SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

[tests] use the correct n_gpu in TrainerIntegrationTest::test_train_and_eval_dataloaders for XPU (#29307)

Browse Source 
* fix n_gpu

* fix style
This commit is contained in:
Fanli Lin
2024-03-08 23:52:25 +08:00
committed by GitHub
parent 1ba89dc2d2
commit 3f6973db06
1 changed files with 4 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
tests/trainer/test_trainer.py
View File

@@ -1029,7 +1029,10 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
self.assertFalse(is_any_loss_nan_or_inf(log_history_filter)) self.assertFalse(is_any_loss_nan_or_inf(log_history_filter))
def test_train_and_eval_dataloaders(self): def test_train_and_eval_dataloaders(self):
n_gpu = max(1, backend_device_count(torch_device)) if torch_device == "cuda":
n_gpu = max(1, backend_device_count(torch_device))
else:
n_gpu = 1
trainer = get_regression_trainer(learning_rate=0.1, per_device_train_batch_size=16) trainer = get_regression_trainer(learning_rate=0.1, per_device_train_batch_size=16)
self.assertEqual(trainer.get_train_dataloader().total_batch_size, 16 * n_gpu) self.assertEqual(trainer.get_train_dataloader().total_batch_size, 16 * n_gpu)
trainer = get_regression_trainer(learning_rate=0.1, per_device_eval_batch_size=16) trainer = get_regression_trainer(learning_rate=0.1, per_device_eval_batch_size=16)