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Only use tb_writer from master #11

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Apr 2, 2020
Merged

Only use tb_writer from master #11

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27 changes: 18 additions & 9 deletions examples/run_glue_tpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,6 @@
from transformers import glue_convert_examples_to_features as convert_examples_to_features

logger = logging.getLogger(__name__)
script_start_time = time.strftime("%Y%m%d_%H%M%S", time.gmtime())

ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (
BertConfig, XLNetConfig, XLMConfig, RobertaConfig, DistilBertConfig)), ())
Expand Down Expand Up @@ -90,7 +89,9 @@ def get_sampler(dataset):

def train(args, train_dataset, model, tokenizer, disable_logging=False):
""" Train the model """
tb_writer = SummaryWriter('./runs/{}/xla{}'.format(script_start_time, xm.get_ordinal()))
if xm.is_master_ordinal():
# Only master writes to Tensorboard
tb_writer = SummaryWriter()

train_sampler = get_sampler(train_dataset)
dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
Expand Down Expand Up @@ -177,15 +178,18 @@ def train(args, train_dataset, model, tokenizer, disable_logging=False):

if args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics.
results = {}
if args.evaluate_during_training:
results = evaluate(args, model, tokenizer, disable_logging=disable_logging)
for key, value in results.items():
tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
loss_scalar = loss.item()
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', loss_scalar, global_step)
logger.info('global_step: {global_step}, lr: {lr:.3f}, loss: {loss:.3f}'.format(
global_step=global_step, lr=scheduler.get_lr()[0], loss=loss_scalar))
if xm.is_master_ordinal():
# All values must be in CPU and not on TPU device
for key, value in results.items():
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similar comment to prev. pr; all the values here need to be on cpu. if so, can you add a comment? it's a subtle point.

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Done, thanks.

tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', loss_scalar, global_step)

if args.max_steps > 0 and global_step > args.max_steps:
epoch_iterator.close()
Expand All @@ -196,13 +200,16 @@ def train(args, train_dataset, model, tokenizer, disable_logging=False):
train_iterator.close()
break

tb_writer.close()
if xm.is_master_ordinal():
tb_writer.close()
return global_step, loss.item()


def evaluate(args, model, tokenizer, prefix="", disable_logging=False):
"""Evaluate the model"""
tb_writer = SummaryWriter('./runs/{}/xla{}'.format(script_start_time, xm.get_ordinal()))
if xm.is_master_ordinal():
# Only master writes to Tensorboard
tb_writer = SummaryWriter()

# Loop to handle MNLI double evaluation (matched, mis-matched)
eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
Expand Down Expand Up @@ -276,7 +283,9 @@ def evaluate(args, model, tokenizer, prefix="", disable_logging=False):
if args.metrics_debug:
xm.master_print(met.metrics_report())

tb_writer.close()
if xm.is_master_ordinal():
tb_writer.close()

return results


Expand Down