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these tests require non-multigpu env (#7059)

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* these tests require non-multigpu env

* cleanup

* clarify
This commit is contained in:
Stas Bekman 2020-09-10 15:52:55 -07:00 committed by GitHub
parent 77950c485a
commit 8fcbe486e1
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2 changed files with 21 additions and 1 deletions
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14
src/transformers/testing_utils.py
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@ -122,6 +122,20 @@ def require_multigpu(test_case):
return test_case
def require_non_multigpu(test_case):
"""
Decorator marking a test that requires 0 or 1 GPU setup (in PyTorch).
"""
if not _torch_available:
return unittest.skip("test requires PyTorch")(test_case)
import torch
if torch.cuda.device_count() > 1:
return unittest.skip("test requires 0 or 1 GPU")(test_case)
return test_case
def require_torch_tpu(test_case):
"""
Decorator marking a test that requires a TPU (in PyTorch).

8
tests/test_trainer.py
View File

@ -4,7 +4,7 @@ import datasets
import numpy as np
from transformers import AutoTokenizer, TrainingArguments, is_torch_available
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.testing_utils import get_tests_dir, require_non_multigpu, require_torch
if is_torch_available():
@ -111,6 +111,7 @@ class TrainerIntegrationTest(unittest.TestCase):
self.n_epochs = args.num_train_epochs
self.batch_size = args.per_device_train_batch_size
@require_non_multigpu
def test_reproducible_training(self):
# Checks that training worked, model trained and seed made a reproducible training.
trainer = get_regression_trainer(learning_rate=0.1)
@ -122,6 +123,7 @@ class TrainerIntegrationTest(unittest.TestCase):
trainer.train()
self.check_trained_model(trainer.model, alternate_seed=True)
@require_non_multigpu
def test_number_of_steps_in_training(self):
# Regular training has n_epochs * len(train_dl) steps
trainer = get_regression_trainer(learning_rate=0.1)
@ -138,6 +140,7 @@ class TrainerIntegrationTest(unittest.TestCase):
train_output = trainer.train()
self.assertEqual(train_output.global_step, 10)
@require_non_multigpu
def test_train_and_eval_dataloaders(self):
trainer = get_regression_trainer(learning_rate=0.1, per_device_train_batch_size=16)
self.assertEqual(trainer.get_train_dataloader().batch_size, 16)
@ -200,6 +203,7 @@ class TrainerIntegrationTest(unittest.TestCase):
x = trainer.eval_dataset.x
self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
@require_non_multigpu
def test_trainer_with_datasets(self):
np.random.seed(42)
x = np.random.normal(size=(64,)).astype(np.float32)
@ -228,6 +232,7 @@ class TrainerIntegrationTest(unittest.TestCase):
trainer.train()
self.check_trained_model(trainer.model)
@require_non_multigpu
def test_custom_optimizer(self):
train_dataset = RegressionDataset()
args = TrainingArguments("./regression")
@ -241,6 +246,7 @@ class TrainerIntegrationTest(unittest.TestCase):
self.assertTrue(torch.abs(trainer.model.b - 2.5656) < 1e-4)
self.assertEqual(trainer.optimizer.state_dict()["param_groups"][0]["lr"], 1.0)
@require_non_multigpu
def test_model_init(self):
train_dataset = RegressionDataset()
args = TrainingArguments("./regression", learning_rate=0.1)