Skip to content

[BugFix] Fix amp usage for evaluation. #3303

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
Sep 24, 2022
Merged

[BugFix] Fix amp usage for evaluation. #3303

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
caade9e
fix eval of amp usage.
ZHUI Sep 19, 2022
fa97159
fix
ZHUI Sep 19, 2022
7bba707
Merge branch 'develop' into fix_eval
ZHUI Sep 20, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
62 changes: 36 additions & 26 deletions model_zoo/ernie-1.0/run_pretrain.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -217,33 +217,43 @@ def run_evaluate(data_loader,
for eval_step, batch in enumerate(data_loader):
input_ids, segment_ids, input_mask, masked_lm_positions, \
masked_lm_labels, next_sentence_labels = batch
with paddle.amp.auto_cast(args.use_amp,
custom_white_list=[
'softmax',
'layer_norm',
'gelu',
],
custom_black_list=[
"c_softmax_with_cross_entropy",
],
level=args.fp16_opt_level):

if args.binary_head:
prediction_scores, seq_relationship_score = model(
input_ids=input_ids,
token_type_ids=segment_ids,
position_ids=None,
attention_mask=input_mask,
masked_positions=masked_lm_positions)

lm_loss, sop_loss = criterion(prediction_scores,
seq_relationship_score,
masked_lm_labels,
next_sentence_labels)
loss = lm_loss + sop_loss
else:
prediction_scores = model(input_ids=input_ids,
token_type_ids=segment_ids,
position_ids=None,
attention_mask=input_mask,
masked_positions=masked_lm_positions)

loss = criterion(prediction_scores, None, masked_lm_labels)

loss_global["loss"] += loss.detach()
if args.binary_head:
loss_global["lm_loss"] += lm_loss.detach()
loss_global["sop_loss"] += sop_loss.detach()
if args.binary_head:
prediction_scores, seq_relationship_score = model(
input_ids=input_ids,
token_type_ids=segment_ids,
position_ids=None,
attention_mask=input_mask,
masked_positions=masked_lm_positions)

lm_loss, sop_loss = criterion(prediction_scores,
seq_relationship_score,
masked_lm_labels,
next_sentence_labels)
loss = lm_loss + sop_loss
else:
prediction_scores = model(input_ids=input_ids,
token_type_ids=segment_ids,
position_ids=None,
attention_mask=input_mask,
masked_positions=masked_lm_positions)

loss = criterion(prediction_scores, None, masked_lm_labels)

loss_global["loss"] += loss.detach()
if args.binary_head:
loss_global["lm_loss"] += lm_loss.detach()
loss_global["sop_loss"] += sop_loss.detach()

if eval_step >= iter_steps - 1:
log_info_dict = dict()
Expand Down