using multi_gpu consistently (#8446)

* s|multiple_gpu|multi_gpu|g; s|multigpu|multi_gpu|g'

* doc

.github/workflows/self-push.yml

@@ -135,7 +135,7 @@ jobs:
           name: run_all_tests_tf_gpu_test_reports
           path: reports
 
-  run_tests_torch_multiple_gpu:
+  run_tests_torch_multi_gpu:
     runs-on: [self-hosted, multi-gpu]
     steps:
       - uses: actions/checkout@v2
@@ -154,7 +154,7 @@ jobs:
         id: cache
         with:
           path: .env
-          key: v1.1-tests_torch_multiple_gpu-${{ hashFiles('setup.py') }}
+          key: v1.1-tests_torch_multi_gpu-${{ hashFiles('setup.py') }}
 
       - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
         run: |
@@ -181,11 +181,11 @@ jobs:
           OMP_NUM_THREADS: 1
         run: |
           source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_torch_multiple_gpu tests
+          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_torch_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
-        run: cat reports/tests_torch_multiple_gpu_failures_short.txt          
+        run: cat reports/tests_torch_multi_gpu_failures_short.txt          
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
@@ -194,7 +194,7 @@ jobs:
           name: run_all_tests_torch_multi_gpu_test_reports
           path: reports
 
-  run_tests_tf_multiple_gpu:
+  run_tests_tf_multi_gpu:
     runs-on: [self-hosted, multi-gpu]
     steps:
       - uses: actions/checkout@v2
@@ -213,7 +213,7 @@ jobs:
         id: cache
         with:
           path: .env
-          key: v1.1-tests_tf_multiple_gpu-${{ hashFiles('setup.py') }}
+          key: v1.1-tests_tf_multi_gpu-${{ hashFiles('setup.py') }}
 
       - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
         run: |
@@ -240,11 +240,11 @@ jobs:
           OMP_NUM_THREADS: 1
         run: |
           source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_tf_multiple_gpu tests
+          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_tf_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
-        run: cat reports/tests_tf_multiple_gpu_failures_short.txt
+        run: cat reports/tests_tf_multi_gpu_failures_short.txt
 
       - name: Test suite reports artifacts
         if: ${{ always() }}

.github/workflows/self-scheduled.yml

@@ -187,7 +187,7 @@ jobs:
           name: run_all_tests_tf_gpu_test_reports
           path: reports
           
-  run_all_tests_torch_multiple_gpu:
+  run_all_tests_torch_multi_gpu:
     runs-on: [self-hosted, multi-gpu]
     steps:
       - uses: actions/checkout@v2
@@ -238,11 +238,11 @@ jobs:
           RUN_SLOW: yes
         run: |
           source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_multiple_gpu tests
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
-        run: cat reports/tests_torch_multiple_gpu_failures_short.txt
+        run: cat reports/tests_torch_multi_gpu_failures_short.txt
 
       - name: Run examples tests on multi-GPU
         env:
@@ -250,11 +250,11 @@ jobs:
           RUN_SLOW: yes
         run: |
           source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=examples_torch_multiple_gpu examples
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=examples_torch_multi_gpu examples
 
       - name: Failure short reports
         if: ${{ always() }}
-        run: cat reports/examples_torch_multiple_gpu_failures_short.txt
+        run: cat reports/examples_torch_multi_gpu_failures_short.txt
 
       - name: Run all pipeline tests on multi-GPU
         if: ${{ always() }}
@@ -265,11 +265,11 @@ jobs:
           RUN_PIPELINE_TESTS: yes
         run: |
           source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_torch_pipeline_multiple_gpu tests
+          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_torch_pipeline_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
-        run: cat reports/tests_torch_pipeline_multiple_gpu_failures_short.txt
+        run: cat reports/tests_torch_pipeline_multi_gpu_failures_short.txt
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
@@ -278,7 +278,7 @@ jobs:
           name: run_all_tests_torch_multi_gpu_test_reports
           path: reports
 
-  run_all_tests_tf_multiple_gpu:
+  run_all_tests_tf_multi_gpu:
     runs-on: [self-hosted, multi-gpu]
     steps:
       - uses: actions/checkout@v2
@@ -329,11 +329,11 @@ jobs:
           RUN_SLOW: yes
         run: |
           source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_tf_multiple_gpu tests
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_tf_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
-        run: cat reports/tests_tf_multiple_gpu_failures_short.txt
+        run: cat reports/tests_tf_multi_gpu_failures_short.txt
 
       - name: Run all pipeline tests on multi-GPU
         if: ${{ always() }}
@@ -344,11 +344,11 @@ jobs:
           RUN_PIPELINE_TESTS: yes
         run: |
           source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_tf_pipelines_multiple_gpu tests
+          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_tf_pipelines_multi_gpu tests
           
       - name: Failure short reports
         if: ${{ always() }}
-        run: cat reports/tests_tf_multiple_gpu_pipelines_failures_short.txt
+        run: cat reports/tests_tf_multi_gpu_pipelines_failures_short.txt
 
       - name: Test suite reports artifacts
         if: ${{ always() }}

docs/source/testing.rst

@@ -405,32 +405,32 @@ decorators are used to set the requirements of tests CPU/GPU/TPU-wise:
 
 * ``require_torch`` - this test will run only under torch
 * ``require_torch_gpu`` - as ``require_torch`` plus requires at least 1 GPU
-* ``require_torch_multigpu`` - as ``require_torch`` plus requires at least 2 GPUs
-* ``require_torch_non_multigpu`` - as ``require_torch`` plus requires 0 or 1 GPUs
+* ``require_torch_multi_gpu`` - as ``require_torch`` plus requires at least 2 GPUs
+* ``require_torch_non_multi_gpu`` - as ``require_torch`` plus requires 0 or 1 GPUs
 * ``require_torch_tpu`` - as ``require_torch`` plus requires at least 1 TPU
 
 Let's depict the GPU requirements in the following table:
 
 
-+----------+---------------------------------+
-| n gpus   |  decorator                      |
-+==========+=================================+
-| ``>= 0`` | ``@require_torch``              |
-+----------+---------------------------------+
-| ``>= 1`` | ``@require_torch_gpu``          |
-+----------+---------------------------------+
-| ``>= 2`` | ``@require_torch_multigpu``     |
-+----------+---------------------------------+
-| ``< 2``  | ``@require_torch_non_multigpu`` |
-+----------+---------------------------------+
++----------+----------------------------------+
+| n gpus   |  decorator                       |
++==========+==================================+
+| ``>= 0`` | ``@require_torch``               |
++----------+----------------------------------+
+| ``>= 1`` | ``@require_torch_gpu``           |
++----------+----------------------------------+
+| ``>= 2`` | ``@require_torch_multi_gpu``     |
++----------+----------------------------------+
+| ``< 2``  | ``@require_torch_non_multi_gpu`` |
++----------+----------------------------------+
 
 
 For example, here is a test that must be run only when there are 2 or more GPUs available and pytorch is installed:
 
 .. code-block:: python
 
-    @require_torch_multigpu
-    def test_example_with_multigpu():
+    @require_torch_multi_gpu
+    def test_example_with_multi_gpu():
 
 If a test requires ``tensorflow`` use the ``require_tf`` decorator. For example:
 
@@ -454,7 +454,7 @@ last for them to work correctly. Here is an example of the correct usage:
 .. code-block:: python
 
     @parameterized.expand(...)
-    @require_torch_multigpu
+    @require_torch_multi_gpu
     def test_integration_foo():
 
 This order problem doesn't exist with ``@pytest.mark.parametrize``, you can put it first or last and it will still

examples/bert-loses-patience/test_run_glue_with_pabee.py

@@ -4,7 +4,7 @@ import sys
 from unittest.mock import patch
 
 import run_glue_with_pabee
-from transformers.testing_utils import TestCasePlus, require_torch_non_multigpu_but_fix_me
+from transformers.testing_utils import TestCasePlus, require_torch_non_multi_gpu_but_fix_me
 
 
 logging.basicConfig(level=logging.DEBUG)
@@ -20,7 +20,7 @@ def get_setup_file():
 
 
 class PabeeTests(TestCasePlus):
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_run_glue(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)

examples/deebert/test_glue_deebert.py

@@ -5,7 +5,7 @@ import unittest
 from unittest.mock import patch
 
 import run_glue_deebert
-from transformers.testing_utils import require_torch_non_multigpu_but_fix_me, slow
+from transformers.testing_utils import require_torch_non_multi_gpu_but_fix_me, slow
 
 
 logging.basicConfig(level=logging.DEBUG)
@@ -26,7 +26,7 @@ class DeeBertTests(unittest.TestCase):
         logger.addHandler(stream_handler)
 
     @slow
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_glue_deebert_train(self):
 
         train_args = """

examples/rag/test_distributed_retriever.py

@@ -16,7 +16,7 @@ from transformers.configuration_dpr import DPRConfig
 from transformers.configuration_rag import RagConfig
 from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
 from transformers.retrieval_rag import CustomHFIndex
-from transformers.testing_utils import require_torch_non_multigpu_but_fix_me
+from transformers.testing_utils import require_torch_non_multi_gpu_but_fix_me
 from transformers.tokenization_bart import BartTokenizer
 from transformers.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
 from transformers.tokenization_dpr import DPRQuestionEncoderTokenizer
@@ -179,7 +179,7 @@ class RagRetrieverTest(TestCase):
             retriever.init_retrieval(port)
         return retriever
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_pytorch_distributed_retriever_retrieve(self):
         n_docs = 1
         retriever = self.get_dummy_pytorch_distributed_retriever(init_retrieval=True)
@@ -195,7 +195,7 @@ class RagRetrieverTest(TestCase):
         self.assertEqual(doc_dicts[1]["id"][0], "0")  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist(), [[1], [0]])
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_custom_hf_index_retriever_retrieve(self):
         n_docs = 1
         retriever = self.get_dummy_custom_hf_index_retriever(init_retrieval=True, from_disk=False)
@@ -211,7 +211,7 @@ class RagRetrieverTest(TestCase):
         self.assertEqual(doc_dicts[1]["id"][0], "0")  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist(), [[1], [0]])
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_custom_pytorch_distributed_retriever_retrieve_from_disk(self):
         n_docs = 1
         retriever = self.get_dummy_custom_hf_index_retriever(init_retrieval=True, from_disk=True)

examples/seq2seq/test_bash_script.py

@@ -13,7 +13,7 @@ from distillation import BartSummarizationDistiller, distill_main
 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_multigpu_but_fix_me, slow
+from transformers.testing_utils import TestCasePlus, require_torch_gpu, require_torch_non_multi_gpu_but_fix_me, slow
 from utils import load_json
 
 
@@ -32,7 +32,7 @@ class TestMbartCc25Enro(TestCasePlus):
 
     @slow
     @require_torch_gpu
-    @require_torch_non_multigpu_but_fix_me
+    @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)
@@ -40,7 +40,7 @@ class TestMbartCc25Enro(TestCasePlus):
     # @timeout_decorator.timeout(1200)
     @slow
     @require_torch_gpu
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_train_mbart_cc25_enro_script(self):
         env_vars_to_replace = {
             "$MAX_LEN": 64,
@@ -75,7 +75,7 @@ class TestMbartCc25Enro(TestCasePlus):
             --num_sanity_val_steps 0
             --eval_beams 2
         """.split()
-        # XXX: args.gpus > 1 : handle multigpu in the future
+        # XXX: args.gpus > 1 : handle multi_gpu in the future
 
         testargs = ["finetune.py"] + bash_script.split() + args
         with patch.object(sys, "argv", testargs):
@@ -129,7 +129,7 @@ class TestDistilMarianNoTeacher(TestCasePlus):
     @timeout_decorator.timeout(600)
     @slow
     @require_torch_gpu
-    @require_torch_non_multigpu_but_fix_me
+    @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 = {
@@ -172,7 +172,7 @@ class TestDistilMarianNoTeacher(TestCasePlus):
             parser = pl.Trainer.add_argparse_args(parser)
             parser = BartSummarizationDistiller.add_model_specific_args(parser, os.getcwd())
             args = parser.parse_args()
-            # assert args.gpus == gpus THIS BREAKS for multigpu
+            # assert args.gpus == gpus THIS BREAKS for multi_gpu
 
             model = distill_main(args)
 

examples/seq2seq/test_datasets.py

@@ -11,7 +11,7 @@ from save_len_file import save_len_file
 from test_seq2seq_examples import ARTICLES, BART_TINY, MARIAN_TINY, MBART_TINY, SUMMARIES, T5_TINY, make_test_data_dir
 from transformers import AutoTokenizer
 from transformers.modeling_bart import shift_tokens_right
-from transformers.testing_utils import TestCasePlus, require_torch_non_multigpu_but_fix_me, slow
+from transformers.testing_utils import TestCasePlus, require_torch_non_multi_gpu_but_fix_me, slow
 from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeq2SeqDataset, Seq2SeqDataset
 
 
@@ -30,7 +30,7 @@ class TestAll(TestCasePlus):
         ],
     )
     @slow
-    @require_torch_non_multigpu_but_fix_me
+    @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())
@@ -70,7 +70,7 @@ class TestAll(TestCasePlus):
             break  # No need to test every batch
 
     @parameterized.expand([BART_TINY, BERT_BASE_CASED])
-    @require_torch_non_multigpu_but_fix_me
+    @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())
@@ -95,7 +95,7 @@ class TestAll(TestCasePlus):
             assert max_len_target > trunc_target  # Truncated
             break  # No need to test every batch
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_pack_dataset(self):
         tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
 
@@ -114,7 +114,7 @@ class TestAll(TestCasePlus):
         assert orig_paths == new_paths
 
     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq")
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_dynamic_batch_size(self):
         if not FAIRSEQ_AVAILABLE:
             return
@@ -139,7 +139,7 @@ class TestAll(TestCasePlus):
         if failures:
             raise AssertionError(f"too many tokens in {len(failures)} batches")
 
-    @require_torch_non_multigpu_but_fix_me
+    @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
@@ -179,7 +179,7 @@ class TestAll(TestCasePlus):
         )
         return ds, max_tokens, tokenizer
 
-    @require_torch_non_multigpu_but_fix_me
+    @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))
@@ -195,7 +195,7 @@ class TestAll(TestCasePlus):
             PEGASUS_XSUM,
         ],
     )
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_dataset_kwargs(self, tok_name):
         tokenizer = AutoTokenizer.from_pretrained(tok_name)
         if tok_name == MBART_TINY:

examples/seq2seq/test_fsmt_bleu_score.py

@@ -22,7 +22,7 @@ from transformers import FSMTForConditionalGeneration, FSMTTokenizer
 from transformers.testing_utils import (
     get_tests_dir,
     require_torch,
-    require_torch_non_multigpu_but_fix_me,
+    require_torch_non_multi_gpu_but_fix_me,
     slow,
     torch_device,
 )
@@ -54,7 +54,7 @@ class ModelEvalTester(unittest.TestCase):
         ]
     )
     @slow
-    @require_torch_non_multigpu_but_fix_me
+    @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_make_student.py

@@ -4,7 +4,7 @@ import unittest
 from make_student import create_student_by_copying_alternating_layers
 from transformers import AutoConfig
 from transformers.file_utils import cached_property
-from transformers.testing_utils import require_torch, require_torch_non_multigpu_but_fix_me
+from transformers.testing_utils import require_torch, require_torch_non_multi_gpu_but_fix_me
 
 
 TINY_BART = "sshleifer/bart-tiny-random"
@@ -17,28 +17,28 @@ class MakeStudentTester(unittest.TestCase):
     def teacher_config(self):
         return AutoConfig.from_pretrained(TINY_BART)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_valid_t5(self):
         student, *_ = create_student_by_copying_alternating_layers(TINY_T5, tempfile.mkdtemp(), e=1, d=1)
         self.assertEqual(student.config.num_hidden_layers, 1)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_asymmetric_t5(self):
         student, *_ = create_student_by_copying_alternating_layers(TINY_T5, tempfile.mkdtemp(), e=1, d=None)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_same_decoder_small_encoder(self):
         student, *_ = create_student_by_copying_alternating_layers(TINY_BART, tempfile.mkdtemp(), e=1, d=None)
         self.assertEqual(student.config.encoder_layers, 1)
         self.assertEqual(student.config.decoder_layers, self.teacher_config.encoder_layers)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_small_enc_small_dec(self):
         student, *_ = create_student_by_copying_alternating_layers(TINY_BART, tempfile.mkdtemp(), e=1, d=1)
         self.assertEqual(student.config.encoder_layers, 1)
         self.assertEqual(student.config.decoder_layers, 1)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_raises_assert(self):
         with self.assertRaises(AssertionError):
             create_student_by_copying_alternating_layers(TINY_BART, tempfile.mkdtemp(), e=None, d=None)

examples/seq2seq/test_seq2seq_examples.py

@@ -24,7 +24,7 @@ from transformers.testing_utils import (
     CaptureStdout,
     TestCasePlus,
     require_torch_gpu,
-    require_torch_non_multigpu_but_fix_me,
+    require_torch_non_multi_gpu_but_fix_me,
     slow,
 )
 from utils import ROUGE_KEYS, label_smoothed_nll_loss, lmap, load_json
@@ -133,7 +133,7 @@ class TestSummarizationDistiller(TestCasePlus):
 
     @slow
     @require_torch_gpu
-    @require_torch_non_multigpu_but_fix_me
+    @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."""
 
@@ -151,12 +151,12 @@ class TestSummarizationDistiller(TestCasePlus):
                 failures.append(m)
         assert not failures, f"The following models could not be loaded through AutoConfig: {failures}"
 
-    @require_torch_non_multigpu_but_fix_me
+    @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_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_distill_checkpointing_with_teacher(self):
         updates = dict(
             student_encoder_layers=2,
@@ -181,7 +181,7 @@ class TestSummarizationDistiller(TestCasePlus):
         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_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_loss_fn(self):
         model = AutoModelForSeq2SeqLM.from_pretrained(BART_TINY, return_dict=True)
         input_ids, mask = model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
@@ -202,7 +202,7 @@ class TestSummarizationDistiller(TestCasePlus):
             # TODO: understand why this breaks
             self.assertEqual(nll_loss, model_computed_loss)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_distill_mbart(self):
         updates = dict(
             student_encoder_layers=2,
@@ -227,7 +227,7 @@ class TestSummarizationDistiller(TestCasePlus):
         assert len(all_files) > 2
         self.assertEqual(len(transformer_ckpts), 2)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_distill_t5(self):
         updates = dict(
             student_encoder_layers=1,
@@ -309,21 +309,21 @@ class TestTheRest(TestCasePlus):
 
     # 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_multigpu_but_fix_me
+    @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_multigpu_but_fix_me
+    @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_multigpu_but_fix_me
+    @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"
@@ -374,7 +374,7 @@ class TestTheRest(TestCasePlus):
     @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY],
     )
-    @require_torch_non_multigpu_but_fix_me
+    @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"
@@ -426,7 +426,7 @@ class TestTheRest(TestCasePlus):
         assert isinstance(example_batch, dict)
         assert len(example_batch) >= 4
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_finetune_extra_model_args(self):
         args_d: dict = CHEAP_ARGS.copy()
 
@@ -477,7 +477,7 @@ class TestTheRest(TestCasePlus):
             model = main(args)
         assert str(excinfo.value) == f"model config doesn't have a `{unsupported_param}` attribute"
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_finetune_lr_schedulers(self):
         args_d: dict = CHEAP_ARGS.copy()
 

examples/seq2seq/test_seq2seq_examples_multi_gpu.py

@@ -8,7 +8,7 @@ from transformers.testing_utils import (
     execute_subprocess_async,
     get_gpu_count,
     require_torch_gpu,
-    require_torch_multigpu,
+    require_torch_multi_gpu,
     slow,
 )
 
@@ -21,8 +21,8 @@ class TestSummarizationDistillerMultiGPU(TestCasePlus):
     def setUpClass(cls):
         return cls
 
-    @require_torch_multigpu
-    def test_multigpu(self):
+    @require_torch_multi_gpu
+    def test_multi_gpu(self):
 
         updates = dict(
             no_teacher=True,

examples/seq2seq/test_tatoeba_conversion.py

@@ -4,7 +4,7 @@ import unittest
 
 from transformers.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
 from transformers.file_utils import cached_property
-from transformers.testing_utils import require_torch_non_multigpu_but_fix_me, slow
+from transformers.testing_utils import require_torch_non_multi_gpu_but_fix_me, slow
 
 
 @unittest.skipUnless(os.path.exists(DEFAULT_REPO), "Tatoeba directory does not exist.")
@@ -15,12 +15,12 @@ class TatoebaConversionTester(unittest.TestCase):
         return TatoebaConverter(save_dir=tmp_dir)
 
     @slow
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_resolver(self):
         self.resolver.convert_models(["heb-eng"])
 
     @slow
-    @require_torch_non_multigpu_but_fix_me
+    @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"

examples/test_examples.py

@@ -23,7 +23,7 @@ from unittest.mock import patch
 import torch
 
 from transformers.file_utils import is_apex_available
-from transformers.testing_utils import TestCasePlus, require_torch_non_multigpu_but_fix_me, torch_device
+from transformers.testing_utils import TestCasePlus, require_torch_non_multi_gpu_but_fix_me, torch_device
 
 
 SRC_DIRS = [
@@ -67,7 +67,7 @@ def is_cuda_and_apex_available():
 
 
 class ExamplesTests(TestCasePlus):
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_run_glue(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -100,7 +100,7 @@ class ExamplesTests(TestCasePlus):
             for value in result.values():
                 self.assertGreaterEqual(value, 0.75)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_run_pl_glue(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -138,7 +138,7 @@ class ExamplesTests(TestCasePlus):
             #         self.assertGreaterEqual(v, 0.75, f"({k})")
             #
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_run_clm(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -170,7 +170,7 @@ class ExamplesTests(TestCasePlus):
             result = run_clm.main()
             self.assertLess(result["perplexity"], 100)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_run_mlm(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -196,7 +196,7 @@ class ExamplesTests(TestCasePlus):
             result = run_mlm.main()
             self.assertLess(result["perplexity"], 42)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_run_ner(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -227,7 +227,7 @@ class ExamplesTests(TestCasePlus):
             self.assertGreaterEqual(result["eval_precision"], 0.75)
             self.assertLess(result["eval_loss"], 0.5)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_run_squad(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -256,7 +256,7 @@ class ExamplesTests(TestCasePlus):
             self.assertGreaterEqual(result["f1"], 25)
             self.assertGreaterEqual(result["exact"], 21)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_generation(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)

examples/test_xla_examples.py

@@ -20,7 +20,7 @@ import unittest
 from time import time
 from unittest.mock import patch
 
-from transformers.testing_utils import require_torch_non_multigpu_but_fix_me, require_torch_tpu
+from transformers.testing_utils import require_torch_non_multi_gpu_but_fix_me, require_torch_tpu
 
 
 logging.basicConfig(level=logging.DEBUG)
@@ -30,7 +30,7 @@ logger = logging.getLogger()
 
 @require_torch_tpu
 class TorchXLAExamplesTests(unittest.TestCase):
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_run_glue(self):
         import xla_spawn
 
@@ -82,7 +82,7 @@ class TorchXLAExamplesTests(unittest.TestCase):
             # Assert that the script takes less than 300 seconds to make sure it doesn't hang.
             self.assertLess(end - start, 500)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_trainer_tpu(self):
         import xla_spawn
 

examples/token-classification/test_ner_examples.py

@@ -4,7 +4,7 @@ import unittest
 from unittest.mock import patch
 
 import run_ner_old as run_ner
-from transformers.testing_utils import require_torch_non_multigpu_but_fix_me, slow
+from transformers.testing_utils import require_torch_non_multi_gpu_but_fix_me, slow
 
 
 logging.basicConfig(level=logging.INFO)
@@ -14,7 +14,7 @@ logger = logging.getLogger()
 
 class ExamplesTests(unittest.TestCase):
     @slow
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_run_ner(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -35,7 +35,7 @@ class ExamplesTests(unittest.TestCase):
             result = run_ner.main()
             self.assertLess(result["eval_loss"], 1.5)
 
-    @require_torch_non_multigpu_but_fix_me
+    @require_torch_non_multi_gpu_but_fix_me
     def test_run_ner_pl(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)

src/transformers/testing_utils.py

@@ -193,13 +193,13 @@ def require_tokenizers(test_case):
         return test_case
 
 
-def require_torch_multigpu(test_case):
+def require_torch_multi_gpu(test_case):
     """
     Decorator marking a test that requires a multi-GPU setup (in PyTorch).
 
     These tests are skipped on a machine without multiple GPUs.
 
-    To run *only* the multigpu tests, assuming all test names contain multigpu: $ pytest -sv ./tests -k "multigpu"
+    To run *only* the multi_gpu tests, assuming all test names contain multi_gpu: $ pytest -sv ./tests -k "multi_gpu"
     """
     if not _torch_available:
         return unittest.skip("test requires PyTorch")(test_case)
@@ -212,7 +212,7 @@ def require_torch_multigpu(test_case):
         return test_case
 
 
-def require_torch_non_multigpu(test_case):
+def require_torch_non_multi_gpu(test_case):
     """
     Decorator marking a test that requires 0 or 1 GPU setup (in PyTorch).
     """
@@ -227,10 +227,10 @@ def require_torch_non_multigpu(test_case):
         return test_case
 
 
-# this is a decorator identical to require_torch_non_multigpu, but is used as a quick band-aid to
+# this is a decorator identical to require_torch_non_multi_gpu, but is used as a quick band-aid to
 # allow all of examples to be run multi-gpu CI and it reminds us that tests decorated with this one
 # need to be ported and aren't so by design.
-require_torch_non_multigpu_but_fix_me = require_torch_non_multigpu
+require_torch_non_multi_gpu_but_fix_me = require_torch_non_multi_gpu
 
 
 def require_torch_tpu(test_case):

tests/test_modeling_common.py

@@ -23,7 +23,7 @@ from typing import List, Tuple
 
 from transformers import is_torch_available
 from transformers.file_utils import WEIGHTS_NAME
-from transformers.testing_utils import require_torch, require_torch_multigpu, slow, torch_device
+from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
 
 
 if is_torch_available():
@@ -928,8 +928,8 @@ class ModelTesterMixin:
             with torch.no_grad():
                 model(**inputs)[0]
 
-    @require_torch_multigpu
-    def test_multigpu_data_parallel_forward(self):
+    @require_torch_multi_gpu
+    def test_multi_gpu_data_parallel_forward(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
         # some params shouldn't be scattered by nn.DataParallel

tests/test_modeling_rag.py

@@ -29,7 +29,7 @@ from transformers.testing_utils import (
     require_sentencepiece,
     require_tokenizers,
     require_torch,
-    require_torch_non_multigpu,
+    require_torch_non_multi_gpu,
     slow,
     torch_device,
 )
@@ -581,7 +581,7 @@ class RagDPRT5Test(RagTestMixin, unittest.TestCase):
 @require_retrieval
 @require_sentencepiece
 @require_tokenizers
-@require_torch_non_multigpu
+@require_torch_non_multi_gpu
 class RagModelIntegrationTests(unittest.TestCase):
     @cached_property
     def sequence_model(self):

tests/test_modeling_reformer.py

@@ -20,7 +20,7 @@ from transformers.testing_utils import (
     require_sentencepiece,
     require_tokenizers,
     require_torch,
-    require_torch_multigpu,
+    require_torch_multi_gpu,
     slow,
     torch_device,
 )
@@ -562,8 +562,8 @@ class ReformerTesterMixin:
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_reformer_model_fp16_generate(*config_and_inputs)
 
-    @require_torch_multigpu
-    def test_multigpu_data_parallel_forward(self):
+    @require_torch_multi_gpu
+    def test_multi_gpu_data_parallel_forward(self):
         # Opt-out of this test.
         pass
 

tests/test_modeling_transfo_xl.py

@@ -17,7 +17,7 @@ import random
 import unittest
 
 from transformers import is_torch_available
-from transformers.testing_utils import require_torch, require_torch_multigpu, slow, torch_device
+from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
 
 from .test_configuration_common import ConfigTester
 from .test_generation_utils import GenerationTesterMixin
@@ -205,8 +205,8 @@ class TransfoXLModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestC
         output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs)
         self.model_tester.check_transfo_xl_lm_head_output(output_result)
 
-    @require_torch_multigpu
-    def test_multigpu_data_parallel_forward(self):
+    @require_torch_multi_gpu
+    def test_multi_gpu_data_parallel_forward(self):
         # Opt-out of this test.
         pass
 

tests/test_trainer_distributed.py

@@ -2,7 +2,7 @@ import sys
 from typing import Dict
 
 from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
-from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multigpu
+from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
 from transformers.utils import logging
 
 
@@ -44,7 +44,7 @@ if is_torch_available():
 
 
 class TestTrainerDistributed(TestCasePlus):
-    @require_torch_multigpu
+    @require_torch_multi_gpu
     def test_trainer(self):
 
         distributed_args = f"""