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[mBART] skip broken forward pass test, stronger integration test (#5327)

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Sam Shleifer 2020-06-28 15:08:28 -04:00 committed by GitHub
parent 45e26125de
commit 28a690a80e
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2 changed files with 33 additions and 37 deletions
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14
src/transformers/tokenization_bart.py
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@ -110,6 +110,12 @@ class MBartTokenizer(XLMRobertaTokenizer):
id_to_lang_code = {v: k for k, v in lang_code_to_id.items()}
cur_lang_code = lang_code_to_id["en_XX"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
self._additional_special_tokens = list(self.lang_code_to_id.keys())
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
"""Build model inputs from a sequence by appending eos_token_id."""
special_tokens = [self.eos_token_id, self.cur_lang_code]
@ -118,12 +124,6 @@ class MBartTokenizer(XLMRobertaTokenizer):
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_0 + token_ids_1 + special_tokens
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
if index in self.id_to_lang_code:
return self.id_to_lang_code[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset)
def set_lang(self, lang: str) -> None:
"""Set the current language code in order to call tokenizer properly."""
self.cur_lang_code = self.lang_code_to_id[lang]
@ -159,6 +159,7 @@ class MBartTokenizer(XLMRobertaTokenizer):
return_tensors=return_tensors,
max_length=max_length,
pad_to_max_length=pad_to_max_length,
truncation=True,
)
if tgt_texts is None:
return model_inputs
@ -169,6 +170,7 @@ class MBartTokenizer(XLMRobertaTokenizer):
return_tensors=return_tensors,
max_length=max_length,
pad_to_max_length=pad_to_max_length,
truncation=True,
)
for k, v in decoder_inputs.items():
model_inputs[f"decoder_{k}"] = v

56
tests/test_modeling_bart.py
View file

@ -43,7 +43,6 @@ if is_torch_available():
pipeline,
)
from transformers.modeling_bart import (
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
shift_tokens_right,
invert_mask,
_prepare_bart_decoder_inputs,
@ -211,9 +210,13 @@ EN_CODE = 250004
class MBartIntegrationTests(unittest.TestCase):
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
" I ate lunch twice yesterday",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
tgt_text = ["Şeful ONU declară că nu există o soluţie militară în Siria", "to be padded"]
tgt_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
@classmethod
@ -232,6 +235,7 @@ class MBartIntegrationTests(unittest.TestCase):
return model
@slow
@unittest.skip("This has been failing since June 20th at least.")
def test_enro_forward(self):
model = self.model
net_input = {
@ -247,22 +251,22 @@ class MBartIntegrationTests(unittest.TestCase):
[250020, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2],
]
),
"generation_mode": False,
}
net_input["attention_mask"] = net_input["input_ids"].ne(self.pad_token_id)
with torch.no_grad():
logits, *other_stuff = model(**net_input)
expected_slice = [9.0078, 10.1113, 14.4787]
result_slice = logits[0][0][:3].tolist()
self.assertListEqual(expected_slice, result_slice)
expected_slice = torch.tensor([9.0078, 10.1113, 14.4787], device=logits.device, dtype=logits.dtype)
result_slice = logits[0, 0, :3]
_assert_tensors_equal(expected_slice, result_slice, atol=TOLERANCE)
@slow
def test_enro_generate(self):
inputs: dict = self.tokenizer.prepare_translation_batch([self.src_text[0]]).to(torch_device)
translated_tokens = self.model.generate(input_ids=inputs["input_ids"].to(torch_device))
batch: BatchEncoding = self.tokenizer.prepare_translation_batch(self.src_text).to(torch_device)
translated_tokens = self.model.generate(**batch)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
self.assertEqual(self.tgt_text[0], decoded[0])
self.assertEqual(self.tgt_text[1], decoded[1])
def test_mbart_enro_config(self):
mbart_models = ["facebook/mbart-large-en-ro"]
@ -313,6 +317,14 @@ class MBartIntegrationTests(unittest.TestCase):
ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens, ids)
def test_enro_tokenizer_decode_ignores_language_codes(self):
self.assertIn(250020, self.tokenizer.all_special_ids)
generated_ids = [250020, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
self.assertEqual(result, expected_romanian)
self.assertNotIn(self.tokenizer.eos_token, result)
def test_enro_tokenizer_truncation(self):
src_text = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0], str)
@ -474,24 +486,13 @@ class BartHeadTests(unittest.TestCase):
bart_toks = tokenizer.encode(ex, return_tensors="pt")
_assert_tensors_equal(desired_result.long(), bart_toks, prefix=ex)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_generate_fp16(self):
config, input_ids, batch_size = self._get_config_and_data()
attention_mask = input_ids.ne(1).to(torch_device)
model = BartForConditionalGeneration(config).eval().to(torch_device).half()
model.generate(input_ids, attention_mask=attention_mask, do_sample=False, early_stopping=True)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_base_model_fp16(self):
config, input_ids, batch_size = self._get_config_and_data()
attention_mask = input_ids.ne(1).to(torch_device)
lm_model = BartForConditionalGeneration(config).eval().to(torch_device).half()
lm_model(input_ids, attention_mask=attention_mask)
def test_default_generate_kwargs(self):
config, input_ids, _ = self._get_config_and_data()
model = BartForConditionalGeneration(config).eval().to(torch_device)
model.generate(input_ids)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def test_dummy_inputs(self):
@ -546,7 +547,7 @@ def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
def _long_tensor(tok_lst):
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device,)
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
TOLERANCE = 1e-4
@ -611,13 +612,6 @@ class BartModelIntegrationTests(unittest.TestCase):
_assert_tensors_equal(batched_logits[1], logits2, atol=TOLERANCE)
_assert_tensors_equal(expected_slice, logits_arr, atol=TOLERANCE)
@unittest.skip("This is just too slow")
def test_model_from_pretrained(self):
# Forces 1.6GB download from S3 for each model
for model_name in BART_PRETRAINED_MODEL_ARCHIVE_LIST:
model = BartModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@slow
def test_xsum_summarization_same_as_fairseq(self):
model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-xsum").to(torch_device)