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Cleanup pytorch tests (#8033)

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Sam Shleifer
2020-10-26 08:59:06 -04:00
committed by GitHub
parent 20a0894d1a
commit 8bbe8247f1
3 changed files with 3 additions and 31 deletions
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29
tests/test_modeling_mbart.py
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@@ -4,7 +4,6 @@ from transformers import is_torch_available
from transformers.file_utils import cached_property
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from .test_modeling_bart import TOLERANCE, _long_tensor, assert_tensors_close
from .test_modeling_common import ModelTesterMixin
@@ -91,32 +90,6 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
]
expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
@slow
@unittest.skip("This has been failing since June 20th at least.")
def test_enro_forward(self):
model = self.model
net_input = {
"input_ids": _long_tensor(
[
[3493, 3060, 621, 104064, 1810, 100, 142, 566, 13158, 6889, 5, 2, 250004],
[64511, 7, 765, 2837, 45188, 297, 4049, 237, 10, 122122, 5, 2, 250004],
]
),
"decoder_input_ids": _long_tensor(
[
[250020, 31952, 144, 9019, 242307, 21980, 55749, 11, 5, 2, 1, 1],
[250020, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2],
]
),
}
net_input["attention_mask"] = net_input["input_ids"].ne(1)
with torch.no_grad():
logits, *other_stuff = model(**net_input)
expected_slice = torch.tensor([9.0078, 10.1113, 14.4787], device=logits.device, dtype=logits.dtype)
result_slice = logits[0, 0, :3]
assert_tensors_close(expected_slice, result_slice, atol=TOLERANCE)
@slow
def test_enro_generate_one(self):
batch: BatchEncoding = self.tokenizer.prepare_seq2seq_batch(
@@ -128,7 +101,7 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
# self.assertEqual(self.tgt_text[1], decoded[1])
@slow
def test_enro_generate(self):
def test_enro_generate_batch(self):
batch: BatchEncoding = self.tokenizer.prepare_seq2seq_batch(self.src_text).to(torch_device)
translated_tokens = self.model.generate(**batch)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)