[examples tests] tests that are fine on multi-gpu

examples/seq2seq/test_bash_script.py

@@ -13,7 +13,7 @@
 from finetune import SummarizationModule, main
 from transformers import MarianMTModel
 from transformers.file_utils import cached_path
-from transformers.testing_utils import TestCasePlus, require_torch_gpu, require_torch_non_multi_gpu_but_fix_me, slow
+from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
 from utils import load_json
 
 
@@ -32,15 +32,13 @@ def setUp(self):
 
     @slow
     @require_torch_gpu
-    @require_torch_non_multi_gpu_but_fix_me
     def test_model_download(self):
         """This warms up the cache so that we can time the next test without including download time, which varies between machines."""
         MarianMTModel.from_pretrained(MARIAN_MODEL)
 
     # @timeout_decorator.timeout(1200)
     @slow
     @require_torch_gpu
-    @require_torch_non_multi_gpu_but_fix_me
     def test_train_mbart_cc25_enro_script(self):
         env_vars_to_replace = {
             "$MAX_LEN": 64,
@@ -129,7 +127,6 @@ class TestDistilMarianNoTeacher(TestCasePlus):
     @timeout_decorator.timeout(600)
     @slow
     @require_torch_gpu
-    @require_torch_non_multi_gpu_but_fix_me
     def test_opus_mt_distill_script(self):
         data_dir = f"{self.test_file_dir_str}/test_data/wmt_en_ro"
         env_vars_to_replace = {

examples/seq2seq/test_fsmt_bleu_score.py

@@ -19,13 +19,7 @@
 
 from parameterized import parameterized
 from transformers import FSMTForConditionalGeneration, FSMTTokenizer
-from transformers.testing_utils import (
-    get_tests_dir,
-    require_torch,
-    require_torch_non_multi_gpu_but_fix_me,
-    slow,
-    torch_device,
-)
+from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
 from utils import calculate_bleu
 
 
@@ -54,7 +48,6 @@ def get_model(self, mname):
         ]
     )
     @slow
-    @require_torch_non_multi_gpu_but_fix_me
     def test_bleu_scores(self, pair, min_bleu_score):
         # note: this test is not testing the best performance since it only evals a small batch
         # but it should be enough to detect a regression in the output quality

examples/seq2seq/test_seq2seq_examples.py

@@ -19,14 +19,7 @@
 from run_eval_search import run_search
 from transformers import AutoConfig, AutoModelForSeq2SeqLM
 from transformers.hf_api import HfApi
-from transformers.testing_utils import (
-    CaptureStderr,
-    CaptureStdout,
-    TestCasePlus,
-    require_torch_gpu,
-    require_torch_non_multi_gpu_but_fix_me,
-    slow,
-)
+from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
 from utils import ROUGE_KEYS, label_smoothed_nll_loss, lmap, load_json
 
 
@@ -135,7 +128,6 @@ def setUpClass(cls):
 
     @slow
     @require_torch_gpu
-    @require_torch_non_multi_gpu_but_fix_me
     def test_hub_configs(self):
         """I put require_torch_gpu cause I only want this to run with self-scheduled."""
 
@@ -153,12 +145,10 @@ def test_hub_configs(self):
                 failures.append(m)
         assert not failures, f"The following models could not be loaded through AutoConfig: {failures}"
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_distill_no_teacher(self):
         updates = dict(student_encoder_layers=2, student_decoder_layers=1, no_teacher=True)
         self._test_distiller_cli(updates)
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_distill_checkpointing_with_teacher(self):
         updates = dict(
             student_encoder_layers=2,
@@ -183,7 +173,6 @@ def test_distill_checkpointing_with_teacher(self):
         convert_pl_to_hf(ckpts[0], transformer_ckpts[0].parent, out_path_new)
         assert os.path.exists(os.path.join(out_path_new, "pytorch_model.bin"))
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_loss_fn(self):
         model = AutoModelForSeq2SeqLM.from_pretrained(BART_TINY)
         input_ids, mask = model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
@@ -204,7 +193,6 @@ def test_loss_fn(self):
             # TODO: understand why this breaks
             self.assertEqual(nll_loss, model_computed_loss)
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_distill_mbart(self):
         updates = dict(
             student_encoder_layers=2,
@@ -229,7 +217,6 @@ def test_distill_mbart(self):
         assert len(all_files) > 2
         self.assertEqual(len(transformer_ckpts), 2)
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_distill_t5(self):
         updates = dict(
             student_encoder_layers=1,
@@ -241,7 +228,6 @@ def test_distill_t5(self):
         )
         self._test_distiller_cli(updates)
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_distill_different_base_models(self):
         updates = dict(
             teacher=T5_TINY,
@@ -321,21 +307,18 @@ def run_eval_tester(self, model):
 
     # test one model to quickly (no-@slow) catch simple problems and do an
     # extensive testing of functionality with multiple models as @slow separately
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_eval(self):
         self.run_eval_tester(T5_TINY)
 
     # any extra models should go into the list here - can be slow
     @parameterized.expand([BART_TINY, MBART_TINY])
     @slow
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_eval_slow(self, model):
         self.run_eval_tester(model)
 
     # testing with 2 models to validate: 1. translation (t5) 2. summarization (mbart)
     @parameterized.expand([T5_TINY, MBART_TINY])
     @slow
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_eval_search(self, model):
         input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
         output_file_name = input_file_name.parent / "utest_output.txt"
@@ -386,7 +369,6 @@ def test_run_eval_search(self, model):
     @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY],
     )
-    @require_torch_non_multi_gpu_but_fix_me
     def test_finetune(self, model):
         args_d: dict = CHEAP_ARGS.copy()
         task = "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
@@ -438,7 +420,6 @@ def test_finetune(self, model):
         assert isinstance(example_batch, dict)
         assert len(example_batch) >= 4
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_finetune_extra_model_args(self):
         args_d: dict = CHEAP_ARGS.copy()
 
@@ -489,7 +470,6 @@ def test_finetune_extra_model_args(self):
             model = main(args)
         assert str(excinfo.value) == f"model config doesn't have a `{unsupported_param}` attribute"
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_finetune_lr_schedulers(self):
         args_d: dict = CHEAP_ARGS.copy()