Fix test

Signed-off-by: Shogo Hida <[email protected]>

tests/bettertransformer/test_bettertransformer_encoder.py

@@ -43,9 +43,9 @@
     "hf-internal-testing/tiny-random-MarkupLMModel",
     "hf-internal-testing/tiny-random-rembert",
     "hf-internal-testing/tiny-random-RobertaModel",
+    "hf-internal-testing/tiny-random-RoCBertModel",
     "hf-internal-testing/tiny-random-SplinterModel",
     "hf-internal-testing/tiny-random-TapasModel",
-    "hf-internal-testing/tiny-random-RoCBertModel",
     "hf-internal-testing/tiny-xlm-roberta",
     "ybelkada/random-tiny-BertGenerationModel",
 ]
@@ -256,6 +256,29 @@ def test_accelerate_compatibility_single_gpu_without_keeping(self):
         self.check_accelerate_compatibility_cpu_gpu(keep_original_model=False, max_memory=max_memory)
 
 
+class BetterTransformersRoCBertTest(BetterTransformersTestMixin, unittest.TestCase):
+    r"""
+    Full testing suite of the `BetterTransformers` integration into Hugging Face
+    `transformers` ecosystem. Check the docstring of each test to understand the
+    purpose of each test. Basically we test:
+    - if the conversion dictionnary is consistent, ie if the converted model exists
+    in HuggingFace `transformers` library.
+    - if the converted model produces the same logits as the original model.
+    - if the converted model is faster than the original model.
+    """
+    all_models_to_test = ALL_ENCODER_MODELS_TO_TEST
+
+    def tearDown(self):
+        gc.collect()
+
+    def prepare_inputs_for_class(self, model_id=None):
+        input_dict = {
+            "input_ids": torch.LongTensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]),
+            "attention_mask": torch.LongTensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0]]),
+        }
+        return input_dict
+
+
 class BetterTransformersEncoderDecoderTest(BetterTransformersTestMixin, unittest.TestCase):
     r"""
     Full testing suite of the `BetterTransformers` integration into Hugging Face