[examples tests on multigpu] resolving require_torch_non_multi_gpu_but_fix_me (#10561)

* batch 1

* this is tpu

* deebert attempt

* the rest

examples/legacy/seq2seq/old_test_datasets.py

@@ -24,7 +24,7 @@ from parameterized import parameterized
 from save_len_file import save_len_file
 from transformers import AutoTokenizer
 from transformers.models.mbart.modeling_mbart import shift_tokens_right
-from transformers.testing_utils import TestCasePlus, require_torch_non_multi_gpu_but_fix_me, slow
+from transformers.testing_utils import TestCasePlus, slow
 from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeq2SeqDataset, Seq2SeqDataset
 
 
@@ -61,7 +61,6 @@ class TestAll(TestCasePlus):
         ],
     )
     @slow
-    @require_torch_non_multi_gpu_but_fix_me
     def test_seq2seq_dataset_truncation(self, tok_name):
         tokenizer = AutoTokenizer.from_pretrained(tok_name)
         tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
@@ -101,7 +100,6 @@ class TestAll(TestCasePlus):
             break  # No need to test every batch
 
     @parameterized.expand([BART_TINY, BERT_BASE_CASED])
-    @require_torch_non_multi_gpu_but_fix_me
     def test_legacy_dataset_truncation(self, tok):
         tokenizer = AutoTokenizer.from_pretrained(tok)
         tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
@@ -126,7 +124,6 @@ class TestAll(TestCasePlus):
             assert max_len_target > trunc_target  # Truncated
             break  # No need to test every batch
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_pack_dataset(self):
         tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
 
@@ -145,7 +142,6 @@ class TestAll(TestCasePlus):
         assert orig_paths == new_paths
 
     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq")
-    @require_torch_non_multi_gpu_but_fix_me
     def test_dynamic_batch_size(self):
         if not FAIRSEQ_AVAILABLE:
             return
@@ -170,7 +166,6 @@ class TestAll(TestCasePlus):
         if failures:
             raise AssertionError(f"too many tokens in {len(failures)} batches")
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_sortish_sampler_reduces_padding(self):
         ds, _, tokenizer = self._get_dataset(max_len=512)
         bs = 2
@@ -210,7 +205,6 @@ class TestAll(TestCasePlus):
         )
         return ds, max_tokens, tokenizer
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_distributed_sortish_sampler_splits_indices_between_procs(self):
         ds, max_tokens, tokenizer = self._get_dataset()
         ids1 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=0, add_extra_examples=False))
@@ -226,7 +220,6 @@ class TestAll(TestCasePlus):
             PEGASUS_XSUM,
         ],
     )
-    @require_torch_non_multi_gpu_but_fix_me
     def test_dataset_kwargs(self, tok_name):
         tokenizer = AutoTokenizer.from_pretrained(tok_name, use_fast=False)
         if tok_name == MBART_TINY:

examples/legacy/seq2seq/old_test_tatoeba_conversion.py

@@ -18,7 +18,7 @@ import unittest
 
 from transformers.file_utils import cached_property
 from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
-from transformers.testing_utils import require_torch_non_multi_gpu_but_fix_me, slow
+from transformers.testing_utils import slow
 
 
 @unittest.skipUnless(os.path.exists(DEFAULT_REPO), "Tatoeba directory does not exist.")
@@ -29,12 +29,10 @@ class TatoebaConversionTester(unittest.TestCase):
         return TatoebaConverter(save_dir=tmp_dir)
 
     @slow
-    @require_torch_non_multi_gpu_but_fix_me
     def test_resolver(self):
         self.resolver.convert_models(["heb-eng"])
 
     @slow
-    @require_torch_non_multi_gpu_but_fix_me
     def test_model_card(self):
         content, mmeta = self.resolver.write_model_card("opus-mt-he-en", dry_run=True)
         assert mmeta["long_pair"] == "heb-eng"