92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
# coding=utf-8
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# Copyright 2018 HuggingFace Inc..
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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import sys
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import unittest
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from time import time
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from unittest.mock import patch
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from transformers.testing_utils import require_torch_tpu
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger()
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def get_setup_file():
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parser = argparse.ArgumentParser()
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parser.add_argument("-f")
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args = parser.parse_args()
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return args.f
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@require_torch_tpu
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class TorchXLAExamplesTests(unittest.TestCase):
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def test_run_glue(self):
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import xla_spawn
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stream_handler = logging.StreamHandler(sys.stdout)
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logger.addHandler(stream_handler)
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output_directory = "run_glue_output"
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testargs = f"""
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text-classification/run_glue.py
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--num_cores=8
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text-classification/run_glue.py
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--do_train
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--do_eval
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--task_name=MRPC
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--data_dir=../glue_data/MRPC
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--cache_dir=./cache_dir
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--num_train_epochs=1
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--max_seq_length=128
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--learning_rate=3e-5
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--output_dir={output_directory}
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--overwrite_output_dir
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--logging_steps=5
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--save_steps=5
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--overwrite_cache
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--tpu_metrics_debug
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--model_name_or_path=bert-base-cased
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--per_device_train_batch_size=64
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--per_device_eval_batch_size=64
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--evaluate_during_training
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--overwrite_cache
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""".split()
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with patch.object(sys, "argv", testargs):
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start = time()
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xla_spawn.main()
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end = time()
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result = {}
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with open(f"{output_directory}/eval_results_mrpc.txt") as f:
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lines = f.readlines()
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for line in lines:
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key, value = line.split(" = ")
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result[key] = float(value)
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del result["eval_loss"]
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for value in result.values():
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# Assert that the model trains
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self.assertGreaterEqual(value, 0.70)
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# Assert that the script takes less than 100 seconds to make sure it doesn't hang.
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self.assertLess(end - start, 100)
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