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fix reshard recover #7083

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fix reshard recover #7083

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30 changes: 25 additions & 5 deletions paddlenlp/trainer/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -429,6 +429,24 @@ def remove_callback(self, callback):
"""
self.callback_handler.remove_callback(callback)

def _check_resume_from_checkpoint(self, resume_from_checkpoint):
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这个函数也不需要

"""load state_dict from_checkpoint, Only load model state dict.

Args:
resume_from_checkpoint (`str` or `bool`, *optional*):
If a `str`, local path to a saved checkpoint as saved by a previous instance of [`Trainer`]. If a
`bool` and equals `True`, load the last checkpoint in *args.output_dir* as saved by a previous instance
of [`Trainer`]. Only load model state dict.
"""
resume_from_checkpoint = None if not resume_from_checkpoint else resume_from_checkpoint
# Load potential model checkpoint
if isinstance(resume_from_checkpoint, bool) and resume_from_checkpoint:
resume_from_checkpoint = get_last_checkpoint(self.args.output_dir)
if resume_from_checkpoint is None:
raise ValueError(f"No valid checkpoint found in output directory ({self.args.output_dir})")

return resume_from_checkpoint

def _load_from_checkpoint(self, resume_from_checkpoint=None):
"""load state_dict from_checkpoint, Only load model state dict.

Expand Down Expand Up @@ -456,10 +474,12 @@ def _load_from_checkpoint(self, resume_from_checkpoint=None):
weight_index_name = PADDLE_WEIGHTS_INDEX_NAME # currently set paddle as default, do not support safetensors.

if self.args.should_load_sharding_stage1_model:
state_dict = self.sharding_io.load_state_dict_from_checkpoint_with_reshard(
resume_from_checkpoint,
base_weight_name=weight_name,
)
if resume_from_checkpoint is not None:
state_dict = self.sharding_io.load_state_dict_from_checkpoint_with_reshard(
resume_from_checkpoint,
base_weight_name=weight_name,
)
self._set_state_dict_in_model(state_dict)
else:
if resume_from_checkpoint is not None and self.args.dataset_rank == 0:

Expand Down Expand Up @@ -594,7 +614,7 @@ def train(
self.create_optimizer_and_scheduler(num_training_steps=max_steps)

self.state = TrainerState()

resume_from_checkpoint = self._check_resume_from_checkpoint(resume_from_checkpoint)
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这个是在训练代码里面做的，不是在这里。不需要加

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不加就报错了

if self.args.should_load_sharding_stage1_model:
model = self._wrap_model_and_load_sharded_checkpoint(resume_from_checkpoint)
elif self.args.should_save_sharding_stage1_model:
Expand Down