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Unverified Commit f284089e authored by Stas Bekman's avatar Stas Bekman Committed by GitHub
Browse files

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

* batch 1

* this is tpu

* deebert attempt

* the rest
parent dfd16af8
...@@ -24,7 +24,7 @@ from parameterized import parameterized ...@@ -24,7 +24,7 @@ from parameterized import parameterized
from save_len_file import save_len_file from save_len_file import save_len_file
from transformers import AutoTokenizer from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right 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 from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeq2SeqDataset, Seq2SeqDataset
...@@ -61,7 +61,6 @@ class TestAll(TestCasePlus): ...@@ -61,7 +61,6 @@ class TestAll(TestCasePlus):
], ],
) )
@slow @slow
@require_torch_non_multi_gpu_but_fix_me
def test_seq2seq_dataset_truncation(self, tok_name): def test_seq2seq_dataset_truncation(self, tok_name):
tokenizer = AutoTokenizer.from_pretrained(tok_name) tokenizer = AutoTokenizer.from_pretrained(tok_name)
tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
...@@ -101,7 +100,6 @@ class TestAll(TestCasePlus): ...@@ -101,7 +100,6 @@ class TestAll(TestCasePlus):
break # No need to test every batch break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED]) @parameterized.expand([BART_TINY, BERT_BASE_CASED])
@require_torch_non_multi_gpu_but_fix_me
def test_legacy_dataset_truncation(self, tok): def test_legacy_dataset_truncation(self, tok):
tokenizer = AutoTokenizer.from_pretrained(tok) tokenizer = AutoTokenizer.from_pretrained(tok)
tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
...@@ -126,7 +124,6 @@ class TestAll(TestCasePlus): ...@@ -126,7 +124,6 @@ class TestAll(TestCasePlus):
assert max_len_target > trunc_target # Truncated assert max_len_target > trunc_target # Truncated
break # No need to test every batch break # No need to test every batch
@require_torch_non_multi_gpu_but_fix_me
def test_pack_dataset(self): def test_pack_dataset(self):
tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25") tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
...@@ -145,7 +142,6 @@ class TestAll(TestCasePlus): ...@@ -145,7 +142,6 @@ class TestAll(TestCasePlus):
assert orig_paths == new_paths assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq") @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): def test_dynamic_batch_size(self):
if not FAIRSEQ_AVAILABLE: if not FAIRSEQ_AVAILABLE:
return return
...@@ -170,7 +166,6 @@ class TestAll(TestCasePlus): ...@@ -170,7 +166,6 @@ class TestAll(TestCasePlus):
if failures: if failures:
raise AssertionError(f"too many tokens in {len(failures)} batches") 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): def test_sortish_sampler_reduces_padding(self):
ds, _, tokenizer = self._get_dataset(max_len=512) ds, _, tokenizer = self._get_dataset(max_len=512)
bs = 2 bs = 2
...@@ -210,7 +205,6 @@ class TestAll(TestCasePlus): ...@@ -210,7 +205,6 @@ class TestAll(TestCasePlus):
) )
return ds, max_tokens, tokenizer return ds, max_tokens, tokenizer
@require_torch_non_multi_gpu_but_fix_me
def test_distributed_sortish_sampler_splits_indices_between_procs(self): def test_distributed_sortish_sampler_splits_indices_between_procs(self):
ds, max_tokens, tokenizer = self._get_dataset() ds, max_tokens, tokenizer = self._get_dataset()
ids1 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=0, add_extra_examples=False)) ids1 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=0, add_extra_examples=False))
...@@ -226,7 +220,6 @@ class TestAll(TestCasePlus): ...@@ -226,7 +220,6 @@ class TestAll(TestCasePlus):
PEGASUS_XSUM, PEGASUS_XSUM,
], ],
) )
@require_torch_non_multi_gpu_but_fix_me
def test_dataset_kwargs(self, tok_name): def test_dataset_kwargs(self, tok_name):
tokenizer = AutoTokenizer.from_pretrained(tok_name, use_fast=False) tokenizer = AutoTokenizer.from_pretrained(tok_name, use_fast=False)
if tok_name == MBART_TINY: if tok_name == MBART_TINY:
......
...@@ -18,7 +18,7 @@ import unittest ...@@ -18,7 +18,7 @@ import unittest
from transformers.file_utils import cached_property from transformers.file_utils import cached_property
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter 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.") @unittest.skipUnless(os.path.exists(DEFAULT_REPO), "Tatoeba directory does not exist.")
...@@ -29,12 +29,10 @@ class TatoebaConversionTester(unittest.TestCase): ...@@ -29,12 +29,10 @@ class TatoebaConversionTester(unittest.TestCase):
return TatoebaConverter(save_dir=tmp_dir) return TatoebaConverter(save_dir=tmp_dir)
@slow @slow
@require_torch_non_multi_gpu_but_fix_me
def test_resolver(self): def test_resolver(self):
self.resolver.convert_models(["heb-eng"]) self.resolver.convert_models(["heb-eng"])
@slow @slow
@require_torch_non_multi_gpu_but_fix_me
def test_model_card(self): def test_model_card(self):
content, mmeta = self.resolver.write_model_card("opus-mt-he-en", dry_run=True) content, mmeta = self.resolver.write_model_card("opus-mt-he-en", dry_run=True)
assert mmeta["long_pair"] == "heb-eng" assert mmeta["long_pair"] == "heb-eng"
...@@ -4,7 +4,7 @@ import sys ...@@ -4,7 +4,7 @@ import sys
from unittest.mock import patch from unittest.mock import patch
import run_glue_with_pabee 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) logging.basicConfig(level=logging.DEBUG)
...@@ -20,7 +20,6 @@ def get_setup_file(): ...@@ -20,7 +20,6 @@ def get_setup_file():
class PabeeTests(TestCasePlus): class PabeeTests(TestCasePlus):
@require_torch_non_multi_gpu_but_fix_me
def test_run_glue(self): def test_run_glue(self):
stream_handler = logging.StreamHandler(sys.stdout) stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler) logger.addHandler(stream_handler)
......
import argparse import argparse
import logging import logging
import sys import sys
import unittest
from unittest.mock import patch from unittest.mock import patch
import run_glue_deebert 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) logging.basicConfig(level=logging.DEBUG)
...@@ -20,17 +19,34 @@ def get_setup_file(): ...@@ -20,17 +19,34 @@ def get_setup_file():
return args.f return args.f
class DeeBertTests(unittest.TestCase): class DeeBertTests(TestCasePlus):
def setup(self) -> None: def setup(self) -> None:
stream_handler = logging.StreamHandler(sys.stdout) stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler) 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 @slow
@require_torch_non_multi_gpu_but_fix_me @require_torch_non_multi_gpu
def test_glue_deebert_train(self): def test_glue_deebert_train(self):
train_args = """ train_args = """
run_glue_deebert.py
--model_type roberta --model_type roberta
--model_name_or_path roberta-base --model_name_or_path roberta-base
--task_name MRPC --task_name MRPC
...@@ -51,13 +67,9 @@ class DeeBertTests(unittest.TestCase): ...@@ -51,13 +67,9 @@ class DeeBertTests(unittest.TestCase):
--overwrite_cache --overwrite_cache
--eval_after_first_stage --eval_after_first_stage
""".split() """.split()
with patch.object(sys, "argv", train_args): self.run_and_check(train_args)
result = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(value, 0.666)
eval_args = """ eval_args = """
run_glue_deebert.py
--model_type roberta --model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC --task_name MRPC
...@@ -72,13 +84,9 @@ class DeeBertTests(unittest.TestCase): ...@@ -72,13 +84,9 @@ class DeeBertTests(unittest.TestCase):
--overwrite_cache --overwrite_cache
--per_gpu_eval_batch_size=1 --per_gpu_eval_batch_size=1
""".split() """.split()
with patch.object(sys, "argv", eval_args): self.run_and_check(eval_args)
result = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(value, 0.666)
entropy_eval_args = """ entropy_eval_args = """
run_glue_deebert.py
--model_type roberta --model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC --task_name MRPC
...@@ -93,7 +101,4 @@ class DeeBertTests(unittest.TestCase): ...@@ -93,7 +101,4 @@ class DeeBertTests(unittest.TestCase):
--overwrite_cache --overwrite_cache
--per_gpu_eval_batch_size=1 --per_gpu_eval_batch_size=1
""".split() """.split()
with patch.object(sys, "argv", entropy_eval_args): self.run_and_check(entropy_eval_args)
result = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(value, 0.666)
...@@ -17,7 +17,7 @@ from transformers.integrations import is_ray_available ...@@ -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.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever 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.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 sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # noqa: E402 # isort:skip
...@@ -265,7 +265,6 @@ class RagRetrieverTest(TestCase): ...@@ -265,7 +265,6 @@ class RagRetrieverTest(TestCase):
self.assertEqual(doc_dicts[1]["id"][0], "0") # max inner product is reached with first doc self.assertEqual(doc_dicts[1]["id"][0], "0") # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist(), [[1], [0]]) self.assertListEqual(doc_ids.tolist(), [[1], [0]])
@require_torch_non_multi_gpu_but_fix_me
def test_pytorch_distributed_retriever_retrieve(self): def test_pytorch_distributed_retriever_retrieve(self):
n_docs = 1 n_docs = 1
hidden_states = np.array( hidden_states = np.array(
...@@ -276,7 +275,6 @@ class RagRetrieverTest(TestCase): ...@@ -276,7 +275,6 @@ class RagRetrieverTest(TestCase):
self.get_dummy_pytorch_distributed_retriever(init_retrieval=True), hidden_states, n_docs 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): def test_custom_hf_index_pytorch_retriever_retrieve(self):
n_docs = 1 n_docs = 1
hidden_states = np.array( hidden_states = np.array(
...@@ -289,7 +287,6 @@ class RagRetrieverTest(TestCase): ...@@ -289,7 +287,6 @@ class RagRetrieverTest(TestCase):
n_docs, n_docs,
) )
@require_torch_non_multi_gpu_but_fix_me
def test_custom_pytorch_distributed_retriever_retrieve_from_disk(self): def test_custom_pytorch_distributed_retriever_retrieve_from_disk(self):
n_docs = 1 n_docs = 1
hidden_states = np.array( hidden_states = np.array(
......
...@@ -4,7 +4,7 @@ import unittest ...@@ -4,7 +4,7 @@ import unittest
from make_student import create_student_by_copying_alternating_layers from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig from transformers import AutoConfig
from transformers.file_utils import cached_property 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" TINY_BART = "sshleifer/bart-tiny-random"
...@@ -17,28 +17,23 @@ class MakeStudentTester(unittest.TestCase): ...@@ -17,28 +17,23 @@ class MakeStudentTester(unittest.TestCase):
def teacher_config(self): def teacher_config(self):
return AutoConfig.from_pretrained(TINY_BART) return AutoConfig.from_pretrained(TINY_BART)
@require_torch_non_multi_gpu_but_fix_me
def test_valid_t5(self): def test_valid_t5(self):
student, *_ = create_student_by_copying_alternating_layers(TINY_T5, tempfile.mkdtemp(), e=1, d=1) student, *_ = create_student_by_copying_alternating_layers(TINY_T5, tempfile.mkdtemp(), e=1, d=1)
self.assertEqual(student.config.num_hidden_layers, 1) self.assertEqual(student.config.num_hidden_layers, 1)
@require_torch_non_multi_gpu_but_fix_me
def test_asymmetric_t5(self): def test_asymmetric_t5(self):
student, *_ = create_student_by_copying_alternating_layers(TINY_T5, tempfile.mkdtemp(), e=1, d=None) 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): def test_same_decoder_small_encoder(self):
student, *_ = create_student_by_copying_alternating_layers(TINY_BART, tempfile.mkdtemp(), e=1, d=None) 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.encoder_layers, 1)
self.assertEqual(student.config.decoder_layers, self.teacher_config.encoder_layers) 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): def test_small_enc_small_dec(self):
student, *_ = create_student_by_copying_alternating_layers(TINY_BART, tempfile.mkdtemp(), e=1, d=1) 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.encoder_layers, 1)
self.assertEqual(student.config.decoder_layers, 1) self.assertEqual(student.config.decoder_layers, 1)
@require_torch_non_multi_gpu_but_fix_me
def test_raises_assert(self): def test_raises_assert(self):
with self.assertRaises(AssertionError): with self.assertRaises(AssertionError):
create_student_by_copying_alternating_layers(TINY_BART, tempfile.mkdtemp(), e=None, d=None) create_student_by_copying_alternating_layers(TINY_BART, tempfile.mkdtemp(), e=None, d=None)
...@@ -24,7 +24,7 @@ from unittest.mock import patch ...@@ -24,7 +24,7 @@ from unittest.mock import patch
import torch import torch
from transformers.file_utils import is_apex_available 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 = [ SRC_DIRS = [
...@@ -82,7 +82,6 @@ def is_cuda_and_apex_available(): ...@@ -82,7 +82,6 @@ def is_cuda_and_apex_available():
class ExamplesTests(TestCasePlus): class ExamplesTests(TestCasePlus):
@require_torch_non_multi_gpu_but_fix_me
def test_run_glue(self): def test_run_glue(self):
stream_handler = logging.StreamHandler(sys.stdout) stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler) logger.addHandler(stream_handler)
...@@ -114,7 +113,6 @@ class ExamplesTests(TestCasePlus): ...@@ -114,7 +113,6 @@ class ExamplesTests(TestCasePlus):
result = get_results(tmp_dir) result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_accuracy"], 0.75) self.assertGreaterEqual(result["eval_accuracy"], 0.75)
@require_torch_non_multi_gpu_but_fix_me
def test_run_clm(self): def test_run_clm(self):
stream_handler = logging.StreamHandler(sys.stdout) stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler) logger.addHandler(stream_handler)
...@@ -147,7 +145,6 @@ class ExamplesTests(TestCasePlus): ...@@ -147,7 +145,6 @@ class ExamplesTests(TestCasePlus):
result = get_results(tmp_dir) result = get_results(tmp_dir)
self.assertLess(result["perplexity"], 100) self.assertLess(result["perplexity"], 100)
@require_torch_non_multi_gpu_but_fix_me
def test_run_mlm(self): def test_run_mlm(self):
stream_handler = logging.StreamHandler(sys.stdout) stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler) logger.addHandler(stream_handler)
...@@ -174,11 +171,13 @@ class ExamplesTests(TestCasePlus): ...@@ -174,11 +171,13 @@ class ExamplesTests(TestCasePlus):
result = get_results(tmp_dir) result = get_results(tmp_dir)
self.assertLess(result["perplexity"], 42) self.assertLess(result["perplexity"], 42)
@require_torch_non_multi_gpu_but_fix_me
def test_run_ner(self): def test_run_ner(self):
stream_handler = logging.StreamHandler(sys.stdout) stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler) 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() tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f""" testargs = f"""
run_ner.py run_ner.py
...@@ -193,7 +192,7 @@ class ExamplesTests(TestCasePlus): ...@@ -193,7 +192,7 @@ class ExamplesTests(TestCasePlus):
--learning_rate=2e-4 --learning_rate=2e-4
--per_device_train_batch_size=2 --per_device_train_batch_size=2
--per_device_eval_batch_size=2 --per_device_eval_batch_size=2
--num_train_epochs=2 --num_train_epochs={epochs}
""".split() """.split()
if torch_device != "cuda": if torch_device != "cuda":
...@@ -206,7 +205,6 @@ class ExamplesTests(TestCasePlus): ...@@ -206,7 +205,6 @@ class ExamplesTests(TestCasePlus):
self.assertGreaterEqual(result["eval_precision"], 0.75) self.assertGreaterEqual(result["eval_precision"], 0.75)
self.assertLess(result["eval_loss"], 0.5) self.assertLess(result["eval_loss"], 0.5)
@require_torch_non_multi_gpu_but_fix_me
def test_run_squad(self): def test_run_squad(self):
stream_handler = logging.StreamHandler(sys.stdout) stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler) logger.addHandler(stream_handler)
...@@ -235,7 +233,6 @@ class ExamplesTests(TestCasePlus): ...@@ -235,7 +233,6 @@ class ExamplesTests(TestCasePlus):
self.assertGreaterEqual(result["f1"], 30) self.assertGreaterEqual(result["f1"], 30)
self.assertGreaterEqual(result["exact"], 30) self.assertGreaterEqual(result["exact"], 30)
@require_torch_non_multi_gpu_but_fix_me
def test_run_swag(self): def test_run_swag(self):
stream_handler = logging.StreamHandler(sys.stdout) stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler) logger.addHandler(stream_handler)
...@@ -262,7 +259,6 @@ class ExamplesTests(TestCasePlus): ...@@ -262,7 +259,6 @@ class ExamplesTests(TestCasePlus):
result = get_results(tmp_dir) result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_accuracy"], 0.8) self.assertGreaterEqual(result["eval_accuracy"], 0.8)
@require_torch_non_multi_gpu_but_fix_me
def test_generation(self): def test_generation(self):
stream_handler = logging.StreamHandler(sys.stdout) stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler) logger.addHandler(stream_handler)
...@@ -281,7 +277,6 @@ class ExamplesTests(TestCasePlus): ...@@ -281,7 +277,6 @@ class ExamplesTests(TestCasePlus):
self.assertGreaterEqual(len(result[0]), 10) self.assertGreaterEqual(len(result[0]), 10)
@slow @slow
@require_torch_non_multi_gpu_but_fix_me
def test_run_seq2seq_summarization(self): def test_run_seq2seq_summarization(self):
stream_handler = logging.StreamHandler(sys.stdout) stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler) logger.addHandler(stream_handler)
...@@ -314,7 +309,6 @@ class ExamplesTests(TestCasePlus): ...@@ -314,7 +309,6 @@ class ExamplesTests(TestCasePlus):
self.assertGreaterEqual(result["eval_rougeLsum"], 7) self.assertGreaterEqual(result["eval_rougeLsum"], 7)
@slow @slow
@require_torch_non_multi_gpu_but_fix_me
def test_run_seq2seq_translation(self): def test_run_seq2seq_translation(self):
stream_handler = logging.StreamHandler(sys.stdout) stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler) logger.addHandler(stream_handler)
......
...@@ -20,7 +20,7 @@ import unittest ...@@ -20,7 +20,7 @@ import unittest
from time import time from time import time
from unittest.mock import patch 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) logging.basicConfig(level=logging.DEBUG)
...@@ -30,7 +30,6 @@ logger = logging.getLogger() ...@@ -30,7 +30,6 @@ logger = logging.getLogger()
@require_torch_tpu @require_torch_tpu
class TorchXLAExamplesTests(unittest.TestCase): class TorchXLAExamplesTests(unittest.TestCase):
@require_torch_non_multi_gpu_but_fix_me
def test_run_glue(self): def test_run_glue(self):
import xla_spawn import xla_spawn
...@@ -82,7 +81,6 @@ class TorchXLAExamplesTests(unittest.TestCase): ...@@ -82,7 +81,6 @@ class TorchXLAExamplesTests(unittest.TestCase):
# Assert that the script takes less than 300 seconds to make sure it doesn't hang. # Assert that the script takes less than 300 seconds to make sure it doesn't hang.
self.assertLess(end - start, 500) self.assertLess(end - start, 500)
@require_torch_non_multi_gpu_but_fix_me
def test_trainer_tpu(self): def test_trainer_tpu(self):
import xla_spawn import xla_spawn
......
...@@ -301,12 +301,6 @@ def require_torch_non_multi_gpu(test_case): ...@@ -301,12 +301,6 @@ def require_torch_non_multi_gpu(test_case):
return 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): def require_torch_tpu(test_case):
""" """
Decorator marking a test that requires a TPU (in PyTorch). Decorator marking a test that requires a TPU (in PyTorch).
......
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