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Test checkpointing (#11682)

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* Add test and see where CI is unhappy

* Load with strict=False
This commit is contained in:
Sylvain Gugger 2021-05-11 12:02:48 -04:00 committed by GitHub
parent d9b286272c
commit f13f1f8fb8
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2 changed files with 19 additions and 1 deletions
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13
src/transformers/trainer.py
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@ -1059,7 +1059,18 @@ class Trainer:
# We load the model state dict on the CPU to avoid an OOM error. # We load the model state dict on the CPU to avoid an OOM error.
state_dict = torch.load(os.path.join(resume_from_checkpoint, WEIGHTS_NAME), map_location="cpu") state_dict = torch.load(os.path.join(resume_from_checkpoint, WEIGHTS_NAME), map_location="cpu")
# If the model is on the GPU, it still works! # If the model is on the GPU, it still works!
self.model.load_state_dict(state_dict) load_result = self.model.load_state_dict(state_dict, strict=False)
if len(load_result.missing_keys) != 0:
if load_result.missing_keys == self.model._keys_to_ignore_on_save:
self.model.tie_weights()
else:
logger.warn(
f"There were missing keys in the checkpoint model loaded: {load_result.missing_keys}."
)
if len(load_result.unexpected_keys) != 0:
logger.warn(
f"There were unexpected keys in the checkpoint model loaded: {load_result.unexpected_keys}."
)
# If model was re-initialized, put it on the right device and update self.model_wrapped # If model was re-initialized, put it on the right device and update self.model_wrapped
if model_reloaded: if model_reloaded:

7
tests/test_modeling_common.py
View File

@ -177,6 +177,13 @@ class ModelTesterMixin:
for k in _keys_to_ignore_on_save: for k in _keys_to_ignore_on_save:
self.assertNotIn(k, state_dict_saved) self.assertNotIn(k, state_dict_saved)
# Test we can load the state dict in the model, necessary for the checkpointing API in Trainer.
load_result = model.load_state_dict(state_dict_saved, strict=False)
self.assertTrue(
len(load_result.missing_keys) == 0 or load_result.missing_keys == model._keys_to_ignore_on_save
)
self.assertTrue(len(load_result.unexpected_keys) == 0)
def _mock_init_weights(self, module): def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None: if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3) module.weight.data.fill_(3)