def metrics_func(model, epoch, output_predictions=False):
print_rank_last('calculating metrics ...')
correct = 0
total = 0
if output_predictions:
assert mpu.get_data_parallel_world_size() == 1
named_predictions = []
names = 'predictions'
for name, dataloader in dataloaders:
output = calculate_correct_answers(name, model, dataloader,
epoch, output_predictions)
if not output_predictions:
correct_ans, total_count = output
else:
correct_ans, total_count, predictions = output
named_predictions.append((name, predictions))
names += '_' + name
correct += correct_ans
total += total_count
if is_last_rank():
percent = float(correct) * 100.0 / float(total)
print(' >> |epoch: {}| overall: correct / total = {} / {} = '
'{:.4f} %'.format(epoch, correct, total, percent))
if output_predictions and is_last_rank():
assert args.load is not None
filename = os.path.join(args.load, names + '.pt')
torch.save(named_predictions, filename)
def evaluate_and_print_results(task, data_loader, model, eval_metric):
"""Evaluate and print results on screen."""
# Evaluate and get results.
output = evaluate(data_loader, model, eval_metric)
string = ' validation results on {} | '.format(task)
if is_last_rank():
if eval_metric == 'loss':
num_tokenized_tokens = data_loader.dataset.num_tokenized_tokens
num_original_tokens = data_loader.dataset.num_original_tokens
val_loss = output / (num_tokenized_tokens - 1)
ppl = math.exp(min(20, val_loss))
token_ratio = (num_tokenized_tokens - 1) / (num_original_tokens - 1)
adjusted_ppl = math.exp(min(20, val_loss * token_ratio))
string += 'avg loss: {:.4E} | '.format(val_loss)
string += 'ppl: {:.4E} | '.format(ppl)
string += 'adjusted ppl: {:.4E} | '.format(adjusted_ppl)
string += 'token ratio: {} |'.format(token_ratio)
elif eval_metric == 'accuracy':
num_examples = len(data_loader.dataset)
acc = output / num_examples
string += 'number correct: {:.4E} | '.format(output)
string += 'total examples: {:.4E} | '.format(num_examples)
string += 'avg accuracy: {:.4E}'.format(acc)
else:
raise NotImplementedError('evaluation method for {} metric is not '
'implemented yet.'.format(eval_metric))
length = len(string) + 1
print('-' * length)
print(string)
print('-' * length)
def evaluate_and_print_results(prefix,
forward_step_func,
data_iterator,
model,
iteration,
verbose=False):
"""Helper function to evaluate and dump results on screen."""
args = get_args()
writer = get_tensorboard_writer()
total_loss_dict = evaluate(forward_step_func, data_iterator, model,
verbose)
string = ' validation loss at {} | '.format(prefix)
for key in total_loss_dict:
string += '{} value: {:.6E} | '.format(key,
total_loss_dict[key].item())
ppl = math.exp(min(20, total_loss_dict[key].item()))
string += '{} PPL: {:.6E} | '.format(key, ppl)
if writer and is_last_rank():
writer.add_scalar('{} value-validation'.format(key),
total_loss_dict[key].item(), iteration)
writer.add_scalar('{} ppl-validation'.format(key), ppl, iteration)
writer.add_scalar('{} value-validation vs samples'.format(key),
total_loss_dict[key].item(),
args.consumed_train_samples)
writer.add_scalar('{} ppl-validation vs samples'.format(key), ppl,
args.consumed_train_samples)
length = len(string) + 1
print_rank_last('-' * length)
print_rank_last(string)
print_rank_last('-' * length)
def add_balance_log(writer, iteration):
from megatron import is_last_rank
balance_dict_tensor = torch.vstack([
torch.tensor(item, device=item[0].device)
for item in balance_dict.values()
]).detach()
world_group = get_torch_default_comm()
world_size = torch.distributed.get_world_size(group=world_group)
torch.distributed.all_reduce(balance_dict_tensor, group=world_group)
balance_dict_tensor /= world_size
if writer and is_last_rank():
for idx, metric_name in enumerate(balance_dict):
for layer_id, val in enumerate(balance_dict_tensor[idx]):
writer.add_scalar(f"balance-{metric_name}/layer-{layer_id}",
val.item(), iteration)
writer.add_scalar(
f"balance-{metric_name}/all",
balance_dict_tensor[idx].mean().item(),
iteration,
)
reset_gate_hook()
def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
loss_scale, report_memory_flag, skipped_iter):
"""Log training information such as losses, timing, ...."""
args = get_args()
timers = get_timers()
writer = get_tensorboard_writer()
# Advanced, skipped, and Nan iterations.
advanced_iters_key = 'advanced iterations'
skipped_iters_key = 'skipped iterations'
nan_iters_key = 'nan iterations'
# Advanced iterations.
if not skipped_iter:
total_loss_dict[advanced_iters_key] = total_loss_dict.get(
advanced_iters_key, 0) + 1
else:
if advanced_iters_key not in total_loss_dict:
total_loss_dict[advanced_iters_key] = 0
# Skipped iterations.
total_loss_dict[skipped_iters_key] = total_loss_dict.get(
skipped_iters_key, 0) + skipped_iter
# Update losses and set nan iterations
got_nan = False
for key in loss_dict:
if not skipped_iter:
total_loss_dict[key] = total_loss_dict.get(
key, torch.cuda.FloatTensor([0.0])) + loss_dict[key]
else:
value = loss_dict[key].float().sum().item()
is_nan = value == float('inf') or \
value == -float('inf') or \
value != value
got_nan = got_nan or is_nan
total_loss_dict[nan_iters_key] = total_loss_dict.get(nan_iters_key,
0) + int(got_nan)
# Logging.
timers_to_log = []
def add_to_logging(name):
if name in timers.timers:
timers_to_log.append(name)
add_to_logging('forward-compute')
add_to_logging('forward-recv')
add_to_logging('forward-send')
add_to_logging('forward-send-backward-recv')
add_to_logging('backward-compute')
add_to_logging('backward-recv')
add_to_logging('backward-send')
add_to_logging('backward-send-forward-recv')
add_to_logging('backward-params-all-reduce')
add_to_logging('backward-embedding-all-reduce')
add_to_logging('optimizer-copy-to-main-grad')
add_to_logging('optimizer-unscale-and-check-inf')
add_to_logging('optimizer-clip-main-grad')
add_to_logging('optimizer-copy-main-to-model-params')
add_to_logging('optimizer')
add_to_logging('batch-generator')
# Calculate batch size.
batch_size = args.micro_batch_size * args.data_parallel_size * \
get_num_microbatches()
total_iterations = total_loss_dict[advanced_iters_key] + \
total_loss_dict[skipped_iters_key]
# Tensorboard values.
if writer and is_last_rank():
writer.add_scalar('learning-rate', learning_rate, iteration)
writer.add_scalar('learning-rate vs samples', learning_rate,
args.consumed_train_samples)
writer.add_scalar('batch-size', batch_size, iteration)
writer.add_scalar('batch-size vs samples', batch_size,
args.consumed_train_samples)
for key in loss_dict:
writer.add_scalar(key, loss_dict[key], iteration)
writer.add_scalar(key + ' vs samples', loss_dict[key],
args.consumed_train_samples)
writer.add_scalar('loss-scale', loss_scale, iteration)
writer.add_scalar('loss-scale vs samples', loss_scale,
args.consumed_train_samples)
timers.write(timers_to_log,
writer,
iteration,
normalizer=total_iterations)
if iteration % args.log_interval == 0:
elapsed_time = timers('interval time').elapsed()
elapsed_time_per_iteration = elapsed_time / total_iterations
if writer and torch.distributed.get_rank() == 0:
writer.add_scalar('iteration-time', elapsed_time_per_iteration,
iteration)
log_string = ' iteration {:8d}/{:8d} |'.format(iteration,
args.train_iters)
log_string += ' consumed samples: {:12d} |'.format(
args.consumed_train_samples)
log_string += ' elapsed time per iteration (ms): {:.1f} |'.format(
elapsed_time_per_iteration * 1000.0)
log_string += ' learning rate: {:.3E} |'.format(learning_rate)
log_string += ' global batch size: {:5d} |'.format(batch_size)
for key in total_loss_dict:
if key not in [
advanced_iters_key, skipped_iters_key, nan_iters_key
]:
avg = total_loss_dict[key].item() / \
float(max(1, total_loss_dict[advanced_iters_key]))
if avg > 0.0:
log_string += ' {}: {:.6E} |'.format(key, avg)
total_loss_dict[key] = torch.cuda.FloatTensor([0.0])
log_string += ' loss scale: {:.1f} |'.format(loss_scale)
log_string += ' number of skipped iterations: {:3d} |'.format(
total_loss_dict[skipped_iters_key])
log_string += ' number of nan iterations: {:3d} |'.format(
total_loss_dict[nan_iters_key])
total_loss_dict[advanced_iters_key] = 0
total_loss_dict[skipped_iters_key] = 0
total_loss_dict[nan_iters_key] = 0
print_rank_last(log_string)
if report_memory_flag and learning_rate > 0.:
# Report memory after optimizer state has been initialized.
report_memory('(after {} iterations)'.format(iteration))
report_memory_flag = False
timers.log(timers_to_log, normalizer=args.log_interval)
return report_memory_flag
