Torchscript test (#13350)

* Torchscript test

* Remove print statement
This commit is contained in:
Lysandre Debut
2021-09-01 10:41:46 +02:00
committed by GitHub
parent b9c6a97694
commit 73a0381282

View File

@@ -12,13 +12,13 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import os
import tempfile
import unittest import unittest
from transformers import BertConfig, is_torch_available from transformers import BertConfig, is_torch_available
from transformers.models.auto import get_values from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from .test_configuration_common import ConfigTester from .test_configuration_common import ConfigTester
from .test_generation_utils import GenerationTesterMixin from .test_generation_utils import GenerationTesterMixin
@@ -556,6 +556,29 @@ class BertModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
model = BertModel.from_pretrained(model_name) model = BertModel.from_pretrained(model_name)
self.assertIsNotNone(model) self.assertIsNotNone(model)
@slow
@require_torch_gpu
def test_torchscript_device_change(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == BertForMultipleChoice:
return
config.torchscript = True
model = model_class(config=config)
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
traced_model = torch.jit.trace(
model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu"))
)
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(traced_model, os.path.join(tmp, "bert.pt"))
loaded = torch.jit.load(os.path.join(tmp, "bert.pt"), map_location=torch_device)
loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device))
@require_torch @require_torch
class BertModelIntegrationTest(unittest.TestCase): class BertModelIntegrationTest(unittest.TestCase):