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using multi_gpu consistently (#8446)

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

* doc
This commit is contained in:
Stas Bekman
2020-11-10 10:23:58 -08:00
committed by GitHub
parent b93569457f
commit 02bdfc0251
22 changed files with 117 additions and 117 deletions
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16
examples/seq2seq/test_datasets.py
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@@ -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: