[tests] remove test_export_to_onnx

tests/models/fsmt/test_modeling_fsmt.py

@@ -262,20 +262,6 @@ def test_save_load_missing_keys(self):
                 model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
             self.assertEqual(info["missing_keys"], [])
 
-    @unittest.skip(reason="Test has a segmentation fault on torch 1.8.0")
-    def test_export_to_onnx(self):
-        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
-        model = FSMTModel(config).to(torch_device)
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            torch.onnx.export(
-                model,
-                (inputs_dict["input_ids"], inputs_dict["attention_mask"]),
-                f"{tmpdirname}/fsmt_test.onnx",
-                export_params=True,
-                opset_version=12,
-                input_names=["input_ids", "attention_mask"],
-            )
-
     def test_ensure_weights_are_shared(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs()
 

tests/models/longt5/test_modeling_longt5.py

@@ -627,20 +627,6 @@ def test_model_from_pretrained(self):
         model = LongT5Model.from_pretrained(model_name)
         self.assertIsNotNone(model)
 
-    @slow
-    def test_export_to_onnx(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        model = LongT5Model(config_and_inputs[0]).to(torch_device)
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            torch.onnx.export(
-                model,
-                (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),
-                f"{tmpdirname}/longt5_test.onnx",
-                export_params=True,
-                opset_version=14,
-                input_names=["input_ids", "decoder_input_ids"],
-            )
-
     def test_generate_with_head_masking(self):
         attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
         config_and_inputs = self.model_tester.prepare_config_and_inputs()

tests/models/mt5/test_modeling_mt5.py

@@ -871,20 +871,6 @@ def test_model_from_pretrained(self):
         model = MT5Model.from_pretrained(model_name)
         self.assertIsNotNone(model)
 
-    @unittest.skip(reason="Test has a segmentation fault on torch 1.8.0")
-    def test_export_to_onnx(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        model = MT5Model(config_and_inputs[0]).to(torch_device)
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            torch.onnx.export(
-                model,
-                (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),
-                f"{tmpdirname}/t5_test.onnx",
-                export_params=True,
-                opset_version=9,
-                input_names=["input_ids", "decoder_input_ids"],
-            )
-
     def test_generate_with_head_masking(self):
         attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
         config_and_inputs = self.model_tester.prepare_config_and_inputs()

tests/models/pop2piano/test_modeling_pop2piano.py

@@ -26,7 +26,6 @@
 from transformers.testing_utils import (
     require_essentia,
     require_librosa,
-    require_onnx,
     require_scipy,
     require_torch,
     slow,
@@ -611,20 +610,6 @@ def test_model_from_pretrained(self):
         model = Pop2PianoForConditionalGeneration.from_pretrained(model_name)
         self.assertIsNotNone(model)
 
-    @require_onnx
-    def test_export_to_onnx(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        model = Pop2PianoForConditionalGeneration(config_and_inputs[0]).to(torch_device)
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            torch.onnx.export(
-                model,
-                (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),
-                f"{tmpdirname}/Pop2Piano_test.onnx",
-                export_params=True,
-                opset_version=14,
-                input_names=["input_ids", "decoder_input_ids"],
-            )
-
     def test_pass_with_input_features(self):
         input_features = BatchFeature(
             {

tests/models/switch_transformers/test_modeling_switch_transformers.py

@@ -709,20 +709,6 @@ def test_model_from_pretrained(self):
         model = SwitchTransformersModel.from_pretrained(model_name)
         self.assertIsNotNone(model)
 
-    @unittest.skip(reason="Test has a segmentation fault on torch 1.8.0")
-    def test_export_to_onnx(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        model = SwitchTransformersModel(config_and_inputs[0]).to(torch_device)
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            torch.onnx.export(
-                model,
-                (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),
-                f"{tmpdirname}/switch_transformers_test.onnx",
-                export_params=True,
-                opset_version=9,
-                input_names=["input_ids", "decoder_input_ids"],
-            )
-
     def test_generate_with_head_masking(self):
         attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
         config_and_inputs = self.model_tester.prepare_config_and_inputs()

tests/models/t5/test_modeling_t5.py

@@ -875,20 +875,6 @@ def test_model_from_pretrained(self):
         model = T5Model.from_pretrained(model_name)
         self.assertIsNotNone(model)
 
-    @unittest.skip(reason="Test has a segmentation fault on torch 1.8.0")
-    def test_export_to_onnx(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        model = T5Model(config_and_inputs[0]).to(torch_device)
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            torch.onnx.export(
-                model,
-                (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),
-                f"{tmpdirname}/t5_test.onnx",
-                export_params=True,
-                opset_version=9,
-                input_names=["input_ids", "decoder_input_ids"],
-            )
-
     def test_generate_with_head_masking(self):
         attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
         config_and_inputs = self.model_tester.prepare_config_and_inputs()

tests/models/umt5/test_modeling_umt5.py

@@ -525,20 +525,6 @@ def test_with_sequence_classification_head(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_with_sequence_classification_head(*config_and_inputs)
 
-    @unittest.skip(reason="Test has a segmentation fault on torch 1.8.0")
-    def test_export_to_onnx(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        model = UMT5Model(config_and_inputs[0]).to(torch_device)
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            torch.onnx.export(
-                model,
-                (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),
-                f"{tmpdirname}/t5_test.onnx",
-                export_params=True,
-                opset_version=9,
-                input_names=["input_ids", "decoder_input_ids"],
-            )
-
     @unittest.skipIf(torch_device == "cpu", "Cant do half precision")
     def test_model_fp16_forward(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()