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[PEFT] make the trainer support resume checkpoint from a named adapter #28531 #28547

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[PEFT] make the trainer support resume checkpoint from a named adapter #28531 #28547

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77a8b16
make the trainer support resume checkpoint from a named adapter #28531
chenbin11200 Jan 17, 2024
9bc32c3
format code
chenbin11200 Jan 19, 2024
9cf826a
add unit test
chenbin11200 Feb 18, 2024
5ea30c4
Merge remote-tracking branch 'origin/main' into fix_resume_named_peft
chenbin11200 Mar 8, 2024
b450265
changes for cr
chenbin11200 Mar 12, 2024
6f2c7d4
reformat
chenbin11200 Mar 12, 2024
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16 changes: 12 additions & 4 deletions src/transformers/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2038,9 +2038,16 @@ def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
if model is None:
model = self.model

named_adapter_subfolder = ''
if _is_peft_model(model):
# adapter with adapter_name will be saved in checkpoint/adapter_name subfolder, therefore join the path
# to the subfolder if necessary
named_adapter_subfolder = model.active_adapter if model.active_adapter not in ['default', None] else ''

config_file = os.path.join(resume_from_checkpoint, CONFIG_NAME)
adapter_weights_file = os.path.join(resume_from_checkpoint, ADAPTER_WEIGHTS_NAME)
adapter_safe_weights_file = os.path.join(resume_from_checkpoint, ADAPTER_SAFE_WEIGHTS_NAME)
adapter_weights_file = os.path.join(resume_from_checkpoint, named_adapter_subfolder, ADAPTER_WEIGHTS_NAME)
adapter_safe_weights_file = os.path.join(resume_from_checkpoint, named_adapter_subfolder,
ADAPTER_SAFE_WEIGHTS_NAME)
weights_file = os.path.join(resume_from_checkpoint, WEIGHTS_NAME)
weights_index_file = os.path.join(resume_from_checkpoint, WEIGHTS_INDEX_NAME)
safe_weights_file = os.path.join(resume_from_checkpoint, SAFE_WEIGHTS_NAME)
Expand Down Expand Up @@ -2129,8 +2136,9 @@ def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
elif _is_peft_model(model):
# If train a model using PEFT & LoRA, assume that adapter have been saved properly.
if hasattr(model, "active_adapter") and hasattr(model, "load_adapter"):
if os.path.exists(resume_from_checkpoint):
model.load_adapter(resume_from_checkpoint, model.active_adapter, is_trainable=True)
adapter_model_path = os.path.join(resume_from_checkpoint, named_adapter_subfolder)
if os.path.exists(adapter_model_path):
model.load_adapter(adapter_model_path, model.active_adapter, is_trainable=True)
else:
logger.warning(
"The intermediate checkpoints of PEFT may not be saved correctly, "
Expand Down