[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"

examples/research_projects/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_multi_gpu_but_fix_me
+from transformers.testing_utils import TestCasePlus
 
 
 logging.basicConfig(level=logging.DEBUG)
@@ -20,7 +20,6 @@ def get_setup_file():
 
 
 class PabeeTests(TestCasePlus):
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_glue(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)

examples/research_projects/deebert/test_glue_deebert.py

 import argparse
 import logging
 import sys
-import unittest
 from unittest.mock import patch
 
 import run_glue_deebert
-from transformers.testing_utils import require_torch_non_multi_gpu_but_fix_me, slow
+from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
 
 
 logging.basicConfig(level=logging.DEBUG)
@@ -20,17 +19,34 @@ def get_setup_file():
     return args.f
 
 
-class DeeBertTests(unittest.TestCase):
+class DeeBertTests(TestCasePlus):
     def setup(self) -> None:
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
 
+    def run_and_check(self, args):
+        n_gpu = get_gpu_count()
+
+        if n_gpu > 1:
+            pass
+            # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
+            # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
+            # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
+            # cmd = [sys.executable] + distributed_args + args
+            # execute_subprocess_async(cmd, env=self.get_env())
+            # XXX: test the results - need to save them first into .json file
+        else:
+            args.insert(0, "run_glue_deebert.py")
+            with patch.object(sys, "argv", args):
+                result = run_glue_deebert.main()
+                for value in result.values():
+                    self.assertGreaterEqual(value, 0.666)
+
     @slow
-    @require_torch_non_multi_gpu_but_fix_me
+    @require_torch_non_multi_gpu
     def test_glue_deebert_train(self):
 
         train_args = """
-            run_glue_deebert.py
             --model_type roberta
             --model_name_or_path roberta-base
             --task_name MRPC
@@ -51,13 +67,9 @@ class DeeBertTests(unittest.TestCase):
             --overwrite_cache
             --eval_after_first_stage
             """.split()
-        with patch.object(sys, "argv", train_args):
-            result = run_glue_deebert.main()
-            for value in result.values():
-                self.assertGreaterEqual(value, 0.666)
+        self.run_and_check(train_args)
 
         eval_args = """
-            run_glue_deebert.py
             --model_type roberta
             --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
             --task_name MRPC
@@ -72,13 +84,9 @@ class DeeBertTests(unittest.TestCase):
             --overwrite_cache
             --per_gpu_eval_batch_size=1
             """.split()
-        with patch.object(sys, "argv", eval_args):
-            result = run_glue_deebert.main()
-            for value in result.values():
-                self.assertGreaterEqual(value, 0.666)
+        self.run_and_check(eval_args)
 
         entropy_eval_args = """
-            run_glue_deebert.py
             --model_type roberta
             --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
             --task_name MRPC
@@ -93,7 +101,4 @@ class DeeBertTests(unittest.TestCase):
             --overwrite_cache
             --per_gpu_eval_batch_size=1
             """.split()
-        with patch.object(sys, "argv", entropy_eval_args):
-            result = run_glue_deebert.main()
-            for value in result.values():
-                self.assertGreaterEqual(value, 0.666)
+        self.run_and_check(entropy_eval_args)

examples/research_projects/rag/test_distributed_retriever.py

@@ -17,7 +17,7 @@ from transformers.integrations import is_ray_available
 from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
 from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
 from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
-from transformers.testing_utils import require_ray, require_torch_non_multi_gpu_but_fix_me
+from transformers.testing_utils import require_ray
 
 
 sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip
@@ -265,7 +265,6 @@ 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_multi_gpu_but_fix_me
     def test_pytorch_distributed_retriever_retrieve(self):
         n_docs = 1
         hidden_states = np.array(
@@ -276,7 +275,6 @@ class RagRetrieverTest(TestCase):
             self.get_dummy_pytorch_distributed_retriever(init_retrieval=True), hidden_states, n_docs
         )
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_custom_hf_index_pytorch_retriever_retrieve(self):
         n_docs = 1
         hidden_states = np.array(
@@ -289,7 +287,6 @@ class RagRetrieverTest(TestCase):
             n_docs,
         )
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_custom_pytorch_distributed_retriever_retrieve_from_disk(self):
         n_docs = 1
         hidden_states = np.array(

examples/research_projects/seq2seq-distillation/_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_multi_gpu_but_fix_me
+from transformers.testing_utils import require_torch
 
 
 TINY_BART = "sshleifer/bart-tiny-random"
@@ -17,28 +17,23 @@ class MakeStudentTester(unittest.TestCase):
     def teacher_config(self):
         return AutoConfig.from_pretrained(TINY_BART)
 
-    @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_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_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_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_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/test_examples.py

@@ -24,7 +24,7 @@ from unittest.mock import patch
 import torch
 
 from transformers.file_utils import is_apex_available
-from transformers.testing_utils import TestCasePlus, require_torch_non_multi_gpu_but_fix_me, slow, torch_device
+from transformers.testing_utils import TestCasePlus, get_gpu_count, slow, torch_device
 
 
 SRC_DIRS = [
@@ -82,7 +82,6 @@ def is_cuda_and_apex_available():
 
 
 class ExamplesTests(TestCasePlus):
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_glue(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -114,7 +113,6 @@ class ExamplesTests(TestCasePlus):
             result = get_results(tmp_dir)
             self.assertGreaterEqual(result["eval_accuracy"], 0.75)
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_clm(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -147,7 +145,6 @@ class ExamplesTests(TestCasePlus):
             result = get_results(tmp_dir)
             self.assertLess(result["perplexity"], 100)
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_mlm(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -174,11 +171,13 @@ class ExamplesTests(TestCasePlus):
             result = get_results(tmp_dir)
             self.assertLess(result["perplexity"], 42)
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_ner(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
 
+        # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
+        epochs = 7 if get_gpu_count() > 1 else 2
+
         tmp_dir = self.get_auto_remove_tmp_dir()
         testargs = f"""
             run_ner.py
@@ -193,7 +192,7 @@ class ExamplesTests(TestCasePlus):
             --learning_rate=2e-4
             --per_device_train_batch_size=2
             --per_device_eval_batch_size=2
-            --num_train_epochs=2
+            --num_train_epochs={epochs}
         """.split()
 
         if torch_device != "cuda":
@@ -206,7 +205,6 @@ class ExamplesTests(TestCasePlus):
             self.assertGreaterEqual(result["eval_precision"], 0.75)
             self.assertLess(result["eval_loss"], 0.5)
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_squad(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -235,7 +233,6 @@ class ExamplesTests(TestCasePlus):
             self.assertGreaterEqual(result["f1"], 30)
             self.assertGreaterEqual(result["exact"], 30)
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_swag(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -262,7 +259,6 @@ class ExamplesTests(TestCasePlus):
             result = get_results(tmp_dir)
             self.assertGreaterEqual(result["eval_accuracy"], 0.8)
 
-    @require_torch_non_multi_gpu_but_fix_me
     def test_generation(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -281,7 +277,6 @@ class ExamplesTests(TestCasePlus):
             self.assertGreaterEqual(len(result[0]), 10)
 
     @slow
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_seq2seq_summarization(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -314,7 +309,6 @@ class ExamplesTests(TestCasePlus):
             self.assertGreaterEqual(result["eval_rougeLsum"], 7)
 
     @slow
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_seq2seq_translation(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_multi_gpu_but_fix_me, require_torch_tpu
+from transformers.testing_utils import require_torch_tpu
 
 
 logging.basicConfig(level=logging.DEBUG)
@@ -30,7 +30,6 @@ logger = logging.getLogger()
 
 @require_torch_tpu
 class TorchXLAExamplesTests(unittest.TestCase):
-    @require_torch_non_multi_gpu_but_fix_me
     def test_run_glue(self):
         import xla_spawn
 
@@ -82,7 +81,6 @@ 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_multi_gpu_but_fix_me
     def test_trainer_tpu(self):
         import xla_spawn
 

src/transformers/testing_utils.py

@@ -301,12 +301,6 @@ def require_torch_non_multi_gpu(test_case):
         return test_case
 
 
-# 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_multi_gpu_but_fix_me = require_torch_non_multi_gpu
-
-
 def require_torch_tpu(test_case):
     """
     Decorator marking a test that requires a TPU (in PyTorch).