Skip to content

fix Megatron-LM api that breaks bert case #138

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
conglongli merged 1 commit into from
Oct 22, 2021
Merged

fix Megatron-LM api that breaks bert case #138

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
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
2 changes: 1 addition & 1 deletion Megatron-LM-v1.1.5-ZeRO3/megatron/training.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -327,7 +327,7 @@ def train_step(forward_step_func, data_iterator,
#see_memory_usage(f'before forward {model.global_steps}', force=True)
# Forward model for one step.
timers('forward').start()
loss, loss_reduced = forward_step_func(data_iterator, model, args.curriculum_learning)
loss, loss_reduced = forward_step_func(data_iterator, model)
timers('forward').stop()

#see_memory_usage(f'before backward {model.global_steps}', force=True)
Expand Down
4 changes: 2 additions & 2 deletions Megatron-LM-v1.1.5-ZeRO3/pretrain_gpt2.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,7 @@ def get_batch(data_iterator):
return tokens, labels, loss_mask, attention_mask, position_ids


def forward_step(data_iterator, model, curriculum_learning=False):
def forward_step(data_iterator, model):
"""Forward step."""
args = get_args()
timers = get_timers()
Expand All @@ -98,7 +98,7 @@ def forward_step(data_iterator, model, curriculum_learning=False):
timers('batch generator').stop()
# Forward model.
losses = model(tokens, position_ids, attention_mask, labels=labels)
if curriculum_learning and args.curriculum_seqlen < args.seq_length:
if args.curriculum_learning and args.curriculum_seqlen < args.seq_length:
loss_mask = loss_mask[:, :args.curriculum_seqlen].contiguous()
loss_mask = loss_mask.view(-1)
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
Expand Down