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Updated test architecture

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This commit is contained in:
LysandreJik 2019-08-08 18:21:34 -04:00
parent 6c41a8f5dc
commit fbd746bd06
3 changed files with 91 additions and 27 deletions
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43
pytorch_transformers/tests/modeling_roberta_test.py
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@ -19,8 +19,9 @@ from __future__ import print_function
import unittest
import shutil
import pytest
import torch
from pytorch_transformers import (RobertaConfig, RobertaModel, RobertaForMaskedLM)
from pytorch_transformers import (RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification)
from pytorch_transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
from .modeling_common_test import (CommonTestCases, ConfigTester, ids_tensor)
@ -156,6 +157,42 @@ class RobertaModelTest(CommonTestCases.CommonModelTester):
inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
def test_inference_masked_lm(self):
model = RobertaForMaskedLM.from_pretrained('roberta-base')
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
expected_shape = torch.Size((1, 11, 50265))
self.assertEqual(
output.shape,
expected_shape
)
# compare the actual values for a slice.
expected_slice = torch.Tensor(
[[[33.8843, -4.3107, 22.7779],
[4.6533, -2.8099, 13.6252],
[1.8222, -3.6898, 8.8600]]]
)
self.assertTrue(
torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
)
# @pytest.mark.slow
def test_inference_no_head(self):
model = RobertaModel.from_pretrained('roberta-base')
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
# compare the actual values for a slice.
expected_slice = torch.Tensor(
[[[-0.0231, 0.0782, 0.0074],
[-0.1854, 0.0539, -0.0174],
[0.0548, 0.0799, 0.1687]]]
)
self.assertTrue(
torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
)
def setUp(self):
self.model_tester = RobertaModelTest.RobertaModelTester(self)
self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=37)
@ -183,7 +220,7 @@ class RobertaModelTest(CommonTestCases.CommonModelTester):
class RobertaModelIntegrationTest(unittest.TestCase):
@pytest.mark.slow
# @pytest.mark.slow
def test_inference_masked_lm(self):
model = RobertaForMaskedLM.from_pretrained('roberta-base')
@ -204,7 +241,7 @@ class RobertaModelIntegrationTest(unittest.TestCase):
torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
)
@pytest.mark.slow
# @pytest.mark.slow
def test_inference_no_head(self):
model = RobertaModel.from_pretrained('roberta-base')

70
pytorch_transformers/tests/tokenization_roberta_test.py
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@ -15,42 +15,68 @@
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import json
import unittest
from pytorch_transformers.tokenization_roberta import RobertaTokenizer, VOCAB_FILES_NAMES
from .tokenization_tests_commons import create_and_check_tokenizer_commons, TemporaryDirectory
from pytorch_transformers.tokenization_roberta import RobertaTokenizer, DICT_FILES_NAMES
from pytorch_transformers.tokenization_gpt2 import GPT2Tokenizer, VOCAB_FILES_NAMES
from .tokenization_tests_commons import CommonTestCases
class RobertaTokenizationTest(unittest.TestCase):
class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = RobertaTokenizer
def test_full_tokenizer(self):
""" Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
def setUp(self):
super(RobertaTokenizationTest, self).setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n",
"lo", "low", "er",
"low", "lowest", "newer", "wider", "<unk>"]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
special_tokens_map = {"unk_token": "<unk>"}
merges = ["#version: 0.2", "l o", "lo w", "e r", ""]
self.special_tokens_map = {"unk_token": "<unk>"}
with TemporaryDirectory() as tmpdirname:
vocab_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
with open(vocab_file, "w") as fp:
[fp.write(f"{vocab} {index}\n") for index, vocab in enumerate(vocab_tokens)]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'])
with open(self.vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(self.merges_file, "w") as fp:
fp.write("\n".join(merges))
input_text = u"lower newer"
output_text = u"lower<unk>newer"
def get_tokenizer(self):
bpe_tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname, **self.special_tokens_map)
return RobertaTokenizer.from_pretrained("roberta-base", bpe_tokenizer=bpe_tokenizer)
create_and_check_tokenizer_commons(self, input_text, output_text, RobertaTokenizer, tmpdirname, **special_tokens_map)
def get_input_output_texts(self):
input_text = u"lower newer"
output_text = u"lower<unk>newer"
return input_text, output_text
tokenizer = RobertaTokenizer(vocab_file, **special_tokens_map)
text = "lower"
bpe_tokens = ["low", "er"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
def test_full_tokenizer(self):
tokenizer = self.get_tokenizer()
text = "lower"
bpe_tokens = ["low", "er"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + [tokenizer.unk_token]
input_bpe_tokens = [13, 12, 17]
self.assertListEqual(
tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
input_tokens = tokens + [tokenizer.unk_token]
input_bpe_tokens = [0, 4, 12, 176, 2]
tokenizer.convert_tokens_to_ids(input_tokens)
self.assertListEqual(
tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
def roberta_dict_integration_testing(self):
tokenizer = self.get_tokenizer()
self.assertListEqual(
tokenizer.encode('Hello world!'),
[0, 31414, 232, 328, 2]
)
self.assertListEqual(
tokenizer.encode('Hello world! cécé herlolip'),
[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]
)
if __name__ == '__main__':

5
pytorch_transformers/tests/tokenization_tests_commons.py
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@ -105,7 +105,7 @@ class CommonTestCases:
self.assertEqual(added_toks, len(new_toks))
self.assertEqual(all_size_2, all_size + len(new_toks))
tokens = tokenizer.encode("aaaaabbbbbb low cccccccccdddddddd l")
tokens = tokenizer.encode("aaaaabbbbbb low cccccccccdddddddd l", no_sep_cls_tokens=True)
self.assertGreaterEqual(len(tokens), 4)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
@ -121,7 +121,8 @@ class CommonTestCases:
self.assertEqual(added_toks_2, len(new_toks_2))
self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
tokens = tokenizer.encode(">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l")
tokens = tokenizer.encode(">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l",
no_sep_cls_tokens=True)
self.assertGreaterEqual(len(tokens), 6)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)