Merge pull request #2217 from aaugustin/test-parallelization

Support running tests in parallel

.circleci/config.yml

 version: 2
 jobs:
-    build_py3_torch_and_tf:
+    run_tests_py3_torch_and_tf:
         working_directory: ~/transformers
         docker:
             - image: circleci/python:3.5
+        environment:
+            OMP_NUM_THREADS: 1
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -11,49 +13,67 @@ jobs:
             - run: sudo pip install torch
             - run: sudo pip install tensorflow
             - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
+            - run: sudo pip install pytest codecov pytest-cov pytest-xdist
             - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./transformers/tests/ --cov
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./transformers/tests/ --cov
             - run: codecov
-    build_py3_torch:
+    run_tests_py3_torch:
         working_directory: ~/transformers
         docker:
             - image: circleci/python:3.5
+        environment:
+            OMP_NUM_THREADS: 1
         resource_class: xlarge
         parallelism: 1
         steps:
             - checkout
             - run: sudo pip install torch
             - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
+            - run: sudo pip install pytest codecov pytest-cov pytest-xdist
             - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./transformers/tests/ --cov
-            - run: python -m pytest -sv ./examples/
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./transformers/tests/ --cov
             - run: codecov
-    build_py3_tf:
+    run_tests_py3_tf:
         working_directory: ~/transformers
         docker:
             - image: circleci/python:3.5
+        environment:
+            OMP_NUM_THREADS: 1
         resource_class: xlarge
         parallelism: 1
         steps:
             - checkout
             - run: sudo pip install tensorflow
             - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
+            - run: sudo pip install pytest codecov pytest-cov pytest-xdist
             - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./transformers/tests/ --cov
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./transformers/tests/ --cov
             - run: codecov
-    build_py3_custom_tokenizers:
+    run_tests_py3_custom_tokenizers:
         working_directory: ~/transformers
         docker:
             - image: circleci/python:3.5
         steps:
             - checkout
             - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest
+            - run: sudo pip install pytest pytest-xdist
             - run: sudo pip install mecab-python3
             - run: RUN_CUSTOM_TOKENIZERS=1 python -m pytest -sv ./transformers/tests/tokenization_bert_japanese_test.py
+    run_examples_py3_torch:
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.5
+        environment:
+            OMP_NUM_THREADS: 1
+        resource_class: xlarge
+        parallelism: 1
+        steps:
+            - checkout
+            - run: sudo pip install torch
+            - run: sudo pip install --progress-bar off .
+            - run: sudo pip install pytest pytest-xdist
+            - run: sudo pip install tensorboardX scikit-learn
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./examples/
     deploy_doc:
         working_directory: ~/transformers
         docker:
@@ -66,7 +86,7 @@ jobs:
             - run: sudo pip install --progress-bar off -r docs/requirements.txt
             - run: sudo pip install --progress-bar off -r requirements.txt
             - run: ./.circleci/deploy.sh
-    repository_consistency:
+    check_repository_consistency:
         working_directory: ~/transformers
         docker:
             - image: circleci/python:3.5
@@ -85,9 +105,10 @@ workflows:
     version: 2
     build_and_test:
         jobs:
-            - repository_consistency
-            - build_py3_custom_tokenizers
-            - build_py3_torch_and_tf
-            - build_py3_torch
-            - build_py3_tf
+            - check_repository_consistency
+            - run_examples_py3_torch
+            - run_tests_py3_custom_tokenizers
+            - run_tests_py3_torch_and_tf
+            - run_tests_py3_torch
+            - run_tests_py3_tf
             - deploy_doc: *workflow_filters

setup.py

@@ -59,6 +59,7 @@ setup(
                                     "tests.*", "tests"]),
     install_requires=['numpy',
                       'boto3',
+                      'filelock',
                       'requests',
                       'tqdm',
                       'regex != 2019.12.17',

templates/adding_a_new_model/tests/modeling_tf_xxx_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import XxxConfig, is_tf_available
 
@@ -245,10 +244,8 @@ class TFXxxModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in ['xxx-base-uncased']:
-            model = TFXxxModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFXxxModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

templates/adding_a_new_model/tests/modeling_xxx_test.py

@@ -17,13 +17,12 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 if is_torch_available():
     from transformers import (XxxConfig, XxxModel, XxxForMaskedLM,
@@ -249,10 +248,8 @@ class XxxModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(XXX_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = XxxModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = XxxModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/file_utils.py

@@ -10,10 +10,9 @@ import json
 import logging
 import os
 import six
-import shutil
 import tempfile
 import fnmatch
-from functools import wraps
+from functools import partial, wraps
 from hashlib import sha256
 from io import open
 
@@ -25,6 +24,8 @@ from tqdm.auto import tqdm
 from contextlib import contextmanager
 from . import __version__
 
+from filelock import FileLock
+
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
 try:
@@ -334,59 +335,60 @@ def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag
     # If we don't have a connection (etag is None) and can't identify the file
     # try to get the last downloaded one
     if not os.path.exists(cache_path) and etag is None:
-        matching_files = fnmatch.filter(os.listdir(cache_dir), filename + '.*')
-        matching_files = list(filter(lambda s: not s.endswith('.json'), matching_files))
+        matching_files = [
+            file
+            for file in fnmatch.filter(os.listdir(cache_dir), filename + '.*')
+            if not file.endswith('.json') and not file.endswith('.lock')
+        ]
         if matching_files:
             cache_path = os.path.join(cache_dir, matching_files[-1])
 
-    if resume_download:
-        incomplete_path = cache_path + '.incomplete'
-        @contextmanager
-        def _resumable_file_manager():
-            with open(incomplete_path,'a+b') as f:
-                yield f
-            os.remove(incomplete_path)
-        temp_file_manager = _resumable_file_manager
-        if os.path.exists(incomplete_path):
-            resume_size = os.stat(incomplete_path).st_size
+    # Prevent parallel downloads of the same file with a lock.
+    lock_path = cache_path + '.lock'
+    with FileLock(lock_path):
+
+        if resume_download:
+            incomplete_path = cache_path + '.incomplete'
+            @contextmanager
+            def _resumable_file_manager():
+                with open(incomplete_path,'a+b') as f:
+                    yield f
+            temp_file_manager = _resumable_file_manager
+            if os.path.exists(incomplete_path):
+                resume_size = os.stat(incomplete_path).st_size
+            else:
+                resume_size = 0
         else:
+            temp_file_manager = partial(tempfile.NamedTemporaryFile, dir=cache_dir, delete=False)
             resume_size = 0
-    else:
-        temp_file_manager = tempfile.NamedTemporaryFile
-        resume_size = 0
-
-    if etag is not None and (not os.path.exists(cache_path) or force_download):
-        # Download to temporary file, then copy to cache dir once finished.
-        # Otherwise you get corrupt cache entries if the download gets interrupted.
-        with temp_file_manager() as temp_file:
-            logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)
-
-            # GET file object
-            if url.startswith("s3://"):
-                if resume_download:
-                    logger.warn('Warning: resumable downloads are not implemented for "s3://" urls')
-                s3_get(url, temp_file, proxies=proxies)
-            else:
-                http_get(url, temp_file, proxies=proxies, resume_size=resume_size, user_agent=user_agent)
-
-            # we are copying the file before closing it, so flush to avoid truncation
-            temp_file.flush()
-            # shutil.copyfileobj() starts at the current position, so go to the start
-            temp_file.seek(0)
-
-            logger.info("copying %s to cache at %s", temp_file.name, cache_path)
-            with open(cache_path, 'wb') as cache_file:
-                shutil.copyfileobj(temp_file, cache_file)
-
-            logger.info("creating metadata file for %s", cache_path)
-            meta = {'url': url, 'etag': etag}
-            meta_path = cache_path + '.json'
-            with open(meta_path, 'w') as meta_file:
-                output_string = json.dumps(meta)
-                if sys.version_info[0] == 2 and isinstance(output_string, str):
-                    output_string = unicode(output_string, 'utf-8')  # The beauty of python 2
-                meta_file.write(output_string)
-
-            logger.info("removing temp file %s", temp_file.name)
+
+        if etag is not None and (not os.path.exists(cache_path) or force_download):
+            # Download to temporary file, then copy to cache dir once finished.
+            # Otherwise you get corrupt cache entries if the download gets interrupted.
+            with temp_file_manager() as temp_file:
+                logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)
+
+                # GET file object
+                if url.startswith("s3://"):
+                    if resume_download:
+                        logger.warn('Warning: resumable downloads are not implemented for "s3://" urls')
+                    s3_get(url, temp_file, proxies=proxies)
+                else:
+                    http_get(url, temp_file, proxies=proxies, resume_size=resume_size, user_agent=user_agent)
+
+                # we are copying the file before closing it, so flush to avoid truncation
+                temp_file.flush()
+
+                logger.info("storing %s in cache at %s", url, cache_path)
+                os.rename(temp_file.name, cache_path)
+
+                logger.info("creating metadata file for %s", cache_path)
+                meta = {'url': url, 'etag': etag}
+                meta_path = cache_path + '.json'
+                with open(meta_path, 'w') as meta_file:
+                    output_string = json.dumps(meta)
+                    if sys.version_info[0] == 2 and isinstance(output_string, str):
+                        output_string = unicode(output_string, 'utf-8')  # The beauty of python 2
+                    meta_file.write(output_string)
 
     return cache_path

transformers/tests/modeling_albert_test.py

@@ -17,13 +17,12 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 if is_torch_available():
     from transformers import (AlbertConfig, AlbertModel, AlbertForMaskedLM,
@@ -230,10 +229,8 @@ class AlbertModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = AlbertModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = AlbertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_bert_test.py

@@ -17,13 +17,12 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
 from .modeling_common_test import (CommonTestCases, ids_tensor, floats_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 if is_torch_available():
     from transformers import (BertConfig, BertModel, BertForMaskedLM,
@@ -360,10 +359,8 @@ class BertModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = BertModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = BertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_common_test.py

@@ -18,7 +18,7 @@ from __future__ import print_function
 
 import copy
 import sys
-import os
+import os.path
 import shutil
 import tempfile
 import json
@@ -30,7 +30,7 @@ import logging
 
 from transformers import is_torch_available
 
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 if is_torch_available():
     import torch
@@ -218,21 +218,22 @@ class CommonTestCases:
                 inputs = inputs_dict['input_ids']  # Let's keep only input_ids
 
                 try:
-                    torch.jit.trace(model, inputs)
+                    traced_gpt2 = torch.jit.trace(model, inputs)
                 except RuntimeError:
                     self.fail("Couldn't trace module.")
 
-                try:
-                    traced_gpt2 = torch.jit.trace(model, inputs)
-                    torch.jit.save(traced_gpt2, "traced_model.pt")
-                except RuntimeError:
-                    self.fail("Couldn't save module.")
+                with TemporaryDirectory() as tmp_dir_name:
+                    pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
 
-                try:
-                    loaded_model = torch.jit.load("traced_model.pt")
-                    os.remove("traced_model.pt")
-                except ValueError:
-                    self.fail("Couldn't load module.")
+                    try:
+                        torch.jit.save(traced_gpt2, pt_file_name)
+                    except Exception:
+                        self.fail("Couldn't save module.")
+
+                    try:
+                        loaded_model = torch.jit.load(pt_file_name)
+                    except Exception:
+                        self.fail("Couldn't load module.")
 
                 model.to(torch_device)
                 model.eval()
@@ -352,12 +353,11 @@ class CommonTestCases:
                 heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
                                 -1: [0]}
                 model.prune_heads(heads_to_prune)
-                directory = "pruned_model"
-                if not os.path.exists(directory):
-                    os.makedirs(directory)
-                model.save_pretrained(directory)
-                model = model_class.from_pretrained(directory)
-                model.to(torch_device)
+
+                with TemporaryDirectory() as temp_dir_name:
+                    model.save_pretrained(temp_dir_name)
+                    model = model_class.from_pretrained(temp_dir_name)
+                    model.to(torch_device)
 
                 with torch.no_grad():
                     outputs = model(**inputs_dict)
@@ -366,7 +366,6 @@ class CommonTestCases:
                 self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
                 self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
 
-                shutil.rmtree(directory)
 
         def test_head_pruning_save_load_from_config_init(self):
             if not self.test_pruning:
@@ -426,14 +425,10 @@ class CommonTestCases:
                 self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
                 self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
 
-                directory = "pruned_model"
-
-                if not os.path.exists(directory):
-                    os.makedirs(directory)
-                model.save_pretrained(directory)
-                model = model_class.from_pretrained(directory)
-                model.to(torch_device)
-                shutil.rmtree(directory)
+                with TemporaryDirectory() as temp_dir_name:
+                    model.save_pretrained(temp_dir_name)
+                    model = model_class.from_pretrained(temp_dir_name)
+                    model.to(torch_device)
 
                 with torch.no_grad():
                     outputs = model(**inputs_dict)
@@ -758,10 +753,8 @@ class CommonTestCases:
                 [[], []])
 
         def create_and_check_model_from_pretrained(self):
-            cache_dir = "/tmp/transformers_test/"
             for model_name in list(self.base_model_class.pretrained_model_archive_map.keys())[:1]:
-                model = self.base_model_class.from_pretrained(model_name, cache_dir=cache_dir)
-                shutil.rmtree(cache_dir)
+                model = self.base_model_class.from_pretrained(model_name, cache_dir=CACHE_DIR)
                 self.parent.assertIsNotNone(model)
 
         def prepare_config_and_inputs_for_common(self):

transformers/tests/modeling_ctrl_test.py

@@ -16,7 +16,6 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import pdb
 
 from transformers import is_torch_available
@@ -27,7 +26,7 @@ if is_torch_available():
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 
 @require_torch
@@ -205,10 +204,8 @@ class CTRLModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = CTRLModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = CTRLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_distilbert_test.py

@@ -27,7 +27,7 @@ if is_torch_available():
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 
 @require_torch
@@ -235,10 +235,8 @@ class DistilBertModelTest(CommonTestCases.CommonModelTester):
 
     # @slow
     # def test_model_from_pretrained(self):
-    #     cache_dir = "/tmp/transformers_test/"
     #     for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-    #         model = DistilBertModel.from_pretrained(model_name, cache_dir=cache_dir)
-    #         shutil.rmtree(cache_dir)
+    #         model = DistilBertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
     #         self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_gpt2_test.py

@@ -17,7 +17,6 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
@@ -27,7 +26,7 @@ if is_torch_available():
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 
 @require_torch
@@ -239,10 +238,8 @@ class GPT2ModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = GPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = GPT2Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_openai_test.py

@@ -17,7 +17,6 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
@@ -27,7 +26,7 @@ if is_torch_available():
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 
 @require_torch
@@ -207,10 +206,8 @@ class OpenAIGPTModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = OpenAIGPTModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = OpenAIGPTModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_roberta_test.py

@@ -17,7 +17,6 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
@@ -29,7 +28,7 @@ if is_torch_available():
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 
 @require_torch
@@ -199,10 +198,8 @@ class RobertaModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = RobertaModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = RobertaModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_t5_test.py

@@ -17,13 +17,12 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
 from .modeling_common_test import (CommonTestCases, ids_tensor, floats_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 if is_torch_available():
     from transformers import (T5Config, T5Model, T5WithLMHeadModel)
@@ -175,10 +174,8 @@ class T5ModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(T5_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = T5Model.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = T5Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_tf_albert_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import AlbertConfig, is_tf_available
 
@@ -217,12 +216,8 @@ class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
-        # for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-        for model_name in ['albert-base-uncased']:
-            model = TFAlbertModel.from_pretrained(
-                model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+        for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+            model = TFAlbertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_tf_auto_test.py

@@ -46,11 +46,11 @@ class TFAutoModelTest(unittest.TestCase):
         logging.basicConfig(level=logging.INFO)
         # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['bert-base-uncased']:
-            config = AutoConfig.from_pretrained(model_name, force_download=True)
+            config = AutoConfig.from_pretrained(model_name)
             self.assertIsNotNone(config)
             self.assertIsInstance(config, BertConfig)
 
-            model = TFAutoModel.from_pretrained(model_name, force_download=True)
+            model = TFAutoModel.from_pretrained(model_name)
             self.assertIsNotNone(model)
             self.assertIsInstance(model, TFBertModel)
 
@@ -59,11 +59,11 @@ class TFAutoModelTest(unittest.TestCase):
         logging.basicConfig(level=logging.INFO)
         # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['bert-base-uncased']:
-            config = AutoConfig.from_pretrained(model_name, force_download=True)
+            config = AutoConfig.from_pretrained(model_name)
             self.assertIsNotNone(config)
             self.assertIsInstance(config, BertConfig)
 
-            model = TFAutoModelWithLMHead.from_pretrained(model_name, force_download=True)
+            model = TFAutoModelWithLMHead.from_pretrained(model_name)
             self.assertIsNotNone(model)
             self.assertIsInstance(model, TFBertForMaskedLM)
 
@@ -72,11 +72,11 @@ class TFAutoModelTest(unittest.TestCase):
         logging.basicConfig(level=logging.INFO)
         # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['bert-base-uncased']:
-            config = AutoConfig.from_pretrained(model_name, force_download=True)
+            config = AutoConfig.from_pretrained(model_name)
             self.assertIsNotNone(config)
             self.assertIsInstance(config, BertConfig)
 
-            model = TFAutoModelForSequenceClassification.from_pretrained(model_name, force_download=True)
+            model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
             self.assertIsNotNone(model)
             self.assertIsInstance(model, TFBertForSequenceClassification)
 
@@ -85,17 +85,17 @@ class TFAutoModelTest(unittest.TestCase):
         logging.basicConfig(level=logging.INFO)
         # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['bert-base-uncased']:
-            config = AutoConfig.from_pretrained(model_name, force_download=True)
+            config = AutoConfig.from_pretrained(model_name)
             self.assertIsNotNone(config)
             self.assertIsInstance(config, BertConfig)
 
-            model = TFAutoModelForQuestionAnswering.from_pretrained(model_name, force_download=True)
+            model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
             self.assertIsNotNone(model)
             self.assertIsInstance(model, TFBertForQuestionAnswering)
 
     def test_from_pretrained_identifier(self):
         logging.basicConfig(level=logging.INFO)
-        model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER, force_download=True)
+        model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
         self.assertIsInstance(model, TFBertForMaskedLM)
 
 

transformers/tests/modeling_tf_bert_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import BertConfig, is_tf_available
 
@@ -310,11 +309,9 @@ class TFBertModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['bert-base-uncased']:
-            model = TFBertModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFBertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_tf_ctrl_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import CTRLConfig, is_tf_available
 
@@ -189,10 +188,8 @@ class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = TFCTRLModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFCTRLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_tf_distilbert_test.py

@@ -20,7 +20,7 @@ import unittest
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import DistilBertConfig, is_tf_available
 
@@ -211,10 +211,8 @@ class TFDistilBertModelTest(TFCommonTestCases.TFCommonModelTester):
 
     # @slow
     # def test_model_from_pretrained(self):
-    #     cache_dir = "/tmp/transformers_test/"
     #     for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-    #         model = DistilBertModel.from_pretrained(model_name, cache_dir=cache_dir)
-    #         shutil.rmtree(cache_dir)
+    #         model = DistilBertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
     #         self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_tf_gpt2_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import GPT2Config, is_tf_available
 
@@ -220,10 +219,8 @@ class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = TFGPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFGPT2Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":