def test_run_glue(self):
import xla_spawn
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
./examples/pytorch/text-classification/run_glue.py
--num_cores=8
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--debug tpu_metrics_debug
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(sys, "argv", testargs):
start = time()
xla_spawn.main()
end = time()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_accuracy"], 0.75)
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start, 500)
def test_trainer_tpu(self):
import xla_spawn
testargs = """
./tests/test_trainer_tpu.py
--num_cores=8
./tests/test_trainer_tpu.py
""".split()
with patch.object(sys, "argv", testargs):
xla_spawn.main()
def test_run_glue(self):
import xla_spawn
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
output_directory = "run_glue_output"
testargs = f"""
text-classification/run_glue.py
--num_cores=8
text-classification/run_glue.py
--do_train
--do_eval
--task_name=MRPC
--data_dir=../glue_data/MRPC
--cache_dir=./cache_dir
--num_train_epochs=1
--max_seq_length=128
--learning_rate=3e-5
--output_dir={output_directory}
--overwrite_output_dir
--logging_steps=5
--save_steps=5
--overwrite_cache
--tpu_metrics_debug
--model_name_or_path=bert-base-cased
--per_device_train_batch_size=64
--per_device_eval_batch_size=64
--evaluate_during_training
--overwrite_cache
""".split()
with patch.object(sys, "argv", testargs):
start = time()
xla_spawn.main()
end = time()
result = {}
with open(f"{output_directory}/eval_results_mrpc.txt") as f:
lines = f.readlines()
for line in lines:
key, value = line.split(" = ")
result[key] = float(value)
del result["eval_loss"]
for value in result.values():
# Assert that the model trains
self.assertGreaterEqual(value, 0.70)
# Assert that the script takes less than 100 seconds to make sure it doesn't hang.
self.assertLess(end - start, 100)