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[textual_inversion] Fix resuming state when using gradient checkpointing #2072

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pcuenca merged 2 commits into from
Jan 24, 2023
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[textual_inversion] Fix resuming state when using gradient checkpointing #2072

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Fix resuming state when using gradient checkpointing.
pcuenca Jan 23, 2023
717a80c
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pcuenca Jan 23, 2023
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25 changes: 16 additions & 9 deletions examples/textual_inversion/textual_inversion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -597,7 +597,7 @@ def main():
text_encoder, optimizer, train_dataloader, lr_scheduler
)

# For mixed precision training we cast the text_encoder and vae weights to half-precision
# For mixed precision training we cast the unet and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
Expand Down Expand Up @@ -643,14 +643,21 @@ def main():
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1]
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])

resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = resume_global_step // num_update_steps_per_epoch
resume_step = resume_global_step % num_update_steps_per_epoch
path = dirs[-1] if len(dirs) > 0 else None

if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])

resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)

# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
Expand Down