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model.tie_weights() should be applied after accelerator.prepare() #18676

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Merged
muellerzr merged 3 commits into from
Aug 18, 2022
Merged

model.tie_weights() should be applied after accelerator.prepare() #18676

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89bc7b3
`model.tie_weights()` should be applied after `accelerator.prepare`
Gladiator07 Aug 18, 2022
f2ab8c3
Merge branch 'huggingface:main' into patch-1
Gladiator07 Aug 18, 2022
23dc3dd
format code
Gladiator07 Aug 18, 2022
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8 changes: 4 additions & 4 deletions examples/pytorch/language-modeling/run_mlm_no_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -518,10 +518,6 @@ def group_texts(examples):
]
optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)

# On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
if accelerator.distributed_type == DistributedType.TPU:
model.tie_weights()

# Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
# shorter in multiprocess)

Expand All @@ -544,6 +540,10 @@ def group_texts(examples):
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
)

# On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
if accelerator.distributed_type == DistributedType.TPU:
model.tie_weights()

# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
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