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Fixes torch.compile() compatible training #6589

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merged 1 commit into from
Jan 17, 2024
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Fixes torch.compile() compatible training #6589

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18 changes: 12 additions & 6 deletions examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
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LGTM :)
@sayakpaul wdyt? since you reviewed the previous linked pr #6566 :)

Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,7 @@
is_wandb_available,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.torch_utils import is_compiled_module


# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
Expand Down Expand Up @@ -1293,6 +1294,11 @@ def main(args):
else:
param.requires_grad = False

def unwrap_model(model):
model = accelerator.unwrap_model(model)
model = model._orig_mod if is_compiled_module(model) else model
return model

# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
Expand All @@ -1303,14 +1309,14 @@ def save_model_hook(models, weights, output_dir):
text_encoder_two_lora_layers_to_save = None

for model in models:
if isinstance(model, type(accelerator.unwrap_model(unet))):
if isinstance(model, type(unwrap_model(unet))):
unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
elif isinstance(model, type(unwrap_model(text_encoder_one))):
if args.train_text_encoder:
text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
)
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
elif isinstance(model, type(unwrap_model(text_encoder_two))):
if args.train_text_encoder:
text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
Expand Down Expand Up @@ -1338,11 +1344,11 @@ def load_model_hook(models, input_dir):
while len(models) > 0:
model = models.pop()

if isinstance(model, type(accelerator.unwrap_model(unet))):
if isinstance(model, type(unwrap_model(unet))):
unet_ = model
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
elif isinstance(model, type(unwrap_model(text_encoder_one))):
text_encoder_one_ = model
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
elif isinstance(model, type(unwrap_model(text_encoder_two))):
text_encoder_two_ = model
else:
raise ValueError(f"unexpected save model: {model.__class__}")
Expand Down