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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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26
examples/seq2seq/test_seq2seq_examples.py
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@@ -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()