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Fix Trainer tests in a multiGPU env (#7458)

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This commit is contained in:
Sylvain Gugger
2020-09-29 14:06:41 -04:00
committed by GitHub
parent d0fd7154c5
commit 8546dc55c2
1 changed files with 9 additions and 6 deletions
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15
tests/test_trainer.py
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@@ -109,12 +109,15 @@ if is_torch_available():
loss = torch.nn.functional.mse_loss(y, labels) loss = torch.nn.functional.mse_loss(y, labels)
return (loss, y, y) if self.double_output else (loss, y) return (loss, y, y) if self.double_output else (loss, y)
def get_regression_trainer(a=0, b=0, double_output=False, train_len=64, eval_len=64, **kwargs): def get_regression_trainer(a=0, b=0, double_output=False, train_len=64, eval_len=64, pretrained=True, **kwargs):
label_names = kwargs.get("label_names", None) label_names = kwargs.get("label_names", None)
train_dataset = RegressionDataset(length=train_len, label_names=label_names) train_dataset = RegressionDataset(length=train_len, label_names=label_names)
eval_dataset = RegressionDataset(length=eval_len, label_names=label_names) eval_dataset = RegressionDataset(length=eval_len, label_names=label_names)
config = RegressionModelConfig(a=a, b=b, double_output=double_output) if pretrained:
model = RegressionPreTrainedModel(config) config = RegressionModelConfig(a=a, b=b, double_output=double_output)
model = RegressionPreTrainedModel(config)
else:
model = RegressionModel(a=a, b=b, double_output=double_output)
compute_metrics = kwargs.pop("compute_metrics", None) compute_metrics = kwargs.pop("compute_metrics", None)
data_collator = kwargs.pop("data_collator", None) data_collator = kwargs.pop("data_collator", None)
optimizers = kwargs.pop("optimizers", (None, None)) optimizers = kwargs.pop("optimizers", (None, None))
@@ -178,6 +181,7 @@ class TrainerIntegrationTest(unittest.TestCase):
best_model = RegressionModel() best_model = RegressionModel()
state_dict = torch.load(os.path.join(checkpoint, WEIGHTS_NAME)) state_dict = torch.load(os.path.join(checkpoint, WEIGHTS_NAME))
best_model.load_state_dict(state_dict) best_model.load_state_dict(state_dict)
best_model.to(trainer.args.device)
self.assertTrue(torch.allclose(best_model.a, trainer.model.a)) self.assertTrue(torch.allclose(best_model.a, trainer.model.a))
self.assertTrue(torch.allclose(best_model.b, trainer.model.b)) self.assertTrue(torch.allclose(best_model.b, trainer.model.b))
@@ -360,8 +364,7 @@ class TrainerIntegrationTest(unittest.TestCase):
# With a regular model that is not a PreTrainedModel # With a regular model that is not a PreTrainedModel
with tempfile.TemporaryDirectory() as tmpdir: with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(output_dir=tmpdir, save_steps=5) trainer = get_regression_trainer(output_dir=tmpdir, save_steps=5, pretrained=False)
trainer.model = RegressionModel()
trainer.train() trainer.train()
self.check_saved_checkpoints(tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), False) self.check_saved_checkpoints(tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), False)
@@ -426,8 +429,8 @@ class TrainerIntegrationTest(unittest.TestCase):
eval_steps=5, eval_steps=5,
evaluation_strategy="steps", evaluation_strategy="steps",
load_best_model_at_end=True, load_best_model_at_end=True,
pretrained=False,
) )
trainer.model = RegressionModel(a=1.5, b=2.5)
self.assertFalse(trainer.args.greater_is_better) self.assertFalse(trainer.args.greater_is_better)
trainer.train() trainer.train()
self.check_saved_checkpoints(tmpdir, 5, total, is_pretrained=False) self.check_saved_checkpoints(tmpdir, 5, total, is_pretrained=False)