nfef_meter.avg,
nfeb_meter.val,
nfeb_meter.avg,
)
)
if len(regularization_coeffs) > 0:
log_message = append_regularization_to_log(
log_message, regularization_fns, reg_states
)
logger.info(log_message)
if itr % args.val_freq == 0 or itr == args.niters:
with torch.no_grad():
train_eval(
device, args, model, growth_model, itr, best_loss, logger, full_data
)
if itr % args.viz_freq == 0:
if args.data.get_shape()[0] > 2:
logger.warning("Skipping vis as data dimension is >2")
else:
with torch.no_grad():
visualize(device, args, model, itr)
if itr % args.save_freq == 0:
utils.save_checkpoint(
{
# 'args': args,
"state_dict": model.state_dict(),
"growth_state_dict": growth_model.state_dict(),
},
args.save,
epoch=itr,
)
end = time.time()
logger.info("Training has finished.")
