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Update no trainer scripts to include gather for metrics #18877

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merged 2 commits into from
Sep 6, 2022
Merged

Update no trainer scripts to include gather for metrics #18877

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13a8342
updating gather function with gather_for_metrics in run_wav2vec2_pret…
Sep 2, 2022
4bc8f97
Merge branch 'huggingface:main' into update_no_trainer_scripts_to_inc…
arun99481 Sep 3, 2022
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12 changes: 6 additions & 6 deletions examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -596,7 +596,7 @@ def prepare_dataset(batch):
# make sure that `num_losses` is summed for distributed training
# and average gradients over losses of all devices
if accelerator.state.num_processes > 1:
num_losses = accelerator.gather(num_losses).sum()
num_losses = accelerator.gather_for_metrics(num_losses).sum()
gradient_multiplier = accelerator.state.num_processes / num_losses
multiply_grads(model.module.parameters(), gradient_multiplier)
else:
Expand Down Expand Up @@ -647,10 +647,10 @@ def prepare_dataset(batch):
outputs.diversity_loss.detach()

if accelerator.state.num_processes > 1:
loss = accelerator.gather(loss).sum()
outputs.contrastive_loss = accelerator.gather(outputs.contrastive_loss).sum()
outputs.diversity_loss = accelerator.gather(outputs.diversity_loss).sum()
percent_masked = accelerator.gather(percent_masked).sum()
loss = accelerator.gather_for_metrics(loss).sum()
outputs.contrastive_loss = accelerator.gather_for_metrics(outputs.contrastive_loss).sum()
outputs.diversity_loss = accelerator.gather_for_metrics(outputs.diversity_loss).sum()
percent_masked = accelerator.gather_for_metrics(percent_masked).sum()

train_logs = {
"loss": (loss * args.gradient_accumulation_steps) / num_losses,
Expand Down Expand Up @@ -713,7 +713,7 @@ def prepare_dataset(batch):

# sum over devices in multi-processing
if accelerator.num_processes > 1:
val_logs = {k: accelerator.gather(v).sum() for k, v in val_logs.items()}
val_logs = {k: accelerator.gather_for_metrics(v).sum() for k, v in val_logs.items()}

val_logs = {k: v / val_logs["val_num_losses"] for k, v in val_logs.items()}

Expand Down