Test XLA examples (#5583)

* Test XLA examples

* Style

* Using `require_torch_tpu`

* Style

* No need for pytest

examples/test_xla_examples.py

@@ -0,0 +1,91 @@
+# coding=utf-8
+# Copyright 2018 HuggingFace Inc..
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import logging
+import sys
+import unittest
+from time import time
+from unittest.mock import patch
+
+from transformers.testing_utils import require_torch_tpu
+
+
+logging.basicConfig(level=logging.DEBUG)
+
+logger = logging.getLogger()
+
+
+def get_setup_file():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-f")
+    args = parser.parse_args()
+    return args.f
+
+
+@require_torch_tpu
+class TorchXLAExamplesTests(unittest.TestCase):
+    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)

src/transformers/testing_utils.py

@@ -2,7 +2,7 @@ import os
 import unittest
 from distutils.util import strtobool
 
-from transformers.file_utils import _tf_available, _torch_available
+from transformers.file_utils import _tf_available, _torch_available, _torch_tpu_available
 
 
 SMALL_MODEL_IDENTIFIER = "julien-c/bert-xsmall-dummy"
@@ -113,6 +113,16 @@ def require_multigpu(test_case):
     return test_case
 
 
+def require_torch_tpu(test_case):
+    """
+    Decorator marking a test that requires a TPU (in PyTorch).
+    """
+    if not _torch_tpu_available:
+        return unittest.skip("test requires PyTorch TPU")
+
+    return test_case
+
+
 if _torch_available:
     # Set the USE_CUDA environment variable to select a GPU.
     torch_device = "cuda" if parse_flag_from_env("USE_CUDA") else "cpu"