[BigBird Pegasus] Make tests faster (#11744)

* improve tests

* remove bogus file

* make style
Co-authored-by: Patrick von Platen <patrick@huggingface.co>

tests/test_modeling_bigbird_pegasus.py

@@ -368,17 +368,24 @@ class BigBirdPegasusModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.
         self._check_batched_forward(attn_type="block_sparse", tolerance=1e-1)
 
     def _check_batched_forward(self, attn_type, tolerance=1e-3):
-        config = BigBirdPegasusConfig(block_size=16, attention_type=attn_type)
+        config, _ = self.model_tester.prepare_config_and_inputs()
+        config.max_position_embeddings = 128
+        config.block_size = 16
+        config.attention_type = attn_type
         model = BigBirdPegasusForConditionalGeneration(config).to(torch_device)
         model.eval()
 
-        sample_with_padding = [3, 8, 11] * 128 + [0] * 128
-        sample_without_padding = [4, 7, 9, 13] * 128
+        chunk_length = 32
+
+        sample_with_padding = [3, 8, 11] * chunk_length + [0] * chunk_length
+        sample_without_padding = [4, 7, 9, 13] * chunk_length
         target_ids_without_padding = [2, 3] * 8
         target_ids_with_padding = [7, 8] * 6 + 4 * [-100]
 
         attention_mask = torch.tensor(
-            [[1] * 3 * 128 + [0] * 128, [1] * 4 * 128], device=torch_device, dtype=torch.long
+            [[1] * 3 * chunk_length + [0] * chunk_length, [1] * 4 * chunk_length],
+            device=torch_device,
+            dtype=torch.long,
         )
 
         input_ids = torch.tensor([sample_with_padding, sample_without_padding], device=torch_device, dtype=torch.long)
@@ -390,7 +397,7 @@ class BigBirdPegasusModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.
             logits_batched = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels).logits
 
         with torch.no_grad():
-            logits_single_first = model(input_ids=input_ids[:1, :-128], labels=labels[:1]).logits
+            logits_single_first = model(input_ids=input_ids[:1, :-chunk_length], labels=labels[:1]).logits
 
         self.assertTrue(torch.allclose(logits_batched[0, -3:], logits_single_first[0, -3:], atol=tolerance))