def train(model, input_data, output_data, learning_rate=1e-3, epochs=1000):
"""Fits model to training data and returns train loss."""
optimizer = create_optimizer(model, learning_rate=learning_rate, weight_decay=0)
loss_fn_kwargs={}
for epoch in range(epochs):
optimizer = train_step(optimizer, input_data, output_data, mse_loss, loss_fn_kwargs)
preds = jnp.squeeze(optimizer.target(input_data), axis=1)
train_loss = jnp.mean(jnp.square(preds-output_data))
return train_loss
def train(model,
optimizer,
lr_scheduler,
train_data_iterator,
val_data_iterator,
timers,
args,
summary_writer=None):
"""Train the model."""
# Turn on training mode which enables dropout.
model.train()
# Tracking loss.
total_lm_loss = 0.0
# Iterations.
skipped_iters = 0
timers('interval time').start()
report_memory_flag = True
mems = []
while args.iteration < args.train_iters:
lm_loss, skipped_iter, mems = train_step(
train_data_iterator,
model,
optimizer,
lr_scheduler,
args,
timers,
mems=mems,
forward_step_func=forward_step)
skipped_iters += skipped_iter
args.iteration += 1
# Update losses.
total_lm_loss += lm_loss.data.detach().float()
# Logging.
if args.iteration % args.log_interval == 0:
learning_rate = optimizer.param_groups[0]['lr']
avg_lm_loss = total_lm_loss.item() / args.log_interval
elapsed_time = timers('interval time').elapsed()
report_iteration_metrics(summary_writer, optimizer, learning_rate,
avg_lm_loss,
elapsed_time * 1000.0 / args.log_interval,
args.iteration, args.train_iters, args)
total_lm_loss = 0.0
if report_memory_flag:
report_memory('after {} iterations'.format(args.iteration))
report_memory_flag = False
# for i in range(torch.distributed.get_world_size()):
# if i == torch.distributed.get_rank():
# print(get_hostname())
# timers.log(['forward', 'backward', 'optimizer',
# 'batch generator', 'data loader'],
# normalizer=args.log_interval, reset=False)
# torch.distributed.barrier()
if args.deepspeed or args.DDP_impl == 'torch':
timers.log([
'forward', 'backward', 'optimizer', 'batch generator',
'data loader'
],
normalizer=args.log_interval)
else:
timers.log([
'forward', 'backward', 'allreduce', 'optimizer',
'batch generator', 'data loader'
],
normalizer=args.log_interval)
# Checkpointing
if args.save and args.save_interval and args.iteration % args.save_interval == 0:
save_checkpoint(args.iteration, model, optimizer, lr_scheduler,
args)
# Evaluation
if args.eval_interval and args.iteration % args.eval_interval == 0 and args.do_valid:
prefix = 'iteration {}'.format(args.iteration)
evaluate_and_print_results(prefix,
val_data_iterator,
model,
args,
timers,
verbose=False,
step=args.iteration,
summary_writer=summary_writer,
forward_step_func=forward_step)
return args.iteration, skipped_iters
def main():
# load datasets
train_loader, test_loader = load_dataset(args.train_set, args.batch_size)
# initialize model
if args.model_file:
try:
total_examples, fixed_noise, gen_losses, disc_losses, gen_loss_per_epoch, disc_loss_per_epoch, \
prev_epoch, gan, disc_optimizer, gen_optimizer, memory, use_EM, use_mcgn \
= load_model(args.model_file, args.cuda, args.learning_rate, args.beta_0, args.beta_1)
print('model loaded successfully! resuming from step {}'.format(
prev_epoch))
args.memory = memory # prevents any contradictions during loading
args.use_EM = use_EM
args.use_mcgn = use_mcgn
except:
print('could not load model! creating new model...')
args.model_file = None
if not args.model_file:
print('creating new model...')
total_examples, fixed_noise, gen_losses, disc_losses, gen_loss_per_epoch, disc_loss_per_epoch, \
prev_epoch, gan, disc_optimizer, gen_optimizer \
= create_new_model(args.train_set, args.cuda, args.learning_rate, args.beta_0,
args.beta_1, args.memory, use_mcgn=args.use_mcgn)
# results save folder
gen_images_dir = 'results/generated_images'
train_summaries_dir = 'results/training_summaries'
checkpoint_dir = 'results/checkpoints'
if not os.path.isdir('results'):
os.mkdir('results')
if not os.path.isdir(gen_images_dir):
os.mkdir(gen_images_dir)
if not os.path.isdir(train_summaries_dir):
os.mkdir(train_summaries_dir)
if not os.path.isdir(checkpoint_dir):
os.mkdir(checkpoint_dir)
np.random.seed(
args.seed
) # reset training seed to ensure that batches remain the same between runs!
try:
for epoch in range(prev_epoch, args.n_epochs):
disc_losses_epoch = []
gen_losses_epoch = []
for idx, (true_batch, _) in enumerate(train_loader):
#time.sleep(1)
disc_train_loss, gen_train_loss, disc_true_accuracy, disc_fake_accuracy \
= train_step(gan=gan, batch_size=args.batch_size, is_cuda=args.cuda, true_batch=true_batch,
grad_clip=args.grad_clip, disc_optimizer=disc_optimizer,
gen_optimizer=gen_optimizer, memory=args.memory, use_EM=args.use_EM)
if (total_examples != 0) and (total_examples %
args.display_result_every == 0):
print(
'epoch {}: step {}/{} disc true acc: {:.4f} disc fake acc: {:.4f} '
'disc loss: {:.4f}, gen loss: {:.4f}'.format(
epoch + 1, idx + 1, len(train_loader),
disc_true_accuracy, disc_fake_accuracy,
disc_train_loss.item(), gen_train_loss.item()))
if args.memory and args.verbose:
next_h, next_k, next_a, next_v = gan.memory.get_info_for_logging(
)
if (total_examples != 0) and (
total_examples % args.display_result_every == 0):
print(
'avg hist: {:.4f} avg age: {:.5f} avg key val: {:.4f}'
.format(next_h.mean(), next_a.mean(),
next_v.mean()))
print(
'min: {:.3f}, max: {:.3f}, median: {:.3f} mean: {:.3f}'
.format(next_h.min().item(),
next_h.max().item(),
next_h.median().item(),
next_h.mean().item()))
# Checkpoint model
total_examples += args.batch_size
if (total_examples != 0) and (total_examples %
args.checkpoint_interval == 0):
disc_losses.extend(disc_losses_epoch)
gen_losses.extend(gen_losses_epoch)
save_all(total_examples=total_examples,
fixed_noise=fixed_noise,
gan=gan,
disc_loss_per_epoch=disc_loss_per_epoch,
gen_loss_per_epoch=gen_loss_per_epoch,
gen_losses=gen_losses,
disc_losses=disc_losses,
epoch=epoch,
checkpoint_dir=checkpoint_dir,
is_cuda=args.cuda,
gen_images_dir=gen_images_dir,
train_summaries_dir=train_summaries_dir,
disc_optimizer=disc_optimizer,
gen_optimizer=gen_optimizer,
train_set=args.train_set,
memory=args.memory,
use_EM=args.use_EM,
use_mcgn=args.use_mcgn)
# Collect information per epoch
disc_losses_epoch.append(disc_train_loss.item())
gen_losses_epoch.append(gen_train_loss.item())
disc_loss_per_epoch.append(np.average(disc_losses_epoch))
gen_loss_per_epoch.append(np.average(gen_losses_epoch))
# Save epoch learning curve
save_learning_curve_epoch(gen_losses=gen_loss_per_epoch,
disc_losses=disc_loss_per_epoch,
total_epochs=epoch + 1,
directory=train_summaries_dir)
print("Saved learning curves!")
print('epoch {}/{} disc loss: {:.4f}, gen loss: {:.4f}'.format(
epoch + 1, args.n_epochs,
np.array(disc_losses_epoch).mean(),
np.array(gen_losses_epoch).mean()))
disc_losses.extend(disc_losses_epoch)
gen_losses.extend(gen_losses_epoch)
except KeyboardInterrupt:
print("Saving before quit...")
save_all(total_examples=total_examples,
fixed_noise=fixed_noise,
gan=gan,
disc_loss_per_epoch=disc_loss_per_epoch,
gen_loss_per_epoch=gen_loss_per_epoch,
gen_losses=gen_losses,
disc_losses=disc_losses,
epoch=epoch,
checkpoint_dir=checkpoint_dir,
is_cuda=args.cuda,
gen_images_dir=gen_images_dir,
train_summaries_dir=train_summaries_dir,
disc_optimizer=disc_optimizer,
gen_optimizer=gen_optimizer,
train_set=args.train_set,
memory=args.memory,
use_EM=args.use_EM,
use_mcgn=args.use_mcgn)
def _train(model,
optimizer,
lr_scheduler,
forward_step,
train_dataloader,
valid_dataloader,
end_of_epoch_callback,
args,
timers,
summary_writer=None):
"""Train the model."""
# Turn on training mode which enables dropout.
model.train()
# Tracking loss.
args.iteration = 0
total_lm_loss = 0.0
best_score, best_iteration = 0, None
# Starting epoch and iteration
start_epoch = args.iteration // args.train_iters_per_epoch
start_iteration = args.iteration % args.train_iters_per_epoch
if not args.block_lm_ratio:
valid_dataloader = valid_dataloader[0]
# For each remaining epoch
timers('interval time').start()
for epoch in range(start_epoch, args.epochs):
print_rank_0('working on epoch {} ...'.format(epoch))
# Set the data loader epoch to shuffle the index iterator.
if mpu.get_model_parallel_rank() == 0:
train_dataloader[0].sampler.set_epoch(args.seed + epoch)
# For all the batches in the dataset.
for iteration_, batch in enumerate(train_dataloader[0]):
# Ignore the iterations before starting value
if iteration_ < start_iteration:
continue
# Set to zero so the next epoch does not skip any batches.
start_iteration = 0
# Train for one step.
if args.block_lm_ratio > 0.0:
data = (batch, train_dataloader[1])
else:
data = batch
lm_loss, skipped_iter, _ = train_step(
data,
model,
optimizer,
lr_scheduler,
args,
timers,
forward_step_func=forward_step,
single_step=True)
args.iteration += 1
total_lm_loss += lm_loss.data.detach().float()
# Logging.
if args.iteration % args.log_interval == 0:
learning_rate = optimizer.param_groups[0]['lr']
avg_lm_loss = total_lm_loss.item() / args.log_interval
elapsed_time = timers('interval time').elapsed()
timers.log([
'forward', 'backward', 'allreduce', 'optimizer',
'batch generator'
],
normalizer=args.log_interval)
report_iteration_metrics(
summary_writer, optimizer, learning_rate, avg_lm_loss,
elapsed_time * 1000.0 / args.log_interval, args.iteration,
args.train_iters, args)
total_lm_loss = 0.0
# Evaluation
if args.eval_interval and valid_dataloader is not None and args.iteration % args.eval_interval == 0:
prefix = 'iteration {}'.format(args.iteration)
evaluate_and_print_results(prefix,
valid_dataloader,
model,
args,
timers,
step=args.iteration,
verbose=False,
forward_step_func=forward_step,
summary_writer=summary_writer)
# Checkpointing at the end of each epoch.
if args.save and (epoch + 1) % args.save_epoch == 0:
save_checkpoint(args.iteration,
model,
optimizer,
lr_scheduler,
args,
only_changed_parameters=True)
# Callback at the end of each epoch.
if end_of_epoch_callback is not None and (epoch +
1) % args.eval_epoch == 0:
score_dict = end_of_epoch_callback(model,
epoch,
summary_writer=summary_writer)
validation_metric = args.validation_metric if args.validation_metric else list(
score_dict.keys())[0]
validation_score = score_dict[validation_metric]
if best_iteration is None or validation_score > best_score:
best_iteration = args.iteration
best_score = validation_score
print_rank_0(
f"Found best {validation_metric} {best_score} at {best_iteration}"
)
save_checkpoint(args.iteration,
model,
optimizer,
lr_scheduler,
args,
tag="best",
barrier=False,
only_changed_parameters=True,
no_deepspeed=True,
no_save_optim=True)
if torch.distributed.get_rank() == 0:
score_dict.update({"type": "validation", "epoch": epoch})
with open(os.path.join(args.log_dir, "results.json"),
"w") as output:
output.write(json.dumps(score_dict) + "\n")
with open(
os.path.join(args.save,
"best_checkpointed_iteration.txt"),
"w") as output:
output.write(str(best_iteration))
torch.distributed.barrier()
return best_iteration
