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[ci] Load pretrained models into the default (long-lived) cache

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There's an inconsistency right now where:
- we load some models into CACHE_DIR
- and some models in the default cache
- and often, in both for the same models

When running the RUN_SLOW tests, this takes a lot of disk space, time, and bandwidth.

I'd rather always use the default cache
This commit is contained in:
Julien Chaumond
2020-04-23 17:12:59 -04:00
parent 6b410bedfc
commit f54dc3f4d5
27 changed files with 50 additions and 53 deletions
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4
tests/test_modeling_albert.py
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@@ -20,7 +20,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -267,5 +267,5 @@ class AlbertModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = AlbertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = AlbertModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_bart.py
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@@ -21,7 +21,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -563,7 +563,7 @@ class BartModelIntegrationTests(unittest.TestCase):
def test_model_from_pretrained(self):
# Forces 1.6GB download from S3 for each model
for model_name in list(BART_PRETRAINED_MODEL_ARCHIVE_MAP.keys()):
model = BartModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = BartModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@slow

4
tests/test_modeling_bert.py
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@@ -20,7 +20,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -501,5 +501,5 @@ class BertModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = BertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = BertModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_ctrl.py
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@@ -19,7 +19,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -211,7 +211,7 @@ class CTRLModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = CTRLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = CTRLModel.from_pretrained(model_name)
self.assertIsNotNone(model)

2
tests/test_modeling_distilbert.py
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@@ -248,5 +248,5 @@ class DistilBertModelTest(ModelTesterMixin, unittest.TestCase):
# @slow
# def test_model_from_pretrained(self):
# for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# model = DistilBertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
# model = DistilBertModel.from_pretrained(model_name)
# self.assertIsNotNone(model)

4
tests/test_modeling_electra.py
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@@ -20,7 +20,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -283,5 +283,5 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = ElectraModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = ElectraModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_flaubert.py
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@@ -20,7 +20,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -388,5 +388,5 @@ class FlaubertModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = FlaubertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = FlaubertModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_gpt2.py
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@@ -20,7 +20,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -335,7 +335,7 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = GPT2Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = GPT2Model.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_openai.py
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@@ -20,7 +20,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -219,7 +219,7 @@ class OpenAIGPTModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = OpenAIGPTModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = OpenAIGPTModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_roberta.py
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@@ -20,7 +20,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -274,7 +274,7 @@ class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = RobertaModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = RobertaModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_create_position_ids_respects_padding_index(self):

4
tests/test_modeling_t5.py
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@@ -20,7 +20,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -358,7 +358,7 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(T5_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = T5Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = T5Model.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_albert.py
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@@ -20,7 +20,7 @@ from transformers import AlbertConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
from .utils import require_tf, slow
if is_tf_available():
@@ -231,5 +231,5 @@ class TFAlbertModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFAlbertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = TFAlbertModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_bert.py
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@@ -20,7 +20,7 @@ from transformers import BertConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
from .utils import require_tf, slow
if is_tf_available():
@@ -313,5 +313,5 @@ class TFBertModelTest(TFModelTesterMixin, unittest.TestCase):
def test_model_from_pretrained(self):
# 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)
model = TFBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_ctrl.py
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@@ -20,7 +20,7 @@ from transformers import CTRLConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
from .utils import require_tf, slow
if is_tf_available():
@@ -201,7 +201,7 @@ class TFCTRLModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFCTRLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = TFCTRLModel.from_pretrained(model_name)
self.assertIsNotNone(model)

2
tests/test_modeling_tf_distilbert.py
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@@ -219,5 +219,5 @@ class TFDistilBertModelTest(TFModelTesterMixin, unittest.TestCase):
# @slow
# def test_model_from_pretrained(self):
# for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# model = DistilBertModesss.from_pretrained(model_name, cache_dir=CACHE_DIR)
# model = DistilBertModesss.from_pretrained(model_name)
# self.assertIsNotNone(model)

4
tests/test_modeling_tf_electra.py
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@@ -20,7 +20,7 @@ from transformers import ElectraConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
from .utils import require_tf, slow
if is_tf_available():
@@ -223,5 +223,5 @@ class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase):
def test_model_from_pretrained(self):
# for model_name in list(TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ["electra-small-discriminator"]:
model = TFElectraModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = TFElectraModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_gpt2.py
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@@ -20,7 +20,7 @@ from transformers import GPT2Config, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
from .utils import require_tf, slow
if is_tf_available():
@@ -324,7 +324,7 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFGPT2Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = TFGPT2Model.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_openai_gpt.py
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@@ -20,7 +20,7 @@ from transformers import OpenAIGPTConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
from .utils import require_tf, slow
if is_tf_available():
@@ -236,7 +236,7 @@ class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFOpenAIGPTModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = TFOpenAIGPTModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_roberta.py
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@@ -20,7 +20,7 @@ from transformers import RobertaConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
from .utils import require_tf, slow
if is_tf_available():
@@ -233,7 +233,7 @@ class TFRobertaModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFRobertaModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = TFRobertaModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_t5.py
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@@ -20,7 +20,7 @@ from transformers import T5Config, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
from .utils import require_tf, slow
if is_tf_available():
@@ -260,7 +260,7 @@ class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in ["t5-small"]:
model = TFT5Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = TFT5Model.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_transfo_xl.py
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@@ -21,7 +21,7 @@ from transformers import TransfoXLConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
from .utils import require_tf, slow
if is_tf_available():
@@ -210,7 +210,7 @@ class TFTransfoXLModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFTransfoXLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = TFTransfoXLModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_xlm.py
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@@ -20,7 +20,7 @@ from transformers import is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
from .utils import require_tf, slow
if is_tf_available():
@@ -309,7 +309,7 @@ class TFXLMModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFXLMModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = TFXLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_xlnet.py
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@@ -21,7 +21,7 @@ from transformers import XLNetConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
from .utils import require_tf, slow
if is_tf_available():
@@ -411,7 +411,7 @@ class TFXLNetModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFXLNetModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = TFXLNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_transfo_xl.py
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@@ -21,7 +21,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -210,7 +210,7 @@ class TransfoXLModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TransfoXLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = TransfoXLModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_xlm.py
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@@ -20,7 +20,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -426,7 +426,7 @@ class XLMModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = XLMModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = XLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_xlnet.py
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@@ -21,7 +21,7 @@ from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
from .utils import require_torch, slow, torch_device
if is_torch_available():
@@ -509,7 +509,7 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = XLNetModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
model = XLNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)

3
tests/utils.py
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@@ -1,13 +1,10 @@
import os
import tempfile
import unittest
from distutils.util import strtobool
from transformers.file_utils import _tf_available, _torch_available
CACHE_DIR = os.path.join(tempfile.gettempdir(), "transformers_test")
SMALL_MODEL_IDENTIFIER = "julien-c/bert-xsmall-dummy"
DUMMY_UNKWOWN_IDENTIFIER = "julien-c/dummy-unknown"
# Used to test Auto{Config, Model, Tokenizer} model_type detection.