[Tests] Fix slow opt tests (#17282)

* fix opt tests

* remove unused tok

* make style

* make flake8 happy

* Update tests/models/opt/test_modeling_opt.py

tests/models/opt/test_modeling_opt.py

@@ -23,7 +23,6 @@ import timeout_decorator  # noqa
 
 from transformers import OPTConfig, is_torch_available
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
-from transformers.utils import cached_property
 
 from ...generation.test_generation_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -270,10 +269,6 @@ def _long_tensor(tok_lst):
 @require_sentencepiece
 @require_tokenizers
 class OPTModelIntegrationTests(unittest.TestCase):
-    @cached_property
-    def default_tokenizer(self):
-        return GPT2Tokenizer.from_pretrained("patrickvonplaten/opt_gpt2_tokenizer")
-
     @slow
     def test_inference_no_head(self):
         model = OPTModel.from_pretrained("facebook/opt-350m").to(torch_device)
@@ -284,7 +279,7 @@ class OPTModelIntegrationTests(unittest.TestCase):
         expected_shape = torch.Size((1, 11, 512))
         self.assertEqual(output.shape, expected_shape)
         expected_slice = torch.tensor(
-            [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], device=torch_device
+            [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]], device=torch_device
         )
         self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3))
 
@@ -307,7 +302,6 @@ class OPTEmbeddingsTest(unittest.TestCase):
         model = OPTForCausalLM.from_pretrained(self.path_model)
         model = model.eval()
         tokenizer = GPT2Tokenizer.from_pretrained(self.path_model)
-        tokenizer.add_special_tokens({"pad_token": "<pad>"})
 
         prompts = [
             "Today is a beautiful day and I want to",
@@ -315,8 +309,9 @@ class OPTEmbeddingsTest(unittest.TestCase):
             "Paris is the capital of France and",
             "Computers and mobile phones have taken",
         ]
-        input_ids = tokenizer(prompts, return_tensors="pt", padding=True).input_ids
-        logits = model(input_ids)[0].mean(dim=-1)
+        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
+        inputs = tokenizer(prompts, return_tensors="pt", padding=True, add_special_tokens=False)
+        logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(dim=-1)
         # logits_meta = torch.load(self.path_logits_meta)
         logits_meta = torch.Tensor(
             [
@@ -326,7 +321,6 @@ class OPTEmbeddingsTest(unittest.TestCase):
                 [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
             ]
         )
-
         assert torch.allclose(logits, logits_meta, atol=1e-4)