Cleanup pytorch tests (#8033)

tests/test_modeling_marian.py

@@ -37,7 +37,6 @@ if is_torch_available():
     from transformers.pipelines import TranslationPipeline
 
 
-@require_torch
 class ModelTester:
     def __init__(self, parent):
         self.config = MarianConfig(

tests/test_modeling_mbart.py

@@ -4,7 +4,6 @@ from transformers import is_torch_available
 from transformers.file_utils import cached_property
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 
-from .test_modeling_bart import TOLERANCE, _long_tensor, assert_tensors_close
 from .test_modeling_common import ModelTesterMixin
 
 
@@ -91,32 +90,6 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
     ]
     expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
 
-    @slow
-    @unittest.skip("This has been failing since June 20th at least.")
-    def test_enro_forward(self):
-        model = self.model
-        net_input = {
-            "input_ids": _long_tensor(
-                [
-                    [3493, 3060, 621, 104064, 1810, 100, 142, 566, 13158, 6889, 5, 2, 250004],
-                    [64511, 7, 765, 2837, 45188, 297, 4049, 237, 10, 122122, 5, 2, 250004],
-                ]
-            ),
-            "decoder_input_ids": _long_tensor(
-                [
-                    [250020, 31952, 144, 9019, 242307, 21980, 55749, 11, 5, 2, 1, 1],
-                    [250020, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2],
-                ]
-            ),
-        }
-        net_input["attention_mask"] = net_input["input_ids"].ne(1)
-        with torch.no_grad():
-            logits, *other_stuff = model(**net_input)
-
-        expected_slice = torch.tensor([9.0078, 10.1113, 14.4787], device=logits.device, dtype=logits.dtype)
-        result_slice = logits[0, 0, :3]
-        assert_tensors_close(expected_slice, result_slice, atol=TOLERANCE)
-
     @slow
     def test_enro_generate_one(self):
         batch: BatchEncoding = self.tokenizer.prepare_seq2seq_batch(
@@ -128,7 +101,7 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
         # self.assertEqual(self.tgt_text[1], decoded[1])
 
     @slow
-    def test_enro_generate(self):
+    def test_enro_generate_batch(self):
         batch: BatchEncoding = self.tokenizer.prepare_seq2seq_batch(self.src_text).to(torch_device)
         translated_tokens = self.model.generate(**batch)
         decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)

tests/test_modeling_pegasus.py

@@ -58,7 +58,7 @@ class PegasusXSUMIntegrationTest(AbstractSeq2SeqIntegrationTest):
     src_text = [PGE_ARTICLE, XSUM_ENTRY_LONGER]
     tgt_text = [
         "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
-        "N-Dubz have said they were surprised to get four nominations for this year's Mobo Awards.",
+        "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
     ]
 
     @cached_property
@@ -72,7 +72,7 @@ class PegasusXSUMIntegrationTest(AbstractSeq2SeqIntegrationTest):
             torch_device
         )
         assert inputs.input_ids.shape == (2, 421)
-        translated_tokens = self.model.generate(**inputs)
+        translated_tokens = self.model.generate(**inputs, num_beams=2)
         decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
         assert self.tgt_text == decoded