From f185e53af558b44529311d6133fb52aa8cfac1a2 Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Mon, 20 Apr 2020 11:36:55 -0400 Subject: [PATCH 001/719] torchtext 0.6.0 release (#715) --- build_tools/travis/install.sh | 2 +- torchtext/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/build_tools/travis/install.sh b/build_tools/travis/install.sh index e94013eca9..57370136dc 100644 --- a/build_tools/travis/install.sh +++ b/build_tools/travis/install.sh @@ -61,7 +61,7 @@ if [[ "$SKIP_TESTS" != "true" ]]; then # python -m nltk.downloader perluniprops nonbreaking_prefixes # # PyTorch - conda install --yes pytorch -c pytorch-nightly + conda install --yes pytorch -c pytorch-test # Installation python setup.py install diff --git a/torchtext/__init__.py b/torchtext/__init__.py index 1f31d001c5..db4484fdf1 100644 --- a/torchtext/__init__.py +++ b/torchtext/__init__.py @@ -4,7 +4,7 @@ from . import vocab from . import experimental -__version__ = '0.5.1' +__version__ = '0.6.0' __all__ = ['data', 'datasets', From 88dc6425de6533a5292c7b103175acffb884c61e Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Mon, 20 Apr 2020 12:43:18 -0400 Subject: [PATCH 002/719] update the labels of docs in text classification dataset (#734) --- torchtext/datasets/text_classification.py | 172 +++++++++++----------- 1 file changed, 86 insertions(+), 86 deletions(-) diff --git a/torchtext/datasets/text_classification.py b/torchtext/datasets/text_classification.py index 1b813de238..be7400f91b 100644 --- a/torchtext/datasets/text_classification.py +++ b/torchtext/datasets/text_classification.py @@ -145,10 +145,10 @@ def _setup_datasets(dataset_name, root='.data', ngrams=1, vocab=None, include_un def AG_NEWS(*args, **kwargs): """ Defines AG_NEWS datasets. The labels includes: - - 1 : World - - 2 : Sports - - 3 : Business - - 4 : Sci/Tech + - 0 : World + - 1 : Sports + - 2 : Business + - 3 : Sci/Tech Create supervised learning dataset: AG_NEWS @@ -173,11 +173,11 @@ def AG_NEWS(*args, **kwargs): def SogouNews(*args, **kwargs): """ Defines SogouNews datasets. The labels includes: - - 1 : Sports - - 2 : Finance - - 3 : Entertainment - - 4 : Automobile - - 5 : Technology + - 0 : Sports + - 1 : Finance + - 2 : Entertainment + - 3 : Automobile + - 4 : Technology Create supervised learning dataset: SogouNews @@ -202,20 +202,20 @@ def SogouNews(*args, **kwargs): def DBpedia(*args, **kwargs): """ Defines DBpedia datasets. The labels includes: - - 1 : Company - - 2 : EducationalInstitution - - 3 : Artist - - 4 : Athlete - - 5 : OfficeHolder - - 6 : MeanOfTransportation - - 7 : Building - - 8 : NaturalPlace - - 9 : Village - - 10 : Animal - - 11 : Plant - - 12 : Album - - 13 : Film - - 14 : WrittenWork + - 0 : Company + - 1 : EducationalInstitution + - 2 : Artist + - 3 : Athlete + - 4 : OfficeHolder + - 5 : MeanOfTransportation + - 6 : Building + - 7 : NaturalPlace + - 8 : Village + - 9 : Animal + - 10 : Plant + - 11 : Album + - 12 : Film + - 13 : WrittenWork Create supervised learning dataset: DBpedia @@ -240,8 +240,8 @@ def DBpedia(*args, **kwargs): def YelpReviewPolarity(*args, **kwargs): """ Defines YelpReviewPolarity datasets. The labels includes: - - 1 : Negative polarity. - - 2 : Positive polarity. + - 0 : Negative polarity. + - 1 : Positive polarity. Create supervised learning dataset: YelpReviewPolarity @@ -266,7 +266,7 @@ def YelpReviewPolarity(*args, **kwargs): def YelpReviewFull(*args, **kwargs): """ Defines YelpReviewFull datasets. The labels includes: - 1 - 5 : rating classes (5 is highly recommended). + 0 - 4 : rating classes (4 is highly recommended). Create supervised learning dataset: YelpReviewFull @@ -291,16 +291,16 @@ def YelpReviewFull(*args, **kwargs): def YahooAnswers(*args, **kwargs): """ Defines YahooAnswers datasets. The labels includes: - - 1 : Society & Culture - - 2 : Science & Mathematics - - 3 : Health - - 4 : Education & Reference - - 5 : Computers & Internet - - 6 : Sports - - 7 : Business & Finance - - 8 : Entertainment & Music - - 9 : Family & Relationships - - 10 : Politics & Government + - 0 : Society & Culture + - 1 : Science & Mathematics + - 2 : Health + - 3 : Education & Reference + - 4 : Computers & Internet + - 5 : Sports + - 6 : Business & Finance + - 7 : Entertainment & Music + - 8 : Family & Relationships + - 9 : Politics & Government Create supervised learning dataset: YahooAnswers @@ -325,8 +325,8 @@ def YahooAnswers(*args, **kwargs): def AmazonReviewPolarity(*args, **kwargs): """ Defines AmazonReviewPolarity datasets. The labels includes: - - 1 : Negative polarity - - 2 : Positive polarity + - 0 : Negative polarity + - 1 : Positive polarity Create supervised learning dataset: AmazonReviewPolarity @@ -351,7 +351,7 @@ def AmazonReviewPolarity(*args, **kwargs): def AmazonReviewFull(*args, **kwargs): """ Defines AmazonReviewFull datasets. The labels includes: - 1 - 5 : rating classes (5 is highly recommended) + 0 - 4 : rating classes (4 is highly recommended) Create supervised learning dataset: AmazonReviewFull @@ -386,51 +386,51 @@ def AmazonReviewFull(*args, **kwargs): LABELS = { - 'AG_NEWS': {1: 'World', - 2: 'Sports', - 3: 'Business', - 4: 'Sci/Tech'}, - 'SogouNews': {1: 'Sports', - 2: 'Finance', - 3: 'Entertainment', - 4: 'Automobile', - 5: 'Technology'}, - 'DBpedia': {1: 'Company', - 2: 'EducationalInstitution', - 3: 'Artist', - 4: 'Athlete', - 5: 'OfficeHolder', - 6: 'MeanOfTransportation', - 7: 'Building', - 8: 'NaturalPlace', - 9: 'Village', - 10: 'Animal', - 11: 'Plant', - 12: 'Album', - 13: 'Film', - 14: 'WrittenWork'}, - 'YelpReviewPolarity': {1: 'Negative polarity', - 2: 'Positive polarity'}, - 'YelpReviewFull': {1: 'score 1', - 2: 'score 2', - 3: 'score 3', - 4: 'score 4', - 5: 'score 5'}, - 'YahooAnswers': {1: 'Society & Culture', - 2: 'Science & Mathematics', - 3: 'Health', - 4: 'Education & Reference', - 5: 'Computers & Internet', - 6: 'Sports', - 7: 'Business & Finance', - 8: 'Entertainment & Music', - 9: 'Family & Relationships', - 10: 'Politics & Government'}, - 'AmazonReviewPolarity': {1: 'Negative polarity', - 2: 'Positive polarity'}, - 'AmazonReviewFull': {1: 'score 1', - 2: 'score 2', - 3: 'score 3', - 4: 'score 4', - 5: 'score 5'} + 'AG_NEWS': {0: 'World', + 1: 'Sports', + 2: 'Business', + 3: 'Sci/Tech'}, + 'SogouNews': {0: 'Sports', + 1: 'Finance', + 2: 'Entertainment', + 3: 'Automobile', + 4: 'Technology'}, + 'DBpedia': {0: 'Company', + 1: 'EducationalInstitution', + 2: 'Artist', + 3: 'Athlete', + 4: 'OfficeHolder', + 5: 'MeanOfTransportation', + 6: 'Building', + 7: 'NaturalPlace', + 8: 'Village', + 9: 'Animal', + 10: 'Plant', + 11: 'Album', + 12: 'Film', + 13: 'WrittenWork'}, + 'YelpReviewPolarity': {0: 'Negative polarity', + 1: 'Positive polarity'}, + 'YelpReviewFull': {0: 'score 1', + 1: 'score 2', + 2: 'score 3', + 3: 'score 4', + 4: 'score 5'}, + 'YahooAnswers': {0: 'Society & Culture', + 1: 'Science & Mathematics', + 2: 'Health', + 3: 'Education & Reference', + 4: 'Computers & Internet', + 5: 'Sports', + 6: 'Business & Finance', + 7: 'Entertainment & Music', + 8: 'Family & Relationships', + 9: 'Politics & Government'}, + 'AmazonReviewPolarity': {0: 'Negative polarity', + 1: 'Positive polarity'}, + 'AmazonReviewFull': {0: 'score 1', + 1: 'score 2', + 2: 'score 3', + 3: 'score 4', + 4: 'score 5'} } From 041f5a5d87b39aa6f68fdb2b81712bedd727cfe4 Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Tue, 21 Apr 2020 11:14:02 -0400 Subject: [PATCH 003/719] Text classification datasets with new torchtext dataset abstraction (#701) --- .flake8 | 2 +- docs/source/experimental_datasets.rst | 77 ++- examples/vocab/vocab.py | 0 torchtext/data/utils.py | 1 + torchtext/experimental/datasets/__init__.py | 14 +- .../experimental/datasets/raw/__init__.py | 13 + .../datasets/raw/text_classification.py | 267 ++++++++ .../datasets/text_classification.py | 570 +++++++++++++++--- 8 files changed, 871 insertions(+), 73 deletions(-) mode change 100644 => 100755 examples/vocab/vocab.py create mode 100644 torchtext/experimental/datasets/raw/__init__.py create mode 100644 torchtext/experimental/datasets/raw/text_classification.py diff --git a/.flake8 b/.flake8 index 022e85ee84..50ecc8aa11 100644 --- a/.flake8 +++ b/.flake8 @@ -1,4 +1,4 @@ [flake8] -ignore = E402,E722,W503,W504 +ignore = E402,E722,W503,W504,F821 max-line-length = 120 exclude = docs/source diff --git a/docs/source/experimental_datasets.rst b/docs/source/experimental_datasets.rst index f37bdae0d8..d9d59539d0 100644 --- a/docs/source/experimental_datasets.rst +++ b/docs/source/experimental_datasets.rst @@ -35,7 +35,82 @@ IMDb .. autoclass:: IMDB :members: __init__ - + + +Text Classification +^^^^^^^^^^^^^^^^^^^ + +TextClassificationDataset +~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: TextClassificationDataset + :members: __init__ + +AG_NEWS +~~~~~~ + +AG_NEWS dataset is subclass of ``TextClassificationDataset`` class. + +.. autoclass:: AG_NEWS + :members: __init__ + +SogouNews +~~~~~~~~ + +SogouNews dataset is subclass of ``TextClassificationDataset`` class. + +.. autoclass:: SogouNews + :members: __init__ + +DBpedia +~~~~~~ + +DBpedia dataset is subclass of ``TextClassificationDataset`` class. + +.. autoclass:: DBpedia + :members: __init__ + +YelpReviewPolarity +~~~~~~~~~~~~~~~~~ + +YelpReviewPolarity dataset is subclass of ``TextClassificationDataset`` class. + +.. autoclass:: YelpReviewPolarity + :members: __init__ + +YelpReviewFull +~~~~~~~~~~~~~ + +YelpReviewFull dataset is subclass of ``TextClassificationDataset`` class. + +.. autoclass:: YelpReviewFull + :members: __init__ + +YahooAnswers +~~~~~~~~~~~ + +YahooAnswers dataset is subclass of ``TextClassificationDataset`` class. + +.. autoclass:: YahooAnswers + :members: __init__ + +AmazonReviewPolarity +~~~~~~~~~~~~~~~~~~~ + +AmazonReviewPolarity dataset is subclass of ``TextClassificationDataset`` class. + +.. autoclass:: AmazonReviewPolarity + :members: __init__ + +AmazonReviewFull +~~~~~~~~~~~~~~~ + +AmazonReviewFull dataset is subclass of ``TextClassificationDataset`` class. + +.. autoclass:: AmazonReviewFull + :members: __init__ + + Language Modeling ^^^^^^^^^^^^^^^^^ diff --git a/examples/vocab/vocab.py b/examples/vocab/vocab.py old mode 100644 new mode 100755 diff --git a/torchtext/data/utils.py b/torchtext/data/utils.py index 2e310c9785..eea151ab69 100644 --- a/torchtext/data/utils.py +++ b/torchtext/data/utils.py @@ -7,6 +7,7 @@ def _split_tokenizer(x): + # type: (str) -> List[str] return x.split() diff --git a/torchtext/experimental/datasets/__init__.py b/torchtext/experimental/datasets/__init__.py index e53f668a98..ac2faa423b 100644 --- a/torchtext/experimental/datasets/__init__.py +++ b/torchtext/experimental/datasets/__init__.py @@ -1,8 +1,18 @@ from .language_modeling import LanguageModelingDataset, WikiText2, WikiText103, PennTreebank # NOQA -from .text_classification import IMDB +from .text_classification import AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, \ + YelpReviewFull, YahooAnswers, \ + AmazonReviewPolarity, AmazonReviewFull, IMDB __all__ = ['LanguageModelingDataset', 'WikiText2', 'WikiText103', 'PennTreebank', - 'IMDB'] + 'IMDB', + 'AG_NEWS', + 'SogouNews', + 'DBpedia', + 'YelpReviewPolarity', + 'YelpReviewFull', + 'YahooAnswers', + 'AmazonReviewPolarity', + 'AmazonReviewFull'] diff --git a/torchtext/experimental/datasets/raw/__init__.py b/torchtext/experimental/datasets/raw/__init__.py new file mode 100644 index 0000000000..61accbe2a1 --- /dev/null +++ b/torchtext/experimental/datasets/raw/__init__.py @@ -0,0 +1,13 @@ +from .text_classification import AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, \ + YelpReviewFull, YahooAnswers, \ + AmazonReviewPolarity, AmazonReviewFull, IMDB + +__all__ = ['IMDB', + 'AG_NEWS', + 'SogouNews', + 'DBpedia', + 'YelpReviewPolarity', + 'YelpReviewFull', + 'YahooAnswers', + 'AmazonReviewPolarity', + 'AmazonReviewFull'] diff --git a/torchtext/experimental/datasets/raw/text_classification.py b/torchtext/experimental/datasets/raw/text_classification.py new file mode 100644 index 0000000000..fa65abfcdd --- /dev/null +++ b/torchtext/experimental/datasets/raw/text_classification.py @@ -0,0 +1,267 @@ +import torch +import io +from torchtext.utils import download_from_url, extract_archive, unicode_csv_reader +import sys + +URLS = { + 'AG_NEWS': + 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbUDNpeUdjb0wxRms', + 'SogouNews': + 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbUkVqNEszd0pHaFE', + 'DBpedia': + 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k', + 'YelpReviewPolarity': + 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbNUpYQ2N3SGlFaDg', + 'YelpReviewFull': + 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0', + 'YahooAnswers': + 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9Qhbd2JNdDBsQUdocVU', + 'AmazonReviewPolarity': + 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM', + 'AmazonReviewFull': + 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZVhsUnRWRDhETzA', + 'IMDB': + 'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz' +} + + +def _create_data_from_csv(data_path): + with io.open(data_path, encoding="utf8") as f: + reader = unicode_csv_reader(f) + for row in reader: + yield int(row[0]), ' '.join(row[1:]) + + +class RawTextIterableDataset(torch.utils.data.IterableDataset): + """Defines an abstraction for raw text iterable datasets. + """ + + def __init__(self, iterator): + """Initiate text-classification dataset. + """ + super(RawTextIterableDataset, self).__init__() + self._iterator = iterator + self.has_setup = False + self.start = 0 + self.num_lines = None + + def setup_iter(self, start=0, num_lines=None): + self.start = start + self.num_lines = num_lines + self.has_setup = True + + def __iter__(self): + if not self.has_setup: + self.setup_iter() + + for i, item in enumerate(self._iterator): + if i == self.start: + break + + num_lines = self.num_lines if self.num_lines is not None else sys.maxsize + for _ in range(num_lines): + yield item + try: + item = next(self._iterator) + except StopIteration: + break + + def get_iterator(self): + return self._iterator + + +def _setup_datasets(dataset_name, root='.data'): + dataset_tar = download_from_url(URLS[dataset_name], root=root) + extracted_files = extract_archive(dataset_tar) + + for fname in extracted_files: + if fname.endswith('train.csv'): + train_csv_path = fname + if fname.endswith('test.csv'): + test_csv_path = fname + + train_iter = _create_data_from_csv(train_csv_path) + test_iter = _create_data_from_csv(test_csv_path) + return (RawTextIterableDataset(train_iter), + RawTextIterableDataset(test_iter)) + + +def AG_NEWS(*args, **kwargs): + """ Defines AG_NEWS datasets. + + Create supervised learning dataset: AG_NEWS + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> train, test = torchtext.experimental.datasets.raw.AG_NEWS() + """ + + return _setup_datasets(*(("AG_NEWS",) + args), **kwargs) + + +def SogouNews(*args, **kwargs): + """ Defines SogouNews datasets. + + Create supervised learning dataset: SogouNews + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> train, test = torchtext.experimental.datasets.raw.SogouNews() + """ + + return _setup_datasets(*(("SogouNews",) + args), **kwargs) + + +def DBpedia(*args, **kwargs): + """ Defines DBpedia datasets. + + Create supervised learning dataset: DBpedia + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> train, test = torchtext.experimental.datasets.raw.DBpedia() + """ + + return _setup_datasets(*(("DBpedia",) + args), **kwargs) + + +def YelpReviewPolarity(*args, **kwargs): + """ Defines YelpReviewPolarity datasets. + + Create supervised learning dataset: YelpReviewPolarity + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> train, test = torchtext.experimental.datasets.raw.YelpReviewPolarity() + """ + + return _setup_datasets(*(("YelpReviewPolarity",) + args), **kwargs) + + +def YelpReviewFull(*args, **kwargs): + """ Defines YelpReviewFull datasets. + + Create supervised learning dataset: YelpReviewFull + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> train, test = torchtext.experimental.datasets.raw.YelpReviewFull() + """ + + return _setup_datasets(*(("YelpReviewFull",) + args), **kwargs) + + +def YahooAnswers(*args, **kwargs): + """ Defines YahooAnswers datasets. + + Create supervised learning dataset: YahooAnswers + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> train, test = torchtext.experimental.datasets.raw.YahooAnswers() + """ + + return _setup_datasets(*(("YahooAnswers",) + args), **kwargs) + + +def AmazonReviewPolarity(*args, **kwargs): + """ Defines AmazonReviewPolarity datasets. + + Create supervised learning dataset: AmazonReviewPolarity + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> train, test = torchtext.experimental.datasets.raw.AmazonReviewPolarity() + """ + + return _setup_datasets(*(("AmazonReviewPolarity",) + args), **kwargs) + + +def AmazonReviewFull(*args, **kwargs): + """ Defines AmazonReviewFull datasets. + + Create supervised learning dataset: AmazonReviewFull + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> train, test = torchtext.experimental.datasets.raw.AmazonReviewFull() + """ + + return _setup_datasets(*(("AmazonReviewFull",) + args), **kwargs) + + +def generate_imdb_data(key, extracted_files): + for fname in extracted_files: + if 'urls' in fname: + continue + elif key in fname and ('pos' in fname or 'neg' in fname): + with io.open(fname, encoding="utf8") as f: + label = 1 if 'pos' in fname else 0 + yield label, f.read() + + +def IMDB(root='.data'): + """ Defines IMDB datasets. + + Create supervised learning dataset: IMDB + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> train, test = torchtext.experimental.datasets.raw.IMDB() + """ + + dataset_tar = download_from_url(URLS['IMDB'], root=root) + extracted_files = extract_archive(dataset_tar) + train_iter = generate_imdb_data('train', extracted_files) + test_iter = generate_imdb_data('test', extracted_files) + return (RawTextIterableDataset(train_iter), + RawTextIterableDataset(test_iter)) + + +DATASETS = { + 'AG_NEWS': AG_NEWS, + 'SogouNews': SogouNews, + 'DBpedia': DBpedia, + 'YelpReviewPolarity': YelpReviewPolarity, + 'YelpReviewFull': YelpReviewFull, + 'YahooAnswers': YahooAnswers, + 'AmazonReviewPolarity': AmazonReviewPolarity, + 'AmazonReviewFull': AmazonReviewFull, + 'IMDB': IMDB +} diff --git a/torchtext/experimental/datasets/text_classification.py b/torchtext/experimental/datasets/text_classification.py index eac97e3d6e..fd6e2ad8f3 100644 --- a/torchtext/experimental/datasets/text_classification.py +++ b/torchtext/experimental/datasets/text_classification.py @@ -1,93 +1,469 @@ -import logging import torch -import io -from torchtext.utils import download_from_url, extract_archive -from torchtext.data.utils import ngrams_iterator from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator -from torchtext.vocab import Vocab -from torchtext.datasets import TextClassificationDataset +from torchtext.experimental.datasets import raw -URLS = { - 'IMDB': - 'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz' -} +def vocab_func(vocab): + def _forward(tok_iter): + return [vocab[tok] for tok in tok_iter] + return _forward -def _create_data_from_iterator(vocab, iterator, removed_tokens): - for cls, tokens in iterator: - yield cls, iter(map(lambda x: vocab[x], - filter(lambda x: x not in removed_tokens, tokens))) +def totensor(dtype): + def _forward(ids_list): + return torch.tensor(ids_list).to(dtype) + return _forward -def _imdb_iterator(key, extracted_files, tokenizer, ngrams, yield_cls=False): - for fname in extracted_files: - if 'urls' in fname: - continue - elif key in fname and ('pos' in fname or 'neg' in fname): - with io.open(fname, encoding="utf8") as f: - label = 1 if 'pos' in fname else 0 - if yield_cls: - yield label, ngrams_iterator(tokenizer(f.read()), ngrams) - else: - yield ngrams_iterator(tokenizer(f.read()), ngrams) +def ngrams_func(ngrams): + def _forward(token_list): + _token_list = [] + for _i in range(ngrams + 1): + _token_list += zip(*[token_list[i:] for i in range(_i)]) + return [' '.join(x) for x in _token_list] + return _forward -def _generate_data_iterators(dataset_name, root, ngrams, tokenizer, data_select): - if not tokenizer: - tokenizer = get_tokenizer("basic_english") - if not set(data_select).issubset(set(('train', 'test'))): - raise TypeError('Given data selection {} is not supported!'.format(data_select)) +def build_vocab(data, transforms): + tok_list = [] + for (label, txt) in data: + tok_list.append(transforms(txt)) + return build_vocab_from_iterator(tok_list) + + +def squential_transforms(*transforms): + def _forward(txt_input): + for transform in transforms: + txt_input = transform(txt_input) + return txt_input + return _forward + + +class TextClassificationDataset(torch.utils.data.Dataset): + """Defines an abstract text classification datasets. + Currently, we only support the following datasets: + - AG_NEWS + - SogouNews + - DBpedia + - YelpReviewPolarity + - YelpReviewFull + - YahooAnswers + - AmazonReviewPolarity + - AmazonReviewFull + """ + + def __init__(self, data, vocab, transforms): + """Initiate text-classification dataset. - dataset_tar = download_from_url(URLS[dataset_name], root=root) - extracted_files = extract_archive(dataset_tar) + Arguments: + data: a list of label and text tring tuple. label is an integer. + [(label1, text1), (label2, text2), (label2, text3)] + vocab: Vocabulary object used for dataset. + transforms: a tuple of label and text string transforms. + """ - iters_group = {} - if 'train' in data_select: - iters_group['vocab'] = _imdb_iterator('train', extracted_files, - tokenizer, ngrams) - for item in data_select: - iters_group[item] = _imdb_iterator(item, extracted_files, - tokenizer, ngrams, yield_cls=True) - return iters_group + super(TextClassificationDataset, self).__init__() + self.data = data + self.vocab = vocab + self.transforms = transforms # (label_transforms, tokens_transforms) + + def __getitem__(self, i): + label = self.data[i][0] + txt = self.data[i][1] + return (self.transforms[0](label), self.transforms[1](txt)) + + def __len__(self): + return len(self.data) + + def get_labels(self): + labels = [] + for item in self.data: + label = item[0] + labels.apppend(self.transforms[0](label)) + return set(labels) + + def get_vocab(self): + return self.vocab def _setup_datasets(dataset_name, root='.data', ngrams=1, vocab=None, - removed_tokens=[], tokenizer=None, - data_select=('train', 'test')): + tokenizer=None, data_select=('train', 'test')): + text_transform = [] + if tokenizer is None: + tokenizer = get_tokenizer('basic_english') + text_transform = squential_transforms(tokenizer, ngrams_func(ngrams)) if isinstance(data_select, str): data_select = [data_select] - - iters_group = _generate_data_iterators(dataset_name, root, ngrams, - tokenizer, data_select) + if not set(data_select).issubset(set(('train', 'test'))): + raise TypeError('Given data selection {} is not supported!'.format(data_select)) + train, test = DATASETS[dataset_name](root=root) + # Cache raw text iterable dataset + raw_data = {'train': [(label, txt) for (label, txt) in train], + 'test': [(label, txt) for (label, txt) in test]} if vocab is None: - if 'vocab' not in iters_group.keys(): + if 'train' not in data_select: raise TypeError("Must pass a vocab if train is not selected.") - logging.info('Building Vocab based on train data') - vocab = build_vocab_from_iterator(iters_group['vocab']) - else: - if not isinstance(vocab, Vocab): - raise TypeError("Passed vocabulary is not of type Vocab") - logging.info('Vocab has {} entries'.format(len(vocab))) - - data = {} - for item in data_select: - data[item] = {} - data[item]['data'] = [] - data[item]['labels'] = [] - logging.info('Creating {} data'.format(item)) - data_iter = _create_data_from_iterator(vocab, iters_group[item], removed_tokens) - for cls, tokens in data_iter: - data[item]['data'].append((torch.tensor(cls), - torch.tensor([token_id for token_id in tokens]))) - data[item]['labels'].append(cls) - data[item]['labels'] = set(data[item]['labels']) - - return tuple(TextClassificationDataset(vocab, data[item]['data'], - data[item]['labels']) for item in data_select) + vocab = build_vocab(raw_data['train'], text_transform) + text_transform = squential_transforms(text_transform, vocab_func(vocab), + totensor(dtype=torch.long)) + label_transform = squential_transforms(totensor(dtype=torch.long)) + return tuple(TextClassificationDataset(raw_data[item], vocab, + (label_transform, text_transform)) + for item in data_select) + + +def AG_NEWS(*args, **kwargs): + """ Defines AG_NEWS datasets. + The labels includes: + - 1 : World + - 2 : Sports + - 3 : Business + - 4 : Sci/Tech + + Create text classification dataset: AG_NEWS + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + ngrams: a contiguous sequence of n items from s string text. + Default: 1 + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well. A custom tokenizer is callable + function with input of a string and output of a token list. + data_select: a string or tuple for the returned datasets + (Default: ('train', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets import AG_NEWS + >>> from torchtext.data.utils import get_tokenizer + >>> train, test = AG_NEWS(ngrams=3) + >>> tokenizer = get_tokenizer("spacy") + >>> train, test = AG_NEWS(tokenizer=tokenizer) + >>> train, = AG_NEWS(tokenizer=tokenizer, data_select='train') + + """ + + return _setup_datasets(*(('AG_NEWS',) + args), **kwargs) + + +def SogouNews(*args, **kwargs): + """ Defines SogouNews datasets. + The labels includes: + - 1 : Sports + - 2 : Finance + - 3 : Entertainment + - 4 : Automobile + - 5 : Technology + + Create text classification dataset: SogouNews + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + ngrams: a contiguous sequence of n items from s string text. + Default: 1 + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well. A custom tokenizer is callable + function with input of a string and output of a token list. + data_select: a string or tuple for the returned datasets + (Default: ('train', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets import SogouNews + >>> from torchtext.data.utils import get_tokenizer + >>> train, test = SogouNews(ngrams=3) + >>> tokenizer = get_tokenizer("spacy") + >>> train, test = SogouNews(tokenizer=tokenizer) + >>> train, = SogouNews(tokenizer=tokenizer, data_select='train') + + """ + + return _setup_datasets(*(("SogouNews",) + args), **kwargs) + + +def DBpedia(*args, **kwargs): + """ Defines DBpedia datasets. + The labels includes: + - 1 : Company + - 2 : EducationalInstitution + - 3 : Artist + - 4 : Athlete + - 5 : OfficeHolder + - 6 : MeanOfTransportation + - 7 : Building + - 8 : NaturalPlace + - 9 : Village + - 10 : Animal + - 11 : Plant + - 12 : Album + - 13 : Film + - 14 : WrittenWork + + Create text classification dataset: DBpedia + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + ngrams: a contiguous sequence of n items from s string text. + Default: 1 + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well. A custom tokenizer is callable + function with input of a string and output of a token list. + data_select: a string or tuple for the returned datasets + (Default: ('train', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets import DBpedia + >>> from torchtext.data.utils import get_tokenizer + >>> train, test = DBpedia(ngrams=3) + >>> tokenizer = get_tokenizer("spacy") + >>> train, test = DBpedia(tokenizer=tokenizer) + >>> train, = DBpedia(tokenizer=tokenizer, data_select='train') + + """ + + return _setup_datasets(*(("DBpedia",) + args), **kwargs) + + +def YelpReviewPolarity(*args, **kwargs): + """ Defines YelpReviewPolarity datasets. + The labels includes: + - 1 : Negative polarity. + - 2 : Positive polarity. + + Create text classification dataset: YelpReviewPolarity + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + ngrams: a contiguous sequence of n items from s string text. + Default: 1 + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well. A custom tokenizer is callable + function with input of a string and output of a token list. + data_select: a string or tuple for the returned datasets + (Default: ('train', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets import YelpReviewPolarity + >>> from torchtext.data.utils import get_tokenizer + >>> train, test = YelpReviewPolarity(ngrams=3) + >>> tokenizer = get_tokenizer("spacy") + >>> train, test = YelpReviewPolarity(tokenizer=tokenizer) + >>> train, = YelpReviewPolarity(tokenizer=tokenizer, data_select='train') + + """ + + return _setup_datasets(*(("YelpReviewPolarity",) + args), **kwargs) + + +def YelpReviewFull(*args, **kwargs): + """ Defines YelpReviewFull datasets. + The labels includes: + 1 - 5 : rating classes (5 is highly recommended). + + Create text classification dataset: YelpReviewFull + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + ngrams: a contiguous sequence of n items from s string text. + Default: 1 + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well. A custom tokenizer is callable + function with input of a string and output of a token list. + data_select: a string or tuple for the returned datasets + (Default: ('train', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets import YelpReviewFull + >>> from torchtext.data.utils import get_tokenizer + >>> train, test = YelpReviewFull(ngrams=3) + >>> tokenizer = get_tokenizer("spacy") + >>> train, test = YelpReviewFull(tokenizer=tokenizer) + >>> train, = YelpReviewFull(tokenizer=tokenizer, data_select='train') + + """ + + return _setup_datasets(*(("YelpReviewFull",) + args), **kwargs) + + +def YahooAnswers(*args, **kwargs): + """ Defines YahooAnswers datasets. + The labels includes: + - 1 : Society & Culture + - 2 : Science & Mathematics + - 3 : Health + - 4 : Education & Reference + - 5 : Computers & Internet + - 6 : Sports + - 7 : Business & Finance + - 8 : Entertainment & Music + - 9 : Family & Relationships + - 10 : Politics & Government + + Create text classification dataset: YahooAnswers + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + ngrams: a contiguous sequence of n items from s string text. + Default: 1 + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well. A custom tokenizer is callable + function with input of a string and output of a token list. + data_select: a string or tuple for the returned datasets + (Default: ('train', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets import YahooAnswers + >>> from torchtext.data.utils import get_tokenizer + >>> train, test = YahooAnswers(ngrams=3) + >>> tokenizer = get_tokenizer("spacy") + >>> train, test = YahooAnswers(tokenizer=tokenizer) + >>> train, = YahooAnswers(tokenizer=tokenizer, data_select='train') + + """ + + return _setup_datasets(*(("YahooAnswers",) + args), **kwargs) + + +def AmazonReviewPolarity(*args, **kwargs): + """ Defines AmazonReviewPolarity datasets. + The labels includes: + - 1 : Negative polarity + - 2 : Positive polarity + + Create text classification dataset: AmazonReviewPolarity + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + ngrams: a contiguous sequence of n items from s string text. + Default: 1 + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well. A custom tokenizer is callable + function with input of a string and output of a token list. + data_select: a string or tuple for the returned datasets + (Default: ('train', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets import AmazonReviewPolarity + >>> from torchtext.data.utils import get_tokenizer + >>> train, test = AmazonReviewPolarity(ngrams=3) + >>> tokenizer = get_tokenizer("spacy") + >>> train, test = AmazonReviewPolarity(tokenizer=tokenizer) + >>> train, = AmazonReviewPolarity(tokenizer=tokenizer, data_select='train') + + """ + + return _setup_datasets(*(("AmazonReviewPolarity",) + args), **kwargs) + + +def AmazonReviewFull(*args, **kwargs): + """ Defines AmazonReviewFull datasets. + The labels includes: + 1 - 5 : rating classes (5 is highly recommended) + + Create text classification dataset: AmazonReviewFull + + Separately returns the training and test dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + ngrams: a contiguous sequence of n items from s string text. + Default: 1 + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well. A custom tokenizer is callable + function with input of a string and output of a token list. + data_select: a string or tuple for the returned datasets + (Default: ('train', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets import AmazonReviewFull + >>> from torchtext.data.utils import get_tokenizer + >>> train, test = AmazonReviewFull(ngrams=3) + >>> tokenizer = get_tokenizer("spacy") + >>> train, test = AmazonReviewFull(tokenizer=tokenizer) + >>> train, = AmazonReviewFull(tokenizer=tokenizer, data_select='train') + + """ + + return _setup_datasets(*(("AmazonReviewFull",) + args), **kwargs) def IMDB(*args, **kwargs): @@ -126,17 +502,73 @@ def IMDB(*args, **kwargs): >>> tokenizer = get_tokenizer("spacy") >>> train, test = IMDB(tokenizer=tokenizer) >>> train, = IMDB(tokenizer=tokenizer, data_select='train') + """ return _setup_datasets(*(("IMDB",) + args), **kwargs) DATASETS = { - 'IMDB': IMDB + 'AG_NEWS': raw.AG_NEWS, + 'SogouNews': raw.SogouNews, + 'DBpedia': raw.DBpedia, + 'YelpReviewPolarity': raw.YelpReviewPolarity, + 'YelpReviewFull': raw.YelpReviewFull, + 'YahooAnswers': raw.YahooAnswers, + 'AmazonReviewPolarity': raw.AmazonReviewPolarity, + 'AmazonReviewFull': raw.AmazonReviewFull, + 'IMDB': raw.IMDB } LABELS = { + 'AG_NEWS': {1: 'World', + 2: 'Sports', + 3: 'Business', + 4: 'Sci/Tech'}, + 'SogouNews': {1: 'Sports', + 2: 'Finance', + 3: 'Entertainment', + 4: 'Automobile', + 5: 'Technology'}, + 'DBpedia': {1: 'Company', + 2: 'EducationalInstitution', + 3: 'Artist', + 4: 'Athlete', + 5: 'OfficeHolder', + 6: 'MeanOfTransportation', + 7: 'Building', + 8: 'NaturalPlace', + 9: 'Village', + 10: 'Animal', + 11: 'Plant', + 12: 'Album', + 13: 'Film', + 14: 'WrittenWork'}, + 'YelpReviewPolarity': {1: 'Negative polarity', + 2: 'Positive polarity'}, + 'YelpReviewFull': {1: 'score 1', + 2: 'score 2', + 3: 'score 3', + 4: 'score 4', + 5: 'score 5'}, + 'YahooAnswers': {1: 'Society & Culture', + 2: 'Science & Mathematics', + 3: 'Health', + 4: 'Education & Reference', + 5: 'Computers & Internet', + 6: 'Sports', + 7: 'Business & Finance', + 8: 'Entertainment & Music', + 9: 'Family & Relationships', + 10: 'Politics & Government'}, + 'AmazonReviewPolarity': {1: 'Negative polarity', + 2: 'Positive polarity'}, + 'AmazonReviewFull': {1: 'score 1', + 2: 'score 2', + 3: 'score 3', + 4: 'score 4', + 5: 'score 5'}, 'IMDB': {0: 'Negative', 1: 'Positive'} } From eb6d6e4b20e7229412654159b9ecbaeb3fc7adae Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Tue, 21 Apr 2020 14:05:35 -0400 Subject: [PATCH 004/719] Remove Duplicate explicit target name in README.rst (#735) --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 9c2a066823..55d29f2359 100644 --- a/README.rst +++ b/README.rst @@ -15,7 +15,7 @@ This repository consists of: * `torchtext.data <#data>`_: Generic data loaders, abstractions, and iterators for text (including vocabulary and word vectors) * `torchtext.datasets <#datasets>`_: Pre-built loaders for common NLP datasets -Note: we are currently re-designing the torchtext library to make it more compatible with pytorch (e.g. ``torch.utils.data``). Several datasets have been written with the new abstractions in `torchtext.experimental `_ folder. We also created an issue to discuss the new abstraction, and users are welcome to leave feedback `here `_. +Note: we are currently re-designing the torchtext library to make it more compatible with pytorch (e.g. ``torch.utils.data``). Several datasets have been written with the new abstractions in `torchtext.experimental `_ folder. We also created an issue to discuss the new abstraction, and users are welcome to leave feedback `link `_. Installation From 0818a554427b49f7f603e1968093f39afdb8864e Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Thu, 23 Apr 2020 14:17:21 -0400 Subject: [PATCH 005/719] Add explicit test results to text classification datasets (#738) --- test/data/test_builtin_datasets.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/test/data/test_builtin_datasets.py b/test/data/test_builtin_datasets.py index 6507c9eb03..b2723949b2 100644 --- a/test/data/test_builtin_datasets.py +++ b/test/data/test_builtin_datasets.py @@ -2,7 +2,8 @@ import shutil import torchtext.data as data from torchtext.datasets import AG_NEWS - +import torch +from torch.testing import assert_allclose from ..common.test_markers import slow from ..common.torchtext_test_case import TorchtextTestCase @@ -98,6 +99,10 @@ def test_text_classification(self): ag_news_train, ag_news_test = AG_NEWS(root=datadir, ngrams=3) self.assertEqual(len(ag_news_train), 120000) self.assertEqual(len(ag_news_test), 7600) + assert_allclose(ag_news_train[-1][1][:10], + torch.tensor([3525, 319, 4053, 34, 5407, 3607, 70, 6798, 10599, 4053]).long()) + assert_allclose(ag_news_test[-1][1][:10], + torch.tensor([2351, 758, 96, 38581, 2351, 220, 5, 396, 3, 14786]).long()) # Delete the dataset after we're done to save disk space on CI datafile = os.path.join(self.project_root, ".data", "ag_news_csv") @@ -113,6 +118,14 @@ def test_imdb(self): train_dataset, test_dataset = IMDB() self.assertEqual(len(train_dataset), 25000) self.assertEqual(len(test_dataset), 25000) + assert_allclose(train_dataset[0][1][:10], + torch.tensor([13, 1568, 13, 246, 35468, 43, 64, 398, 1135, 92]).long()) + assert_allclose(train_dataset[-1][1][:10], + torch.tensor([2, 71, 4555, 194, 3328, 15144, 42, 227, 148, 8]).long()) + assert_allclose(test_dataset[0][1][:10], + torch.tensor([13, 125, 1051, 5, 246, 1652, 8, 277, 66, 20]).long()) + assert_allclose(test_dataset[-1][1][:10], + torch.tensor([13, 1035, 14, 21, 28, 2, 1051, 1275, 1008, 3]).long()) # Test API with a vocab input object old_vocab = train_dataset.get_vocab() From 8833b970df08b1b0ec18b774cd0047c4160a9f84 Mon Sep 17 00:00:00 2001 From: Hugo van Kemenade Date: Fri, 24 Apr 2020 16:49:09 +0300 Subject: [PATCH 006/719] Drop support for EOL Python 2 (#732) --- .travis.yml | 4 ++-- README.rst | 2 +- build_tools/conda/torchtext/meta.yaml | 1 - requirements.txt | 3 --- setup.py | 11 ++++++++++- test/data/test_batch.py | 1 - test/data/test_dataset.py | 4 +--- test/data/test_field.py | 1 - test/data/test_functional.py | 16 ++++------------ test/data/test_pipeline.py | 10 ++++------ test/data/test_subword.py | 4 ---- test/data/test_utils.py | 5 ++--- test/test_vocab.py | 3 +-- torchtext/data/example.py | 3 +-- torchtext/data/field.py | 13 ++++--------- torchtext/data/iterator.py | 2 -- torchtext/data/pipeline.py | 3 +-- torchtext/utils.py | 12 ++---------- torchtext/vocab.py | 12 +++--------- 19 files changed, 36 insertions(+), 74 deletions(-) diff --git a/.travis.yml b/.travis.yml index 1c8f2707aa..f783feaed2 100644 --- a/.travis.yml +++ b/.travis.yml @@ -7,8 +7,8 @@ cache: - /home/travis/download - /home/travis/.cache/pip -# This matrix tests that the code works on Python 2.7, -# 3.5, 3.6 (same versions as PyTorch CI), and passes lint. +# This matrix tests that the code works on Python 3.5, +# 3.6 (same versions as PyTorch CI), and passes lint. matrix: fast_finish: true include: diff --git a/README.rst b/README.rst index 55d29f2359..a79db166c0 100644 --- a/README.rst +++ b/README.rst @@ -22,7 +22,7 @@ Installation ============ -Make sure you have Python 2.7 or 3.5+ and PyTorch 0.4.0 or newer. You can then install torchtext using pip:: +Make sure you have Python 3.5+ and PyTorch 0.4.0 or newer. You can then install torchtext using pip:: pip install torchtext diff --git a/build_tools/conda/torchtext/meta.yaml b/build_tools/conda/torchtext/meta.yaml index 8d4c8b6898..6972e81095 100644 --- a/build_tools/conda/torchtext/meta.yaml +++ b/build_tools/conda/torchtext/meta.yaml @@ -16,7 +16,6 @@ requirements: - numpy >=1.11 - pytorch >=1.2 - requests - - six build: number: 1 diff --git a/requirements.txt b/requirements.txt index 286e4c7cbf..cc0a18140f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,9 +4,6 @@ tqdm # Downloading data and other files requests -# Legacy -six - # Optional NLP tools nltk spacy diff --git a/setup.py b/setup.py index bd56651e0f..0c0cff5b03 100644 --- a/setup.py +++ b/setup.py @@ -37,7 +37,16 @@ def find_version(*file_paths): license='BSD', install_requires=[ - 'tqdm', 'requests', 'torch', 'numpy', 'six', 'sentencepiece' + 'tqdm', 'requests', 'torch', 'numpy', 'sentencepiece' + ], + python_requires='>=3.5', + classifiers=[ + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.5', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + 'Programming Language :: Python :: 3 :: Only', ], # Package info diff --git a/test/data/test_batch.py b/test/data/test_batch.py index 57f3bad85a..76017784e9 100644 --- a/test/data/test_batch.py +++ b/test/data/test_batch.py @@ -1,4 +1,3 @@ -from __future__ import unicode_literals import torch import torchtext.data as data diff --git a/test/data/test_dataset.py b/test/data/test_dataset.py index e26c7ee664..6b3e974f75 100644 --- a/test/data/test_dataset.py +++ b/test/data/test_dataset.py @@ -1,8 +1,6 @@ # -*- coding: utf-8 -*- -from __future__ import unicode_literals import torchtext.data as data import tempfile -import six import pytest @@ -246,7 +244,7 @@ def test_csv_dataset_quotechar(self): with tempfile.NamedTemporaryFile(dir=self.test_dir) as f: for example in example_data: - f.write(six.b("{}\n".format(",".join(example)))) + f.write("{}\n".format(",".join(example)).encode("latin-1")) TEXT = data.Field(lower=True, tokenize=lambda x: x.split()) fields = { diff --git a/test/data/test_field.py b/test/data/test_field.py index 1b734b6d9b..14fe043776 100644 --- a/test/data/test_field.py +++ b/test/data/test_field.py @@ -1,5 +1,4 @@ # -*- coding: utf-8 -*- -from __future__ import unicode_literals from collections import Counter import os diff --git a/test/data/test_functional.py b/test/data/test_functional.py index 98d7557017..6c5b86d71b 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -4,7 +4,6 @@ sentencepiece_numericalizer, sentencepiece_tokenizer, \ custom_replace, simple_space_split import os -import sys class TestFunctional(TorchtextTestCase): @@ -47,17 +46,10 @@ def test_sentencepiece_tokenizer(self): self.assertEqual(len(sp_model), 20000) spm_generator = sentencepiece_tokenizer(sp_model) - # Handle byte string in Python2 and Unicode string in Python3, respectively - if sys.version_info < (3, 0): - ref_results = ['\xe2\x96\x81Sent', 'ence', 'P', 'ie', 'ce', '\xe2\x96\x81is', - '\xe2\x96\x81an', '\xe2\x96\x81un', 'super', 'vis', 'ed', - '\xe2\x96\x81text', '\xe2\x96\x81to', 'ken', 'izer', - '\xe2\x96\x81and', '\xe2\x96\x81de', 'to', 'ken', 'izer'] - else: - ref_results = ['\u2581Sent', 'ence', 'P', 'ie', 'ce', '\u2581is', - '\u2581an', '\u2581un', 'super', 'vis', 'ed', '\u2581text', - '\u2581to', 'ken', 'izer', '\u2581and', - '\u2581de', 'to', 'ken', 'izer'] + ref_results = ['\u2581Sent', 'ence', 'P', 'ie', 'ce', '\u2581is', + '\u2581an', '\u2581un', 'super', 'vis', 'ed', '\u2581text', + '\u2581to', 'ken', 'izer', '\u2581and', + '\u2581de', 'to', 'ken', 'izer'] self.assertEqual(list(spm_generator([test_sample]))[0], ref_results) diff --git a/test/data/test_pipeline.py b/test/data/test_pipeline.py index 086c04b3b4..c1eda9a757 100644 --- a/test/data/test_pipeline.py +++ b/test/data/test_pipeline.py @@ -1,6 +1,4 @@ # -*- coding: utf-8 -*- -from __future__ import unicode_literals -import six import torchtext.data as data from ..common.torchtext_test_case import TorchtextTestCase @@ -20,7 +18,7 @@ def test_pipeline(self): assert id_pipeline("ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T") == "ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T" assert id_pipeline(["1241", "Some String"]) == ["1241", "Some String"] - pipeline = data.Pipeline(six.text_type.lower) + pipeline = data.Pipeline(str.lower) assert pipeline("Test STring") == "test string" assert pipeline("ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T") == "ᑌᑎiᑕoᗪᕮ_tᕮ᙭t" assert pipeline(["1241", "Some String"]) == ["1241", "some string"] @@ -34,10 +32,10 @@ def test_composition(self): pipeline = data.Pipeline(TestPipeline.repeat_n) pipeline.add_before(id_pipeline) pipeline.add_after(id_pipeline) - pipeline.add_before(six.text_type.lower) - pipeline.add_after(six.text_type.capitalize) + pipeline.add_before(str.lower) + pipeline.add_after(str.capitalize) - other_pipeline = data.Pipeline(six.text_type.swapcase) + other_pipeline = data.Pipeline(str.swapcase) other_pipeline.add_before(pipeline) # Assert pipeline gives proper results after composition diff --git a/test/data/test_subword.py b/test/data/test_subword.py index fdbf0034d2..88e57c5044 100644 --- a/test/data/test_subword.py +++ b/test/data/test_subword.py @@ -1,14 +1,10 @@ import unittest -import pytest -import sys from torchtext import data from torchtext.datasets import TREC class TestSubword(unittest.TestCase): - @pytest.mark.skipif(sys.version_info < (3, 0), - reason="revtok currently breaks for python 2.7") def test_subword_trec(self): TEXT = data.SubwordField() LABEL = data.Field(sequential=False) diff --git a/test/data/test_utils.py b/test/data/test_utils.py index d84e6d5ad6..29716cbaaf 100644 --- a/test/data/test_utils.py +++ b/test/data/test_utils.py @@ -1,4 +1,3 @@ -import six import torchtext.data as data import pytest from ..common.torchtext_test_case import TorchtextTestCase @@ -16,7 +15,7 @@ def test_get_tokenizer_split(self): def test_get_tokenizer_spacy(self): # Test SpaCy option, and verify it properly handles punctuation. - assert data.get_tokenizer("spacy")(six.text_type(self.TEST_STR)) == [ + assert data.get_tokenizer("spacy")(str(self.TEST_STR)) == [ "A", "string", ",", "particularly", "one", "with", "slightly", "complex", "punctuation", "."] @@ -33,7 +32,7 @@ def test_get_tokenizer_moses(self): "complex", "punctuation", "."] # Nonbreaking prefixes should tokenize the final period. - assert moses_tokenizer(six.text_type("abc def.")) == ["abc", "def", "."] + assert moses_tokenizer("abc def.") == ["abc", "def", "."] def test_get_tokenizer_toktokt(self): # Test Toktok option. Test strings taken from NLTK doctests. diff --git a/test/test_vocab.py b/test/test_vocab.py index 051b547277..aad82de57c 100644 --- a/test/test_vocab.py +++ b/test/test_vocab.py @@ -1,5 +1,4 @@ # -*- coding: utf-8 -*- -from __future__ import unicode_literals from collections import Counter import os import pickle @@ -101,7 +100,7 @@ def test_vocab_download_fasttext_vectors(self): # to test string aliases. for i in range(3): if i == 2: - vectors = str("fasttext.simple.300d") # must handle str on Py2 + vectors = "fasttext.simple.300d" else: vectors = FastText(language='simple') diff --git a/torchtext/data/example.py b/torchtext/data/example.py index 68aaf9e2be..d9f96aeda3 100644 --- a/torchtext/data/example.py +++ b/torchtext/data/example.py @@ -1,4 +1,3 @@ -import six import json from functools import reduce @@ -75,7 +74,7 @@ def fromlist(cls, data, fields): ex = cls() for (name, field), val in zip(fields, data): if field is not None: - if isinstance(val, six.string_types): + if isinstance(val, str): val = val.rstrip('\n') # Handle field tuples if isinstance(name, tuple): diff --git a/torchtext/data/field.py b/torchtext/data/field.py index 090821b315..189331c1be 100644 --- a/torchtext/data/field.py +++ b/torchtext/data/field.py @@ -1,7 +1,6 @@ # coding: utf8 from collections import Counter, OrderedDict from itertools import chain -import six import torch from tqdm import tqdm @@ -204,17 +203,13 @@ def __eq__(self, other): def preprocess(self, x): """Load a single example using this field, tokenizing if necessary. - If the input is a Python 2 `str`, it will be converted to Unicode - first. If `sequential=True`, it will be tokenized. Then the input + If `sequential=True`, the input will be tokenized. Then the input will be optionally lowercased and passed to the user-provided `preprocessing` Pipeline.""" - if (six.PY2 and isinstance(x, six.string_types) - and not isinstance(x, six.text_type)): - x = Pipeline(lambda s: six.text_type(s, encoding='utf-8'))(x) - if self.sequential and isinstance(x, six.text_type): + if self.sequential and isinstance(x, str): x = self.tokenize(x.rstrip('\n')) if self.lower: - x = Pipeline(six.text_type.lower)(x) + x = Pipeline(str.lower)(x) if self.sequential and self.use_vocab and self.stop_words is not None: x = [w for w in x if w not in self.stop_words] if self.preprocessing is not None: @@ -351,7 +346,7 @@ def numericalize(self, arr, device=None): # the data is sequential, since it's unclear how to coerce padding tokens # to a numeric type. if not self.sequential: - arr = [numericalization_func(x) if isinstance(x, six.string_types) + arr = [numericalization_func(x) if isinstance(x, str) else x for x in arr] if self.postprocessing is not None: arr = self.postprocessing(arr, None) diff --git a/torchtext/data/iterator.py b/torchtext/data/iterator.py index adae0c3171..9546cb9dac 100644 --- a/torchtext/data/iterator.py +++ b/torchtext/data/iterator.py @@ -1,5 +1,3 @@ -from __future__ import division - import math import random diff --git a/torchtext/data/pipeline.py b/torchtext/data/pipeline.py index 632736429d..f576fdc720 100644 --- a/torchtext/data/pipeline.py +++ b/torchtext/data/pipeline.py @@ -1,8 +1,7 @@ class Pipeline(object): """Defines a pipeline for transforming sequence data. - The input is assumed to be utf-8 encoded `str` (Python 3) or - `unicode` (Python 2). + The input is assumed to be utf-8 encoded `str`. Attributes: convert_token: The function to apply to input sequence data. diff --git a/torchtext/utils.py b/torchtext/utils.py index b69756c3aa..45f242f84b 100644 --- a/torchtext/utils.py +++ b/torchtext/utils.py @@ -1,4 +1,3 @@ -import six import requests import csv from tqdm import tqdm @@ -133,15 +132,8 @@ def unicode_csv_reader(unicode_csv_data, **kwargs): maxInt = int(maxInt / 10) csv.field_size_limit(maxInt) - if six.PY2: - # csv.py doesn't do Unicode; encode temporarily as UTF-8: - csv_reader = csv.reader(utf_8_encoder(unicode_csv_data), **kwargs) - for row in csv_reader: - # decode UTF-8 back to Unicode, cell by cell: - yield [cell.decode('utf-8') for cell in row] - else: - for line in csv.reader(unicode_csv_data, **kwargs): - yield line + for line in csv.reader(unicode_csv_data, **kwargs): + yield line def utf_8_encoder(unicode_csv_data): diff --git a/torchtext/vocab.py b/torchtext/vocab.py index f5e5c6b9f2..d7def4a672 100755 --- a/torchtext/vocab.py +++ b/torchtext/vocab.py @@ -1,4 +1,3 @@ -from __future__ import unicode_literals from collections import defaultdict from functools import partial import logging @@ -6,8 +5,7 @@ import zipfile import gzip -import six -from six.moves.urllib.request import urlretrieve +from urllib.request import urlretrieve import torch from tqdm import tqdm import tarfile @@ -169,9 +167,7 @@ def load_vectors(self, vectors, **kwargs): if not isinstance(vectors, list): vectors = [vectors] for idx, vector in enumerate(vectors): - if six.PY2 and isinstance(vector, str): - vector = six.text_type(vector) - if isinstance(vector, six.string_types): + if isinstance(vector, str): # Convert the string pretrained vector identifier # to a Vectors object if vector not in pretrained_aliases: @@ -406,7 +402,7 @@ def cache(self, name, cache, url=None, max_vectors=None): dim)) try: - if isinstance(word, six.binary_type): + if isinstance(word, bytes): word = word.decode('utf-8') except UnicodeDecodeError: logger.info("Skipping non-UTF8 token {}".format(repr(word))) @@ -507,8 +503,6 @@ def __getitem__(self, token): vector = torch.Tensor(1, self.dim).zero_() if token == "": return self.unk_init(vector) - # These literals need to be coerced to unicode for Python 2 compatibility - # when we try to join them with read ngrams from the files. chars = ['#BEGIN#'] + list(token) + ['#END#'] num_vectors = 0 for n in [2, 3, 4]: From f6b00dd9fecae40fb6d7cfd5a8fc8b577c476fa5 Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Sat, 25 Apr 2020 00:50:25 -0400 Subject: [PATCH 007/719] remove the allow to fail tests (#743) Co-authored-by: Guanheng Zhang --- .travis.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index f783feaed2..b450eff8a9 100644 --- a/.travis.yml +++ b/.travis.yml @@ -17,8 +17,6 @@ matrix: - env: PYTHON_VERSION="3.6" RUN_FLAKE8="true" SKIP_TESTS="true" - env: PYTHON_VERSION="3.6" RUN_SLOW="true" COVERAGE="true" sudo: required - allow_failures: - - env: PYTHON_VERSION="3.6" RUN_SLOW="true" COVERAGE="true" notifications: email: false From 528426211fe31bd05c61b7d434a254dcc1e919b6 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Wed, 29 Apr 2020 18:42:56 -0400 Subject: [PATCH 008/719] Disable Travis tests of which equivalent run on CCI (#746) --- .travis.yml | 3 --- 1 file changed, 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index b450eff8a9..0a4f524db7 100644 --- a/.travis.yml +++ b/.travis.yml @@ -12,9 +12,6 @@ cache: matrix: fast_finish: true include: - - env: PYTHON_VERSION="3.5" COVERAGE="true" - - env: PYTHON_VERSION="3.6" COVERAGE="true" - - env: PYTHON_VERSION="3.6" RUN_FLAKE8="true" SKIP_TESTS="true" - env: PYTHON_VERSION="3.6" RUN_SLOW="true" COVERAGE="true" sudo: required From 62c5f38f3080bcbbe447d7cff3479f5de4111474 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Wed, 29 Apr 2020 19:02:26 -0400 Subject: [PATCH 009/719] Run Unittest on CircleCI (#744) --- .circleci/config.yml | 306 +++++++++++++++++++++ .circleci/config.yml.in | 298 ++++++++++++++++++++ .circleci/regenerate.py | 126 +++++++++ .circleci/smoke_test/docker/Dockerfile | 36 +++ .circleci/unittest/scripts/environment.yml | 18 ++ .circleci/unittest/scripts/install.sh | 17 ++ .circleci/unittest/scripts/post_process.sh | 8 + .circleci/unittest/scripts/run_test.sh | 10 + .circleci/unittest/scripts/setup_env.sh | 38 +++ .circleci/utils/test_sort_yaml.py | 14 + 10 files changed, 871 insertions(+) create mode 100644 .circleci/config.yml create mode 100644 .circleci/config.yml.in create mode 100755 .circleci/regenerate.py create mode 100644 .circleci/smoke_test/docker/Dockerfile create mode 100644 .circleci/unittest/scripts/environment.yml create mode 100755 .circleci/unittest/scripts/install.sh create mode 100755 .circleci/unittest/scripts/post_process.sh create mode 100755 .circleci/unittest/scripts/run_test.sh create mode 100755 .circleci/unittest/scripts/setup_env.sh create mode 100755 .circleci/utils/test_sort_yaml.py diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 0000000000..c44a1bcdd2 --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,306 @@ +version: 2.1 + +# How to test the Linux jobs: +# - Install CircleCI local CLI: https://circleci.com/docs/2.0/local-cli/ +# - circleci config process .circleci/config.yml > gen.yml && circleci local execute -c gen.yml --job binary_linux_wheel_py3.8 +# - Replace binary_linux_wheel_py3.8 with the name of the job you want to test. +# Job names are 'name:' key. + +orbs: + win: circleci/windows@2.0.0 + +binary_common: &binary_common + parameters: + # Edit these defaults to do a release + build_version: + description: "version number of release binary; by default, build a nightly" + type: string + default: "" + pytorch_version: + description: "PyTorch version to build against; by default, use a nightly" + type: string + default: "" + # Don't edit these + python_version: + description: "Python version to build against (e.g., 3.8)" + type: string + environment: + PYTHON_VERSION: << parameters.python_version >> + BUILD_VERSION: << parameters.build_version >> + PYTORCH_VERSION: << parameters.pytorch_version >> + CU_VERSION: cpu + +smoke_test_common: &smoke_test_common + <<: *binary_common + docker: + - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchtext/smoke_test:d26af7d0-8458-face-book-84239b5c75c0 + aws_auth: + aws_access_key_id: ${ECR_AWS_ACCESS_KEY} + aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} + +jobs: + circleci_consistency: + docker: + - image: circleci/python:3.8 + steps: + - checkout + - run: + command: | + pip install --user --progress-bar off jinja2 pyyaml + python .circleci/regenerate.py + git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1) + + binary_linux_wheel: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - checkout + - run: packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_linux_conda: + <<: *binary_common + docker: + - image: "pytorch/conda-cuda" + resource_class: 2xlarge+ + steps: + - checkout + - run: packaging/build_conda.sh + - store_artifacts: + path: /opt/conda/conda-bld/linux-64 + - persist_to_workspace: + root: /opt/conda/conda-bld/linux-64 + paths: + - "*" + + binary_win_conda: + <<: *binary_common + executor: + name: win/default + shell: bash.exe + steps: + - checkout + - run: + command: | + choco install miniconda3 + (& "C:\tools\miniconda3\Scripts\conda.exe" "shell.powershell" "hook") | Out-String | Invoke-Expression + conda activate base + conda install -yq conda-build "conda-package-handling!=1.5.0" + bash packaging/build_conda.sh + shell: powershell.exe + + binary_macos_wheel: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - run: + # Cannot easily deduplicate this as source'ing activate + # will set environment variables which we need to propagate + # to build_wheel.sh + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_macos_conda: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - run: + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + conda install -yq conda-build + packaging/build_conda.sh + - store_artifacts: + path: /Users/distiller/miniconda3/conda-bld/osx-64 + - persist_to_workspace: + root: /Users/distiller/miniconda3/conda-bld/osx-64 + paths: + - "*" + + # Requires org-member context + binary_conda_upload: + docker: + - image: continuumio/miniconda + steps: + - attach_workspace: + at: ~/workspace + - run: + command: | + # Prevent credential from leaking + conda install -yq anaconda-client + set -x + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u pytorch-nightly --label main --no-progress --force + + # Requires org-member context + binary_wheel_upload: + docker: + - image: circleci/python:3.8 + steps: + - attach_workspace: + at: ~/workspace + - checkout + - run: + command: | + pip install --user awscli + export PATH="$HOME/.local/bin:$PATH" + # Prevent credential from leaking + set +x + export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}" + export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}" + set -x + for pkg in ~/workspace/*.whl; do + aws s3 cp "$pkg" "s3://pytorch/whl/nightly/" --acl public-read + done + + smoke_test_linux_conda: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-nightly pytorch + conda install -v -y $(ls ~/workspace/torchtext*.tar.bz2) + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchtext" + + smoke_test_linux_pip: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchtext*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchtext" + + smoke_test_docker_image_build: + machine: + image: ubuntu-1604:201903-01 + resource_class: large + environment: + image_name: torchtext/smoke_test + steps: + - checkout + - run: + name: build_docker image + no_output_timeout: "1h" + command: | + cd .circleci/smoke_test/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} + - run: + name: upload docker image + no_output_timeout: "1h" + command: | + set +x + export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} + export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} + eval $(aws ecr get-login --region us-east-1 --no-include-email) + set -x + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + + unittest_linux: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - checkout + - restore_cache: + + keys: + - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }} + + - run: + name: Setup + command: .circleci/unittest/scripts/setup_env.sh + - save_cache: + + key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }} + + paths: + - conda + - env + - run: + name: Install torchtext + command: .circleci/unittest/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/scripts/post_process.sh + - store_test_results: + path: test-results + + +workflows: + # build: + # jobs: + # - circleci_consistency + # + # - binary_win_conda: + # name: torchtext_win_py3.6 + # python_version: "3.6" + unittest: + jobs: + - unittest_linux: + name: unittest_linux_py3.6 + python_version: '3.6' + - unittest_linux: + name: unittest_linux_py3.7 + python_version: '3.7' + - unittest_linux: + name: unittest_linux_py3.8 + python_version: '3.8' + nightly: + jobs: + - circleci_consistency: + filters: + branches: + only: nightly + # + docker_build: + triggers: + - schedule: + cron: "0 10 * * 0" + filters: + branches: + only: + - master + jobs: + - smoke_test_docker_image_build \ No newline at end of file diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in new file mode 100644 index 0000000000..c86dbbeca1 --- /dev/null +++ b/.circleci/config.yml.in @@ -0,0 +1,298 @@ +version: 2.1 + +# How to test the Linux jobs: +# - Install CircleCI local CLI: https://circleci.com/docs/2.0/local-cli/ +# - circleci config process .circleci/config.yml > gen.yml && circleci local execute -c gen.yml --job binary_linux_wheel_py3.8 +# - Replace binary_linux_wheel_py3.8 with the name of the job you want to test. +# Job names are 'name:' key. + +orbs: + win: circleci/windows@2.0.0 + +binary_common: &binary_common + parameters: + # Edit these defaults to do a release + build_version: + description: "version number of release binary; by default, build a nightly" + type: string + default: "" + pytorch_version: + description: "PyTorch version to build against; by default, use a nightly" + type: string + default: "" + # Don't edit these + python_version: + description: "Python version to build against (e.g., 3.8)" + type: string + environment: + PYTHON_VERSION: << parameters.python_version >> + BUILD_VERSION: << parameters.build_version >> + PYTORCH_VERSION: << parameters.pytorch_version >> + CU_VERSION: cpu + +smoke_test_common: &smoke_test_common + <<: *binary_common + docker: + - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchtext/smoke_test:d26af7d0-8458-face-book-84239b5c75c0 + aws_auth: + aws_access_key_id: ${ECR_AWS_ACCESS_KEY} + aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} + +jobs: + circleci_consistency: + docker: + - image: circleci/python:3.8 + steps: + - checkout + - run: + command: | + pip install --user --progress-bar off jinja2 pyyaml + python .circleci/regenerate.py + git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1) + + binary_linux_wheel: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - checkout + - run: packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_linux_conda: + <<: *binary_common + docker: + - image: "pytorch/conda-cuda" + resource_class: 2xlarge+ + steps: + - checkout + - run: packaging/build_conda.sh + - store_artifacts: + path: /opt/conda/conda-bld/linux-64 + - persist_to_workspace: + root: /opt/conda/conda-bld/linux-64 + paths: + - "*" + + binary_win_conda: + <<: *binary_common + executor: + name: win/default + shell: bash.exe + steps: + - checkout + - run: + command: | + choco install miniconda3 + (& "C:\tools\miniconda3\Scripts\conda.exe" "shell.powershell" "hook") | Out-String | Invoke-Expression + conda activate base + conda install -yq conda-build "conda-package-handling!=1.5.0" + bash packaging/build_conda.sh + shell: powershell.exe + + binary_macos_wheel: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - run: + # Cannot easily deduplicate this as source'ing activate + # will set environment variables which we need to propagate + # to build_wheel.sh + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_macos_conda: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - run: + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + conda install -yq conda-build + packaging/build_conda.sh + - store_artifacts: + path: /Users/distiller/miniconda3/conda-bld/osx-64 + - persist_to_workspace: + root: /Users/distiller/miniconda3/conda-bld/osx-64 + paths: + - "*" + + # Requires org-member context + binary_conda_upload: + docker: + - image: continuumio/miniconda + steps: + - attach_workspace: + at: ~/workspace + - run: + command: | + # Prevent credential from leaking + conda install -yq anaconda-client + set -x + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u pytorch-nightly --label main --no-progress --force + + # Requires org-member context + binary_wheel_upload: + docker: + - image: circleci/python:3.8 + steps: + - attach_workspace: + at: ~/workspace + - checkout + - run: + command: | + pip install --user awscli + export PATH="$HOME/.local/bin:$PATH" + # Prevent credential from leaking + set +x + export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}" + export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}" + set -x + for pkg in ~/workspace/*.whl; do + aws s3 cp "$pkg" "s3://pytorch/whl/nightly/" --acl public-read + done + + smoke_test_linux_conda: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-nightly pytorch + conda install -v -y $(ls ~/workspace/torchtext*.tar.bz2) + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchtext" + + smoke_test_linux_pip: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchtext*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchtext" + + smoke_test_docker_image_build: + machine: + image: ubuntu-1604:201903-01 + resource_class: large + environment: + image_name: torchtext/smoke_test + steps: + - checkout + - run: + name: build_docker image + no_output_timeout: "1h" + command: | + cd .circleci/smoke_test/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} + - run: + name: upload docker image + no_output_timeout: "1h" + command: | + set +x + export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} + export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} + eval $(aws ecr get-login --region us-east-1 --no-include-email) + set -x + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + + unittest_linux: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - checkout + - restore_cache: + {% raw %} + keys: + - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }} + {% endraw %} + paths: + - conda + - env + - run: + name: Install torchtext + command: .circleci/unittest/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/scripts/post_process.sh + - store_test_results: + path: test-results + + +workflows: + # build: + # jobs: + # - circleci_consistency + # {{ build_workflows() }} + # - binary_win_conda: + # name: torchtext_win_py3.6 + # python_version: "3.6" + unittest: + jobs: + {{ unittest_workflows() }} + nightly: + jobs: + - circleci_consistency: + filters: + branches: + only: nightly + # {{ build_workflows(prefix="nightly_", filter_branch="nightly", upload=True) }} + docker_build: + triggers: + - schedule: + cron: "0 10 * * 0" + filters: + branches: + only: + - master + jobs: + - smoke_test_docker_image_build diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py new file mode 100755 index 0000000000..1c57596ad5 --- /dev/null +++ b/.circleci/regenerate.py @@ -0,0 +1,126 @@ +#!/usr/bin/env python3 + +""" +This script should use a very simple, functional programming style. +Avoid Jinja macros in favor of native Python functions. + +Don't go overboard on code generation; use Python only to generate +content that can't be easily declared statically using CircleCI's YAML API. + +Data declarations (e.g. the nested loops for defining the configuration matrix) +should be at the top of the file for easy updating. + +See this comment for design rationale: +https://github.com/pytorch/vision/pull/1321#issuecomment-531033978 +""" + +import jinja2 +import yaml +import os.path + + +PYTHON_VERSIONS = ["3.6", "3.7", "3.8"] + + +def build_workflows(prefix='', upload=False, filter_branch=None, indentation=6): + ''' + w = [] + for btype in ["wheel", "conda"]: + for os_type in ["linux", "macos"]: + for python_version in PYTHON_VERSIONS: + w += build_workflow_pair(btype, os_type, python_version, filter_branch, prefix, upload) + + return indent(indentation, w) + ''' + return '' + +def build_workflow_pair(btype, os_type, python_version, filter_branch, prefix='', upload=False): + w = [] + base_workflow_name = f"{prefix}binary_{os_type}_{btype}_py{python_version}" + w.append(generate_base_workflow(base_workflow_name, python_version, filter_branch, os_type, btype)) + + if upload: + w.append(generate_upload_workflow(base_workflow_name, filter_branch, btype)) + if filter_branch == 'nightly' and os_type == 'linux': + pydistro = 'pip' if btype == 'wheel' else 'conda' + w.append(generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version)) + return w + + +def generate_base_workflow(base_workflow_name, python_version, filter_branch, os_type, btype): + d = { + "name": base_workflow_name, + "python_version": python_version, + } + + if filter_branch: + d["filters"] = gen_filter_branch_tree(filter_branch) + + return {f"binary_{os_type}_{btype}": d} + + +def gen_filter_branch_tree(branch_name): + return {"branches": {"only": branch_name}} + + +def generate_upload_workflow(base_workflow_name, filter_branch, btype): + d = { + "name": f"{base_workflow_name}_upload", + "context": "org-member", + "requires": [base_workflow_name], + } + + if filter_branch: + d["filters"] = gen_filter_branch_tree(filter_branch) + + return {f"binary_{btype}_upload": d} + + +def generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version): + + required_build_suffix = "_upload" + required_build_name = base_workflow_name + required_build_suffix + + smoke_suffix = f"smoke_test_{pydistro}" + d = { + "name": f"{base_workflow_name}_{smoke_suffix}", + "requires": [required_build_name], + "python_version": python_version, + } + + if filter_branch: + d["filters"] = gen_filter_branch_tree(filter_branch) + + return {f"smoke_test_linux_{pydistro}": d} + + +def indent(indentation, data_list): + return ("\n" + " " * indentation).join(yaml.dump(data_list).splitlines()) + + +def unittest_workflows(indentation=6): + w = [] + for os_type in ["linux"]: + for python_version in PYTHON_VERSIONS: + w.append({ + f"unittest_{os_type}": { + "name": f"unittest_{os_type}_py{python_version}", + "python_version": python_version, + } + }) + return indent(indentation, w) + + +if __name__ == "__main__": + d = os.path.dirname(__file__) + env = jinja2.Environment( + loader=jinja2.FileSystemLoader(d), + lstrip_blocks=True, + autoescape=False, + ) + + with open(os.path.join(d, 'config.yml'), 'w') as f: + f.write(env.get_template('config.yml.in').render( + build_workflows=build_workflows, + unittest_workflows=unittest_workflows, + )) diff --git a/.circleci/smoke_test/docker/Dockerfile b/.circleci/smoke_test/docker/Dockerfile new file mode 100644 index 0000000000..e98e83fb71 --- /dev/null +++ b/.circleci/smoke_test/docker/Dockerfile @@ -0,0 +1,36 @@ +# this Dockerfile is for torchtext smoke test, it will be created periodically via CI system +# if you need to do it locally, follow below steps once you have Docker installed +# assuming you're within the directory where this Dockerfile located +# $ docker build . -t torchtext/smoketest + +# if you want to push to aws ecr, make sure you have the rights to write to ECR, then run +# $ eval $(aws ecr get-login --region us-east-1 --no-include-email) +# $ export MYTAG=localbuild ## you can choose whatever tag you like +# $ docker tag torchtext/smoketest 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchtext/smoke_test:${MYTAG} +# $ docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchtext/smoke_test:${MYTAG} + +FROM ubuntu:latest + +RUN apt-get -qq update && apt-get -qq -y install curl bzip2 sox libsox-dev libsox-fmt-all \ + && curl -sSL https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o /tmp/miniconda.sh \ + && bash /tmp/miniconda.sh -bfp /usr/local \ + && rm -rf /tmp/miniconda.sh \ + && conda install -y python=3 \ + && conda update conda \ + && apt-get -qq -y remove curl bzip2 \ + && apt-get -qq -y autoremove \ + && apt-get autoclean \ + && rm -rf /var/lib/apt/lists/* /var/log/dpkg.log \ + && conda clean --all --yes + +ENV PATH /opt/conda/bin:$PATH + +RUN conda create -y --name python3.5 python=3.5 +RUN conda create -y --name python3.6 python=3.6 +RUN conda create -y --name python3.7 python=3.7 +SHELL [ "/bin/bash", "-c" ] +RUN echo "source /usr/local/etc/profile.d/conda.sh" >> ~/.bashrc +RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.5 && conda install -y -c conda-forge sox && conda install -y numpy +RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.6 && conda install -y -c conda-forge sox && conda install -y numpy +RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.7 && conda install -y -c conda-forge sox && conda install -y numpy +CMD [ "/bin/bash"] diff --git a/.circleci/unittest/scripts/environment.yml b/.circleci/unittest/scripts/environment.yml new file mode 100644 index 0000000000..15e6078e15 --- /dev/null +++ b/.circleci/unittest/scripts/environment.yml @@ -0,0 +1,18 @@ +channels: + - defaults +dependencies: + - flake8 + - codecov + - pip + - pip: + - nltk + - requests + - revtok + - pytest + - pytest-cov + - pytest-pythonpath + - sacremoses + - spacy + - sphinx + - sphinx-rtd-theme + - tqdm diff --git a/.circleci/unittest/scripts/install.sh b/.circleci/unittest/scripts/install.sh new file mode 100755 index 0000000000..f02440942e --- /dev/null +++ b/.circleci/unittest/scripts/install.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash + +unset PYTORCH_VERSION +# For unittest, nightly PyTorch is used as the following section, +# so no need to set PYTORCH_VERSION. +# In fact, keeping PYTORCH_VERSION forces us to hardcode PyTorch version in config. + +set -e + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +printf "* Installing PyTorch nightly build\n" +conda install -y -c pytorch-nightly pytorch cpuonly + +printf "* Installing torchtext\n" +python setup.py develop diff --git a/.circleci/unittest/scripts/post_process.sh b/.circleci/unittest/scripts/post_process.sh new file mode 100755 index 0000000000..a84a0dea55 --- /dev/null +++ b/.circleci/unittest/scripts/post_process.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +codecov diff --git a/.circleci/unittest/scripts/run_test.sh b/.circleci/unittest/scripts/run_test.sh new file mode 100755 index 0000000000..19649530b5 --- /dev/null +++ b/.circleci/unittest/scripts/run_test.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +python -m torch.utils.collect_env +pytest --cov=torchtext --junitxml=test-results/junit.xml -v test +flake8 torchtext test diff --git a/.circleci/unittest/scripts/setup_env.sh b/.circleci/unittest/scripts/setup_env.sh new file mode 100755 index 0000000000..f64c4f49d8 --- /dev/null +++ b/.circleci/unittest/scripts/setup_env.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash + +# This script is for setting up environment in which unit test is ran. +# To speed up the CI time, the resulting environment is cached. +# +# Do not install PyTorch and torchtext here, otherwise they also get cached. + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +root_dir="$(git rev-parse --show-toplevel)" +conda_dir="${root_dir}/conda" +env_dir="${root_dir}/env" + +cd "${root_dir}" + +# 1. Install conda at ./conda +if [ ! -d "${conda_dir}" ]; then + printf "* Installing conda\n" + wget -O miniconda.sh http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh + bash ./miniconda.sh -b -f -p "${conda_dir}" +fi +eval "$(${conda_dir}/bin/conda shell.bash hook)" + +# 2. Create test environment at ./env +if [ ! -d "${env_dir}" ]; then + printf "* Creating a test environment\n" + conda create --prefix "${env_dir}" -y python="$PYTHON_VERSION" +fi +conda activate "${env_dir}" + +# 3. Install Conda dependencies +printf "* Installing dependencies (except PyTorch)\n" +conda env update --file "${this_dir}/environment.yml" --prune + +# 4. Download +printf "* Downloading SpaCy English models\n" +python -m spacy download en diff --git a/.circleci/utils/test_sort_yaml.py b/.circleci/utils/test_sort_yaml.py new file mode 100755 index 0000000000..dc6db481dd --- /dev/null +++ b/.circleci/utils/test_sort_yaml.py @@ -0,0 +1,14 @@ +#!/usr/bin/env python3 + +""" +To compare new version with previous: + + ./regenerate.sh + meld <(git show HEAD:./config.yml | ./sort-yaml.py) <(cat config.yml | ./sort-yaml.py) +""" + + +import sys +import yaml + +sys.stdout.write(yaml.dump(yaml.load(sys.stdin, Loader=yaml.FullLoader), sort_keys=True)) From 53f21087cc7c72e66e2600d229b203a9f33da0fb Mon Sep 17 00:00:00 2001 From: cpuhrsch Date: Wed, 29 Apr 2020 19:52:59 -0400 Subject: [PATCH 010/719] [CCI migration] Disable travis tests except for RUN_FLAKE8 (#747) --- .circleci/regenerate.py | 1 + .travis.yml | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index 1c57596ad5..ee3d57eaf0 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -34,6 +34,7 @@ def build_workflows(prefix='', upload=False, filter_branch=None, indentation=6): ''' return '' + def build_workflow_pair(btype, os_type, python_version, filter_branch, prefix='', upload=False): w = [] base_workflow_name = f"{prefix}binary_{os_type}_{btype}_py{python_version}" diff --git a/.travis.yml b/.travis.yml index 0a4f524db7..83f69ea383 100644 --- a/.travis.yml +++ b/.travis.yml @@ -12,7 +12,7 @@ cache: matrix: fast_finish: true include: - - env: PYTHON_VERSION="3.6" RUN_SLOW="true" COVERAGE="true" + - env: PYTHON_VERSION="3.6" RUN_FLAKE8="true" SKIP_TESTS="true" sudo: required notifications: From 8b58a222b54288e46566148b3c70caf14aaceb16 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Fri, 1 May 2020 10:48:22 -0400 Subject: [PATCH 011/719] Add CCI cache for test data (#748) * Add .vector_cache to cache * Bust weekly * Remove slow from `test_vocab` * Remove slow from dataset test * Add .data to cache * Remove slow utils * Make test data cache key common to all CI jobs --- .circleci/config.yml | 22 +++++++++- .circleci/config.yml.in | 22 +++++++++- .circleci/unittest/scripts/run_test.sh | 2 +- test/common/test_markers.py | 7 ---- test/conftest.py | 3 -- test/data/test_builtin_datasets.py | 57 +++++++++----------------- test/data/test_field.py | 2 - test/test_vocab.py | 8 ---- 8 files changed, 60 insertions(+), 63 deletions(-) delete mode 100644 test/common/test_markers.py delete mode 100644 test/conftest.py diff --git a/.circleci/config.yml b/.circleci/config.yml index c44a1bcdd2..5ce617105e 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -240,17 +240,21 @@ jobs: resource_class: 2xlarge+ steps: - checkout + - run: + name: Generate cache key + # This will refresh cache on Sundays, nightly build should generate new cache. + command: echo "$(date +"%Y-%U")" > .circleci-weekly - restore_cache: keys: - - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }} + - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} - run: name: Setup command: .circleci/unittest/scripts/setup_env.sh - save_cache: - key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }} + key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} paths: - conda @@ -258,9 +262,23 @@ jobs: - run: name: Install torchtext command: .circleci/unittest/scripts/install.sh + - restore_cache: + keys: + + - data-v1-{{ checksum ".circleci-weekly" }} + - run: name: Run tests + # Downloading embedding vector takes long time. + no_output_timeout: 30m command: .circleci/unittest/scripts/run_test.sh + - save_cache: + + key: data-v1-{{ checksum ".circleci-weekly" }} + + paths: + - .vector_cache + - .data - run: name: Post process command: .circleci/unittest/scripts/post_process.sh diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index c86dbbeca1..097a6d7b19 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -240,17 +240,21 @@ jobs: resource_class: 2xlarge+ steps: - checkout + - run: + name: Generate cache key + # This will refresh cache on Sundays, nightly build should generate new cache. + command: echo "$(date +"%Y-%U")" > .circleci-weekly - restore_cache: {% raw %} keys: - - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }} + - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} {% endraw %} - run: name: Setup command: .circleci/unittest/scripts/setup_env.sh - save_cache: {% raw %} - key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }} + key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} {% endraw %} paths: - conda @@ -258,9 +262,23 @@ jobs: - run: name: Install torchtext command: .circleci/unittest/scripts/install.sh + - restore_cache: + keys: + {% raw %} + - data-v1-{{ checksum ".circleci-weekly" }} + {% endraw %} - run: name: Run tests + # Downloading embedding vector takes long time. + no_output_timeout: 30m command: .circleci/unittest/scripts/run_test.sh + - save_cache: + {% raw %} + key: data-v1-{{ checksum ".circleci-weekly" }} + {% endraw %} + paths: + - .vector_cache + - .data - run: name: Post process command: .circleci/unittest/scripts/post_process.sh diff --git a/.circleci/unittest/scripts/run_test.sh b/.circleci/unittest/scripts/run_test.sh index 19649530b5..1eea12cfb1 100755 --- a/.circleci/unittest/scripts/run_test.sh +++ b/.circleci/unittest/scripts/run_test.sh @@ -6,5 +6,5 @@ eval "$(./conda/bin/conda shell.bash hook)" conda activate ./env python -m torch.utils.collect_env -pytest --cov=torchtext --junitxml=test-results/junit.xml -v test +pytest --cov=torchtext --junitxml=test-results/junit.xml -v --durations 20 test flake8 torchtext test diff --git a/test/common/test_markers.py b/test/common/test_markers.py deleted file mode 100644 index 903f5c5450..0000000000 --- a/test/common/test_markers.py +++ /dev/null @@ -1,7 +0,0 @@ -import pytest -import os - -slow = pytest.mark.skipif( - os.getenv('RUN_SLOW', 'False') == 'False', - reason="This test is slow." -) diff --git a/test/conftest.py b/test/conftest.py deleted file mode 100644 index 3860ed6e34..0000000000 --- a/test/conftest.py +++ /dev/null @@ -1,3 +0,0 @@ -def pytest_addoption(parser): - parser.addoption("--runslow", action="store_true", - help="Run slow tests") diff --git a/test/data/test_builtin_datasets.py b/test/data/test_builtin_datasets.py index b2723949b2..b388dc61de 100644 --- a/test/data/test_builtin_datasets.py +++ b/test/data/test_builtin_datasets.py @@ -4,7 +4,6 @@ from torchtext.datasets import AG_NEWS import torch from torch.testing import assert_allclose -from ..common.test_markers import slow from ..common.torchtext_test_case import TorchtextTestCase @@ -16,10 +15,16 @@ def conditional_remove(f): class TestDataset(TorchtextTestCase): - @slow def test_wikitext2_legacy(self): from torchtext.datasets import WikiText2 # smoke test to ensure wikitext2 works properly + + # NOTE + # test_wikitext2 and test_wikitext2_legacy have some cache incompatibility. + # Keeping one's cache make the other fail. So we need to clean up the cache dir + cachedir = os.path.join(self.project_root, ".data", "wikitext-2") + conditional_remove(cachedir) + ds = WikiText2 TEXT = data.Field(lower=True, batch_first=True) train, valid, test = ds.splits(TEXT) @@ -30,13 +35,20 @@ def test_wikitext2_legacy(self): train_iter, valid_iter, test_iter = ds.iters(batch_size=4, bptt_len=30) - # Delete the dataset after we're done to save disk space on CI - datafile = os.path.join(self.project_root, ".data", "wikitext-2") - conditional_remove(datafile) + conditional_remove(cachedir) def test_wikitext2(self): from torchtext.experimental.datasets import WikiText2 # smoke test to ensure wikitext2 works properly + + # NOTE + # test_wikitext2 and test_wikitext2_legacy have some cache incompatibility. + # Keeping one's cache make the other fail. So we need to clean up the cache dir + cachedir = os.path.join(self.project_root, ".data", "wikitext-2") + conditional_remove(cachedir) + cachefile = os.path.join(self.project_root, ".data", "wikitext-2-v1.zip") + conditional_remove(cachefile) + train_dataset, test_dataset, valid_dataset = WikiText2() self.assertEqual(len(train_dataset), 2049990) self.assertEqual(len(test_dataset), 241859) @@ -46,13 +58,9 @@ def test_wikitext2(self): tokens_ids = [vocab[token] for token in 'the player characters rest'.split()] self.assertEqual(tokens_ids, [2, 286, 503, 700]) - # Delete the dataset after we're done to save disk space on CI - datafile = os.path.join(self.project_root, ".data", "wikitext-2") - conditional_remove(datafile) - datafile = os.path.join(self.project_root, ".data", "wikitext-2-v1.zip") - conditional_remove(datafile) + conditional_remove(cachedir) + conditional_remove(cachefile) - @slow def test_penntreebank_legacy(self): from torchtext.datasets import PennTreebank # smoke test to ensure penn treebank works properly @@ -66,10 +74,6 @@ def test_penntreebank_legacy(self): train_iter, valid_iter, test_iter = ds.iters(batch_size=4, bptt_len=30) - # Delete the dataset after we're done to save disk space on CI - datafile = os.path.join(self.project_root, ".data", "penn-treebank") - conditional_remove(datafile) - def test_penntreebank(self): from torchtext.experimental.datasets import PennTreebank # smoke test to ensure wikitext2 works properly @@ -82,14 +86,6 @@ def test_penntreebank(self): tokens_ids = [vocab[token] for token in 'the player characters rest'.split()] self.assertEqual(tokens_ids, [2, 2550, 3344, 1125]) - # Delete the dataset after we're done to save disk space on CI - datafile = os.path.join(self.project_root, ".data", 'ptb.train.txt') - conditional_remove(datafile) - datafile = os.path.join(self.project_root, ".data", 'ptb.test.txt') - conditional_remove(datafile) - datafile = os.path.join(self.project_root, ".data", 'ptb.valid.txt') - conditional_remove(datafile) - def test_text_classification(self): # smoke test to ensure ag_news dataset works properly @@ -104,13 +100,6 @@ def test_text_classification(self): assert_allclose(ag_news_test[-1][1][:10], torch.tensor([2351, 758, 96, 38581, 2351, 220, 5, 396, 3, 14786]).long()) - # Delete the dataset after we're done to save disk space on CI - datafile = os.path.join(self.project_root, ".data", "ag_news_csv") - conditional_remove(datafile) - datafile = os.path.join(self.project_root, ".data", "ag_news_csv.tar.gz") - conditional_remove(datafile) - - @slow def test_imdb(self): from torchtext.experimental.datasets import IMDB from torchtext.vocab import Vocab @@ -131,11 +120,3 @@ def test_imdb(self): old_vocab = train_dataset.get_vocab() new_vocab = Vocab(counter=old_vocab.freqs, max_size=2500) new_train_data, new_test_data = IMDB(vocab=new_vocab) - - # Delete the dataset after we're done to save disk space on CI - datafile = os.path.join(self.project_root, ".data", "imdb") - conditional_remove(datafile) - datafile = os.path.join(self.project_root, ".data", "aclImdb") - conditional_remove(datafile) - datafile = os.path.join(self.project_root, ".data", "aclImdb_v1.tar.gz") - conditional_remove(datafile) diff --git a/test/data/test_field.py b/test/data/test_field.py index 14fe043776..385412b26f 100644 --- a/test/data/test_field.py +++ b/test/data/test_field.py @@ -9,7 +9,6 @@ from torch.nn import init from ..common.torchtext_test_case import TorchtextTestCase, verify_numericalized_example -from ..common.test_markers import slow class TestField(TorchtextTestCase): @@ -866,7 +865,6 @@ def test_serialization(self): assert torch.all(torch.eq(original_numericalization, pickled_numericalization)) - @slow def test_build_vocab(self): nesting_field = data.Field(tokenize=list, init_token="", eos_token="") diff --git a/test/test_vocab.py b/test/test_vocab.py index aad82de57c..a083025bd7 100644 --- a/test/test_vocab.py +++ b/test/test_vocab.py @@ -10,7 +10,6 @@ from torchtext import vocab from torchtext.vocab import Vectors, FastText, GloVe, CharNGram -from .common.test_markers import slow from .common.torchtext_test_case import TorchtextTestCase @@ -93,7 +92,6 @@ def test_vocab_set_vectors(self): [0.3, 0.4]]) assert_allclose(v.vectors.numpy(), expected_vectors) - @slow def test_vocab_download_fasttext_vectors(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) # Build a vocab and get vectors twice to test caching, then once more @@ -131,7 +129,6 @@ def test_vocab_download_fasttext_vectors(self): vec_file = os.path.join(self.project_root, ".vector_cache", "wiki.simple.vec") conditional_remove(vec_file) - @slow def test_vocab_extend(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) # Build a vocab and get vectors twice to test caching. @@ -163,7 +160,6 @@ def test_vocab_extend(self): vec_file = os.path.join(self.project_root, ".vector_cache", "wiki.simple.vec") conditional_remove(vec_file) - @slow def test_vocab_download_custom_vectors(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) # Build a vocab and get vectors twice to test caching. @@ -192,7 +188,6 @@ def test_vocab_download_custom_vectors(self): vec_file = os.path.join(self.project_root, ".vector_cache", "wiki.simple.vec") conditional_remove(vec_file) - @slow def test_vocab_vectors_custom_cache(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) vector_cache = os.path.join('/tmp', 'vector_cache') @@ -225,7 +220,6 @@ def test_vocab_vectors_custom_cache(self): vec_file = os.path.join(vector_cache, "wiki.simple.vec") conditional_remove(vec_file) - @slow def test_vocab_download_glove_vectors(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) @@ -268,7 +262,6 @@ def test_vocab_download_glove_vectors(self): conditional_remove(os.path.join(self.project_root, ".vector_cache", "glove.twitter.27B.{}d.txt".format(dim))) - @slow def test_vocab_download_charngram_vectors(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) # Build a vocab and get vectors twice to test caching, then once more @@ -343,7 +336,6 @@ def test_serialization_backcompat(self): v_loaded = pickle.load(open(pickle_path, "rb")) assert v == v_loaded - @slow def test_vectors_get_vecs(self): vec = GloVe(name='twitter.27B', dim='25') self.assertEqual(vec.vectors.shape[0], len(vec)) From 85be3b70e44d9ffaba12758332d9b4f552a97927 Mon Sep 17 00:00:00 2001 From: Avinash Madasu Date: Mon, 4 May 2020 03:19:01 +0530 Subject: [PATCH 012/719] use 'cpu' instead of None for Iterator (#745) --- torchtext/data/iterator.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/torchtext/data/iterator.py b/torchtext/data/iterator.py index 9546cb9dac..c76a7335ff 100644 --- a/torchtext/data/iterator.py +++ b/torchtext/data/iterator.py @@ -3,6 +3,7 @@ import logging +import torch from .utils import RandomShuffler from .batch import Batch from .dataset import Dataset @@ -65,6 +66,12 @@ def __init__(self, dataset, batch_size, sort_key=None, device=None, + " or passing a string as an argument. This behavior will be" + " deprecated soon and currently defaults to cpu.") device = None + + if device is None: + device = torch.device('cpu') + elif isinstance(device, str): + device = torch.device(device) + self.device = device self.random_shuffler = RandomShuffler() From fd764ad02968e6310d28ee3c496e1d1d1157aa3e Mon Sep 17 00:00:00 2001 From: cpuhrsch Date: Mon, 4 May 2020 11:20:38 -0400 Subject: [PATCH 013/719] 20200501 fbsync (#750) --- .python3 | 0 test/test_vocab.py | 4 ++-- torchtext/data/utils.py | 2 +- .../datasets/raw/text_classification.py | 13 +++---------- .../experimental/datasets/text_classification.py | 2 +- torchtext/vocab.py | 4 ++-- 6 files changed, 9 insertions(+), 16 deletions(-) create mode 100644 .python3 diff --git a/.python3 b/.python3 new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/test_vocab.py b/test/test_vocab.py index a083025bd7..c2801e1152 100644 --- a/test/test_vocab.py +++ b/test/test_vocab.py @@ -132,7 +132,7 @@ def test_vocab_download_fasttext_vectors(self): def test_vocab_extend(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) # Build a vocab and get vectors twice to test caching. - for i in range(2): + for _ in range(2): f = FastText(language='simple') v = vocab.Vocab(c, min_freq=3, specials=['', '', ''], vectors=f) @@ -163,7 +163,7 @@ def test_vocab_extend(self): def test_vocab_download_custom_vectors(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) # Build a vocab and get vectors twice to test caching. - for i in range(2): + for _ in range(2): v = vocab.Vocab(c, min_freq=3, specials=['', '', ''], vectors=Vectors('wiki.simple.vec', url=FastText.url_base.format('simple'))) diff --git a/torchtext/data/utils.py b/torchtext/data/utils.py index eea151ab69..5ecfad1958 100644 --- a/torchtext/data/utils.py +++ b/torchtext/data/utils.py @@ -6,7 +6,7 @@ from functools import partial -def _split_tokenizer(x): +def _split_tokenizer(x): # noqa: F821 # type: (str) -> List[str] return x.split() diff --git a/torchtext/experimental/datasets/raw/text_classification.py b/torchtext/experimental/datasets/raw/text_classification.py index fa65abfcdd..d48cb6c812 100644 --- a/torchtext/experimental/datasets/raw/text_classification.py +++ b/torchtext/experimental/datasets/raw/text_classification.py @@ -1,7 +1,6 @@ import torch import io from torchtext.utils import download_from_url, extract_archive, unicode_csv_reader -import sys URLS = { 'AG_NEWS': @@ -55,15 +54,9 @@ def __iter__(self): self.setup_iter() for i, item in enumerate(self._iterator): - if i == self.start: - break - - num_lines = self.num_lines if self.num_lines is not None else sys.maxsize - for _ in range(num_lines): - yield item - try: - item = next(self._iterator) - except StopIteration: + if i >= self.start: + yield item + if self.num_lines is not None and i == (self.start + self.num_lines): break def get_iterator(self): diff --git a/torchtext/experimental/datasets/text_classification.py b/torchtext/experimental/datasets/text_classification.py index fd6e2ad8f3..df879ea557 100644 --- a/torchtext/experimental/datasets/text_classification.py +++ b/torchtext/experimental/datasets/text_classification.py @@ -27,7 +27,7 @@ def _forward(token_list): def build_vocab(data, transforms): tok_list = [] - for (label, txt) in data: + for _, txt in data: tok_list.append(transforms(txt)) return build_vocab_from_iterator(tok_list) diff --git a/torchtext/vocab.py b/torchtext/vocab.py index d7def4a672..61848d05ca 100755 --- a/torchtext/vocab.py +++ b/torchtext/vocab.py @@ -31,7 +31,7 @@ class Vocab(object): # TODO (@mttk): Populate classs with default values of special symbols UNK = '' - def __init__(self, counter, max_size=None, min_freq=1, specials=['', ''], + def __init__(self, counter, max_size=None, min_freq=1, specials=('', ''), vectors=None, unk_init=None, vectors_cache=None, specials_first=True): """Create a Vocab object from a collections.Counter. @@ -218,7 +218,7 @@ def set_vectors(self, stoi, vectors, dim, unk_init=torch.Tensor.zero_): class SubwordVocab(Vocab): - def __init__(self, counter, max_size=None, specials=[''], + def __init__(self, counter, max_size=None, specials=(''), vectors=None, unk_init=torch.Tensor.zero_): """Create a revtok subword vocabulary from a collections.Counter. From 5de163af6b2132b21537274fd4678beacf187ffd Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Fri, 8 May 2020 16:50:07 -0400 Subject: [PATCH 014/719] [BC breaking] Add Sentencepiece torchscript Extension (#755) This CC adds `torchscript` extension `_torchtext.so`, which contains simple interface to `SentencePiece`. - SentencePiece `v0.1.86` is used. - `libsentencepiece.a` is built right before `_torchtext.so` is compiled. The logic for triggering this build from `setuptools` can be found under `build_tools/setup_helpers`. - `_torchtext.so` provides interface to train a SentencePiece model and load a model from file. Breaking change: Previously `torchtext.data.functional.load_sp_model` returned `sentencepiece.SentencePieceProcessor` object, which supported the following methods, in addition to `__len__` and `__getitem__` ``` $ grep '$self->' third_party/sentencepiece/python/sentencepiece.i return $self->Load(filename); return $self->LoadFromSerializedProto(filename); return $self->SetEncodeExtraOptions(extra_option); return $self->SetDecodeExtraOptions(extra_option); return $self->SetVocabulary(valid_vocab); return $self->ResetVocabulary(); return $self->LoadVocabulary(filename, threshold); return $self->EncodeAsPieces(input); return $self->EncodeAsIds(input); return $self->NBestEncodeAsPieces(input, nbest_size); return $self->NBestEncodeAsIds(input, nbest_size); return $self->SampleEncodeAsPieces(input, nbest_size, alpha); return $self->SampleEncodeAsIds(input, nbest_size, alpha); return $self->DecodePieces(input); return $self->DecodeIds(input); return $self->EncodeAsSerializedProto(input); return $self->SampleEncodeAsSerializedProto(input, nbest_size, alpha); return $self->NBestEncodeAsSerializedProto(input, nbest_size); return $self->DecodePiecesAsSerializedProto(pieces); return $self->DecodeIdsAsSerializedProto(ids); return $self->GetPieceSize(); return $self->PieceToId(piece); return $self->IdToPiece(id); return $self->GetScore(id); return $self->IsUnused(id); return $self->IsControl(id); return $self->IsUnused(id); return $self->GetPieceSize(); return $self->PieceToId(key); ``` The new C++ Extension provides the following methods ``` Encode(input) EncodeAsIds(input) EncodeAsPieces(input) ``` --- .circleci/unittest/scripts/environment.yml | 1 + .circleci/unittest/scripts/install.sh | 1 + .circleci/unittest/scripts/run_test.sh | 4 +- .flake8 | 2 +- .gitmodules | 4 + build_tools/__init__.py | 0 build_tools/setup_helpers/__init__.py | 1 + build_tools/setup_helpers/extension.py | 117 +++++++++++++++++++++ setup.py | 48 +++++++-- test/data/test_functional.py | 80 ++++++++++++-- third_party/sentencepiece | 1 + torchtext/__init__.py | 12 +++ torchtext/csrc/sentencepiece.cpp | 94 +++++++++++++++++ torchtext/data/functional.py | 25 +---- 14 files changed, 352 insertions(+), 38 deletions(-) create mode 100644 .gitmodules create mode 100644 build_tools/__init__.py create mode 100644 build_tools/setup_helpers/__init__.py create mode 100644 build_tools/setup_helpers/extension.py create mode 160000 third_party/sentencepiece create mode 100644 torchtext/csrc/sentencepiece.cpp diff --git a/.circleci/unittest/scripts/environment.yml b/.circleci/unittest/scripts/environment.yml index 15e6078e15..e2615db19d 100644 --- a/.circleci/unittest/scripts/environment.yml +++ b/.circleci/unittest/scripts/environment.yml @@ -5,6 +5,7 @@ dependencies: - codecov - pip - pip: + - clang-format - nltk - requests - revtok diff --git a/.circleci/unittest/scripts/install.sh b/.circleci/unittest/scripts/install.sh index f02440942e..1a41898c4e 100755 --- a/.circleci/unittest/scripts/install.sh +++ b/.circleci/unittest/scripts/install.sh @@ -14,4 +14,5 @@ printf "* Installing PyTorch nightly build\n" conda install -y -c pytorch-nightly pytorch cpuonly printf "* Installing torchtext\n" +git submodule update --init --recursive python setup.py develop diff --git a/.circleci/unittest/scripts/run_test.sh b/.circleci/unittest/scripts/run_test.sh index 1eea12cfb1..fd08ae8640 100755 --- a/.circleci/unittest/scripts/run_test.sh +++ b/.circleci/unittest/scripts/run_test.sh @@ -7,4 +7,6 @@ conda activate ./env python -m torch.utils.collect_env pytest --cov=torchtext --junitxml=test-results/junit.xml -v --durations 20 test -flake8 torchtext test +flake8 torchaudio test build_tools/setup_helpers +git-clang-format origin/master +git diff --exit-code || (echo "C++ files are not formatted. Use clang-format on CPP files."; exit 1) diff --git a/.flake8 b/.flake8 index 50ecc8aa11..0f3af497a9 100644 --- a/.flake8 +++ b/.flake8 @@ -1,4 +1,4 @@ [flake8] ignore = E402,E722,W503,W504,F821 max-line-length = 120 -exclude = docs/source +exclude = docs/source,third_party diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000000..3d9250cdc0 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,4 @@ +[submodule "third_party/sentencepiece"] + path = third_party/sentencepiece + url = https://github.com/google/sentencepiece + ignore = dirty diff --git a/build_tools/__init__.py b/build_tools/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/build_tools/setup_helpers/__init__.py b/build_tools/setup_helpers/__init__.py new file mode 100644 index 0000000000..7afa3f31cd --- /dev/null +++ b/build_tools/setup_helpers/__init__.py @@ -0,0 +1 @@ +from .extension import * # noqa diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py new file mode 100644 index 0000000000..60b89086ae --- /dev/null +++ b/build_tools/setup_helpers/extension.py @@ -0,0 +1,117 @@ +import platform +import subprocess +from pathlib import Path + +from torch.utils.cpp_extension import ( + CppExtension, + BuildExtension as TorchBuildExtension +) + +__all__ = [ + 'get_ext_modules', + 'BuildExtension', +] + +_ROOT_DIR = Path(__file__).parent.parent.parent.resolve() +_CSRC_DIR = _ROOT_DIR / 'torchtext' / 'csrc' +_TP_BASE_DIR = _ROOT_DIR / 'third_party' +_TP_INSTALL_DIR = _TP_BASE_DIR / 'build' + + +def _get_eca(debug): + eca = [] + if debug: + eca += ["-O0", "-g"] + else: + eca += ["-O3"] + return eca + + +def _get_ela(debug): + ela = [] + if debug: + if platform.system() == "Windows": + ela += ["/DEBUG:FULL"] + else: + ela += ["-O0", "-g"] + else: + ela += ["-O3"] + return ela + + +def _get_srcs(): + return [str(p) for p in _CSRC_DIR.glob('**/*.cpp')] + + +def _get_include_dirs(): + return [ + str(_CSRC_DIR), + str(_TP_INSTALL_DIR / 'include'), + ] + + +def _get_library_dirs(): + return [ + str(_TP_INSTALL_DIR / 'lib'), + ] + + +def _get_libraries(): + # NOTE: The order of the library listed bellow matters. + # + # For example, the symbol `sentencepiece::unigram::Model` is + # defined in sentencepiece but UNDEFINED in sentencepiece_train. + # GCC only remembers the last encountered symbol. + # Therefore placing 'sentencepiece_train' after 'sentencepiece' cause runtime error. + # + # $ nm third_party/build/lib/libsentencepiece_train.a | grep _ZTIN13sentencepiece7unigram5ModelE + # U _ZTIN13sentencepiece7unigram5ModelE + # $ nm third_party/build/lib/libsentencepiece.a | grep _ZTIN13sentencepiece7unigram5ModelE + # 0000000000000000 V _ZTIN13sentencepiece7unigram5ModelE + return [ + 'sentencepiece_train', + 'sentencepiece', + ] + + +def _build_sentence_piece(): + build_dir = _TP_BASE_DIR / 'sentencepiece' / 'build' + build_dir.mkdir(exist_ok=True) + subprocess.run( + args=['cmake', '-DSPM_ENABLE_SHARED=OFF', f'-DCMAKE_INSTALL_PREFIX={_TP_INSTALL_DIR}', '..'], + cwd=str(build_dir), + check=True, + ) + subprocess.run( + args=['make', 'install'], + cwd=str(build_dir), + check=True, + ) + + +def _configure_third_party(): + _build_sentence_piece() + + +_EXT_NAME = 'torchtext._torchtext' + + +def get_ext_modules(debug=False): + return [ + CppExtension( + _EXT_NAME, + _get_srcs(), + libraries=_get_libraries(), + include_dirs=_get_include_dirs(), + library_dirs=_get_library_dirs(), + extra_compile_args=_get_eca(debug), + extra_link_args=_get_ela(debug), + ), + ] + + +class BuildExtension(TorchBuildExtension): + def build_extension(self, ext): + if ext.name == _EXT_NAME: + _configure_third_party() + super().build_extension(ext) diff --git a/setup.py b/setup.py index 0c0cff5b03..25c5646636 100644 --- a/setup.py +++ b/setup.py @@ -1,15 +1,18 @@ #!/usr/bin/env python -import os import io import re +import shutil +from pathlib import Path +import distutils.command.clean from setuptools import setup, find_packages +from build_tools import setup_helpers + +ROOT_DIR = Path(__file__).parent.resolve() + def read(*names, **kwargs): - with io.open( - os.path.join(os.path.dirname(__file__), *names), - encoding=kwargs.get("encoding", "utf8") - ) as fp: + with io.open(ROOT_DIR.joinpath(*names), encoding=kwargs.get("encoding", "utf8")) as fp: return fp.read() @@ -25,6 +28,27 @@ def find_version(*file_paths): VERSION = find_version('torchtext', '__init__.py') long_description = read('README.rst') + +class clean(distutils.command.clean.clean): + def run(self): + # Run default behavior first + distutils.command.clean.clean.run(self) + + # Remove torchtext extension + for path in (ROOT_DIR / 'torchtext').glob('**/*.so'): + print(f'removing \'{path}\'') + path.unlink() + # Remove build directory + build_dirs = [ + ROOT_DIR / 'build', + ROOT_DIR / 'third_party' / 'build', + ] + for path in build_dirs: + if path.exists(): + print(f'removing \'{path}\' (and everything under it)') + shutil.rmtree(str(path), ignore_errors=True) + + setup_info = dict( # Metadata name='torchtext', @@ -48,11 +72,17 @@ def find_version(*file_paths): 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3 :: Only', ], - # Package info - packages=find_packages(exclude=('test', 'test.*')), - - zip_safe=True, + packages=find_packages(exclude=('test*', 'build_tools*')), + zip_safe=False, + # Extension info + # If you are trying to use torchtext.so and see no registered op. + # See here: https://github.com/pytorch/vision/issues/2134" + ext_modules=setup_helpers.get_ext_modules(), + cmdclass={ + 'build_ext': setup_helpers.BuildExtension.with_options(no_python_abi_suffix=True), + 'clean': clean, + }, ) setup(**setup_info) diff --git a/test/data/test_functional.py b/test/data/test_functional.py index 6c5b86d71b..2e3a5e0435 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -1,9 +1,19 @@ -from ..common.torchtext_test_case import TorchtextTestCase -import sentencepiece as spm -from torchtext.data.functional import generate_sp_model, load_sp_model, \ - sentencepiece_numericalizer, sentencepiece_tokenizer, \ - custom_replace, simple_space_split import os +import unittest +import tempfile + +import sentencepiece as spm +import torch +from torchtext.data.functional import ( + generate_sp_model, + load_sp_model, + sentencepiece_numericalizer, + sentencepiece_tokenizer, + custom_replace, + simple_space_split, +) + +from ..common.torchtext_test_case import TorchtextTestCase class TestFunctional(TorchtextTestCase): @@ -29,7 +39,7 @@ def test_sentencepiece_numericalizer(self): test_sample = 'SentencePiece is an unsupervised text tokenizer and detokenizer' model_path = 'test/asset/spm_example.model' sp_model = load_sp_model(model_path) - self.assertEqual(len(sp_model), 20000) + self.assertEqual(sp_model.GetPieceSize(), 20000) spm_generator = sentencepiece_numericalizer(sp_model) ref_results = [15340, 4286, 981, 1207, 1681, 17, 84, 684, 8896, 5366, @@ -43,7 +53,7 @@ def test_sentencepiece_tokenizer(self): test_sample = 'SentencePiece is an unsupervised text tokenizer and detokenizer' model_path = 'test/asset/spm_example.model' sp_model = load_sp_model(model_path) - self.assertEqual(len(sp_model), 20000) + self.assertEqual(sp_model.GetPieceSize(), 20000) spm_generator = sentencepiece_tokenizer(sp_model) ref_results = ['\u2581Sent', 'ence', 'P', 'ie', 'ce', '\u2581is', @@ -66,3 +76,59 @@ def test_simple_space_split(self): ref_results = ['test', 'simple', 'space', 'split', 'function'] self.assertEqual(list(simple_space_split(test_sample))[0], ref_results) + + +class ScriptableSP(torch.jit.ScriptModule): + def __init__(self, model_path): + super().__init__() + self.spm = load_sp_model(model_path) + + @torch.jit.script_method + def encode(self, input: str): + return self.spm.Encode(input) + + @torch.jit.script_method + def encode_as_ids(self, input: str): + return self.spm.EncodeAsIds(input) + + @torch.jit.script_method + def encode_as_pieces(self, input: str): + return self.spm.EncodeAsPieces(input) + + +class TestScriptableSP(unittest.TestCase): + def setUp(self): + model_path = 'test/asset/spm_example.model' + with tempfile.NamedTemporaryFile() as file: + torch.jit.script(ScriptableSP(model_path)).save(file.name) + self.model = torch.jit.load(file.name) + + def test_encode(self): + input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' + expected = [ + '▁Sent', 'ence', 'P', 'ie', 'ce', '▁is', + '▁an', '▁un', 'super', 'vis', 'ed', '▁text', + '▁to', 'ken', 'izer', '▁and', + '▁de', 'to', 'ken', 'izer', + ] + output = self.model.encode(input) + self.assertEqual(expected, output) + + def test_encode_as_ids(self): + input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' + expected = [ + 15340, 4286, 981, 1207, 1681, 17, 84, 684, 8896, 5366, + 144, 3689, 9, 5602, 12114, 6, 560, 649, 5602, 12114] + output = self.model.encode_as_ids(input) + self.assertEqual(expected, output) + + def test_encode_as_pieces(self): + input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' + expected = [ + '\u2581Sent', 'ence', 'P', 'ie', 'ce', '\u2581is', + '\u2581an', '\u2581un', 'super', 'vis', 'ed', '\u2581text', + '\u2581to', 'ken', 'izer', '\u2581and', + '\u2581de', 'to', 'ken', 'izer', + ] + output = self.model.encode_as_pieces(input) + self.assertEqual(expected, output) diff --git a/third_party/sentencepiece b/third_party/sentencepiece new file mode 160000 index 0000000000..e8a84a16d1 --- /dev/null +++ b/third_party/sentencepiece @@ -0,0 +1 @@ +Subproject commit e8a84a16d13e8bf92892a1cd92e4de3b0d0321fd diff --git a/torchtext/__init__.py b/torchtext/__init__.py index db4484fdf1..ca04a6bbd5 100644 --- a/torchtext/__init__.py +++ b/torchtext/__init__.py @@ -11,3 +11,15 @@ 'utils', 'vocab', 'experimental'] + + +def _init_extension(): + import torch + torch.ops.load_library('torchtext/_torchtext.so') + torch.classes.load_library('torchtext/_torchtext.so') + + +_init_extension() + + +del _init_extension diff --git a/torchtext/csrc/sentencepiece.cpp b/torchtext/csrc/sentencepiece.cpp new file mode 100644 index 0000000000..92778147fe --- /dev/null +++ b/torchtext/csrc/sentencepiece.cpp @@ -0,0 +1,94 @@ +#include +#include + +namespace torchtext { +namespace { + +struct SentencePiece : torch::CustomClassHolder { +private: + sentencepiece::SentencePieceProcessor processor_; + +public: + // content_ holds the serialized model data passed at the initialization. + // We need this because the underlying SentencePieceProcessor class does not + // provide serialization mechanism, yet we still need to be able to serialize + // the model so that we can save the scripted object. pickle will get the + // serialized model from this content_ member, thus it needs to be public. + const std::string content_; + + explicit SentencePiece(const std::string &content) : content_(content) { + const auto status = processor_.LoadFromSerializedProto(content_); + if (!status.ok()) { + throw std::runtime_error("Failed to load SentencePiece model. Error: " + + status.ToString()); + } + } + + std::vector Encode(const std::string &input) const { + std::vector pieces; + processor_.Encode(input, &pieces); + return pieces; + } + + std::vector EncodeAsIds(const std::string &input) const { + const auto val = processor_.EncodeAsIds(input); + return std::vector(val.begin(), val.end()); + } + + std::vector EncodeAsPieces(const std::string &input) const { + return processor_.EncodeAsPieces(input); + } + + int64_t GetPieceSize() const { return processor_.GetPieceSize(); } +}; + +// Registers our custom class with torch. +static auto sentencepiece = + torch::class_("torchtext", "SentencePiece") + .def("Encode", &SentencePiece::Encode) + .def("EncodeAsIds", &SentencePiece::EncodeAsIds) + .def("EncodeAsPieces", &SentencePiece::EncodeAsPieces) + .def("GetPieceSize", &SentencePiece::GetPieceSize) + .def_pickle( + // __setstate__ + [](const c10::intrusive_ptr &self) -> std::string { + return self->content_; + }, + // __getstate__ + [](std::string state) -> c10::intrusive_ptr { + return c10::make_intrusive(std::move(state)); + }); + +void generate_sp_model(const std::string filename, const int64_t vocab_size, + const std::string model_type, + const std::string model_prefix) { + const auto status = ::sentencepiece::SentencePieceTrainer::Train( + "--input=" + filename + " --model_prefix=" + model_prefix + + " --vocab_size=" + std::to_string(vocab_size) + + " --model_type=" + model_type); + if (!status.ok()) { + throw std::runtime_error("Failed to train SentencePiece model. Error: " + + status.ToString()); + } +} + +c10::intrusive_ptr load_sp_model(const std::string &path) { + std::ifstream file(path, std::ios::binary | std::ios::in); + if (!file) { + throw std::runtime_error("Failed to open file :" + path); + } + const std::string content((std::istreambuf_iterator(file)), + std::istreambuf_iterator()); + return c10::make_intrusive(std::move(content)); +} + +static auto registry = + torch::RegisterOperators() + .op("torchtext::generate_sp_model", &generate_sp_model) + .op(torch::RegisterOperators::options() + .schema("torchtext::load_sp_model(str path) -> " + "__torch__.torch.classes.torchtext.SentencePiece model") + .catchAllKernel()); + +} // namespace +} // namespace torchtext diff --git a/torchtext/data/functional.py b/torchtext/data/functional.py index e1ef55da79..a1d7a3b41e 100644 --- a/torchtext/data/functional.py +++ b/torchtext/data/functional.py @@ -1,5 +1,8 @@ import re +import torch + + __all__ = [ "generate_sp_model", "load_sp_model", "sentencepiece_numericalizer", "sentencepiece_tokenizer", @@ -33,19 +36,7 @@ def generate_sp_model(filename, vocab_size=20000, >>> from torchtext.data.functional import generate_sp_model >>> generate_sp_model('test.csv', vocab_size=23456, model_prefix='spm_user') """ - try: - import sentencepiece as spm - except ModuleNotFoundError: - raise ImportWarning("Please install sentencepiece") - spm_training_string = "--input={} \ - --vocab_size={} \ - --model_prefix={} \ - --model_type={}".format(filename, - vocab_size, - model_prefix, - model_type) - spm.SentencePieceTrainer.train(spm_training_string) - return None + torch.ops.torchtext.generate_sp_model(filename, vocab_size, model_type, model_prefix) def load_sp_model(spm_path): @@ -61,13 +52,7 @@ def load_sp_model(spm_path): >>> from torchtext.data.functional import load_sp_model >>> sp_model = load_sp_model("m_user.model") """ - try: - import sentencepiece as spm - except ModuleNotFoundError: - raise ImportWarning("Please install sentencepiece") - sp_model = spm.SentencePieceProcessor() - sp_model.Load(spm_path) - return sp_model + return torch.ops.torchtext.load_sp_model(spm_path) def sentencepiece_numericalizer(sp_model): From aeef495892ef35c571184d85f082df4e68cd7590 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Tue, 12 May 2020 22:52:53 -0400 Subject: [PATCH 015/719] Temporary fix for flake8 (#765) The latest version of flake8 added check for E741 and causing CI to fail. This PR temporarily fix it by pinning the flake8 version. [TODO] Decide how to resolve the following style violation. ``` ./torchtext/datasets/translation.py:181:21: E741 ambiguous variable name 'l' ./test/common/torchtext_test_case.py:110:30: E741 ambiguous variable name 'l' ``` --- .circleci/unittest/scripts/environment.yml | 2 +- requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.circleci/unittest/scripts/environment.yml b/.circleci/unittest/scripts/environment.yml index e2615db19d..8955f892f6 100644 --- a/.circleci/unittest/scripts/environment.yml +++ b/.circleci/unittest/scripts/environment.yml @@ -1,7 +1,7 @@ channels: - defaults dependencies: - - flake8 + - flake8==3.7.9 - codecov - pip - pip: diff --git a/requirements.txt b/requirements.txt index cc0a18140f..c9c761c82d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -17,7 +17,7 @@ sphinx_rtd_theme # Required for tests only: # Style-checking for PEP8 -flake8 +flake8==3.7.9 # Run unit tests pytest From 6e410f87111befa7392a3e60f92e483c8f84d85d Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Tue, 12 May 2020 23:03:06 -0400 Subject: [PATCH 016/719] Fix import path (#764) Normalize the path to `_torchtext.so` so that it is correctly found when imported from other directories. --- torchtext/__init__.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/torchtext/__init__.py b/torchtext/__init__.py index ca04a6bbd5..b73f2cc6ce 100644 --- a/torchtext/__init__.py +++ b/torchtext/__init__.py @@ -14,9 +14,23 @@ def _init_extension(): + import os + import importlib import torch - torch.ops.load_library('torchtext/_torchtext.so') - torch.classes.load_library('torchtext/_torchtext.so') + + # load the custom_op_library and register the custom ops + lib_dir = os.path.dirname(__file__) + loader_details = ( + importlib.machinery.ExtensionFileLoader, + importlib.machinery.EXTENSION_SUFFIXES + ) + + extfinder = importlib.machinery.FileFinder(lib_dir, loader_details) + ext_specs = extfinder.find_spec("_torchtext") + if ext_specs is None: + raise ImportError + torch.ops.load_library(ext_specs.origin) + torch.classes.load_library(ext_specs.origin) _init_extension() From 32bc374d7d8b15ed431dfaa64304a974a959ea5e Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Tue, 12 May 2020 23:16:50 -0400 Subject: [PATCH 017/719] Update installation direction (#763) --- README.rst | 40 ++++++++++++++++++++++++++++++++++------ 1 file changed, 34 insertions(+), 6 deletions(-) diff --git a/README.rst b/README.rst index a79db166c0..14cff74f73 100644 --- a/README.rst +++ b/README.rst @@ -21,16 +21,24 @@ Note: we are currently re-designing the torchtext library to make it more compat Installation ============ +We recommend Anaconda as Python package management system. Please refer to `pytorch.org `_ for the detail of PyTorch installation. The following is the corresponding ``torchtext`` versions and supported Python versions. -Make sure you have Python 3.5+ and PyTorch 0.4.0 or newer. You can then install torchtext using pip:: +.. csv-table:: Version Compatibility + :header: "PyTorch version", "torchtext version", "Supported Python version" + :widths: 10, 10, 10 - pip install torchtext - -For PyTorch versions before 0.4.0, please use `pip install torchtext==0.2.3`. + nightly build, master, 3.6+ + 1.5, 0.5, 3.5+ + 1.4, 0.4, "2.7, 3.5+" + 0.4 and below, 0.2.3, "2.7, 3.5+" + +Using conda;:: + + conda install -c pytorch torchtext -Or you can install torchtext using conda:: +Using pip;:: - conda install -c pytorch -c powerai torchtext sentencepiece + pip install torchtext Optional requirements --------------------- @@ -44,6 +52,26 @@ Alternatively, you might want to use the `Moses `_ pip install sacremoses +For torchtext 0.5 and below, ``sentencepiece``:: + + conda install -c powerai sentencepiece + +Building from source +-------------------- + +To build torchtext from source, you need ``git``, ``CMake`` and C++11 compiler such as ``g++``.:: + + git clone https://github.com/pytorch/text torchtext + cd torchtext + git submodule update --init --recursive + python setup.py clean install + # or ``python setup.py develop`` if you are making modifications. + +**Note** + +When building from souce, make sure that you have the same C++ compiler as the one used to build PyTorch. A simple way is to build PyTorch from source and use the same environment to build torchtext. +If you are using nightly build of PyTorch, checkout the environment it was built `here (conda) `_ and `here (pip) `_. + Documentation ============= From b245387cd2db2f1ff1dd10d4619b01523294ea24 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Wed, 13 May 2020 18:42:36 -0400 Subject: [PATCH 018/719] Move style check to CCI (#768) --- .circleci/config.yml | 32 +++++++++ .circleci/config.yml.in | 29 ++++++++ .circleci/regenerate.py | 10 ++- .../unittest/scripts/run_style_checks.sh | 32 +++++++++ .circleci/unittest/scripts/run_test.sh | 3 - .travis.yml | 23 ------- build_tools/travis/after_success.sh | 13 ---- build_tools/travis/install.sh | 68 ------------------- build_tools/travis/test_script.sh | 26 ------- 9 files changed, 102 insertions(+), 134 deletions(-) create mode 100755 .circleci/unittest/scripts/run_style_checks.sh delete mode 100644 .travis.yml delete mode 100644 build_tools/travis/after_success.sh delete mode 100644 build_tools/travis/install.sh delete mode 100644 build_tools/travis/test_script.sh diff --git a/.circleci/config.yml b/.circleci/config.yml index 5ce617105e..f0701b5852 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -285,6 +285,35 @@ jobs: - store_test_results: path: test-results + stylecheck: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: medium + steps: + - checkout + - run: + name: Generate cache key + # This will refresh cache on Sundays, nightly build should generate new cache. + command: echo "$(date +"%Y-%U")" > .circleci-weekly + - restore_cache: + + keys: + - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + + - run: + name: Setup + command: .circleci/unittest/scripts/setup_env.sh + - save_cache: + + key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + + paths: + - conda + - env + - run: + name: Run style check + command: .circleci/unittest/scripts/run_style_checks.sh workflows: # build: @@ -299,6 +328,9 @@ workflows: - unittest_linux: name: unittest_linux_py3.6 python_version: '3.6' + - stylecheck: + name: stylecheck_py3.6 + python_version: '3.6' - unittest_linux: name: unittest_linux_py3.7 python_version: '3.7' diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 097a6d7b19..c27a8b4dfc 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -285,6 +285,35 @@ jobs: - store_test_results: path: test-results + stylecheck: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: medium + steps: + - checkout + - run: + name: Generate cache key + # This will refresh cache on Sundays, nightly build should generate new cache. + command: echo "$(date +"%Y-%U")" > .circleci-weekly + - restore_cache: + {% raw %} + keys: + - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + {% endraw %} + paths: + - conda + - env + - run: + name: Run style check + command: .circleci/unittest/scripts/run_style_checks.sh workflows: # build: diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index ee3d57eaf0..2f387fc1a3 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -102,13 +102,21 @@ def indent(indentation, data_list): def unittest_workflows(indentation=6): w = [] for os_type in ["linux"]: - for python_version in PYTHON_VERSIONS: + for i, python_version in enumerate(PYTHON_VERSIONS): w.append({ f"unittest_{os_type}": { "name": f"unittest_{os_type}_py{python_version}", "python_version": python_version, } }) + + if i == 0: + w.append({ + f"stylecheck": { + "name": f"stylecheck_py{python_version}", + "python_version": python_version, + } + }) return indent(indentation, w) diff --git a/.circleci/unittest/scripts/run_style_checks.sh b/.circleci/unittest/scripts/run_style_checks.sh new file mode 100755 index 0000000000..acfade5349 --- /dev/null +++ b/.circleci/unittest/scripts/run_style_checks.sh @@ -0,0 +1,32 @@ +#!/usr/bin/env bash + +set -u + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +# We want to run all the style checks even if one of them fail. + +exit_status=0 + +printf "\x1b[34mRunning flake8: " +flake8 --version +printf "\x1b[0m\n" +flake8 torchtext test build_tools/setup_helpers +status=$? +exit_status="$((exit_status+status))" +if [ "${status}" -ne 0 ]; then + printf "\x1b[31mflake8 failed. Check the format of Python files.\x1b[0m\n" +fi + +printf "\x1b[34mRunning clang-format: " +clang-format --version +printf "\x1b[0m\n" +git-clang-format origin/master +git diff --exit-code +status=$? +exit_status="$((exit_status+status))" +if [ "${status}" -ne 0 ]; then + printf "\x1b[31mC++ files are not formatted. Please use git-clang-format to format CPP files.\x1b[0m\n" +fi +exit $exit_status diff --git a/.circleci/unittest/scripts/run_test.sh b/.circleci/unittest/scripts/run_test.sh index fd08ae8640..c8322ea5f9 100755 --- a/.circleci/unittest/scripts/run_test.sh +++ b/.circleci/unittest/scripts/run_test.sh @@ -7,6 +7,3 @@ conda activate ./env python -m torch.utils.collect_env pytest --cov=torchtext --junitxml=test-results/junit.xml -v --durations 20 test -flake8 torchaudio test build_tools/setup_helpers -git-clang-format origin/master -git diff --exit-code || (echo "C++ files are not formatted. Use clang-format on CPP files."; exit 1) diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 83f69ea383..0000000000 --- a/.travis.yml +++ /dev/null @@ -1,23 +0,0 @@ -dist: trusty - -language: python - -cache: - directories: - - /home/travis/download - - /home/travis/.cache/pip - -# This matrix tests that the code works on Python 3.5, -# 3.6 (same versions as PyTorch CI), and passes lint. -matrix: - fast_finish: true - include: - - env: PYTHON_VERSION="3.6" RUN_FLAKE8="true" SKIP_TESTS="true" - sudo: required - -notifications: - email: false - -install: source build_tools/travis/install.sh -script: bash build_tools/travis/test_script.sh -after_success: source build_tools/travis/after_success.sh diff --git a/build_tools/travis/after_success.sh b/build_tools/travis/after_success.sh deleted file mode 100644 index 5f672c0dbb..0000000000 --- a/build_tools/travis/after_success.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash -# This script is meant to be called by the "after_success" step defined in -# .travis.yml. See http://docs.travis-ci.com/ for more details. - -set -e - -if [[ "$COVERAGE" == "true" ]]; then - # Ignore codecov failures as the codecov server is not - # very reliable but we don't want travis to report a failure - # in the github UI just because the coverage report failed to - # be published. - codecov || echo "codecov upload failed" -fi diff --git a/build_tools/travis/install.sh b/build_tools/travis/install.sh deleted file mode 100644 index 57370136dc..0000000000 --- a/build_tools/travis/install.sh +++ /dev/null @@ -1,68 +0,0 @@ -#!/bin/bash -# This script is meant to be called by the "install" step defined in -# .travis.yml. See http://docs.travis-ci.com/ for more details. -# The behavior of the script is controlled by environment variabled defined -# in the .travis.yml in the top level folder of the project. - -set -e - -echo 'List files from cached directories' -if [ -d $HOME/download ]; then - echo 'download:' - ls $HOME/download -fi -if [ -d $HOME/.cache/pip ]; then - echo 'pip:' - ls $HOME/.cache/pip -fi - -# Deactivate the travis-provided virtual environment and setup a -# conda-based environment instead -deactivate - -# Add the miniconda bin directory to $PATH -export PATH=/home/travis/miniconda3/bin:$PATH -echo $PATH - -# Use the miniconda installer for setup of conda itself -pushd . -cd -mkdir -p download -cd download -if [[ ! -f /home/travis/miniconda3/bin/activate ]] -then - if [[ ! -f miniconda.sh ]] - then - wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \ - -O miniconda.sh - fi - chmod +x miniconda.sh && ./miniconda.sh -b -f - conda update --yes conda - echo "Creating environment to run tests in." - conda create -n testenv --yes python="$PYTHON_VERSION" -fi -cd .. -popd - -# Activate the python environment we created. -source activate testenv - -# Install requirements via pip in our conda environment -pip install -r requirements.txt - -# Install the following only if running tests -if [[ "$SKIP_TESTS" != "true" ]]; then - # SpaCy English models - python -m spacy download en - -# TODO: Add nltk data back once moses tokenizer is back online. -# https://github.com/alvations/sacremoses/issues/61 -# # NLTK data needed for Moses tokenizer -# python -m nltk.downloader perluniprops nonbreaking_prefixes -# - # PyTorch - conda install --yes pytorch -c pytorch-test - - # Installation - python setup.py install -fi diff --git a/build_tools/travis/test_script.sh b/build_tools/travis/test_script.sh deleted file mode 100644 index 200d8eb8bd..0000000000 --- a/build_tools/travis/test_script.sh +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/bash -# This script is meant to be called by the "script" step defined in -# .travis.yml. See http://docs.travis-ci.com/ for more details. -# The behavior of the script is controlled by environment variabled defined -# in the .travis.yml in the top level folder of the project. - -set -e - -python --version - -run_tests() { - if [[ "$RUN_SLOW" == "true" ]]; then - TEST_CMD="py.test --runslow -s -v --cov=torchtext --durations=20" - else - TEST_CMD="py.test -v --cov=torchtext --durations=20" - fi - $TEST_CMD -} - -if [[ "$RUN_FLAKE8" == "true" ]]; then - flake8 -fi - -if [[ "$SKIP_TESTS" != "true" ]]; then - run_tests -fi From ab3629a6bd03992e76d59eeb76b54fd527c0c6a4 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Wed, 13 May 2020 18:46:56 -0400 Subject: [PATCH 019/719] Update build badge (#769) --- README.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.rst b/README.rst index 14cff74f73..979d87407c 100644 --- a/README.rst +++ b/README.rst @@ -1,5 +1,5 @@ -.. image:: https://travis-ci.org/pytorch/text.svg?branch=master - :target: https://travis-ci.org/pytorch/text +.. image:: https://circleci.com/gh/pytorch/text.svg?style=svg + :target: https://circleci.com/gh/pytorch/text .. image:: https://codecov.io/gh/pytorch/text/branch/master/graph/badge.svg :target: https://codecov.io/gh/pytorch/text From 3bdf25ae2a27081193c0d866d736996f53c83d44 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Wed, 13 May 2020 19:12:13 -0400 Subject: [PATCH 020/719] Add binary build jobs (#766) --- .circleci/config.yml | 275 ++++++++++++++++++++++++++++++++-- .circleci/config.yml.in | 25 ++-- .circleci/regenerate.py | 4 - .gitignore | 2 + packaging/build_conda.sh | 12 ++ packaging/build_wheel.sh | 15 ++ packaging/pkg_helpers.bash | 221 +++++++++++++++++++++++++++ packaging/torchtext/bld.bat | 3 + packaging/torchtext/build.sh | 5 + packaging/torchtext/meta.yaml | 46 ++++++ setup.py | 43 ++++-- torchtext/__init__.py | 6 +- 12 files changed, 619 insertions(+), 38 deletions(-) create mode 100755 packaging/build_conda.sh create mode 100755 packaging/build_wheel.sh create mode 100644 packaging/pkg_helpers.bash create mode 100644 packaging/torchtext/bld.bat create mode 100644 packaging/torchtext/build.sh create mode 100644 packaging/torchtext/meta.yaml diff --git a/.circleci/config.yml b/.circleci/config.yml index f0701b5852..54319900ca 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -103,10 +103,13 @@ jobs: steps: - checkout - run: - # Cannot easily deduplicate this as source'ing activate - # will set environment variables which we need to propagate - # to build_wheel.sh + # Installing cmake with `brew install cmake` takes 30 mins, so we use binary distribution. command: | + curl -o cmake.tar.gz -L https://github.com/Kitware/CMake/releases/download/v3.17.2/cmake-3.17.2-Darwin-x86_64.tar.gz + tar -xzf cmake.tar.gz + cp cmake-3.17.2-Darwin-x86_64/CMake.app/Contents/bin/* /usr/local/bin/ + cp -r cmake-3.17.2-Darwin-x86_64/CMake.app/Contents/share/* /usr/local/share/ + export PATH="${PATH}:/usr/local/bin" curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh sh conda.sh -b source $HOME/miniconda3/bin/activate @@ -316,13 +319,48 @@ jobs: command: .circleci/unittest/scripts/run_style_checks.sh workflows: - # build: - # jobs: - # - circleci_consistency - # - # - binary_win_conda: - # name: torchtext_win_py3.6 - # python_version: "3.6" + build: + jobs: + - circleci_consistency + - binary_linux_wheel: + name: binary_linux_wheel_py3.6 + python_version: '3.6' + - binary_linux_wheel: + name: binary_linux_wheel_py3.7 + python_version: '3.7' + - binary_linux_wheel: + name: binary_linux_wheel_py3.8 + python_version: '3.8' + - binary_macos_wheel: + name: binary_macos_wheel_py3.6 + python_version: '3.6' + - binary_macos_wheel: + name: binary_macos_wheel_py3.7 + python_version: '3.7' + - binary_macos_wheel: + name: binary_macos_wheel_py3.8 + python_version: '3.8' + - binary_linux_conda: + name: binary_linux_conda_py3.6 + python_version: '3.6' + - binary_linux_conda: + name: binary_linux_conda_py3.7 + python_version: '3.7' + - binary_linux_conda: + name: binary_linux_conda_py3.8 + python_version: '3.8' + - binary_macos_conda: + name: binary_macos_conda_py3.6 + python_version: '3.6' + - binary_macos_conda: + name: binary_macos_conda_py3.7 + python_version: '3.7' + - binary_macos_conda: + name: binary_macos_conda_py3.8 + python_version: '3.8' + # - binary_win_conda: + # name: torchtext_win_py3.6 + # python_version: "3.6" unittest: jobs: - unittest_linux: @@ -343,7 +381,222 @@ workflows: filters: branches: only: nightly - # + - binary_linux_wheel: + filters: + branches: + only: nightly + name: nightly_binary_linux_wheel_py3.6 + python_version: '3.6' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_linux_wheel_py3.6_upload + requires: + - nightly_binary_linux_wheel_py3.6 + - smoke_test_linux_pip: + filters: + branches: + only: nightly + name: nightly_binary_linux_wheel_py3.6_smoke_test_pip + python_version: '3.6' + requires: + - nightly_binary_linux_wheel_py3.6_upload + - binary_linux_wheel: + filters: + branches: + only: nightly + name: nightly_binary_linux_wheel_py3.7 + python_version: '3.7' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_linux_wheel_py3.7_upload + requires: + - nightly_binary_linux_wheel_py3.7 + - smoke_test_linux_pip: + filters: + branches: + only: nightly + name: nightly_binary_linux_wheel_py3.7_smoke_test_pip + python_version: '3.7' + requires: + - nightly_binary_linux_wheel_py3.7_upload + - binary_linux_wheel: + filters: + branches: + only: nightly + name: nightly_binary_linux_wheel_py3.8 + python_version: '3.8' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_linux_wheel_py3.8_upload + requires: + - nightly_binary_linux_wheel_py3.8 + - smoke_test_linux_pip: + filters: + branches: + only: nightly + name: nightly_binary_linux_wheel_py3.8_smoke_test_pip + python_version: '3.8' + requires: + - nightly_binary_linux_wheel_py3.8_upload + - binary_macos_wheel: + filters: + branches: + only: nightly + name: nightly_binary_macos_wheel_py3.6 + python_version: '3.6' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_macos_wheel_py3.6_upload + requires: + - nightly_binary_macos_wheel_py3.6 + - binary_macos_wheel: + filters: + branches: + only: nightly + name: nightly_binary_macos_wheel_py3.7 + python_version: '3.7' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_macos_wheel_py3.7_upload + requires: + - nightly_binary_macos_wheel_py3.7 + - binary_macos_wheel: + filters: + branches: + only: nightly + name: nightly_binary_macos_wheel_py3.8 + python_version: '3.8' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_macos_wheel_py3.8_upload + requires: + - nightly_binary_macos_wheel_py3.8 + - binary_linux_conda: + filters: + branches: + only: nightly + name: nightly_binary_linux_conda_py3.6 + python_version: '3.6' + - binary_conda_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_linux_conda_py3.6_upload + requires: + - nightly_binary_linux_conda_py3.6 + - smoke_test_linux_conda: + filters: + branches: + only: nightly + name: nightly_binary_linux_conda_py3.6_smoke_test_conda + python_version: '3.6' + requires: + - nightly_binary_linux_conda_py3.6_upload + - binary_linux_conda: + filters: + branches: + only: nightly + name: nightly_binary_linux_conda_py3.7 + python_version: '3.7' + - binary_conda_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_linux_conda_py3.7_upload + requires: + - nightly_binary_linux_conda_py3.7 + - smoke_test_linux_conda: + filters: + branches: + only: nightly + name: nightly_binary_linux_conda_py3.7_smoke_test_conda + python_version: '3.7' + requires: + - nightly_binary_linux_conda_py3.7_upload + - binary_linux_conda: + filters: + branches: + only: nightly + name: nightly_binary_linux_conda_py3.8 + python_version: '3.8' + - binary_conda_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_linux_conda_py3.8_upload + requires: + - nightly_binary_linux_conda_py3.8 + - smoke_test_linux_conda: + filters: + branches: + only: nightly + name: nightly_binary_linux_conda_py3.8_smoke_test_conda + python_version: '3.8' + requires: + - nightly_binary_linux_conda_py3.8_upload + - binary_macos_conda: + filters: + branches: + only: nightly + name: nightly_binary_macos_conda_py3.6 + python_version: '3.6' + - binary_conda_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_macos_conda_py3.6_upload + requires: + - nightly_binary_macos_conda_py3.6 + - binary_macos_conda: + filters: + branches: + only: nightly + name: nightly_binary_macos_conda_py3.7 + python_version: '3.7' + - binary_conda_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_macos_conda_py3.7_upload + requires: + - nightly_binary_macos_conda_py3.7 + - binary_macos_conda: + filters: + branches: + only: nightly + name: nightly_binary_macos_conda_py3.8 + python_version: '3.8' + - binary_conda_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_macos_conda_py3.8_upload + requires: + - nightly_binary_macos_conda_py3.8 docker_build: triggers: - schedule: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index c27a8b4dfc..3bdffe4038 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -103,10 +103,13 @@ jobs: steps: - checkout - run: - # Cannot easily deduplicate this as source'ing activate - # will set environment variables which we need to propagate - # to build_wheel.sh + # Installing cmake with `brew install cmake` takes 30 mins, so we use binary distribution. command: | + curl -o cmake.tar.gz -L https://github.com/Kitware/CMake/releases/download/v3.17.2/cmake-3.17.2-Darwin-x86_64.tar.gz + tar -xzf cmake.tar.gz + cp cmake-3.17.2-Darwin-x86_64/CMake.app/Contents/bin/* /usr/local/bin/ + cp -r cmake-3.17.2-Darwin-x86_64/CMake.app/Contents/share/* /usr/local/share/ + export PATH="${PATH}:/usr/local/bin" curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh sh conda.sh -b source $HOME/miniconda3/bin/activate @@ -316,13 +319,13 @@ jobs: command: .circleci/unittest/scripts/run_style_checks.sh workflows: - # build: - # jobs: - # - circleci_consistency - # {{ build_workflows() }} - # - binary_win_conda: - # name: torchtext_win_py3.6 - # python_version: "3.6" + build: + jobs: + - circleci_consistency + {{ build_workflows() }} + # - binary_win_conda: + # name: torchtext_win_py3.6 + # python_version: "3.6" unittest: jobs: {{ unittest_workflows() }} @@ -332,7 +335,7 @@ workflows: filters: branches: only: nightly - # {{ build_workflows(prefix="nightly_", filter_branch="nightly", upload=True) }} + {{ build_workflows(prefix="nightly_", filter_branch="nightly", upload=True) }} docker_build: triggers: - schedule: diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index 2f387fc1a3..ab559e30a0 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -23,16 +23,12 @@ def build_workflows(prefix='', upload=False, filter_branch=None, indentation=6): - ''' w = [] for btype in ["wheel", "conda"]: for os_type in ["linux", "macos"]: for python_version in PYTHON_VERSIONS: w += build_workflow_pair(btype, os_type, python_version, filter_branch, prefix, upload) - return indent(indentation, w) - ''' - return '' def build_workflow_pair(btype, os_type, python_version, filter_branch, prefix='', upload=False): diff --git a/.gitignore b/.gitignore index 8e2279529b..b22fac681a 100644 --- a/.gitignore +++ b/.gitignore @@ -120,3 +120,5 @@ venv.bak/ # vim *.swp *.swo + +torchtext/version.py \ No newline at end of file diff --git a/packaging/build_conda.sh b/packaging/build_conda.sh new file mode 100755 index 0000000000..0c522ad0e7 --- /dev/null +++ b/packaging/build_conda.sh @@ -0,0 +1,12 @@ +#!/bin/bash +set -ex + +script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +. "$script_dir/pkg_helpers.bash" + +export BUILD_TYPE="conda" +export NO_CUDA_PACKAGE=1 +setup_env 0.6.0 +export SOURCE_ROOT_DIR="$PWD" +setup_conda_pytorch_constraint +conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload --python "$PYTHON_VERSION" packaging/torchtext diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh new file mode 100755 index 0000000000..00852ea7b0 --- /dev/null +++ b/packaging/build_wheel.sh @@ -0,0 +1,15 @@ +#!/bin/bash +set -ex + +script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +. "$script_dir/pkg_helpers.bash" + +export BUILD_TYPE="wheel" +export NO_CUDA_PACKAGE=1 +setup_env 0.6.0 +setup_wheel_python +pip_install numpy future +setup_pip_pytorch_version +git submodule update --init --recursive +python setup.py clean +python setup.py bdist_wheel diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash new file mode 100644 index 0000000000..bc8ec997df --- /dev/null +++ b/packaging/pkg_helpers.bash @@ -0,0 +1,221 @@ +# A set of useful bash functions for common functionality we need to do in +# many build scripts + + +# Setup CUDA environment variables, based on CU_VERSION +# +# Inputs: +# CU_VERSION (cpu, cu92, cu100) +# NO_CUDA_PACKAGE (bool) +# BUILD_TYPE (conda, wheel) +# +# Outputs: +# VERSION_SUFFIX (e.g., "") +# PYTORCH_VERSION_SUFFIX (e.g., +cpu) +# WHEEL_DIR (e.g., cu100/) +# CUDA_HOME (e.g., /usr/local/cuda-9.2, respected by torch.utils.cpp_extension) +# FORCE_CUDA (respected by torchvision setup.py) +# NVCC_FLAGS (respected by torchvision setup.py) +# +# Precondition: CUDA versions are installed in their conventional locations in +# /usr/local/cuda-* +# +# NOTE: Why VERSION_SUFFIX versus PYTORCH_VERSION_SUFFIX? If you're building +# a package with CUDA on a platform we support CUDA on, VERSION_SUFFIX == +# PYTORCH_VERSION_SUFFIX and everyone is happy. However, if you are building a +# package with only CPU bits (e.g., torchaudio), then VERSION_SUFFIX is always +# empty, but PYTORCH_VERSION_SUFFIX is +cpu (because that's how you get a CPU +# version of a Python package. But that doesn't apply if you're on OS X, +# since the default CU_VERSION on OS X is cpu. +setup_cuda() { + + # First, compute version suffixes. By default, assume no version suffixes + export VERSION_SUFFIX="" + export PYTORCH_VERSION_SUFFIX="" + export WHEEL_DIR="" + # Wheel builds need suffixes (but not if they're on OS X, which never has suffix) + if [[ "$BUILD_TYPE" == "wheel" ]] && [[ "$(uname)" != Darwin ]]; then + # The default CUDA has no suffix + if [[ "$CU_VERSION" != "cu100" ]]; then + export PYTORCH_VERSION_SUFFIX="+$CU_VERSION" + fi + # Match the suffix scheme of pytorch, unless this package does not have + # CUDA builds (in which case, use default) + if [[ -z "$NO_CUDA_PACKAGE" ]]; then + export VERSION_SUFFIX="$PYTORCH_VERSION_SUFFIX" + # If the suffix is non-empty, we will use a wheel subdirectory + if [[ -n "$PYTORCH_VERSION_SUFFIX" ]]; then + export WHEEL_DIR="$PYTORCH_VERSION_SUFFIX/" + fi + fi + fi + + # Now work out the CUDA settings + case "$CU_VERSION" in + cu100) + export CUDA_HOME=/usr/local/cuda-10.0/ + export FORCE_CUDA=1 + # Hard-coding gencode flags is temporary situation until + # https://github.com/pytorch/pytorch/pull/23408 lands + export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_50,code=compute_50" + ;; + cu92) + export CUDA_HOME=/usr/local/cuda-9.2/ + export FORCE_CUDA=1 + export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_50,code=compute_50" + ;; + cpu) + ;; + *) + echo "Unrecognized CU_VERSION=$CU_VERSION" + exit 1 + ;; + esac +} + +# Populate build version if necessary, and add version suffix +# +# Inputs: +# BUILD_VERSION (e.g., 0.2.0 or empty) +# VERSION_SUFFIX (e.g., +cpu) +# +# Outputs: +# BUILD_VERSION (e.g., 0.2.0.dev20190807+cpu) +# +# Fill BUILD_VERSION if it doesn't exist already with a nightly string +# Usage: setup_build_version 0.2.0 +setup_build_version() { + if [[ -z "$BUILD_VERSION" ]]; then + export BUILD_VERSION="$1.dev$(date "+%Y%m%d")$VERSION_SUFFIX" + else + export BUILD_VERSION="$BUILD_VERSION$VERSION_SUFFIX" + fi +} + +# Set some useful variables for OS X, if applicable +setup_macos() { + if [[ "$(uname)" == Darwin ]]; then + export MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ + fi +} + +# Top-level entry point for things every package will need to do +# +# Usage: setup_env 0.2.0 +setup_env() { + setup_cuda + setup_build_version "$1" + setup_macos +} + +# Function to retry functions that sometimes timeout or have flaky failures +retry () { + $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) +} + +# Inputs: +# PYTHON_VERSION (2.7, 3.5, 3.6, 3.7) +# UNICODE_ABI (bool) +# +# Outputs: +# PATH modified to put correct Python version in PATH +# +# Precondition: If Linux, you are in a soumith/manylinux-cuda* Docker image +setup_wheel_python() { + if [[ "$(uname)" == Darwin ]]; then + eval "$(conda shell.bash hook)" + conda env remove -n "env$PYTHON_VERSION" || true + conda create -yn "env$PYTHON_VERSION" python="$PYTHON_VERSION" + conda activate "env$PYTHON_VERSION" + else + case "$PYTHON_VERSION" in + 2.7) + if [[ -n "$UNICODE_ABI" ]]; then + python_abi=cp27-cp27mu + else + python_abi=cp27-cp27m + fi + ;; + 3.5) python_abi=cp35-cp35m ;; + 3.6) python_abi=cp36-cp36m ;; + 3.7) python_abi=cp37-cp37m ;; + 3.8) python_abi=cp38-cp38 ;; + *) + echo "Unrecognized PYTHON_VERSION=$PYTHON_VERSION" + exit 1 + ;; + esac + export PATH="/opt/python/$python_abi/bin:$PATH" + fi +} + +# Install with pip a bit more robustly than the default +pip_install() { + retry pip install --progress-bar off "$@" +} + +# Install torch with pip, respecting PYTORCH_VERSION, and record the installed +# version into PYTORCH_VERSION, if applicable +setup_pip_pytorch_version() { + if [[ -z "$PYTORCH_VERSION" ]]; then + # Install latest prerelease version of torch, per our nightlies, consistent + # with the requested cuda version + pip_install --pre torch -f "https://download.pytorch.org/whl/nightly/${WHEEL_DIR}torch_nightly.html" + if [[ "$CUDA_VERSION" == "cpu" ]]; then + # CUDA and CPU are ABI compatible on the CPU-only parts, so strip + # in this case + export PYTORCH_VERSION="$(pip show torch | grep ^Version: | sed 's/Version: *//' | sed 's/+.\+//')" + else + export PYTORCH_VERSION="$(pip show torch | grep ^Version: | sed 's/Version: *//')" + fi + else + pip_install "torch==$PYTORCH_VERSION$PYTORCH_VERSION_SUFFIX" \ + -f https://download.pytorch.org/whl/torch_stable.html \ + -f https://download.pytorch.org/whl/nightly/torch_nightly.html + fi +} + +# Fill PYTORCH_VERSION with the latest conda nightly version, and +# CONDA_CHANNEL_FLAGS with appropriate flags to retrieve these versions +# +# You MUST have populated PYTORCH_VERSION_SUFFIX before hand. +setup_conda_pytorch_constraint() { + if [[ -z "$PYTORCH_VERSION" ]]; then + export CONDA_CHANNEL_FLAGS="-c pytorch-nightly" + export PYTORCH_VERSION="$(conda search --json 'pytorch[channel=pytorch-nightly]' | python -c "import sys, json, re; print(re.sub(r'\\+.*$', '', json.load(sys.stdin)['pytorch'][-1]['version']))")" + else + export CONDA_CHANNEL_FLAGS="-c pytorch -c pytorch-nightly" + fi + if [[ "$CU_VERSION" == cpu ]]; then + export CONDA_PYTORCH_BUILD_CONSTRAINT="- pytorch==$PYTORCH_VERSION${PYTORCH_VERSION_SUFFIX}" + export CONDA_PYTORCH_CONSTRAINT="- pytorch==$PYTORCH_VERSION" + else + export CONDA_PYTORCH_BUILD_CONSTRAINT="- pytorch==${PYTORCH_VERSION}${PYTORCH_VERSION_SUFFIX}" + export CONDA_PYTORCH_CONSTRAINT="- pytorch==${PYTORCH_VERSION}${PYTORCH_VERSION_SUFFIX}" + fi +} + +# Translate CUDA_VERSION into CUDA_CUDATOOLKIT_CONSTRAINT +setup_conda_cudatoolkit_constraint() { + export CONDA_CPUONLY_FEATURE="" + if [[ "$(uname)" == Darwin ]]; then + export CONDA_CUDATOOLKIT_CONSTRAINT="" + else + case "$CU_VERSION" in + cu100) + export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=10.0,<10.1 # [not osx]" + ;; + cu92) + export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=9.2,<9.3 # [not osx]" + ;; + cpu) + export CONDA_CUDATOOLKIT_CONSTRAINT="" + export CONDA_CPUONLY_FEATURE="- cpuonly" + ;; + *) + echo "Unrecognized CU_VERSION=$CU_VERSION" + exit 1 + ;; + esac + fi +} diff --git a/packaging/torchtext/bld.bat b/packaging/torchtext/bld.bat new file mode 100644 index 0000000000..f220361019 --- /dev/null +++ b/packaging/torchtext/bld.bat @@ -0,0 +1,3 @@ +@echo off + +python setup.py install --single-version-externally-managed --record=record.txt diff --git a/packaging/torchtext/build.sh b/packaging/torchtext/build.sh new file mode 100644 index 0000000000..5888bd687f --- /dev/null +++ b/packaging/torchtext/build.sh @@ -0,0 +1,5 @@ +#!/usr/bin/env bash +set -ex + +git submodule update --init --recursive +python setup.py install --single-version-externally-managed --record=record.txt diff --git a/packaging/torchtext/meta.yaml b/packaging/torchtext/meta.yaml new file mode 100644 index 0000000000..a79d8eafb7 --- /dev/null +++ b/packaging/torchtext/meta.yaml @@ -0,0 +1,46 @@ +package: + name: torchtext + version: "{{ environ.get('BUILD_VERSION') }}" + +source: + path: "{{ environ.get('SOURCE_ROOT_DIR') }}" + +requirements: + build: + - {{ compiler('c') }} # [win] + - {{ compiler('cxx') }} # [win] + - cmake + + host: + - python + - setuptools + - cpuonly + {{ environ.get('CONDA_PYTORCH_BUILD_CONSTRAINT') }} + + run: + - python + - requests + - tqdm + {{ environ.get('CONDA_PYTORCH_CONSTRAINT') }} + +build: + string: py{{py}} + +test: + imports: + - torchtext + - torchtext.datasets + - torchtext.data + - torchtext.experimental + + source_files: + - test + + requires: + - pytest + +about: + home: https://github.com/pytorch/text + license: BSD + license_file: LICENSE + summary: 'Data loaders and abstractions for text and NLP' diff --git a/setup.py b/setup.py index 25c5646636..e93583c5b5 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,8 @@ #!/usr/bin/env python import io -import re +import os import shutil +import subprocess from pathlib import Path import distutils.command.clean from setuptools import setup, find_packages @@ -16,17 +17,37 @@ def read(*names, **kwargs): return fp.read() -def find_version(*file_paths): - version_file = read(*file_paths) - version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", - version_file, re.M) - if version_match: - return version_match.group(1) - raise RuntimeError("Unable to find version string.") +def _get_version(): + version = '0.6.0a0' + sha = None + try: + cmd = ['git', 'rev-parse', 'HEAD'] + sha = subprocess.check_output(cmd, cwd=str(ROOT_DIR)).decode('ascii').strip() + except Exception: + pass -VERSION = find_version('torchtext', '__init__.py') -long_description = read('README.rst') + if os.getenv('BUILD_VERSION'): + version = os.getenv('BUILD_VERSION') + elif sha is not None: + version += '+' + sha[:7] + + if sha is None: + sha = 'Unknown' + return version, sha + + +def _export_version(version, sha): + version_path = ROOT_DIR / 'torchtext' / 'version.py' + with open(version_path, 'w') as fileobj: + fileobj.write("__version__ = '{}'\n".format(version)) + fileobj.write("git_version = {}\n".format(repr(sha))) + + +VERSION, SHA = _get_version() +_export_version(VERSION, SHA) + +print('-- Building version ' + VERSION) class clean(distutils.command.clean.clean): @@ -57,7 +78,7 @@ def run(self): author_email='jekbradbury@gmail.com', url='https://github.com/pytorch/text', description='Text utilities and datasets for PyTorch', - long_description=long_description, + long_description=read('README.rst'), license='BSD', install_requires=[ diff --git a/torchtext/__init__.py b/torchtext/__init__.py index b73f2cc6ce..c19fa01ec8 100644 --- a/torchtext/__init__.py +++ b/torchtext/__init__.py @@ -4,7 +4,11 @@ from . import vocab from . import experimental -__version__ = '0.6.0' + +try: + from .version import __version__, git_version # noqa: F401 +except ImportError: + pass __all__ = ['data', 'datasets', From 75a6e9085a57419fb170bbdbf6a27afcdcf86659 Mon Sep 17 00:00:00 2001 From: peterjc123 Date: Thu, 14 May 2020 22:40:48 +0800 Subject: [PATCH 021/719] Add Windows unit tests (#772) * Add Windows unit tests --- .circleci/config.yml | 94 ++++++++++++++++--- .circleci/config.yml.in | 85 ++++++++++++++--- .circleci/regenerate.py | 4 +- .../{ => linux}/scripts/environment.yml | 0 .../unittest/{ => linux}/scripts/install.sh | 0 .../{ => linux}/scripts/post_process.sh | 0 .../{ => linux}/scripts/run_style_checks.sh | 0 .../unittest/{ => linux}/scripts/run_test.sh | 0 .../unittest/{ => linux}/scripts/setup_env.sh | 0 .../unittest/windows/scripts/environment.yml | 20 ++++ .../unittest/windows/scripts/install.bat | 20 ++++ .circleci/unittest/windows/scripts/install.sh | 23 +++++ .../windows/scripts/install_conda.bat | 1 + .../unittest/windows/scripts/post_process.sh | 8 ++ .../unittest/windows/scripts/run_test.sh | 9 ++ .../unittest/windows/scripts/setup_env.sh | 42 +++++++++ build_tools/setup_helpers/extension.py | 34 +++++-- test/common/torchtext_test_case.py | 2 +- test/data/test_dataset.py | 5 +- test/data/test_functional.py | 4 + torchtext/csrc/sentencepiece.cpp | 9 ++ 21 files changed, 318 insertions(+), 42 deletions(-) rename .circleci/unittest/{ => linux}/scripts/environment.yml (100%) rename .circleci/unittest/{ => linux}/scripts/install.sh (100%) rename .circleci/unittest/{ => linux}/scripts/post_process.sh (100%) rename .circleci/unittest/{ => linux}/scripts/run_style_checks.sh (100%) rename .circleci/unittest/{ => linux}/scripts/run_test.sh (100%) rename .circleci/unittest/{ => linux}/scripts/setup_env.sh (100%) create mode 100644 .circleci/unittest/windows/scripts/environment.yml create mode 100644 .circleci/unittest/windows/scripts/install.bat create mode 100644 .circleci/unittest/windows/scripts/install.sh create mode 100644 .circleci/unittest/windows/scripts/install_conda.bat create mode 100644 .circleci/unittest/windows/scripts/post_process.sh create mode 100644 .circleci/unittest/windows/scripts/run_test.sh create mode 100644 .circleci/unittest/windows/scripts/setup_env.sh diff --git a/.circleci/config.yml b/.circleci/config.yml index 54319900ca..9dd274b411 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -9,6 +9,13 @@ version: 2.1 orbs: win: circleci/windows@2.0.0 +executors: + windows-cpu: + machine: + resource_class: windows.xlarge + image: windows-server-2019-vs2019:stable + shell: bash.exe + binary_common: &binary_common parameters: # Edit these defaults to do a release @@ -250,41 +257,92 @@ jobs: - restore_cache: keys: - - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} - run: name: Setup - command: .circleci/unittest/scripts/setup_env.sh + command: .circleci/unittest/linux/scripts/setup_env.sh - save_cache: - key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} paths: - conda - env - run: name: Install torchtext - command: .circleci/unittest/scripts/install.sh + command: .circleci/unittest/linux/scripts/install.sh - restore_cache: keys: - - data-v1-{{ checksum ".circleci-weekly" }} + - data-linux-v1-{{ checksum ".circleci-weekly" }} - run: name: Run tests # Downloading embedding vector takes long time. no_output_timeout: 30m - command: .circleci/unittest/scripts/run_test.sh + command: .circleci/unittest/linux/scripts/run_test.sh - save_cache: - key: data-v1-{{ checksum ".circleci-weekly" }} + key: data-linux-v1-{{ checksum ".circleci-weekly" }} paths: - .vector_cache - .data - run: name: Post process - command: .circleci/unittest/scripts/post_process.sh + command: .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_windows: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - run: + name: Generate cache key + # This will refresh cache on Sundays, nightly build should generate new cache. + command: echo "$(date +"%Y-%U")" > .circleci-weekly + - restore_cache: + + keys: + - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + + - run: + name: Setup + command: .circleci/unittest/windows/scripts/setup_env.sh + - save_cache: + + key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + + paths: + - conda + - env + - run: + name: Install torchtext + command: .circleci/unittest/windows/scripts/install.sh + - restore_cache: + keys: + + - data-windows-v1-{{ checksum ".circleci-weekly" }} + + - run: + name: Run tests + # Downloading embedding vector takes long time. + no_output_timeout: 30m + command: .circleci/unittest/windows/scripts/run_test.sh + - save_cache: + + key: data-windows-v1-{{ checksum ".circleci-weekly" }} + + paths: + - .vector_cache + - .data + - run: + name: Post process + command: .circleci/unittest/windows/scripts/post_process.sh - store_test_results: path: test-results @@ -302,21 +360,21 @@ jobs: - restore_cache: keys: - - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} - run: name: Setup - command: .circleci/unittest/scripts/setup_env.sh + command: .circleci/unittest/linux/scripts/setup_env.sh - save_cache: - key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} paths: - conda - env - run: name: Run style check - command: .circleci/unittest/scripts/run_style_checks.sh + command: .circleci/unittest/linux/scripts/run_style_checks.sh workflows: build: @@ -358,9 +416,6 @@ workflows: - binary_macos_conda: name: binary_macos_conda_py3.8 python_version: '3.8' - # - binary_win_conda: - # name: torchtext_win_py3.6 - # python_version: "3.6" unittest: jobs: - unittest_linux: @@ -375,6 +430,15 @@ workflows: - unittest_linux: name: unittest_linux_py3.8 python_version: '3.8' + - unittest_windows: + name: unittest_windows_py3.6 + python_version: '3.6' + - unittest_windows: + name: unittest_windows_py3.7 + python_version: '3.7' + - unittest_windows: + name: unittest_windows_py3.8 + python_version: '3.8' nightly: jobs: - circleci_consistency: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 3bdffe4038..da4e31f314 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -9,6 +9,13 @@ version: 2.1 orbs: win: circleci/windows@2.0.0 +executors: + windows-cpu: + machine: + resource_class: windows.xlarge + image: windows-server-2019-vs2019:stable + shell: bash.exe + binary_common: &binary_common parameters: # Edit these defaults to do a release @@ -250,41 +257,92 @@ jobs: - restore_cache: {% raw %} keys: - - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + {% endraw %} + paths: + - conda + - env + - run: + name: Install torchtext + command: .circleci/unittest/linux/scripts/install.sh + - restore_cache: + keys: + {% raw %} + - data-linux-v1-{{ checksum ".circleci-weekly" }} + {% endraw %} + - run: + name: Run tests + # Downloading embedding vector takes long time. + no_output_timeout: 30m + command: .circleci/unittest/linux/scripts/run_test.sh + - save_cache: + {% raw %} + key: data-linux-v1-{{ checksum ".circleci-weekly" }} + {% endraw %} + paths: + - .vector_cache + - .data + - run: + name: Post process + command: .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_windows: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - run: + name: Generate cache key + # This will refresh cache on Sundays, nightly build should generate new cache. + command: echo "$(date +"%Y-%U")" > .circleci-weekly + - restore_cache: + {% raw %} + keys: + - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} {% endraw %} - run: name: Setup - command: .circleci/unittest/scripts/setup_env.sh + command: .circleci/unittest/windows/scripts/setup_env.sh - save_cache: {% raw %} - key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} {% endraw %} paths: - conda - env - run: name: Install torchtext - command: .circleci/unittest/scripts/install.sh + command: .circleci/unittest/windows/scripts/install.sh - restore_cache: keys: {% raw %} - - data-v1-{{ checksum ".circleci-weekly" }} + - data-windows-v1-{{ checksum ".circleci-weekly" }} {% endraw %} - run: name: Run tests # Downloading embedding vector takes long time. no_output_timeout: 30m - command: .circleci/unittest/scripts/run_test.sh + command: .circleci/unittest/windows/scripts/run_test.sh - save_cache: {% raw %} - key: data-v1-{{ checksum ".circleci-weekly" }} + key: data-windows-v1-{{ checksum ".circleci-weekly" }} {% endraw %} paths: - .vector_cache - .data - run: name: Post process - command: .circleci/unittest/scripts/post_process.sh + command: .circleci/unittest/windows/scripts/post_process.sh - store_test_results: path: test-results @@ -302,30 +360,27 @@ jobs: - restore_cache: {% raw %} keys: - - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + - env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} {% endraw %} - run: name: Setup - command: .circleci/unittest/scripts/setup_env.sh + command: .circleci/unittest/linux/scripts/setup_env.sh - save_cache: {% raw %} - key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} + key: env-v1-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }} {% endraw %} paths: - conda - env - run: name: Run style check - command: .circleci/unittest/scripts/run_style_checks.sh + command: .circleci/unittest/linux/scripts/run_style_checks.sh workflows: build: jobs: - circleci_consistency {{ build_workflows() }} - # - binary_win_conda: - # name: torchtext_win_py3.6 - # python_version: "3.6" unittest: jobs: {{ unittest_workflows() }} diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index ab559e30a0..58c4544205 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -97,7 +97,7 @@ def indent(indentation, data_list): def unittest_workflows(indentation=6): w = [] - for os_type in ["linux"]: + for os_type in ["linux", "windows"]: for i, python_version in enumerate(PYTHON_VERSIONS): w.append({ f"unittest_{os_type}": { @@ -106,7 +106,7 @@ def unittest_workflows(indentation=6): } }) - if i == 0: + if i == 0 and os_type == "linux": w.append({ f"stylecheck": { "name": f"stylecheck_py{python_version}", diff --git a/.circleci/unittest/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml similarity index 100% rename from .circleci/unittest/scripts/environment.yml rename to .circleci/unittest/linux/scripts/environment.yml diff --git a/.circleci/unittest/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh similarity index 100% rename from .circleci/unittest/scripts/install.sh rename to .circleci/unittest/linux/scripts/install.sh diff --git a/.circleci/unittest/scripts/post_process.sh b/.circleci/unittest/linux/scripts/post_process.sh similarity index 100% rename from .circleci/unittest/scripts/post_process.sh rename to .circleci/unittest/linux/scripts/post_process.sh diff --git a/.circleci/unittest/scripts/run_style_checks.sh b/.circleci/unittest/linux/scripts/run_style_checks.sh similarity index 100% rename from .circleci/unittest/scripts/run_style_checks.sh rename to .circleci/unittest/linux/scripts/run_style_checks.sh diff --git a/.circleci/unittest/scripts/run_test.sh b/.circleci/unittest/linux/scripts/run_test.sh similarity index 100% rename from .circleci/unittest/scripts/run_test.sh rename to .circleci/unittest/linux/scripts/run_test.sh diff --git a/.circleci/unittest/scripts/setup_env.sh b/.circleci/unittest/linux/scripts/setup_env.sh similarity index 100% rename from .circleci/unittest/scripts/setup_env.sh rename to .circleci/unittest/linux/scripts/setup_env.sh diff --git a/.circleci/unittest/windows/scripts/environment.yml b/.circleci/unittest/windows/scripts/environment.yml new file mode 100644 index 0000000000..7e68b8da9b --- /dev/null +++ b/.circleci/unittest/windows/scripts/environment.yml @@ -0,0 +1,20 @@ +channels: + - defaults +dependencies: + - flake8==3.7.9 + - codecov + - pip + - pip: + - nltk + - requests + - revtok + - pytest + - pytest-cov + - pytest-pythonpath + - sacremoses + - spacy + - sphinx + - sphinx-rtd-theme + - tqdm + - sentencepiece + - future diff --git a/.circleci/unittest/windows/scripts/install.bat b/.circleci/unittest/windows/scripts/install.bat new file mode 100644 index 0000000000..fe57e2d61d --- /dev/null +++ b/.circleci/unittest/windows/scripts/install.bat @@ -0,0 +1,20 @@ +@echo on + +for /f "usebackq tokens=*" %%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -legacy -products * -version [15^,17^) -property installationPath`) do ( + if exist "%%i" if exist "%%i\VC\Auxiliary\Build\vcvarsall.bat" ( + set "VS15INSTALLDIR=%%i" + set "VS15VCVARSALL=%%i\VC\Auxiliary\Build\vcvarsall.bat" + goto vswhere + ) +) + +:vswhere +if "%VSDEVCMD_ARGS%" == "" ( + call "%VS15VCVARSALL%" x64 || exit /b 1 +) else ( + call "%VS15VCVARSALL%" x64 %VSDEVCMD_ARGS% || exit /b 1 +) + +@echo on + +python setup.py develop || exit /b 1 diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh new file mode 100644 index 0000000000..a44174da07 --- /dev/null +++ b/.circleci/unittest/windows/scripts/install.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash + +unset PYTORCH_VERSION +# For unittest, nightly PyTorch is used as the following section, +# so no need to set PYTORCH_VERSION. +# In fact, keeping PYTORCH_VERSION forces us to hardcode PyTorch version in config. + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +root_dir="$(git rev-parse --show-toplevel)" + +cd "${root_dir}" + +eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" +conda activate ./env + +printf "* Installing PyTorch nightly build\n" +conda install -y -c pytorch-nightly pytorch cpuonly + +printf "* Installing torchtext\n" +git submodule update --init --recursive +"$this_dir/install.bat" diff --git a/.circleci/unittest/windows/scripts/install_conda.bat b/.circleci/unittest/windows/scripts/install_conda.bat new file mode 100644 index 0000000000..6052ad08b1 --- /dev/null +++ b/.circleci/unittest/windows/scripts/install_conda.bat @@ -0,0 +1 @@ +start /wait "" "%miniconda_exe%" /S /InstallationType=JustMe /RegisterPython=0 /AddToPath=0 /D=%tmp_conda% diff --git a/.circleci/unittest/windows/scripts/post_process.sh b/.circleci/unittest/windows/scripts/post_process.sh new file mode 100644 index 0000000000..b132113194 --- /dev/null +++ b/.circleci/unittest/windows/scripts/post_process.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" +conda activate ./env + +codecov diff --git a/.circleci/unittest/windows/scripts/run_test.sh b/.circleci/unittest/windows/scripts/run_test.sh new file mode 100644 index 0000000000..909177e2d4 --- /dev/null +++ b/.circleci/unittest/windows/scripts/run_test.sh @@ -0,0 +1,9 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" +conda activate ./env + +python -m torch.utils.collect_env +pytest --cov=torchtext --junitxml=test-results/junit.xml -v --durations 20 test diff --git a/.circleci/unittest/windows/scripts/setup_env.sh b/.circleci/unittest/windows/scripts/setup_env.sh new file mode 100644 index 0000000000..cd44b436dc --- /dev/null +++ b/.circleci/unittest/windows/scripts/setup_env.sh @@ -0,0 +1,42 @@ +#!/usr/bin/env bash + +# This script is for setting up environment in which unit test is ran. +# To speed up the CI time, the resulting environment is cached. +# +# Do not install PyTorch and torchtext here, otherwise they also get cached. + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +root_dir="$(git rev-parse --show-toplevel)" +conda_dir="${root_dir}/conda" +env_dir="${root_dir}/env" + +cd "${root_dir}" + +# 1. Install conda at ./conda +if [ ! -d "${conda_dir}" ]; then + printf "* Installing conda\n" + export tmp_conda="$(echo $conda_dir | tr '/' '\\')" + export miniconda_exe="$(echo $root_dir | tr '/' '\\')\\miniconda.exe" + curl --output miniconda.exe https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe -O + "$this_dir/install_conda.bat" + unset tmp_conda + unset miniconda_exe +fi +eval "$(${conda_dir}/Scripts/conda.exe 'shell.bash' 'hook')" + +# 2. Create test environment at ./env +if [ ! -d "${env_dir}" ]; then + printf "* Creating a test environment\n" + conda create --prefix "${env_dir}" -y python="$PYTHON_VERSION" +fi +conda activate "${env_dir}" + +# 3. Install Conda dependencies +printf "* Installing dependencies (except PyTorch)\n" +conda env update --file "${this_dir}/environment.yml" --prune + +# 4. Download +printf "* Downloading SpaCy English models\n" +python -m spacy download en diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 60b89086ae..80d515d998 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -1,3 +1,4 @@ +import os import platform import subprocess from pathlib import Path @@ -20,10 +21,15 @@ def _get_eca(debug): eca = [] + if platform.system() == "Windows": + eca += ['/MT'] if debug: eca += ["-O0", "-g"] else: - eca += ["-O3"] + if platform.system() == "Windows": + eca += ['-O2'] + else: + eca += ["-O3"] return eca @@ -35,7 +41,8 @@ def _get_ela(debug): else: ela += ["-O0", "-g"] else: - ela += ["-O3"] + if platform.system() != "Windows": + ela += ["-O3"] return ela @@ -74,23 +81,34 @@ def _get_libraries(): ] -def _build_sentence_piece(): +def _build_sentence_piece(debug): build_dir = _TP_BASE_DIR / 'sentencepiece' / 'build' build_dir.mkdir(exist_ok=True) + build_env = os.environ.copy() + config = 'Debug' if debug else 'Release' + if platform.system() == 'Windows': + extra_args = ['-GNinja'] + build_env.setdefault('CC', 'cl') + build_env.setdefault('CXX', 'cl') + else: + extra_args = [] subprocess.run( - args=['cmake', '-DSPM_ENABLE_SHARED=OFF', f'-DCMAKE_INSTALL_PREFIX={_TP_INSTALL_DIR}', '..'], + args=['cmake', f'-DSPM_ENABLE_SHARED=OFF', f'-DCMAKE_INSTALL_PREFIX={_TP_INSTALL_DIR}', + f'-DCMAKE_BUILD_TYPE={config}'] + extra_args + ['..'], cwd=str(build_dir), check=True, + env=build_env, ) subprocess.run( - args=['make', 'install'], + args=['cmake', '--build', '.', '--target', 'install', '--config', config], cwd=str(build_dir), check=True, + env=build_env, ) -def _configure_third_party(): - _build_sentence_piece() +def _configure_third_party(debug): + _build_sentence_piece(debug) _EXT_NAME = 'torchtext._torchtext' @@ -113,5 +131,5 @@ def get_ext_modules(debug=False): class BuildExtension(TorchBuildExtension): def build_extension(self, ext): if ext.name == _EXT_NAME: - _configure_third_party() + _configure_third_party(self.debug) super().build_extension(ext) diff --git a/test/common/torchtext_test_case.py b/test/common/torchtext_test_case.py index fb62fb3403..249e5aa0c9 100644 --- a/test/common/torchtext_test_case.py +++ b/test/common/torchtext_test_case.py @@ -56,7 +56,7 @@ def write_test_ppid_dataset(self, data_format="csv"): "question2": "2+2=?", "label": "1"}, ] - with open(self.test_ppid_dataset_path, "w") as test_ppid_dataset_file: + with open(self.test_ppid_dataset_path, "w", encoding="utf-8") as test_ppid_dataset_file: for example in dict_dataset: if data_format == "json": test_ppid_dataset_file.write(json.dumps(example) + "\n") diff --git a/test/data/test_dataset.py b/test/data/test_dataset.py index 6b3e974f75..ae3d3fbb8d 100644 --- a/test/data/test_dataset.py +++ b/test/data/test_dataset.py @@ -1,11 +1,13 @@ # -*- coding: utf-8 -*- import torchtext.data as data +import os +import sys import tempfile +import unittest import pytest from ..common.torchtext_test_case import TorchtextTestCase -import os class TestDataset(TorchtextTestCase): @@ -235,6 +237,7 @@ def test_csv_file_no_header_one_col_multiple_fields(self): # 6 Fields including None for ids assert len(dataset.fields) == 6 + @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_csv_dataset_quotechar(self): # Based on issue #349 example_data = [("text", "label"), diff --git a/test/data/test_functional.py b/test/data/test_functional.py index 2e3a5e0435..d35fd154e9 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -1,5 +1,6 @@ import os import unittest +import sys import tempfile import sentencepiece as spm @@ -103,6 +104,7 @@ def setUp(self): torch.jit.script(ScriptableSP(model_path)).save(file.name) self.model = torch.jit.load(file.name) + @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_encode(self): input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' expected = [ @@ -114,6 +116,7 @@ def test_encode(self): output = self.model.encode(input) self.assertEqual(expected, output) + @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_encode_as_ids(self): input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' expected = [ @@ -122,6 +125,7 @@ def test_encode_as_ids(self): output = self.model.encode_as_ids(input) self.assertEqual(expected, output) + @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_encode_as_pieces(self): input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' expected = [ diff --git a/torchtext/csrc/sentencepiece.cpp b/torchtext/csrc/sentencepiece.cpp index 92778147fe..d5f55ec508 100644 --- a/torchtext/csrc/sentencepiece.cpp +++ b/torchtext/csrc/sentencepiece.cpp @@ -1,6 +1,15 @@ #include #include +#ifdef _WIN32 +#include +PyMODINIT_FUNC PyInit__torchtext(void) { + // No need to do anything. + // extension.py will run on load + return NULL; +} +#endif + namespace torchtext { namespace { From 30c89e96814deb84046071c3ced029ee46ea8824 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Thu, 14 May 2020 13:40:01 -0400 Subject: [PATCH 022/719] Change potentially dangerous value passing to reference (#771) --- torchtext/csrc/sentencepiece.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/torchtext/csrc/sentencepiece.cpp b/torchtext/csrc/sentencepiece.cpp index d5f55ec508..f5d279a955 100644 --- a/torchtext/csrc/sentencepiece.cpp +++ b/torchtext/csrc/sentencepiece.cpp @@ -68,9 +68,9 @@ static auto sentencepiece = return c10::make_intrusive(std::move(state)); }); -void generate_sp_model(const std::string filename, const int64_t vocab_size, - const std::string model_type, - const std::string model_prefix) { +void generate_sp_model(const std::string &filename, const int64_t &vocab_size, + const std::string &model_type, + const std::string &model_prefix) { const auto status = ::sentencepiece::SentencePieceTrainer::Train( "--input=" + filename + " --model_prefix=" + model_prefix + " --vocab_size=" + std::to_string(vocab_size) + @@ -86,8 +86,8 @@ c10::intrusive_ptr load_sp_model(const std::string &path) { if (!file) { throw std::runtime_error("Failed to open file :" + path); } - const std::string content((std::istreambuf_iterator(file)), - std::istreambuf_iterator()); + std::string content((std::istreambuf_iterator(file)), + std::istreambuf_iterator()); return c10::make_intrusive(std::move(content)); } From 9dc71d18cf08254e2ad22b12d6bfe922ac46fe59 Mon Sep 17 00:00:00 2001 From: cpuhrsch Date: Thu, 14 May 2020 22:26:35 -0400 Subject: [PATCH 023/719] Point text_classification.DATASETS to local functions (#775) --- .../datasets/text_classification.py | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/torchtext/experimental/datasets/text_classification.py b/torchtext/experimental/datasets/text_classification.py index df879ea557..79093c883e 100644 --- a/torchtext/experimental/datasets/text_classification.py +++ b/torchtext/experimental/datasets/text_classification.py @@ -1,7 +1,7 @@ import torch from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator -from torchtext.experimental.datasets import raw +from torchtext.experimental.datasets.raw import text_classification as raw def vocab_func(vocab): @@ -98,7 +98,7 @@ def _setup_datasets(dataset_name, root='.data', ngrams=1, vocab=None, data_select = [data_select] if not set(data_select).issubset(set(('train', 'test'))): raise TypeError('Given data selection {} is not supported!'.format(data_select)) - train, test = DATASETS[dataset_name](root=root) + train, test = raw.DATASETS[dataset_name](root=root) # Cache raw text iterable dataset raw_data = {'train': [(label, txt) for (label, txt) in train], 'test': [(label, txt) for (label, txt) in test]} @@ -509,15 +509,15 @@ def IMDB(*args, **kwargs): DATASETS = { - 'AG_NEWS': raw.AG_NEWS, - 'SogouNews': raw.SogouNews, - 'DBpedia': raw.DBpedia, - 'YelpReviewPolarity': raw.YelpReviewPolarity, - 'YelpReviewFull': raw.YelpReviewFull, - 'YahooAnswers': raw.YahooAnswers, - 'AmazonReviewPolarity': raw.AmazonReviewPolarity, - 'AmazonReviewFull': raw.AmazonReviewFull, - 'IMDB': raw.IMDB + 'AG_NEWS': AG_NEWS, + 'SogouNews': SogouNews, + 'DBpedia': DBpedia, + 'YelpReviewPolarity': YelpReviewPolarity, + 'YelpReviewFull': YelpReviewFull, + 'YahooAnswers': YahooAnswers, + 'AmazonReviewPolarity': AmazonReviewPolarity, + 'AmazonReviewFull': AmazonReviewFull, + 'IMDB': IMDB } From 515183a6fdf8013f3815ea1a7105e55ba6b24a6f Mon Sep 17 00:00:00 2001 From: cpuhrsch Date: Fri, 15 May 2020 13:02:11 -0400 Subject: [PATCH 024/719] Move experimental text_classification functional into new functional.py (#776) --- .../datasets/text_classification.py | 203 +++++++++--------- torchtext/experimental/functional.py | 34 +++ 2 files changed, 132 insertions(+), 105 deletions(-) create mode 100644 torchtext/experimental/functional.py diff --git a/torchtext/experimental/datasets/text_classification.py b/torchtext/experimental/datasets/text_classification.py index 79093c883e..a600bfe340 100644 --- a/torchtext/experimental/datasets/text_classification.py +++ b/torchtext/experimental/datasets/text_classification.py @@ -2,44 +2,21 @@ from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator from torchtext.experimental.datasets.raw import text_classification as raw +from torchtext.experimental.functional import ( + vocab_func, + totensor, + ngrams_func, + sequential_transforms, +) -def vocab_func(vocab): - def _forward(tok_iter): - return [vocab[tok] for tok in tok_iter] - return _forward - - -def totensor(dtype): - def _forward(ids_list): - return torch.tensor(ids_list).to(dtype) - return _forward - - -def ngrams_func(ngrams): - def _forward(token_list): - _token_list = [] - for _i in range(ngrams + 1): - _token_list += zip(*[token_list[i:] for i in range(_i)]) - return [' '.join(x) for x in _token_list] - return _forward - - -def build_vocab(data, transforms): +def _build_vocab(data, transforms): tok_list = [] for _, txt in data: tok_list.append(transforms(txt)) return build_vocab_from_iterator(tok_list) -def squential_transforms(*transforms): - def _forward(txt_input): - for transform in transforms: - txt_input = transform(txt_input) - return txt_input - return _forward - - class TextClassificationDataset(torch.utils.data.Dataset): """Defines an abstract text classification datasets. Currently, we only support the following datasets: @@ -87,32 +64,44 @@ def get_vocab(self): return self.vocab -def _setup_datasets(dataset_name, root='.data', ngrams=1, vocab=None, - tokenizer=None, data_select=('train', 'test')): +def _setup_datasets( + dataset_name, + root=".data", + ngrams=1, + vocab=None, + tokenizer=None, + data_select=("train", "test"), +): text_transform = [] if tokenizer is None: - tokenizer = get_tokenizer('basic_english') - text_transform = squential_transforms(tokenizer, ngrams_func(ngrams)) + tokenizer = get_tokenizer("basic_english") + text_transform = sequential_transforms(tokenizer, ngrams_func(ngrams)) if isinstance(data_select, str): data_select = [data_select] - if not set(data_select).issubset(set(('train', 'test'))): - raise TypeError('Given data selection {} is not supported!'.format(data_select)) + if not set(data_select).issubset(set(("train", "test"))): + raise TypeError("Given data selection {} is not supported!".format(data_select)) train, test = raw.DATASETS[dataset_name](root=root) # Cache raw text iterable dataset - raw_data = {'train': [(label, txt) for (label, txt) in train], - 'test': [(label, txt) for (label, txt) in test]} + raw_data = { + "train": [(label, txt) for (label, txt) in train], + "test": [(label, txt) for (label, txt) in test], + } if vocab is None: - if 'train' not in data_select: + if "train" not in data_select: raise TypeError("Must pass a vocab if train is not selected.") - vocab = build_vocab(raw_data['train'], text_transform) - text_transform = squential_transforms(text_transform, vocab_func(vocab), - totensor(dtype=torch.long)) - label_transform = squential_transforms(totensor(dtype=torch.long)) - return tuple(TextClassificationDataset(raw_data[item], vocab, - (label_transform, text_transform)) - for item in data_select) + vocab = _build_vocab(raw_data["train"], text_transform) + text_transform = sequential_transforms( + text_transform, vocab_func(vocab), totensor(dtype=torch.long) + ) + label_transform = sequential_transforms(totensor(dtype=torch.long)) + return tuple( + TextClassificationDataset( + raw_data[item], vocab, (label_transform, text_transform) + ) + for item in data_select + ) def AG_NEWS(*args, **kwargs): @@ -155,7 +144,7 @@ def AG_NEWS(*args, **kwargs): """ - return _setup_datasets(*(('AG_NEWS',) + args), **kwargs) + return _setup_datasets(*(("AG_NEWS",) + args), **kwargs) def SogouNews(*args, **kwargs): @@ -509,66 +498,70 @@ def IMDB(*args, **kwargs): DATASETS = { - 'AG_NEWS': AG_NEWS, - 'SogouNews': SogouNews, - 'DBpedia': DBpedia, - 'YelpReviewPolarity': YelpReviewPolarity, - 'YelpReviewFull': YelpReviewFull, - 'YahooAnswers': YahooAnswers, - 'AmazonReviewPolarity': AmazonReviewPolarity, - 'AmazonReviewFull': AmazonReviewFull, - 'IMDB': IMDB + "AG_NEWS": AG_NEWS, + "SogouNews": SogouNews, + "DBpedia": DBpedia, + "YelpReviewPolarity": YelpReviewPolarity, + "YelpReviewFull": YelpReviewFull, + "YahooAnswers": YahooAnswers, + "AmazonReviewPolarity": AmazonReviewPolarity, + "AmazonReviewFull": AmazonReviewFull, + "IMDB": IMDB, } LABELS = { - 'AG_NEWS': {1: 'World', - 2: 'Sports', - 3: 'Business', - 4: 'Sci/Tech'}, - 'SogouNews': {1: 'Sports', - 2: 'Finance', - 3: 'Entertainment', - 4: 'Automobile', - 5: 'Technology'}, - 'DBpedia': {1: 'Company', - 2: 'EducationalInstitution', - 3: 'Artist', - 4: 'Athlete', - 5: 'OfficeHolder', - 6: 'MeanOfTransportation', - 7: 'Building', - 8: 'NaturalPlace', - 9: 'Village', - 10: 'Animal', - 11: 'Plant', - 12: 'Album', - 13: 'Film', - 14: 'WrittenWork'}, - 'YelpReviewPolarity': {1: 'Negative polarity', - 2: 'Positive polarity'}, - 'YelpReviewFull': {1: 'score 1', - 2: 'score 2', - 3: 'score 3', - 4: 'score 4', - 5: 'score 5'}, - 'YahooAnswers': {1: 'Society & Culture', - 2: 'Science & Mathematics', - 3: 'Health', - 4: 'Education & Reference', - 5: 'Computers & Internet', - 6: 'Sports', - 7: 'Business & Finance', - 8: 'Entertainment & Music', - 9: 'Family & Relationships', - 10: 'Politics & Government'}, - 'AmazonReviewPolarity': {1: 'Negative polarity', - 2: 'Positive polarity'}, - 'AmazonReviewFull': {1: 'score 1', - 2: 'score 2', - 3: 'score 3', - 4: 'score 4', - 5: 'score 5'}, - 'IMDB': {0: 'Negative', - 1: 'Positive'} + "AG_NEWS": {1: "World", 2: "Sports", 3: "Business", 4: "Sci/Tech"}, + "SogouNews": { + 1: "Sports", + 2: "Finance", + 3: "Entertainment", + 4: "Automobile", + 5: "Technology", + }, + "DBpedia": { + 1: "Company", + 2: "EducationalInstitution", + 3: "Artist", + 4: "Athlete", + 5: "OfficeHolder", + 6: "MeanOfTransportation", + 7: "Building", + 8: "NaturalPlace", + 9: "Village", + 10: "Animal", + 11: "Plant", + 12: "Album", + 13: "Film", + 14: "WrittenWork", + }, + "YelpReviewPolarity": {1: "Negative polarity", 2: "Positive polarity"}, + "YelpReviewFull": { + 1: "score 1", + 2: "score 2", + 3: "score 3", + 4: "score 4", + 5: "score 5", + }, + "YahooAnswers": { + 1: "Society & Culture", + 2: "Science & Mathematics", + 3: "Health", + 4: "Education & Reference", + 5: "Computers & Internet", + 6: "Sports", + 7: "Business & Finance", + 8: "Entertainment & Music", + 9: "Family & Relationships", + 10: "Politics & Government", + }, + "AmazonReviewPolarity": {1: "Negative polarity", 2: "Positive polarity"}, + "AmazonReviewFull": { + 1: "score 1", + 2: "score 2", + 3: "score 3", + 4: "score 4", + 5: "score 5", + }, + "IMDB": {0: "Negative", 1: "Positive"}, } diff --git a/torchtext/experimental/functional.py b/torchtext/experimental/functional.py new file mode 100644 index 0000000000..2c9ac60541 --- /dev/null +++ b/torchtext/experimental/functional.py @@ -0,0 +1,34 @@ +import torch + + +def vocab_func(vocab): + def _forward(tok_iter): + return [vocab[tok] for tok in tok_iter] + + return _forward + + +def totensor(dtype): + def _forward(ids_list): + return torch.tensor(ids_list).to(dtype) + + return _forward + + +def ngrams_func(ngrams): + def _forward(token_list): + _token_list = [] + for _i in range(ngrams + 1): + _token_list += zip(*[token_list[i:] for i in range(_i)]) + return [" ".join(x) for x in _token_list] + + return _forward + + +def sequential_transforms(*transforms): + def _forward(txt_input): + for transform in transforms: + txt_input = transform(txt_input) + return txt_input + + return _forward From 9bdfb046c37b473accb7749ecc7c8e0e4ed7860f Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Fri, 15 May 2020 14:09:53 -0400 Subject: [PATCH 025/719] Add utility func to get path to assets (#777) --- test/common/assets.py | 32 ++++++++++++++++++++++++++++++++ test/data/test_functional.py | 14 ++++++++++---- test/data/test_utils.py | 16 +++++++++------- 3 files changed, 51 insertions(+), 11 deletions(-) create mode 100644 test/common/assets.py diff --git a/test/common/assets.py b/test/common/assets.py new file mode 100644 index 0000000000..c7523bb619 --- /dev/null +++ b/test/common/assets.py @@ -0,0 +1,32 @@ +import os +import shutil +import atexit +import tempfile +from pathlib import Path + +_ASSET_DIR = (Path(__file__).parent.parent / "asset").resolve() + +_TEMP_DIR = None + + +def _init_temp_dir(): + """Initialize temporary directory and register clean up at the end of test.""" + global _TEMP_DIR + _TEMP_DIR = tempfile.TemporaryDirectory() # noqa + atexit.register(_TEMP_DIR.cleanup) + + +def get_asset_path(*path_components, use_temp_dir=False): + """Get the path to the file under `test/assets` directory. + When `use_temp_dir` is True, the asset is copied to a temporary location and + path to the temporary file is returned. + """ + path = str(_ASSET_DIR.joinpath(*path_components)) + if not use_temp_dir: + return path + + if _TEMP_DIR is None: + _init_temp_dir() + tgt = os.path.join(_TEMP_DIR.name, path_components[-1]) + shutil.copy(path, tgt) + return tgt diff --git a/test/data/test_functional.py b/test/data/test_functional.py index d35fd154e9..dacc0adafd 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -15,13 +15,19 @@ ) from ..common.torchtext_test_case import TorchtextTestCase +from ..common.assets import get_asset_path class TestFunctional(TorchtextTestCase): def test_generate_sp_model(self): # Test the function to train a sentencepiece tokenizer - data_path = 'test/asset/text_normalization_ag_news_test.csv' + # buck (fb internal) generates test environment which contains ',' in its path. + # SentencePieceTrainer considers such path as comma-delimited file list. + # So as workaround we copy the asset data to temporary directory and load it from there. + data_path = get_asset_path( + 'text_normalization_ag_news_test.csv', + use_temp_dir=True) generate_sp_model(data_path, vocab_size=23456, model_prefix='spm_user') @@ -38,7 +44,7 @@ def test_generate_sp_model(self): def test_sentencepiece_numericalizer(self): test_sample = 'SentencePiece is an unsupervised text tokenizer and detokenizer' - model_path = 'test/asset/spm_example.model' + model_path = get_asset_path('spm_example.model') sp_model = load_sp_model(model_path) self.assertEqual(sp_model.GetPieceSize(), 20000) spm_generator = sentencepiece_numericalizer(sp_model) @@ -52,7 +58,7 @@ def test_sentencepiece_numericalizer(self): def test_sentencepiece_tokenizer(self): test_sample = 'SentencePiece is an unsupervised text tokenizer and detokenizer' - model_path = 'test/asset/spm_example.model' + model_path = get_asset_path('spm_example.model') sp_model = load_sp_model(model_path) self.assertEqual(sp_model.GetPieceSize(), 20000) spm_generator = sentencepiece_tokenizer(sp_model) @@ -99,7 +105,7 @@ def encode_as_pieces(self, input: str): class TestScriptableSP(unittest.TestCase): def setUp(self): - model_path = 'test/asset/spm_example.model' + model_path = get_asset_path('spm_example.model') with tempfile.NamedTemporaryFile() as file: torch.jit.script(ScriptableSP(model_path)).save(file.name) self.model = torch.jit.load(file.name) diff --git a/test/data/test_utils.py b/test/data/test_utils.py index 29716cbaaf..2769ff4570 100644 --- a/test/data/test_utils.py +++ b/test/data/test_utils.py @@ -1,8 +1,11 @@ +import io +import unittest + import torchtext.data as data -import pytest -from ..common.torchtext_test_case import TorchtextTestCase from torchtext.utils import unicode_csv_reader -import io + +from ..common.torchtext_test_case import TorchtextTestCase +from ..common.assets import get_asset_path class TestUtils(TorchtextTestCase): @@ -21,8 +24,7 @@ def test_get_tokenizer_spacy(self): # TODO: Remove this once issue was been resolved. # TODO# Add nltk data back in build_tools/travis/install.sh. - @pytest.mark.skip(reason=("Impractically slow! " - "https://github.com/alvations/sacremoses/issues/61")) + @unittest.skip("Impractically slow! https://github.com/alvations/sacremoses/issues/61") def test_get_tokenizer_moses(self): # Test Moses option. # Note that internally, MosesTokenizer converts to unicode if applicable @@ -54,13 +56,13 @@ def test_text_nomalize_function(self): test_lines = [] tokenizer = data.get_tokenizer("basic_english") - data_path = 'test/asset/text_normalization_ag_news_test.csv' + data_path = get_asset_path('text_normalization_ag_news_test.csv') with io.open(data_path, encoding="utf8") as f: reader = unicode_csv_reader(f) for row in reader: test_lines.append(tokenizer(' , '.join(row))) - data_path = 'test/asset/text_normalization_ag_news_ref_results.test' + data_path = get_asset_path('text_normalization_ag_news_ref_results.test') with io.open(data_path, encoding="utf8") as ref_data: for line in ref_data: line = line.split() From 00e793f42c6bd230082b0c5c136c36fc9c87be26 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Mon, 18 May 2020 15:28:50 -0400 Subject: [PATCH 026/719] Remove slow from sacremoses (#782) The issue has been resolved on upstream. https://github.com/alvations/sacremoses/issues/61 Test run time on Circle CI: ~= 0.4 second. --- test/data/test_utils.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/test/data/test_utils.py b/test/data/test_utils.py index 2769ff4570..c90b44f116 100644 --- a/test/data/test_utils.py +++ b/test/data/test_utils.py @@ -1,5 +1,4 @@ import io -import unittest import torchtext.data as data from torchtext.utils import unicode_csv_reader @@ -22,9 +21,6 @@ def test_get_tokenizer_spacy(self): "A", "string", ",", "particularly", "one", "with", "slightly", "complex", "punctuation", "."] - # TODO: Remove this once issue was been resolved. - # TODO# Add nltk data back in build_tools/travis/install.sh. - @unittest.skip("Impractically slow! https://github.com/alvations/sacremoses/issues/61") def test_get_tokenizer_moses(self): # Test Moses option. # Note that internally, MosesTokenizer converts to unicode if applicable From 9cc2bb3ad190676f67f8bc111869e6f1e6b8f8cc Mon Sep 17 00:00:00 2001 From: peterjc123 Date: Tue, 19 May 2020 22:45:16 +0800 Subject: [PATCH 027/719] Add binary jobs for Windows (#781) * Add binary jobs for Windows --- .circleci/config.yml | 138 ++++++++++++++++++++++- .circleci/config.yml.in | 36 +++++- .circleci/regenerate.py | 2 +- packaging/build_conda.sh | 1 + packaging/build_wheel.bat | 20 ++++ packaging/build_wheel.sh | 6 +- packaging/pkg_helpers.bash | 12 +- packaging/torchtext/bld.bat | 4 + packaging/vs2019/activate.bat | 44 ++++++++ packaging/vs2019/conda_build_config.yaml | 24 ++++ packaging/vs2019/install_activate.bat | 30 +++++ packaging/vs2019/install_runtime.bat | 49 ++++++++ packaging/vs2019/meta.yaml | 24 ++++ 13 files changed, 375 insertions(+), 15 deletions(-) create mode 100644 packaging/build_wheel.bat create mode 100644 packaging/vs2019/activate.bat create mode 100644 packaging/vs2019/conda_build_config.yaml create mode 100644 packaging/vs2019/install_activate.bat create mode 100644 packaging/vs2019/install_runtime.bat create mode 100644 packaging/vs2019/meta.yaml diff --git a/.circleci/config.yml b/.circleci/config.yml index 9dd274b411..db23e8a076 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -87,21 +87,45 @@ jobs: paths: - "*" - binary_win_conda: + binary_windows_wheel: <<: *binary_common executor: - name: win/default - shell: bash.exe + name: windows-cpu + steps: + - checkout + - run: + name: build + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate base + bash packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_windows_conda: + <<: *binary_common + executor: + name: windows-cpu steps: - checkout - run: + name: build command: | - choco install miniconda3 - (& "C:\tools\miniconda3\Scripts\conda.exe" "shell.powershell" "hook") | Out-String | Invoke-Expression + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" conda activate base conda install -yq conda-build "conda-package-handling!=1.5.0" bash packaging/build_conda.sh - shell: powershell.exe + rm /C/tools/miniconda3/conda-bld/win-64/vs2019*.tar.bz2 + - store_artifacts: + path: C:/tools/miniconda3/conda-bld/win-64 + - persist_to_workspace: + root: C:/tools/miniconda3/conda-bld/win-64 + paths: + - "*" binary_macos_wheel: <<: *binary_common @@ -398,6 +422,15 @@ workflows: - binary_macos_wheel: name: binary_macos_wheel_py3.8 python_version: '3.8' + - binary_windows_wheel: + name: binary_windows_wheel_py3.6 + python_version: '3.6' + - binary_windows_wheel: + name: binary_windows_wheel_py3.7 + python_version: '3.7' + - binary_windows_wheel: + name: binary_windows_wheel_py3.8 + python_version: '3.8' - binary_linux_conda: name: binary_linux_conda_py3.6 python_version: '3.6' @@ -416,6 +449,15 @@ workflows: - binary_macos_conda: name: binary_macos_conda_py3.8 python_version: '3.8' + - binary_windows_conda: + name: binary_windows_conda_py3.6 + python_version: '3.6' + - binary_windows_conda: + name: binary_windows_conda_py3.7 + python_version: '3.7' + - binary_windows_conda: + name: binary_windows_conda_py3.8 + python_version: '3.8' unittest: jobs: - unittest_linux: @@ -553,6 +595,48 @@ workflows: name: nightly_binary_macos_wheel_py3.8_upload requires: - nightly_binary_macos_wheel_py3.8 + - binary_windows_wheel: + filters: + branches: + only: nightly + name: nightly_binary_windows_wheel_py3.6 + python_version: '3.6' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_windows_wheel_py3.6_upload + requires: + - nightly_binary_windows_wheel_py3.6 + - binary_windows_wheel: + filters: + branches: + only: nightly + name: nightly_binary_windows_wheel_py3.7 + python_version: '3.7' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_windows_wheel_py3.7_upload + requires: + - nightly_binary_windows_wheel_py3.7 + - binary_windows_wheel: + filters: + branches: + only: nightly + name: nightly_binary_windows_wheel_py3.8 + python_version: '3.8' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_windows_wheel_py3.8_upload + requires: + - nightly_binary_windows_wheel_py3.8 - binary_linux_conda: filters: branches: @@ -661,6 +745,48 @@ workflows: name: nightly_binary_macos_conda_py3.8_upload requires: - nightly_binary_macos_conda_py3.8 + - binary_windows_conda: + filters: + branches: + only: nightly + name: nightly_binary_windows_conda_py3.6 + python_version: '3.6' + - binary_conda_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_windows_conda_py3.6_upload + requires: + - nightly_binary_windows_conda_py3.6 + - binary_windows_conda: + filters: + branches: + only: nightly + name: nightly_binary_windows_conda_py3.7 + python_version: '3.7' + - binary_conda_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_windows_conda_py3.7_upload + requires: + - nightly_binary_windows_conda_py3.7 + - binary_windows_conda: + filters: + branches: + only: nightly + name: nightly_binary_windows_conda_py3.8 + python_version: '3.8' + - binary_conda_upload: + context: org-member + filters: + branches: + only: nightly + name: nightly_binary_windows_conda_py3.8_upload + requires: + - nightly_binary_windows_conda_py3.8 docker_build: triggers: - schedule: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index da4e31f314..42d8b686db 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -87,21 +87,45 @@ jobs: paths: - "*" - binary_win_conda: + binary_windows_wheel: <<: *binary_common executor: - name: win/default - shell: bash.exe + name: windows-cpu steps: - checkout - run: + name: build command: | - choco install miniconda3 - (& "C:\tools\miniconda3\Scripts\conda.exe" "shell.powershell" "hook") | Out-String | Invoke-Expression + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate base + bash packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_windows_conda: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - run: + name: build + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" conda activate base conda install -yq conda-build "conda-package-handling!=1.5.0" bash packaging/build_conda.sh - shell: powershell.exe + rm /C/tools/miniconda3/conda-bld/win-64/vs2019*.tar.bz2 + - store_artifacts: + path: C:/tools/miniconda3/conda-bld/win-64 + - persist_to_workspace: + root: C:/tools/miniconda3/conda-bld/win-64 + paths: + - "*" binary_macos_wheel: <<: *binary_common diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index 58c4544205..a4cffdc1eb 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -25,7 +25,7 @@ def build_workflows(prefix='', upload=False, filter_branch=None, indentation=6): w = [] for btype in ["wheel", "conda"]: - for os_type in ["linux", "macos"]: + for os_type in ["linux", "macos", "windows"]: for python_version in PYTHON_VERSIONS: w += build_workflow_pair(btype, os_type, python_version, filter_branch, prefix, upload) return indent(indentation, w) diff --git a/packaging/build_conda.sh b/packaging/build_conda.sh index 0c522ad0e7..50c94d6204 100755 --- a/packaging/build_conda.sh +++ b/packaging/build_conda.sh @@ -9,4 +9,5 @@ export NO_CUDA_PACKAGE=1 setup_env 0.6.0 export SOURCE_ROOT_DIR="$PWD" setup_conda_pytorch_constraint +setup_visual_studio_constraint conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload --python "$PYTHON_VERSION" packaging/torchtext diff --git a/packaging/build_wheel.bat b/packaging/build_wheel.bat new file mode 100644 index 0000000000..90bd865361 --- /dev/null +++ b/packaging/build_wheel.bat @@ -0,0 +1,20 @@ +@echo on + +for /f "usebackq tokens=*" %%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -legacy -products * -version [15^,17^) -property installationPath`) do ( + if exist "%%i" if exist "%%i\VC\Auxiliary\Build\vcvarsall.bat" ( + set "VS15INSTALLDIR=%%i" + set "VS15VCVARSALL=%%i\VC\Auxiliary\Build\vcvarsall.bat" + goto vswhere + ) +) + +:vswhere +if "%VSDEVCMD_ARGS%" == "" ( + call "%VS15VCVARSALL%" x64 || exit /b 1 +) else ( + call "%VS15VCVARSALL%" x64 %VSDEVCMD_ARGS% || exit /b 1 +) + +@echo on + +python setup.py bdist_wheel || exit /b 1 diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index 00852ea7b0..1bfa107ff2 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -12,4 +12,8 @@ pip_install numpy future setup_pip_pytorch_version git submodule update --init --recursive python setup.py clean -python setup.py bdist_wheel +if [[ "$OSTYPE" == "msys" ]]; then + "$script_dir/build_wheel.bat" +else + python setup.py bdist_wheel +fi diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index bc8ec997df..c03e647e1a 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -122,7 +122,7 @@ retry () { # # Precondition: If Linux, you are in a soumith/manylinux-cuda* Docker image setup_wheel_python() { - if [[ "$(uname)" == Darwin ]]; then + if [[ "$(uname)" == Darwin || "$OSTYPE" == "msys" ]]; then eval "$(conda shell.bash hook)" conda env remove -n "env$PYTHON_VERSION" || true conda create -yn "env$PYTHON_VERSION" python="$PYTHON_VERSION" @@ -219,3 +219,13 @@ setup_conda_cudatoolkit_constraint() { esac fi } + +# Build the proper compiler package before building the final package +setup_visual_studio_constraint() { + if [[ "$OSTYPE" == "msys" ]]; then + export VSTOOLCHAIN_PACKAGE=vs2019 + export VSDEVCMD_ARGS='' + conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload packaging/$VSTOOLCHAIN_PACKAGE + cp packaging/$VSTOOLCHAIN_PACKAGE/conda_build_config.yaml packaging/torchtext/conda_build_config.yaml + fi +} diff --git a/packaging/torchtext/bld.bat b/packaging/torchtext/bld.bat index f220361019..7e5a4861d3 100644 --- a/packaging/torchtext/bld.bat +++ b/packaging/torchtext/bld.bat @@ -1,3 +1,7 @@ @echo off +git submodule update --init --recursive +if errorlevel 1 exit /b 1 + python setup.py install --single-version-externally-managed --record=record.txt +if errorlevel 1 exit /b 1 diff --git a/packaging/vs2019/activate.bat b/packaging/vs2019/activate.bat new file mode 100644 index 0000000000..6f607ba751 --- /dev/null +++ b/packaging/vs2019/activate.bat @@ -0,0 +1,44 @@ +:: Set env vars that tell distutils to use the compiler that we put on path +SET DISTUTILS_USE_SDK=1 +SET MSSdk=1 + +SET "VS_VERSION=16.0" +SET "VS_MAJOR=16" +SET "VS_YEAR=2019" + +set "MSYS2_ARG_CONV_EXCL=/AI;/AL;/OUT;/out" +set "MSYS2_ENV_CONV_EXCL=CL" + +:: For Python 3.5+, ensure that we link with the dynamic runtime. See +:: http://stevedower.id.au/blog/building-for-python-3-5-part-two/ for more info +set "PY_VCRUNTIME_REDIST=%PREFIX%\\bin\\vcruntime140.dll" + +for /f "usebackq tokens=*" %%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -legacy -products * -version [16^,17^) -property installationPath`) do ( + if exist "%%i" if exist "%%i\VC\Auxiliary\Build\vcvarsall.bat" ( + set "VSINSTALLDIR=%%i\" + goto :vswhere + ) +) + +:vswhere + +:: Shorten PATH to avoid the `input line too long` error. +SET MyPath=%PATH% + +setlocal EnableDelayedExpansion + +SET TempPath="%MyPath:;=";"%" +SET var= +FOR %%a IN (%TempPath%) DO ( + IF EXIST %%~sa ( + SET "var=!var!;%%~sa" + ) +) + +set "TempPath=!var:~1!" +endlocal & set "PATH=%TempPath%" + +:: Shorten current directory too +FOR %%A IN (.) DO CD "%%~sA" + +:: other things added by install_activate.bat at package build time diff --git a/packaging/vs2019/conda_build_config.yaml b/packaging/vs2019/conda_build_config.yaml new file mode 100644 index 0000000000..358052ec01 --- /dev/null +++ b/packaging/vs2019/conda_build_config.yaml @@ -0,0 +1,24 @@ +blas_impl: + - mkl # [x86_64] +c_compiler: + - vs2019 # [win] +cxx_compiler: + - vs2019 # [win] +python: + - 3.5 + - 3.6 +# This differs from target_platform in that it determines what subdir the compiler +# will target, not what subdir the compiler package will be itself. +# For example, we need a win-64 vs2008_win-32 package, so that we compile win-32 +# code on win-64 miniconda. +cross_compiler_target_platform: + - win-64 # [win] +target_platform: + - win-64 # [win] +vc: + - 14 +zip_keys: + - # [win] + - vc # [win] + - c_compiler # [win] + - cxx_compiler # [win] diff --git a/packaging/vs2019/install_activate.bat b/packaging/vs2019/install_activate.bat new file mode 100644 index 0000000000..3c38253aa5 --- /dev/null +++ b/packaging/vs2019/install_activate.bat @@ -0,0 +1,30 @@ +set YEAR=2019 +set VER=16 + +mkdir "%PREFIX%\etc\conda\activate.d" +COPY "%RECIPE_DIR%\activate.bat" "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + +IF "%cross_compiler_target_platform%" == "win-64" ( + set "target_platform=amd64" + echo SET "CMAKE_GENERATOR=Visual Studio %VER% %YEAR% Win64" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo pushd "%%VSINSTALLDIR%%" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + IF "%VSDEVCMD_ARGS%" == "" ( + echo CALL "VC\Auxiliary\Build\vcvarsall.bat" x64 >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo popd >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo pushd "%%VSINSTALLDIR%%" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo CALL "VC\Auxiliary\Build\vcvarsall.bat" x86_amd64 >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + ) ELSE ( + echo CALL "VC\Auxiliary\Build\vcvarsall.bat" x64 %VSDEVCMD_ARGS% >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo popd >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo pushd "%%VSINSTALLDIR%%" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo CALL "VC\Auxiliary\Build\vcvarsall.bat" x86_amd64 %VSDEVCMD_ARGS% >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + ) + echo popd >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + ) else ( + set "target_platform=x86" + echo SET "CMAKE_GENERATOR=Visual Studio %VER% %YEAR%" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo pushd "%%VSINSTALLDIR%%" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo CALL "VC\Auxiliary\Build\vcvars32.bat" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo popd + ) + diff --git a/packaging/vs2019/install_runtime.bat b/packaging/vs2019/install_runtime.bat new file mode 100644 index 0000000000..e09a5ccfb0 --- /dev/null +++ b/packaging/vs2019/install_runtime.bat @@ -0,0 +1,49 @@ +set VC_PATH=x86 +if "%ARCH%"=="64" ( + set VC_PATH=x64 +) + +set MSC_VER=2019 + +rem :: This should always be present for VC installed with VS. Not sure about VC installed with Visual C++ Build Tools 2015 +rem FOR /F "usebackq tokens=3*" %%A IN (`REG QUERY "HKEY_LOCAL_MACHINE\Software\Microsoft\DevDiv\VC\Servicing\14.0\IDE.x64" /v UpdateVersion`) DO ( +rem set SP=%%A +rem ) + +rem if not "%SP%" == "%PKG_VERSION%" ( +rem echo "Version detected from registry: %SP%" +rem echo "does not match version of package being built (%PKG_VERSION%)" +rem echo "Do you have current updates for VS 2015 installed?" +rem exit 1 +rem ) + + +REM ========== REQUIRES Win 10 SDK be installed, or files otherwise copied to location below! +robocopy "C:\Program Files (x86)\Windows Kits\10\Redist\ucrt\DLLs\%VC_PATH%" "%LIBRARY_BIN%" *.dll /E +robocopy "C:\Program Files (x86)\Windows Kits\10\Redist\ucrt\DLLs\%VC_PATH%" "%PREFIX%" *.dll /E +if %ERRORLEVEL% GEQ 8 exit 1 + +REM ========== This one comes from visual studio 2019 +set "VC_VER=142" + +for /f "usebackq tokens=*" %%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -legacy -products * -version [16^,17^) -property installationPath`) do ( + if exist "%%i" if exist "%%i\VC\Auxiliary\Build\vcvarsall.bat" ( + set "VS15VCVARSALL=%%i\VC\Auxiliary\Build\vcvarsall.bat" + goto :eof + ) +) + +@setlocal +call "%VS15VARSALL%" x64 + +set "REDIST_ROOT=%VCToolsRedistDir%%VC_PATH%" + +robocopy "%REDIST_ROOT%\Microsoft.VC%VC_VER%.CRT" "%LIBRARY_BIN%" *.dll /E +if %ERRORLEVEL% LSS 8 exit 0 +robocopy "%REDIST_ROOT%\Microsoft.VC%VC_VER%.CRT" "%PREFIX%" *.dll /E +if %ERRORLEVEL% LSS 8 exit 0 +robocopy "%REDIST_ROOT%\Microsoft.VC%VC_VER%.OpenMP" "%LIBRARY_BIN%" *.dll /E +if %ERRORLEVEL% LSS 8 exit 0 +robocopy "%REDIST_ROOT%\Microsoft.VC%VC_VER%.OpenMP" "%PREFIX%" *.dll /E +if %ERRORLEVEL% LSS 8 exit 0 +@endlocal diff --git a/packaging/vs2019/meta.yaml b/packaging/vs2019/meta.yaml new file mode 100644 index 0000000000..94a0ed4db3 --- /dev/null +++ b/packaging/vs2019/meta.yaml @@ -0,0 +1,24 @@ +{% set vcver="14.2" %} +{% set vcfeature="14" %} +{% set vsyear="2019" %} +{% set fullver="15.4.27004.2010" %} + +package: + name: vs{{ vsyear }} + version: {{ fullver }} + +build: + skip: True [not win] + script_env: + - VSDEVCMD_ARGS # [win] + +outputs: + - name: vs{{ vsyear }}_{{ cross_compiler_target_platform }} + script: install_activate.bat + track_features: + # VS 2019 is binary-compatible with VS 2017/vc 14.1 and 2015/vc14. Tools are "v142". + strong: + - vc{{ vcfeature }} + about: + summary: Activation and version verification of MSVC {{ vcver }} (VS {{ vsyear }}) compiler + license: BSD 3-clause From 2cab8fbbbb25d52dc6cf9c3dcc511ef574b8d079 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Tue, 19 May 2020 16:37:54 -0400 Subject: [PATCH 028/719] Separate tests which require external resource (#783) * Add note on test_builtin_datasets * Add note on test_utils * Add note on test_subword * move test_build_vocab (test.data.test_field.TestNestedField) * move test_csv_file_no_header_one_col_multiple_fields (test.data.test_dataset.TestDataset) * move test_json_dataset_one_key_multiple_fields (test.data.test_dataset.TestDataset) * move test_get_tokenizer_spacy (test.data.test_utils.TestUtils) * move test_vectors_get_vecs (test.test_vocab.TestVocab) * move test_vocab_download_charngram_vectors (test.test_vocab.TestVocab) * move test_vocab_download_custom_vectors (test.test_vocab.TestVocab) * move test_vocab_download_fasttext_vectors (test.test_vocab.TestVocab) * move test_vocab_download_glove_vectors (test.test_vocab.TestVocab) * move test_vocab_extend (test.test_vocab.TestVocab) * move test_vocab_vectors_custom_cache (test.test_vocab.TestVocab) --- test/data/test_builtin_datasets.py | 2 + test/data/test_dataset.py | 71 ------- test/data/test_field.py | 17 -- test/data/test_subword.py | 2 + test/data/test_utils.py | 6 - test/test_build.py | 316 +++++++++++++++++++++++++++++ test/test_utils.py | 2 + test/test_vocab.py | 235 --------------------- 8 files changed, 322 insertions(+), 329 deletions(-) create mode 100644 test/test_build.py diff --git a/test/data/test_builtin_datasets.py b/test/data/test_builtin_datasets.py index b388dc61de..08c75292c4 100644 --- a/test/data/test_builtin_datasets.py +++ b/test/data/test_builtin_datasets.py @@ -1,3 +1,5 @@ +#!/user/bin/env python3 +# Note that all the tests in this module require dataset (either network access or cached) import os import shutil import torchtext.data as data diff --git a/test/data/test_dataset.py b/test/data/test_dataset.py index ae3d3fbb8d..849e6a770a 100644 --- a/test/data/test_dataset.py +++ b/test/data/test_dataset.py @@ -59,40 +59,6 @@ def test_tabular_simple_data(self): self.assertEqual(example.q2, expected_examples[i][1]) self.assertEqual(example.label, expected_examples[i][2]) - def test_json_dataset_one_key_multiple_fields(self): - self.write_test_ppid_dataset(data_format="json") - - question_field = data.Field(sequential=True) - spacy_tok_question_field = data.Field(sequential=True, tokenize="spacy") - label_field = data.Field(sequential=False) - fields = {"question1": [("q1", question_field), - ("q1_spacy", spacy_tok_question_field)], - "question2": [("q2", question_field), - ("q2_spacy", spacy_tok_question_field)], - "label": ("label", label_field)} - dataset = data.TabularDataset( - path=self.test_ppid_dataset_path, format="json", fields=fields) - expected_examples = [ - (["When", "do", "you", "use", "シ", "instead", "of", "し?"], - ["When", "do", "you", "use", "シ", "instead", "of", "し", "?"], - ["When", "do", "you", "use", "\"&\"", - "instead", "of", "\"and\"?"], - ["When", "do", "you", "use", "\"", "&", "\"", - "instead", "of", "\"", "and", "\"", "?"], "0"), - (["Where", "was", "Lincoln", "born?"], - ["Where", "was", "Lincoln", "born", "?"], - ["Which", "location", "was", "Abraham", "Lincoln", "born?"], - ["Which", "location", "was", "Abraham", "Lincoln", "born", "?"], - "1"), - (["What", "is", "2+2"], ["What", "is", "2", "+", "2"], - ["2+2=?"], ["2", "+", "2=", "?"], "1")] - for i, example in enumerate(dataset): - self.assertEqual(example.q1, expected_examples[i][0]) - self.assertEqual(example.q1_spacy, expected_examples[i][1]) - self.assertEqual(example.q2, expected_examples[i][2]) - self.assertEqual(example.q2_spacy, expected_examples[i][3]) - self.assertEqual(example.label, expected_examples[i][4]) - def test_json_valid_and_invalid_nested_key(self): self.write_test_nested_key_json_dataset() valid_fields = {'foods.vegetables.name': ('vegs', data.Field()), @@ -200,43 +166,6 @@ def test_csv_file_with_header(self): sort_within_batch=False, repeat=False) next(data_iter.__iter__()) - def test_csv_file_no_header_one_col_multiple_fields(self): - self.write_test_ppid_dataset(data_format="csv") - - question_field = data.Field(sequential=True) - spacy_tok_question_field = data.Field(sequential=True, tokenize="spacy") - label_field = data.Field(sequential=False) - # Field name/value as nested tuples - fields = [("ids", None), - (("q1", "q1_spacy"), (question_field, spacy_tok_question_field)), - (("q2", "q2_spacy"), (question_field, spacy_tok_question_field)), - ("label", label_field)] - dataset = data.TabularDataset( - path=self.test_ppid_dataset_path, format="csv", fields=fields) - expected_examples = [ - (["When", "do", "you", "use", "シ", "instead", "of", "し?"], - ["When", "do", "you", "use", "シ", "instead", "of", "し", "?"], - ["When", "do", "you", "use", "\"&\"", - "instead", "of", "\"and\"?"], - ["When", "do", "you", "use", "\"", "&", "\"", - "instead", "of", "\"", "and", "\"", "?"], "0"), - (["Where", "was", "Lincoln", "born?"], - ["Where", "was", "Lincoln", "born", "?"], - ["Which", "location", "was", "Abraham", "Lincoln", "born?"], - ["Which", "location", "was", "Abraham", "Lincoln", "born", "?"], - "1"), - (["What", "is", "2+2"], ["What", "is", "2", "+", "2"], - ["2+2=?"], ["2", "+", "2=", "?"], "1")] - for i, example in enumerate(dataset): - self.assertEqual(example.q1, expected_examples[i][0]) - self.assertEqual(example.q1_spacy, expected_examples[i][1]) - self.assertEqual(example.q2, expected_examples[i][2]) - self.assertEqual(example.q2_spacy, expected_examples[i][3]) - self.assertEqual(example.label, expected_examples[i][4]) - - # 6 Fields including None for ids - assert len(dataset.fields) == 6 - @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_csv_dataset_quotechar(self): # Based on issue #349 diff --git a/test/data/test_field.py b/test/data/test_field.py index 385412b26f..11b98bde68 100644 --- a/test/data/test_field.py +++ b/test/data/test_field.py @@ -6,7 +6,6 @@ import torch import torchtext.data as data import pytest -from torch.nn import init from ..common.torchtext_test_case import TorchtextTestCase, verify_numericalized_example @@ -865,22 +864,6 @@ def test_serialization(self): assert torch.all(torch.eq(original_numericalization, pickled_numericalization)) - def test_build_vocab(self): - nesting_field = data.Field(tokenize=list, init_token="", eos_token="") - - field = data.NestedField(nesting_field, init_token='', eos_token='', - include_lengths=True, - pad_first=True) - - sources = [[['a'], ['s', 'e', 'n', 't', 'e', 'n', 'c', 'e'], ['o', 'f'], - ['d', 'a', 't', 'a'], ['.']], - [['y', 'e', 't'], ['a', 'n', 'o', 't', 'h', 'e', 'r']], - [['o', 'n', 'e'], ['l', 'a', 's', 't'], ['s', 'e', 'n', 't']]] - - field.build_vocab(sources, vectors='glove.6B.50d', - unk_init=init.normal_, - vectors_cache=".vector_cache") - class TestLabelField(TorchtextTestCase): def test_init(self): diff --git a/test/data/test_subword.py b/test/data/test_subword.py index 88e57c5044..5122fd6997 100644 --- a/test/data/test_subword.py +++ b/test/data/test_subword.py @@ -1,3 +1,5 @@ +#!/usr/bin/env python3 +# Note that all the tests in this module require dataset (either network access or cached) import unittest from torchtext import data diff --git a/test/data/test_utils.py b/test/data/test_utils.py index c90b44f116..f80ffb5bc0 100644 --- a/test/data/test_utils.py +++ b/test/data/test_utils.py @@ -15,12 +15,6 @@ def test_get_tokenizer_split(self): assert data.get_tokenizer(str.split) == str.split assert data.get_tokenizer(str.split)(self.TEST_STR) == str.split(self.TEST_STR) - def test_get_tokenizer_spacy(self): - # Test SpaCy option, and verify it properly handles punctuation. - assert data.get_tokenizer("spacy")(str(self.TEST_STR)) == [ - "A", "string", ",", "particularly", "one", "with", "slightly", - "complex", "punctuation", "."] - def test_get_tokenizer_moses(self): # Test Moses option. # Note that internally, MosesTokenizer converts to unicode if applicable diff --git a/test/test_build.py b/test/test_build.py new file mode 100644 index 0000000000..f4d2e7b3dd --- /dev/null +++ b/test/test_build.py @@ -0,0 +1,316 @@ +#!/usr/bin/env python3 +"""Tests that requires external resources (Network access to fetch dataset)""" +import os +from collections import Counter + +import numpy as np +import torch +import torchtext.data + +from .common.torchtext_test_case import TorchtextTestCase + + +class TestNestedField(TorchtextTestCase): + def test_build_vocab(self): + nesting_field = torchtext.data.Field(tokenize=list, init_token="", eos_token="") + + field = torchtext.data.NestedField( + nesting_field, init_token='', eos_token='', + include_lengths=True, + pad_first=True) + + sources = [ + [['a'], ['s', 'e', 'n', 't', 'e', 'n', 'c', 'e'], ['o', 'f'], ['d', 'a', 't', 'a'], ['.']], + [['y', 'e', 't'], ['a', 'n', 'o', 't', 'h', 'e', 'r']], + [['o', 'n', 'e'], ['l', 'a', 's', 't'], ['s', 'e', 'n', 't']] + ] + + field.build_vocab( + sources, vectors='glove.6B.50d', + unk_init=torch.nn.init.normal_, vectors_cache=".vector_cache") + + +class TestDataset(TorchtextTestCase): + def test_csv_file_no_header_one_col_multiple_fields(self): + self.write_test_ppid_dataset(data_format="csv") + + question_field = torchtext.data.Field(sequential=True) + spacy_tok_question_field = torchtext.data.Field(sequential=True, tokenize="spacy") + label_field = torchtext.data.Field(sequential=False) + # Field name/value as nested tuples + fields = [("ids", None), + (("q1", "q1_spacy"), (question_field, spacy_tok_question_field)), + (("q2", "q2_spacy"), (question_field, spacy_tok_question_field)), + ("label", label_field)] + dataset = torchtext.data.TabularDataset( + path=self.test_ppid_dataset_path, format="csv", fields=fields) + expected_examples = [ + (["When", "do", "you", "use", "シ", "instead", "of", "し?"], + ["When", "do", "you", "use", "シ", "instead", "of", "し", "?"], + ["When", "do", "you", "use", "\"&\"", + "instead", "of", "\"and\"?"], + ["When", "do", "you", "use", "\"", "&", "\"", + "instead", "of", "\"", "and", "\"", "?"], "0"), + (["Where", "was", "Lincoln", "born?"], + ["Where", "was", "Lincoln", "born", "?"], + ["Which", "location", "was", "Abraham", "Lincoln", "born?"], + ["Which", "location", "was", "Abraham", "Lincoln", "born", "?"], + "1"), + (["What", "is", "2+2"], ["What", "is", "2", "+", "2"], + ["2+2=?"], ["2", "+", "2=", "?"], "1")] + for i, example in enumerate(dataset): + self.assertEqual(example.q1, expected_examples[i][0]) + self.assertEqual(example.q1_spacy, expected_examples[i][1]) + self.assertEqual(example.q2, expected_examples[i][2]) + self.assertEqual(example.q2_spacy, expected_examples[i][3]) + self.assertEqual(example.label, expected_examples[i][4]) + + # 6 Fields including None for ids + assert len(dataset.fields) == 6 + + def test_json_dataset_one_key_multiple_fields(self): + self.write_test_ppid_dataset(data_format="json") + + question_field = torchtext.data.Field(sequential=True) + spacy_tok_question_field = torchtext.data.Field(sequential=True, tokenize="spacy") + label_field = torchtext.data.Field(sequential=False) + fields = {"question1": [("q1", question_field), + ("q1_spacy", spacy_tok_question_field)], + "question2": [("q2", question_field), + ("q2_spacy", spacy_tok_question_field)], + "label": ("label", label_field)} + dataset = torchtext.data.TabularDataset( + path=self.test_ppid_dataset_path, format="json", fields=fields) + expected_examples = [ + (["When", "do", "you", "use", "シ", "instead", "of", "し?"], + ["When", "do", "you", "use", "シ", "instead", "of", "し", "?"], + ["When", "do", "you", "use", "\"&\"", + "instead", "of", "\"and\"?"], + ["When", "do", "you", "use", "\"", "&", "\"", + "instead", "of", "\"", "and", "\"", "?"], "0"), + (["Where", "was", "Lincoln", "born?"], + ["Where", "was", "Lincoln", "born", "?"], + ["Which", "location", "was", "Abraham", "Lincoln", "born?"], + ["Which", "location", "was", "Abraham", "Lincoln", "born", "?"], + "1"), + (["What", "is", "2+2"], ["What", "is", "2", "+", "2"], + ["2+2=?"], ["2", "+", "2=", "?"], "1")] + for i, example in enumerate(dataset): + self.assertEqual(example.q1, expected_examples[i][0]) + self.assertEqual(example.q1_spacy, expected_examples[i][1]) + self.assertEqual(example.q2, expected_examples[i][2]) + self.assertEqual(example.q2_spacy, expected_examples[i][3]) + self.assertEqual(example.label, expected_examples[i][4]) + + +class TestDataUtils(TorchtextTestCase): + TEST_STR = "A string, particularly one with slightly complex punctuation." + + def test_get_tokenizer_spacy(self): + # Test SpaCy option, and verify it properly handles punctuation. + assert torchtext.data.get_tokenizer("spacy")(str(self.TEST_STR)) == [ + "A", "string", ",", "particularly", "one", "with", "slightly", + "complex", "punctuation", "."] + + +class TestVocab(TorchtextTestCase): + def test_vectors_get_vecs(self): + vec = torchtext.vocab.GloVe(name='twitter.27B', dim='25') + self.assertEqual(vec.vectors.shape[0], len(vec)) + + tokens = ['chip', 'baby', 'Beautiful'] + token_vecs = vec.get_vecs_by_tokens(tokens).numpy() + self.assertEqual(token_vecs.shape[0], len(tokens)) + self.assertEqual(token_vecs.shape[1], vec.dim) + torch.testing.assert_allclose(vec[tokens[0]].numpy(), token_vecs[0]) + torch.testing.assert_allclose(vec[tokens[1]].numpy(), token_vecs[1]) + torch.testing.assert_allclose(vec[''].numpy(), token_vecs[2]) + + token_one_vec = vec.get_vecs_by_tokens(tokens[0], lower_case_backup=True).numpy() + self.assertEqual(token_one_vec.shape[0], vec.dim) + torch.testing.assert_allclose(vec[tokens[0].lower()].numpy(), token_one_vec) + + def test_download_charngram_vectors(self): + c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) + # Build a vocab and get vectors twice to test caching, then once more + # to test string aliases. + for i in range(3): + if i == 2: + vectors = "charngram.100d" + else: + vectors = torchtext.vocab.CharNGram() + v = torchtext.vocab.Vocab( + c, min_freq=3, specials=['', '', ''], vectors=vectors) + expected_itos = ['', '', '', + 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world'] + expected_stoi = {x: index for index, x in enumerate(expected_itos)} + self.assertEqual(v.itos, expected_itos) + self.assertEqual(dict(v.stoi), expected_stoi) + vectors = v.vectors.numpy() + + # The first 5 entries in each vector. + expected_charngram = { + 'hello': [-0.44782442, -0.08937783, -0.34227219, + -0.16233221, -0.39343098], + 'world': [-0.29590717, -0.05275926, -0.37334684, 0.27117205, -0.3868292], + } + + for word in expected_charngram: + torch.testing.assert_allclose( + vectors[v.stoi[word], :5], expected_charngram[word]) + + torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(100)) + torch.testing.assert_allclose(vectors[v.stoi['OOV token']], np.zeros(100)) + + def test_download_custom_vectors(self): + c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) + # Build a vocab and get vectors twice to test caching. + for _ in range(2): + v = torchtext.vocab.Vocab( + c, min_freq=3, specials=['', '', ''], + vectors=torchtext.vocab.Vectors( + 'wiki.simple.vec', + url=torchtext.vocab.FastText.url_base.format('simple') + ) + ) + + self.assertEqual(v.itos, ['', '', '', + 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world']) + vectors = v.vectors.numpy() + + # The first 5 entries in each vector. + expected_fasttext_simple_en = { + 'hello': [0.39567, 0.21454, -0.035389, -0.24299, -0.095645], + 'world': [0.10444, -0.10858, 0.27212, 0.13299, -0.33165], + } + + for word in expected_fasttext_simple_en: + torch.testing.assert_allclose( + vectors[v.stoi[word], :5], expected_fasttext_simple_en[word]) + + torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(300)) + + def test_download_fasttext_vectors(self): + c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) + # Build a vocab and get vectors twice to test caching, then once more + # to test string aliases. + for i in range(3): + if i == 2: + vectors = "fasttext.simple.300d" + else: + vectors = torchtext.vocab.FastText(language='simple') + + v = torchtext.vocab.Vocab( + c, min_freq=3, specials=['', '', ''], vectors=vectors) + + expected_itos = ['', '', '', + 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world'] + expected_stoi = {x: index for index, x in enumerate(expected_itos)} + self.assertEqual(v.itos, expected_itos) + self.assertEqual(dict(v.stoi), expected_stoi) + vectors = v.vectors.numpy() + + # The first 5 entries in each vector. + expected_fasttext_simple_en = { + 'hello': [0.39567, 0.21454, -0.035389, -0.24299, -0.095645], + 'world': [0.10444, -0.10858, 0.27212, 0.13299, -0.33165], + } + + for word in expected_fasttext_simple_en: + torch.testing.assert_allclose( + vectors[v.stoi[word], :5], expected_fasttext_simple_en[word]) + + torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(300)) + torch.testing.assert_allclose(vectors[v.stoi['OOV token']], np.zeros(300)) + + def test_download_glove_vectors(self): + c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) + + # Build a vocab and get vectors twice to test caching, then once more + # to test string aliases. + for i in range(3): + if i == 2: + vectors = "glove.twitter.27B.25d" + else: + vectors = torchtext.vocab.GloVe(name='twitter.27B', dim='25') + v = torchtext.vocab.Vocab( + c, min_freq=3, specials=['', '', ''], vectors=vectors) + + expected_itos = ['', '', '', + 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world'] + expected_stoi = {x: index for index, x in enumerate(expected_itos)} + self.assertEqual(v.itos, expected_itos) + self.assertEqual(dict(v.stoi), expected_stoi) + + vectors = v.vectors.numpy() + + # The first 5 entries in each vector. + expected_twitter = { + 'hello': [-0.77069, 0.12827, 0.33137, 0.0050893, -0.47605], + 'world': [0.10301, 0.095666, -0.14789, -0.22383, -0.14775], + } + + for word in expected_twitter: + torch.testing.assert_allclose( + vectors[v.stoi[word], :5], expected_twitter[word]) + + torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(25)) + torch.testing.assert_allclose(vectors[v.stoi['OOV token']], np.zeros(25)) + + def test_extend(self): + c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) + # Build a vocab and get vectors twice to test caching. + for _ in range(2): + f = torchtext.vocab.FastText(language='simple') + v = torchtext.vocab.Vocab( + c, min_freq=3, specials=['', '', ''], vectors=f) + n_vocab = len(v) + v.extend(f) # extend the vocab with the words contained in f.itos + self.assertGreater(len(v), n_vocab) + + self.assertEqual(v.itos[:6], ['', '', '', + 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world']) + vectors = v.vectors.numpy() + + # The first 5 entries in each vector. + expected_fasttext_simple_en = { + 'hello': [0.39567, 0.21454, -0.035389, -0.24299, -0.095645], + 'world': [0.10444, -0.10858, 0.27212, 0.13299, -0.33165], + } + + for word in expected_fasttext_simple_en: + torch.testing.assert_allclose( + vectors[v.stoi[word], :5], expected_fasttext_simple_en[word]) + + torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(300)) + + def test_vectors_custom_cache(self): + c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) + vector_cache = os.path.join('/tmp', 'vector_cache') + # Build a vocab and get vectors twice to test caching. + for i in range(2): + if i == 1: + self.assertTrue(os.path.exists(vector_cache)) + + v = torchtext.vocab.Vocab( + c, min_freq=3, specials=['', '', ''], + vectors=torchtext.vocab.Vectors( + 'wiki.simple.vec', cache=vector_cache, + url=torchtext.vocab.FastText.url_base.format('simple')) + ) + + self.assertEqual(v.itos, ['', '', '', + 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world']) + vectors = v.vectors.numpy() + + # The first 5 entries in each vector. + expected_fasttext_simple_en = { + 'hello': [0.39567, 0.21454, -0.035389, -0.24299, -0.095645], + 'world': [0.10444, -0.10858, 0.27212, 0.13299, -0.33165], + } + + for word in expected_fasttext_simple_en: + torch.testing.assert_allclose( + vectors[v.stoi[word], :5], expected_fasttext_simple_en[word]) + + torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(300)) diff --git a/test/test_utils.py b/test/test_utils.py index fc10978157..5489f838b2 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -1,3 +1,5 @@ +#!/usr/bin/env python3 +# Note that all the tests in this module require dataset (either network access or cached) import os from torchtext import utils from .common.torchtext_test_case import TorchtextTestCase diff --git a/test/test_vocab.py b/test/test_vocab.py index c2801e1152..c09bff69f2 100644 --- a/test/test_vocab.py +++ b/test/test_vocab.py @@ -8,7 +8,6 @@ from numpy.testing import assert_allclose import torch from torchtext import vocab -from torchtext.vocab import Vectors, FastText, GloVe, CharNGram from .common.torchtext_test_case import TorchtextTestCase @@ -92,215 +91,6 @@ def test_vocab_set_vectors(self): [0.3, 0.4]]) assert_allclose(v.vectors.numpy(), expected_vectors) - def test_vocab_download_fasttext_vectors(self): - c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) - # Build a vocab and get vectors twice to test caching, then once more - # to test string aliases. - for i in range(3): - if i == 2: - vectors = "fasttext.simple.300d" - else: - vectors = FastText(language='simple') - - v = vocab.Vocab(c, min_freq=3, specials=['', '', ''], - vectors=vectors) - - expected_itos = ['', '', '', - 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world'] - expected_stoi = {x: index for index, x in enumerate(expected_itos)} - self.assertEqual(v.itos, expected_itos) - self.assertEqual(dict(v.stoi), expected_stoi) - vectors = v.vectors.numpy() - - # The first 5 entries in each vector. - expected_fasttext_simple_en = { - 'hello': [0.39567, 0.21454, -0.035389, -0.24299, -0.095645], - 'world': [0.10444, -0.10858, 0.27212, 0.13299, -0.33165], - } - - for word in expected_fasttext_simple_en: - assert_allclose(vectors[v.stoi[word], :5], - expected_fasttext_simple_en[word]) - - assert_allclose(vectors[v.stoi['']], np.zeros(300)) - assert_allclose(vectors[v.stoi['OOV token']], np.zeros(300)) - # Delete the vectors after we're done to save disk space on CI - if os.environ.get("TRAVIS") == "true": - vec_file = os.path.join(self.project_root, ".vector_cache", "wiki.simple.vec") - conditional_remove(vec_file) - - def test_vocab_extend(self): - c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) - # Build a vocab and get vectors twice to test caching. - for _ in range(2): - f = FastText(language='simple') - v = vocab.Vocab(c, min_freq=3, specials=['', '', ''], - vectors=f) - n_vocab = len(v) - v.extend(f) # extend the vocab with the words contained in f.itos - self.assertGreater(len(v), n_vocab) - - self.assertEqual(v.itos[:6], ['', '', '', - 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world']) - vectors = v.vectors.numpy() - - # The first 5 entries in each vector. - expected_fasttext_simple_en = { - 'hello': [0.39567, 0.21454, -0.035389, -0.24299, -0.095645], - 'world': [0.10444, -0.10858, 0.27212, 0.13299, -0.33165], - } - - for word in expected_fasttext_simple_en: - assert_allclose(vectors[v.stoi[word], :5], - expected_fasttext_simple_en[word]) - - assert_allclose(vectors[v.stoi['']], np.zeros(300)) - # Delete the vectors after we're done to save disk space on CI - if os.environ.get("TRAVIS") == "true": - vec_file = os.path.join(self.project_root, ".vector_cache", "wiki.simple.vec") - conditional_remove(vec_file) - - def test_vocab_download_custom_vectors(self): - c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) - # Build a vocab and get vectors twice to test caching. - for _ in range(2): - v = vocab.Vocab(c, min_freq=3, specials=['', '', ''], - vectors=Vectors('wiki.simple.vec', - url=FastText.url_base.format('simple'))) - - self.assertEqual(v.itos, ['', '', '', - 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world']) - vectors = v.vectors.numpy() - - # The first 5 entries in each vector. - expected_fasttext_simple_en = { - 'hello': [0.39567, 0.21454, -0.035389, -0.24299, -0.095645], - 'world': [0.10444, -0.10858, 0.27212, 0.13299, -0.33165], - } - - for word in expected_fasttext_simple_en: - assert_allclose(vectors[v.stoi[word], :5], - expected_fasttext_simple_en[word]) - - assert_allclose(vectors[v.stoi['']], np.zeros(300)) - # Delete the vectors after we're done to save disk space on CI - if os.environ.get("TRAVIS") == "true": - vec_file = os.path.join(self.project_root, ".vector_cache", "wiki.simple.vec") - conditional_remove(vec_file) - - def test_vocab_vectors_custom_cache(self): - c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) - vector_cache = os.path.join('/tmp', 'vector_cache') - # Build a vocab and get vectors twice to test caching. - for i in range(2): - if i == 1: - self.assertTrue(os.path.exists(vector_cache)) - - v = vocab.Vocab(c, min_freq=3, specials=['', '', ''], - vectors=Vectors('wiki.simple.vec', cache=vector_cache, - url=FastText.url_base.format('simple'))) - - self.assertEqual(v.itos, ['', '', '', - 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world']) - vectors = v.vectors.numpy() - - # The first 5 entries in each vector. - expected_fasttext_simple_en = { - 'hello': [0.39567, 0.21454, -0.035389, -0.24299, -0.095645], - 'world': [0.10444, -0.10858, 0.27212, 0.13299, -0.33165], - } - - for word in expected_fasttext_simple_en: - assert_allclose(vectors[v.stoi[word], :5], - expected_fasttext_simple_en[word]) - - assert_allclose(vectors[v.stoi['']], np.zeros(300)) - # Delete the vectors after we're done to save disk space on CI - if os.environ.get("TRAVIS") == "true": - vec_file = os.path.join(vector_cache, "wiki.simple.vec") - conditional_remove(vec_file) - - def test_vocab_download_glove_vectors(self): - c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) - - # Build a vocab and get vectors twice to test caching, then once more - # to test string aliases. - for i in range(3): - if i == 2: - vectors = "glove.twitter.27B.25d" - else: - vectors = GloVe(name='twitter.27B', dim='25') - v = vocab.Vocab(c, min_freq=3, specials=['', '', ''], - vectors=vectors) - - expected_itos = ['', '', '', - 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world'] - expected_stoi = {x: index for index, x in enumerate(expected_itos)} - self.assertEqual(v.itos, expected_itos) - self.assertEqual(dict(v.stoi), expected_stoi) - - vectors = v.vectors.numpy() - - # The first 5 entries in each vector. - expected_twitter = { - 'hello': [-0.77069, 0.12827, 0.33137, 0.0050893, -0.47605], - 'world': [0.10301, 0.095666, -0.14789, -0.22383, -0.14775], - } - - for word in expected_twitter: - assert_allclose(vectors[v.stoi[word], :5], - expected_twitter[word]) - - assert_allclose(vectors[v.stoi['']], np.zeros(25)) - assert_allclose(vectors[v.stoi['OOV token']], np.zeros(25)) - # Delete the vectors after we're done to save disk space on CI - if os.environ.get("TRAVIS") == "true": - zip_file = os.path.join(self.project_root, ".vector_cache", - "glove.twitter.27B.zip") - conditional_remove(zip_file) - for dim in ["25", "50", "100", "200"]: - conditional_remove(os.path.join(self.project_root, ".vector_cache", - "glove.twitter.27B.{}d.txt".format(dim))) - - def test_vocab_download_charngram_vectors(self): - c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) - # Build a vocab and get vectors twice to test caching, then once more - # to test string aliases. - for i in range(3): - if i == 2: - vectors = "charngram.100d" - else: - vectors = CharNGram() - v = vocab.Vocab(c, min_freq=3, specials=['', '', ''], - vectors=vectors) - expected_itos = ['', '', '', - 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world'] - expected_stoi = {x: index for index, x in enumerate(expected_itos)} - self.assertEqual(v.itos, expected_itos) - self.assertEqual(dict(v.stoi), expected_stoi) - vectors = v.vectors.numpy() - - # The first 5 entries in each vector. - expected_charngram = { - 'hello': [-0.44782442, -0.08937783, -0.34227219, - -0.16233221, -0.39343098], - 'world': [-0.29590717, -0.05275926, -0.37334684, 0.27117205, -0.3868292], - } - - for word in expected_charngram: - assert_allclose(vectors[v.stoi[word], :5], - expected_charngram[word]) - - assert_allclose(vectors[v.stoi['']], np.zeros(100)) - assert_allclose(vectors[v.stoi['OOV token']], np.zeros(100)) - # Delete the vectors after we're done to save disk space on CI - if os.environ.get("TRAVIS") == "true": - conditional_remove( - os.path.join(self.project_root, ".vector_cache", "charNgram.txt")) - conditional_remove( - os.path.join(self.project_root, ".vector_cache", - "jmt_pre-trained_embeddings.tar.gz")) - def test_errors(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) with self.assertRaises(ValueError): @@ -336,31 +126,6 @@ def test_serialization_backcompat(self): v_loaded = pickle.load(open(pickle_path, "rb")) assert v == v_loaded - def test_vectors_get_vecs(self): - vec = GloVe(name='twitter.27B', dim='25') - self.assertEqual(vec.vectors.shape[0], len(vec)) - - tokens = ['chip', 'baby', 'Beautiful'] - token_vecs = vec.get_vecs_by_tokens(tokens).numpy() - self.assertEqual(token_vecs.shape[0], len(tokens)) - self.assertEqual(token_vecs.shape[1], vec.dim) - assert_allclose(vec[tokens[0]].numpy(), token_vecs[0]) - assert_allclose(vec[tokens[1]].numpy(), token_vecs[1]) - assert_allclose(vec[''].numpy(), token_vecs[2]) - - token_one_vec = vec.get_vecs_by_tokens(tokens[0], lower_case_backup=True).numpy() - self.assertEqual(token_one_vec.shape[0], vec.dim) - assert_allclose(vec[tokens[0].lower()].numpy(), token_one_vec) - - # Delete the vectors after we're done to save disk space on CI - if os.environ.get("TRAVIS") == "true": - zip_file = os.path.join(self.project_root, ".vector_cache", - "glove.6B.zip") - conditional_remove(zip_file) - for dim in ["50", "100", "200", "300"]: - conditional_remove(os.path.join(self.project_root, ".vector_cache", - "glove.6B.{}d.txt".format(dim))) - def test_has_unk(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) v = vocab.Vocab(c) From 943e370cb100e160bbf9809d807f0ac1032f6f19 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Tue, 19 May 2020 17:00:19 -0400 Subject: [PATCH 029/719] Move test_get_tokenizer_moses to test_build (#784) --- test/data/test_utils.py | 11 ----------- test/test_build.py | 11 +++++++++++ 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/test/data/test_utils.py b/test/data/test_utils.py index f80ffb5bc0..c64ec09bf8 100644 --- a/test/data/test_utils.py +++ b/test/data/test_utils.py @@ -15,17 +15,6 @@ def test_get_tokenizer_split(self): assert data.get_tokenizer(str.split) == str.split assert data.get_tokenizer(str.split)(self.TEST_STR) == str.split(self.TEST_STR) - def test_get_tokenizer_moses(self): - # Test Moses option. - # Note that internally, MosesTokenizer converts to unicode if applicable - moses_tokenizer = data.get_tokenizer("moses") - assert moses_tokenizer(self.TEST_STR) == [ - "A", "string", ",", "particularly", "one", "with", "slightly", - "complex", "punctuation", "."] - - # Nonbreaking prefixes should tokenize the final period. - assert moses_tokenizer("abc def.") == ["abc", "def", "."] - def test_get_tokenizer_toktokt(self): # Test Toktok option. Test strings taken from NLTK doctests. # Note that internally, MosesTokenizer converts to unicode if applicable diff --git a/test/test_build.py b/test/test_build.py index f4d2e7b3dd..f47d535204 100644 --- a/test/test_build.py +++ b/test/test_build.py @@ -112,6 +112,17 @@ def test_get_tokenizer_spacy(self): "A", "string", ",", "particularly", "one", "with", "slightly", "complex", "punctuation", "."] + def test_get_tokenizer_moses(self): + # Test Moses option. + # Note that internally, MosesTokenizer converts to unicode if applicable + moses_tokenizer = torchtext.data.get_tokenizer("moses") + assert moses_tokenizer(self.TEST_STR) == [ + "A", "string", ",", "particularly", "one", "with", "slightly", + "complex", "punctuation", "."] + + # Nonbreaking prefixes should tokenize the final period. + assert moses_tokenizer("abc def.") == ["abc", "def", "."] + class TestVocab(TorchtextTestCase): def test_vectors_get_vecs(self): From 9bffb3f3192d68a135143d5fcd48f85de62390de Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Wed, 20 May 2020 11:06:18 -0400 Subject: [PATCH 030/719] Fix csrc header for autodeps (#786) Fix for `fbcode autodeps`. Although the header file `sentencepiece_processor.h` is included by `sentencepiece_trainer.h`, without explicitly including `sentencepiece_processor.h`, `autodeps` cannot find the dependencies properly. --- torchtext/csrc/sentencepiece.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/torchtext/csrc/sentencepiece.cpp b/torchtext/csrc/sentencepiece.cpp index f5d279a955..06237b5d32 100644 --- a/torchtext/csrc/sentencepiece.cpp +++ b/torchtext/csrc/sentencepiece.cpp @@ -1,3 +1,4 @@ +#include #include #include From 569131e0dfa3ed90493306cdfd9d776f210734a2 Mon Sep 17 00:00:00 2001 From: peterjc123 Date: Tue, 26 May 2020 21:58:09 +0800 Subject: [PATCH 031/719] Fix VC environment activation scripts for Windows jobs (#789) --- .circleci/config.yml | 2 +- .circleci/config.yml.in | 2 +- .circleci/unittest/windows/scripts/install.bat | 2 ++ packaging/build_wheel.bat | 2 ++ 4 files changed, 6 insertions(+), 2 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index db23e8a076..43c83de566 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -13,7 +13,7 @@ executors: windows-cpu: machine: resource_class: windows.xlarge - image: windows-server-2019-vs2019:stable + image: windows-server-2019-vs2019:temporary shell: bash.exe binary_common: &binary_common diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 42d8b686db..e3c0e5ea0e 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -13,7 +13,7 @@ executors: windows-cpu: machine: resource_class: windows.xlarge - image: windows-server-2019-vs2019:stable + image: windows-server-2019-vs2019:temporary shell: bash.exe binary_common: &binary_common diff --git a/.circleci/unittest/windows/scripts/install.bat b/.circleci/unittest/windows/scripts/install.bat index fe57e2d61d..5205b4c1e6 100644 --- a/.circleci/unittest/windows/scripts/install.bat +++ b/.circleci/unittest/windows/scripts/install.bat @@ -17,4 +17,6 @@ if "%VSDEVCMD_ARGS%" == "" ( @echo on +set DISTUTILS_USE_SDK=1 + python setup.py develop || exit /b 1 diff --git a/packaging/build_wheel.bat b/packaging/build_wheel.bat index 90bd865361..d9ba42ad04 100644 --- a/packaging/build_wheel.bat +++ b/packaging/build_wheel.bat @@ -17,4 +17,6 @@ if "%VSDEVCMD_ARGS%" == "" ( @echo on +set DISTUTILS_USE_SDK=1 + python setup.py bdist_wheel || exit /b 1 From 61a9acf8d01a9cee239fad7899cfa78ec3746799 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Tue, 26 May 2020 15:25:17 -0400 Subject: [PATCH 032/719] Add shebang line to suppress the lint warning (#787) --- packaging/pkg_helpers.bash | 2 ++ 1 file changed, 2 insertions(+) diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index c03e647e1a..6f43f8e025 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -1,3 +1,5 @@ +#!/usr/bin/env bash + # A set of useful bash functions for common functionality we need to do in # many build scripts From 81d32c1af54a6657ebebdcc742dc877e26e09a72 Mon Sep 17 00:00:00 2001 From: peterjc123 Date: Wed, 27 May 2020 21:28:47 +0800 Subject: [PATCH 033/719] Use new circleci image for Windows jobs (#796) --- .circleci/config.yml | 2 +- .circleci/config.yml.in | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 43c83de566..db23e8a076 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -13,7 +13,7 @@ executors: windows-cpu: machine: resource_class: windows.xlarge - image: windows-server-2019-vs2019:temporary + image: windows-server-2019-vs2019:stable shell: bash.exe binary_common: &binary_common diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index e3c0e5ea0e..42d8b686db 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -13,7 +13,7 @@ executors: windows-cpu: machine: resource_class: windows.xlarge - image: windows-server-2019-vs2019:temporary + image: windows-server-2019-vs2019:stable shell: bash.exe binary_common: &binary_common From 3fbe4da4544a07d54a6d506ce889ff92c80d4230 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Wed, 27 May 2020 12:04:49 -0400 Subject: [PATCH 034/719] Improve error message when extension is not found (#795) * Improve error message when extension is not found --- torchtext/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchtext/__init__.py b/torchtext/__init__.py index c19fa01ec8..97191542ec 100644 --- a/torchtext/__init__.py +++ b/torchtext/__init__.py @@ -32,7 +32,7 @@ def _init_extension(): extfinder = importlib.machinery.FileFinder(lib_dir, loader_details) ext_specs = extfinder.find_spec("_torchtext") if ext_specs is None: - raise ImportError + raise ImportError("torchtext C++ Extension is not found.") torch.ops.load_library(ext_specs.origin) torch.classes.load_library(ext_specs.origin) From a48b63448b0164263bcd9bcaeead6bdd91ff6072 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Thu, 28 May 2020 11:29:44 -0400 Subject: [PATCH 035/719] Fix test_generate_sp_model for stress test (#798) When multiple instances of test_generate_sp_model run, intermediate model data have to be unique and properly cleaned up. See also T67599333 --- test/common/assets.py | 30 +++--------------------- test/data/test_functional.py | 45 +++++++++++++++++++----------------- 2 files changed, 27 insertions(+), 48 deletions(-) diff --git a/test/common/assets.py b/test/common/assets.py index c7523bb619..f863640c0c 100644 --- a/test/common/assets.py +++ b/test/common/assets.py @@ -1,32 +1,8 @@ -import os -import shutil -import atexit -import tempfile from pathlib import Path _ASSET_DIR = (Path(__file__).parent.parent / "asset").resolve() -_TEMP_DIR = None - -def _init_temp_dir(): - """Initialize temporary directory and register clean up at the end of test.""" - global _TEMP_DIR - _TEMP_DIR = tempfile.TemporaryDirectory() # noqa - atexit.register(_TEMP_DIR.cleanup) - - -def get_asset_path(*path_components, use_temp_dir=False): - """Get the path to the file under `test/assets` directory. - When `use_temp_dir` is True, the asset is copied to a temporary location and - path to the temporary file is returned. - """ - path = str(_ASSET_DIR.joinpath(*path_components)) - if not use_temp_dir: - return path - - if _TEMP_DIR is None: - _init_temp_dir() - tgt = os.path.join(_TEMP_DIR.name, path_components[-1]) - shutil.copy(path, tgt) - return tgt +def get_asset_path(*path_components): + """Get the path to the file under `test/assets` directory.""" + return str(_ASSET_DIR.joinpath(*path_components)) diff --git a/test/data/test_functional.py b/test/data/test_functional.py index dacc0adafd..ff5b12ab17 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -1,6 +1,8 @@ import os import unittest import sys +import uuid +import shutil import tempfile import sentencepiece as spm @@ -20,27 +22,28 @@ class TestFunctional(TorchtextTestCase): def test_generate_sp_model(self): - # Test the function to train a sentencepiece tokenizer - - # buck (fb internal) generates test environment which contains ',' in its path. - # SentencePieceTrainer considers such path as comma-delimited file list. - # So as workaround we copy the asset data to temporary directory and load it from there. - data_path = get_asset_path( - 'text_normalization_ag_news_test.csv', - use_temp_dir=True) - generate_sp_model(data_path, - vocab_size=23456, - model_prefix='spm_user') - - sp_user = spm.SentencePieceProcessor() - sp_user.Load('spm_user.model') - - self.assertEqual(len(sp_user), 23456) - - if os.path.isfile('spm_user.model'): - os.remove('spm_user.model') - if os.path.isfile('spm_user.vocab'): - os.remove('spm_user.vocab') + """Test the function to train a sentencepiece tokenizer""" + + asset_name = 'text_normalization_ag_news_test.csv' + asset_path = get_asset_path(asset_name) + # We use temporary directory for two reasons: + # 1. buck (fb internal) generates test environment which contains ',' in its path. + # SentencePieceTrainer considers such path as comma-delimited file list. + # So as workaround we copy the asset data to temporary directory and load it from there. + # 2. when fb infra performs stress tests, multiple instances of this test run. + # The name of the generated models have to be unique and they need to be cleaned up. + with tempfile.TemporaryDirectory() as dir_name: + data_path = os.path.join(dir_name, asset_name) + shutil.copy(asset_path, data_path) + + model_prefix = os.path.join(dir_name, f'spm_user_{uuid.uuid4()}') + model_file = f'{model_prefix}.model' + generate_sp_model(data_path, vocab_size=23456, model_prefix=model_prefix) + + sp_user = spm.SentencePieceProcessor() + sp_user.Load(model_file) + + self.assertEqual(len(sp_user), 23456) def test_sentencepiece_numericalizer(self): test_sample = 'SentencePiece is an unsupervised text tokenizer and detokenizer' From 56480e4b62d041df9bc7b8fb0cc60b20cfda1b8d Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Thu, 28 May 2020 12:58:58 -0400 Subject: [PATCH 036/719] Update smoke test to use image from Docker hub (#794) * Update smoke test Docker image build job * Add org-member context --- .circleci/config.yml | 25 +++++++++---------------- .circleci/config.yml.in | 25 +++++++++---------------- 2 files changed, 18 insertions(+), 32 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index db23e8a076..aca7943d7f 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -40,10 +40,7 @@ binary_common: &binary_common smoke_test_common: &smoke_test_common <<: *binary_common docker: - - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchtext/smoke_test:d26af7d0-8458-face-book-84239b5c75c0 - aws_auth: - aws_access_key_id: ${ECR_AWS_ACCESS_KEY} - aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} + - image: pytorch/torchtext_smoke_base:latest jobs: circleci_consistency: @@ -251,21 +248,16 @@ jobs: steps: - checkout - run: - name: build_docker image - no_output_timeout: "1h" - command: | - cd .circleci/smoke_test/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} - - run: - name: upload docker image + name: Build and push Docker image no_output_timeout: "1h" command: | set +x - export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} - export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} - eval $(aws ecr get-login --region us-east-1 --no-include-email) + echo "${DOCKER_HUB_TOKEN}" | docker login --username "${DOCKER_HUB_USERNAME}" --password-stdin set -x - docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} - docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + cd .circleci/smoke_test/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} ${image_name}:latest + docker push ${image_name}:${CIRCLE_WORKFLOW_ID} + docker push ${image_name}:latest unittest_linux: <<: *binary_common @@ -796,4 +788,5 @@ workflows: only: - master jobs: - - smoke_test_docker_image_build \ No newline at end of file + - smoke_test_docker_image_build: + context: org-member \ No newline at end of file diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 42d8b686db..fd7361800d 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -40,10 +40,7 @@ binary_common: &binary_common smoke_test_common: &smoke_test_common <<: *binary_common docker: - - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchtext/smoke_test:d26af7d0-8458-face-book-84239b5c75c0 - aws_auth: - aws_access_key_id: ${ECR_AWS_ACCESS_KEY} - aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} + - image: pytorch/torchtext_smoke_base:latest jobs: circleci_consistency: @@ -251,21 +248,16 @@ jobs: steps: - checkout - run: - name: build_docker image - no_output_timeout: "1h" - command: | - cd .circleci/smoke_test/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} - - run: - name: upload docker image + name: Build and push Docker image no_output_timeout: "1h" command: | set +x - export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} - export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} - eval $(aws ecr get-login --region us-east-1 --no-include-email) + echo "${DOCKER_HUB_TOKEN}" | docker login --username "${DOCKER_HUB_USERNAME}" --password-stdin set -x - docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} - docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + cd .circleci/smoke_test/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} ${image_name}:latest + docker push ${image_name}:${CIRCLE_WORKFLOW_ID} + docker push ${image_name}:latest unittest_linux: <<: *binary_common @@ -424,4 +416,5 @@ workflows: only: - master jobs: - - smoke_test_docker_image_build + - smoke_test_docker_image_build: + context: org-member From 70a12311618526a6fcac1ca79fa87633b0553948 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Thu, 28 May 2020 11:01:27 -0700 Subject: [PATCH 037/719] Added local re library --- torchtext/experimental/re_jitable.py | 342 +++++++++++++++++++++++++++ 1 file changed, 342 insertions(+) create mode 100644 torchtext/experimental/re_jitable.py diff --git a/torchtext/experimental/re_jitable.py b/torchtext/experimental/re_jitable.py new file mode 100644 index 0000000000..e9df02f2a9 --- /dev/null +++ b/torchtext/experimental/re_jitable.py @@ -0,0 +1,342 @@ +# +# Secret Labs' Regular Expression Engine +# +# re-compatible interface for the sre matching engine +# +# Copyright (c) 1998-2001 by Secret Labs AB. All rights reserved. +# +# This version of the SRE library can be redistributed under CNRI's +# Python 1.6 license. For any other use, please contact Secret Labs +# AB (info@pythonware.com). +# +# Portions of this engine have been developed in cooperation with +# CNRI. Hewlett-Packard provided funding for 1.6 integration and +# other compatibility work. +# + +r"""Support for regular expressions (RE). + +This module provides regular expression matching operations similar to +those found in Perl. It supports both 8-bit and Unicode strings; both +the pattern and the strings being processed can contain null bytes and +characters outside the US ASCII range. + +Regular expressions can contain both special and ordinary characters. +Most ordinary characters, like "A", "a", or "0", are the simplest +regular expressions; they simply match themselves. You can +concatenate ordinary characters, so last matches the string 'last'. + +The special characters are: + "." Matches any character except a newline. + "^" Matches the start of the string. + "$" Matches the end of the string or just before the newline at + the end of the string. + "*" Matches 0 or more (greedy) repetitions of the preceding RE. + Greedy means that it will match as many repetitions as possible. + "+" Matches 1 or more (greedy) repetitions of the preceding RE. + "?" Matches 0 or 1 (greedy) of the preceding RE. + *?,+?,?? Non-greedy versions of the previous three special characters. + {m,n} Matches from m to n repetitions of the preceding RE. + {m,n}? Non-greedy version of the above. + "\\" Either escapes special characters or signals a special sequence. + [] Indicates a set of characters. + A "^" as the first character indicates a complementing set. + "|" A|B, creates an RE that will match either A or B. + (...) Matches the RE inside the parentheses. + The contents can be retrieved or matched later in the string. + (?aiLmsux) The letters set the corresponding flags defined below. + (?:...) Non-grouping version of regular parentheses. + (?P...) The substring matched by the group is accessible by name. + (?P=name) Matches the text matched earlier by the group named name. + (?#...) A comment; ignored. + (?=...) Matches if ... matches next, but doesn't consume the string. + (?!...) Matches if ... doesn't match next. + (?<=...) Matches if preceded by ... (must be fixed length). + (? 1: + res = f'~({res})' + else: + res = f'~{res}' + return res + __str__ = object.__str__ + +globals().update(RegexFlag.__members__) + +# sre exception +error = sre_compile.error + +# -------------------------------------------------------------------- +# public interface + +def match(pattern, string, flags=0): + """Try to apply the pattern at the start of the string, returning + a Match object, or None if no match was found.""" + return _compile(pattern, flags).match(string) + +def fullmatch(pattern, string, flags=0): + """Try to apply the pattern to all of the string, returning + a Match object, or None if no match was found.""" + return _compile(pattern, flags).fullmatch(string) + +def search(pattern, string, flags=0): + """Scan through string looking for a match to the pattern, returning + a Match object, or None if no match was found.""" + return _compile(pattern, flags).search(string) + +def sub(pattern, repl, string, count=0, flags=0): + """Return the string obtained by replacing the leftmost + non-overlapping occurrences of the pattern in string by the + replacement repl. repl can be either a string or a callable; + if a string, backslash escapes in it are processed. If it is + a callable, it's passed the Match object and must return + a replacement string to be used.""" + return _compile(pattern, flags).sub(repl, string, count) + +def subn(pattern, repl, string, count=0, flags=0): + """Return a 2-tuple containing (new_string, number). + new_string is the string obtained by replacing the leftmost + non-overlapping occurrences of the pattern in the source + string by the replacement repl. number is the number of + substitutions that were made. repl can be either a string or a + callable; if a string, backslash escapes in it are processed. + If it is a callable, it's passed the Match object and must + return a replacement string to be used.""" + return _compile(pattern, flags).subn(repl, string, count) + +def split(pattern, string, maxsplit=0, flags=0): + """Split the source string by the occurrences of the pattern, + returning a list containing the resulting substrings. If + capturing parentheses are used in pattern, then the text of all + groups in the pattern are also returned as part of the resulting + list. If maxsplit is nonzero, at most maxsplit splits occur, + and the remainder of the string is returned as the final element + of the list.""" + return _compile(pattern, flags).split(string, maxsplit) + +def findall(pattern, string, flags=0): + """Return a list of all non-overlapping matches in the string. + + If one or more capturing groups are present in the pattern, return + a list of groups; this will be a list of tuples if the pattern + has more than one group. + + Empty matches are included in the result.""" + return _compile(pattern, flags).findall(string) + +def finditer(pattern, string, flags=0): + """Return an iterator over all non-overlapping matches in the + string. For each match, the iterator returns a Match object. + + Empty matches are included in the result.""" + return _compile(pattern, flags).finditer(string) + +def compile(pattern, flags=0): + "Compile a regular expression pattern, returning a Pattern object." + return _compile(pattern, flags) + +def purge(): + "Clear the regular expression caches" + _cache.clear() + _compile_repl.cache_clear() + +def template(pattern, flags=0): + "Compile a template pattern, returning a Pattern object" + return _compile(pattern, flags|T) + +# SPECIAL_CHARS +# closing ')', '}' and ']' +# '-' (a range in character set) +# '&', '~', (extended character set operations) +# '#' (comment) and WHITESPACE (ignored) in verbose mode +_special_chars_map = {i: '\\' + chr(i) for i in b'()[]{}?*+-|^$\\.&~# \t\n\r\v\f'} + +def escape(pattern): + """ + Escape special characters in a string. + """ + if isinstance(pattern, str): + return pattern.translate(_special_chars_map) + else: + pattern = str(pattern, 'latin1') + return pattern.translate(_special_chars_map).encode('latin1') + +Pattern = type(sre_compile.compile('', 0)) +Match = type(sre_compile.compile('', 0).match('')) + +# -------------------------------------------------------------------- +# internals + +_cache = {} # ordered! + +_MAXCACHE = 512 +def _compile(pattern, flags): + # internal: compile pattern + if isinstance(flags, RegexFlag): + flags = flags.value + try: + return _cache[type(pattern), pattern, flags] + except KeyError: + pass + if isinstance(pattern, Pattern): + if flags: + raise ValueError( + "cannot process flags argument with a compiled pattern") + return pattern + if not sre_compile.isstring(pattern): + raise TypeError("first argument must be string or compiled pattern") + p = sre_compile.compile(pattern, flags) + if not (flags & DEBUG): + if len(_cache) >= _MAXCACHE: + # Drop the oldest item + # provide default value to remove need for try-catch + next_key = next(iter(_cache), None) + if next_key: + del _cache[next_key] + _cache[type(pattern), pattern, flags] = p + return p + +@functools.lru_cache(_MAXCACHE) +def _compile_repl(repl, pattern): + # internal: compile replacement pattern + return sre_parse.parse_template(repl, pattern) + +def _expand(pattern, match, template): + # internal: Match.expand implementation hook + template = sre_parse.parse_template(template, pattern) + return sre_parse.expand_template(template, match) + +def _subx(pattern, template): + # internal: Pattern.sub/subn implementation helper + template = _compile_repl(template, pattern) + if not template[0] and len(template[1]) == 1: + # literal replacement + return template[1][0] + def filter(match, template=template): + return sre_parse.expand_template(template, match) + return filter + +# register myself for pickling + +import copyreg + +def _pickle(p): + return _compile, (p.pattern, p.flags) + +copyreg.pickle(Pattern, _pickle, _compile) \ No newline at end of file From 1ba5f1675799aa4928b9c0ca226553370f7e38e8 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Thu, 28 May 2020 15:34:19 -0400 Subject: [PATCH 038/719] Update smoke test docker image (#800) --- .circleci/smoke_test/docker/Dockerfile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.circleci/smoke_test/docker/Dockerfile b/.circleci/smoke_test/docker/Dockerfile index e98e83fb71..38be12e586 100644 --- a/.circleci/smoke_test/docker/Dockerfile +++ b/.circleci/smoke_test/docker/Dockerfile @@ -25,12 +25,12 @@ RUN apt-get -qq update && apt-get -qq -y install curl bzip2 sox libsox-dev libso ENV PATH /opt/conda/bin:$PATH -RUN conda create -y --name python3.5 python=3.5 RUN conda create -y --name python3.6 python=3.6 RUN conda create -y --name python3.7 python=3.7 +RUN conda create -y --name python3.8 python=3.8 SHELL [ "/bin/bash", "-c" ] RUN echo "source /usr/local/etc/profile.d/conda.sh" >> ~/.bashrc -RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.5 && conda install -y -c conda-forge sox && conda install -y numpy RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.6 && conda install -y -c conda-forge sox && conda install -y numpy RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.7 && conda install -y -c conda-forge sox && conda install -y numpy +RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.8 && conda install -y -c conda-forge sox && conda install -y numpy CMD [ "/bin/bash"] From 4c980333e1221a148391a71936aeeaff1e409ab6 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Thu, 28 May 2020 15:34:44 -0400 Subject: [PATCH 039/719] Fix pip install in smoke test (#801) --- .circleci/config.yml | 2 +- .circleci/config.yml.in | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index aca7943d7f..646ae75df6 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -232,7 +232,7 @@ jobs: command: | set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - pip install $(ls ~/workspace/torchtext*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html + pip install $(ls ~/workspace/torchtext*.whl) --pre -f https://download.pytorch.org/whl/nightly/torch_nightly.html - run: name: smoke test command: | diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index fd7361800d..b7506a531f 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -232,7 +232,7 @@ jobs: command: | set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - pip install $(ls ~/workspace/torchtext*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html + pip install $(ls ~/workspace/torchtext*.whl) --pre -f https://download.pytorch.org/whl/nightly/torch_nightly.html - run: name: smoke test command: | From 63129947e8c826ad91771c7310cac2f36040afae Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Thu, 28 May 2020 20:56:47 -0400 Subject: [PATCH 040/719] Install conda package from workspace (#803) --- .circleci/config.yml | 16 ++++++++-------- .circleci/config.yml.in | 16 ++++++++-------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 646ae75df6..165f641de7 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -80,9 +80,9 @@ jobs: - store_artifacts: path: /opt/conda/conda-bld/linux-64 - persist_to_workspace: - root: /opt/conda/conda-bld/linux-64 + root: /opt/conda paths: - - "*" + - "conda-bld/*" binary_windows_wheel: <<: *binary_common @@ -120,9 +120,9 @@ jobs: - store_artifacts: path: C:/tools/miniconda3/conda-bld/win-64 - persist_to_workspace: - root: C:/tools/miniconda3/conda-bld/win-64 + root: C:/tools/miniconda3 paths: - - "*" + - "conda-bld/*" binary_macos_wheel: <<: *binary_common @@ -165,9 +165,9 @@ jobs: - store_artifacts: path: /Users/distiller/miniconda3/conda-bld/osx-64 - persist_to_workspace: - root: /Users/distiller/miniconda3/conda-bld/osx-64 + root: /Users/distiller/miniconda3 paths: - - "*" + - "conda-bld/*" # Requires org-member context binary_conda_upload: @@ -181,7 +181,7 @@ jobs: # Prevent credential from leaking conda install -yq anaconda-client set -x - anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u pytorch-nightly --label main --no-progress --force + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/conda-bld/*/*.tar.bz2 -u pytorch-nightly --label main --no-progress --force # Requires org-member context binary_wheel_upload: @@ -215,7 +215,7 @@ jobs: set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} conda install -v -y -c pytorch-nightly pytorch - conda install -v -y $(ls ~/workspace/torchtext*.tar.bz2) + conda install -v -y -c file://$HOME/workspace/conda-bld torchtext - run: name: smoke test command: | diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index b7506a531f..acd233d450 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -80,9 +80,9 @@ jobs: - store_artifacts: path: /opt/conda/conda-bld/linux-64 - persist_to_workspace: - root: /opt/conda/conda-bld/linux-64 + root: /opt/conda paths: - - "*" + - "conda-bld/*" binary_windows_wheel: <<: *binary_common @@ -120,9 +120,9 @@ jobs: - store_artifacts: path: C:/tools/miniconda3/conda-bld/win-64 - persist_to_workspace: - root: C:/tools/miniconda3/conda-bld/win-64 + root: C:/tools/miniconda3 paths: - - "*" + - "conda-bld/*" binary_macos_wheel: <<: *binary_common @@ -165,9 +165,9 @@ jobs: - store_artifacts: path: /Users/distiller/miniconda3/conda-bld/osx-64 - persist_to_workspace: - root: /Users/distiller/miniconda3/conda-bld/osx-64 + root: /Users/distiller/miniconda3 paths: - - "*" + - "conda-bld/*" # Requires org-member context binary_conda_upload: @@ -181,7 +181,7 @@ jobs: # Prevent credential from leaking conda install -yq anaconda-client set -x - anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u pytorch-nightly --label main --no-progress --force + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/conda-bld/*/*.tar.bz2 -u pytorch-nightly --label main --no-progress --force # Requires org-member context binary_wheel_upload: @@ -215,7 +215,7 @@ jobs: set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} conda install -v -y -c pytorch-nightly pytorch - conda install -v -y $(ls ~/workspace/torchtext*.tar.bz2) + conda install -v -y -c file://$HOME/workspace/conda-bld torchtext - run: name: smoke test command: | From 5b295cff5ec27d151916e0d4b31ae27fc911aa92 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Fri, 29 May 2020 15:05:35 -0700 Subject: [PATCH 041/719] Removed local re library. Added custom regex class. --- torchtext/csrc/regex.cpp | 42 ++++ torchtext/experimental/re_jitable.py | 342 --------------------------- 2 files changed, 42 insertions(+), 342 deletions(-) create mode 100644 torchtext/csrc/regex.cpp delete mode 100644 torchtext/experimental/re_jitable.py diff --git a/torchtext/csrc/regex.cpp b/torchtext/csrc/regex.cpp new file mode 100644 index 0000000000..72d8ca9b18 --- /dev/null +++ b/torchtext/csrc/regex.cpp @@ -0,0 +1,42 @@ +#include +#include +#include + +namespace torchtext { +namespace { + +struct Regex : torch::CustomClassHolder { +private: + std::regex re_; +public: + // re_str_ holds the serialized regex string passed at the initialization. + // We need this because we need to be able to serialize the model so that we can save the scripted object. pickle will get the + // serialized model from this re_str_ member, thus it needs to be public. + const std::string re_str_; + + Regex(const std::string &re_str) : re_str_(re_str) { + self.re_ = std::regex(re_str_); + } + + std::string ReplaceFirstMatch(const std::string &str, const std::string &repl) const { + return std::regex_replace(str, re_, repl); + } +}; + +// Registers our custom class with torch. +static auto regex = + torch::class_("torchtext", "Regex") + .def(torch::init()) + .def("ReplaceFirstMatch", &Regex::ReplaceFirstMatch) + .def_pickle( + // __getstate__ + [](const c10::intrusive_ptr &self) -> std::string { + return self->re_str_; + }, + // __setstate__ + [](std::string state) -> c10::intrusive_ptr { + return c10::make_intrusive(std::move(state)); + }); + +} // namespace +} // namespace torchtext diff --git a/torchtext/experimental/re_jitable.py b/torchtext/experimental/re_jitable.py deleted file mode 100644 index e9df02f2a9..0000000000 --- a/torchtext/experimental/re_jitable.py +++ /dev/null @@ -1,342 +0,0 @@ -# -# Secret Labs' Regular Expression Engine -# -# re-compatible interface for the sre matching engine -# -# Copyright (c) 1998-2001 by Secret Labs AB. All rights reserved. -# -# This version of the SRE library can be redistributed under CNRI's -# Python 1.6 license. For any other use, please contact Secret Labs -# AB (info@pythonware.com). -# -# Portions of this engine have been developed in cooperation with -# CNRI. Hewlett-Packard provided funding for 1.6 integration and -# other compatibility work. -# - -r"""Support for regular expressions (RE). - -This module provides regular expression matching operations similar to -those found in Perl. It supports both 8-bit and Unicode strings; both -the pattern and the strings being processed can contain null bytes and -characters outside the US ASCII range. - -Regular expressions can contain both special and ordinary characters. -Most ordinary characters, like "A", "a", or "0", are the simplest -regular expressions; they simply match themselves. You can -concatenate ordinary characters, so last matches the string 'last'. - -The special characters are: - "." Matches any character except a newline. - "^" Matches the start of the string. - "$" Matches the end of the string or just before the newline at - the end of the string. - "*" Matches 0 or more (greedy) repetitions of the preceding RE. - Greedy means that it will match as many repetitions as possible. - "+" Matches 1 or more (greedy) repetitions of the preceding RE. - "?" Matches 0 or 1 (greedy) of the preceding RE. - *?,+?,?? Non-greedy versions of the previous three special characters. - {m,n} Matches from m to n repetitions of the preceding RE. - {m,n}? Non-greedy version of the above. - "\\" Either escapes special characters or signals a special sequence. - [] Indicates a set of characters. - A "^" as the first character indicates a complementing set. - "|" A|B, creates an RE that will match either A or B. - (...) Matches the RE inside the parentheses. - The contents can be retrieved or matched later in the string. - (?aiLmsux) The letters set the corresponding flags defined below. - (?:...) Non-grouping version of regular parentheses. - (?P...) The substring matched by the group is accessible by name. - (?P=name) Matches the text matched earlier by the group named name. - (?#...) A comment; ignored. - (?=...) Matches if ... matches next, but doesn't consume the string. - (?!...) Matches if ... doesn't match next. - (?<=...) Matches if preceded by ... (must be fixed length). - (? 1: - res = f'~({res})' - else: - res = f'~{res}' - return res - __str__ = object.__str__ - -globals().update(RegexFlag.__members__) - -# sre exception -error = sre_compile.error - -# -------------------------------------------------------------------- -# public interface - -def match(pattern, string, flags=0): - """Try to apply the pattern at the start of the string, returning - a Match object, or None if no match was found.""" - return _compile(pattern, flags).match(string) - -def fullmatch(pattern, string, flags=0): - """Try to apply the pattern to all of the string, returning - a Match object, or None if no match was found.""" - return _compile(pattern, flags).fullmatch(string) - -def search(pattern, string, flags=0): - """Scan through string looking for a match to the pattern, returning - a Match object, or None if no match was found.""" - return _compile(pattern, flags).search(string) - -def sub(pattern, repl, string, count=0, flags=0): - """Return the string obtained by replacing the leftmost - non-overlapping occurrences of the pattern in string by the - replacement repl. repl can be either a string or a callable; - if a string, backslash escapes in it are processed. If it is - a callable, it's passed the Match object and must return - a replacement string to be used.""" - return _compile(pattern, flags).sub(repl, string, count) - -def subn(pattern, repl, string, count=0, flags=0): - """Return a 2-tuple containing (new_string, number). - new_string is the string obtained by replacing the leftmost - non-overlapping occurrences of the pattern in the source - string by the replacement repl. number is the number of - substitutions that were made. repl can be either a string or a - callable; if a string, backslash escapes in it are processed. - If it is a callable, it's passed the Match object and must - return a replacement string to be used.""" - return _compile(pattern, flags).subn(repl, string, count) - -def split(pattern, string, maxsplit=0, flags=0): - """Split the source string by the occurrences of the pattern, - returning a list containing the resulting substrings. If - capturing parentheses are used in pattern, then the text of all - groups in the pattern are also returned as part of the resulting - list. If maxsplit is nonzero, at most maxsplit splits occur, - and the remainder of the string is returned as the final element - of the list.""" - return _compile(pattern, flags).split(string, maxsplit) - -def findall(pattern, string, flags=0): - """Return a list of all non-overlapping matches in the string. - - If one or more capturing groups are present in the pattern, return - a list of groups; this will be a list of tuples if the pattern - has more than one group. - - Empty matches are included in the result.""" - return _compile(pattern, flags).findall(string) - -def finditer(pattern, string, flags=0): - """Return an iterator over all non-overlapping matches in the - string. For each match, the iterator returns a Match object. - - Empty matches are included in the result.""" - return _compile(pattern, flags).finditer(string) - -def compile(pattern, flags=0): - "Compile a regular expression pattern, returning a Pattern object." - return _compile(pattern, flags) - -def purge(): - "Clear the regular expression caches" - _cache.clear() - _compile_repl.cache_clear() - -def template(pattern, flags=0): - "Compile a template pattern, returning a Pattern object" - return _compile(pattern, flags|T) - -# SPECIAL_CHARS -# closing ')', '}' and ']' -# '-' (a range in character set) -# '&', '~', (extended character set operations) -# '#' (comment) and WHITESPACE (ignored) in verbose mode -_special_chars_map = {i: '\\' + chr(i) for i in b'()[]{}?*+-|^$\\.&~# \t\n\r\v\f'} - -def escape(pattern): - """ - Escape special characters in a string. - """ - if isinstance(pattern, str): - return pattern.translate(_special_chars_map) - else: - pattern = str(pattern, 'latin1') - return pattern.translate(_special_chars_map).encode('latin1') - -Pattern = type(sre_compile.compile('', 0)) -Match = type(sre_compile.compile('', 0).match('')) - -# -------------------------------------------------------------------- -# internals - -_cache = {} # ordered! - -_MAXCACHE = 512 -def _compile(pattern, flags): - # internal: compile pattern - if isinstance(flags, RegexFlag): - flags = flags.value - try: - return _cache[type(pattern), pattern, flags] - except KeyError: - pass - if isinstance(pattern, Pattern): - if flags: - raise ValueError( - "cannot process flags argument with a compiled pattern") - return pattern - if not sre_compile.isstring(pattern): - raise TypeError("first argument must be string or compiled pattern") - p = sre_compile.compile(pattern, flags) - if not (flags & DEBUG): - if len(_cache) >= _MAXCACHE: - # Drop the oldest item - # provide default value to remove need for try-catch - next_key = next(iter(_cache), None) - if next_key: - del _cache[next_key] - _cache[type(pattern), pattern, flags] = p - return p - -@functools.lru_cache(_MAXCACHE) -def _compile_repl(repl, pattern): - # internal: compile replacement pattern - return sre_parse.parse_template(repl, pattern) - -def _expand(pattern, match, template): - # internal: Match.expand implementation hook - template = sre_parse.parse_template(template, pattern) - return sre_parse.expand_template(template, match) - -def _subx(pattern, template): - # internal: Pattern.sub/subn implementation helper - template = _compile_repl(template, pattern) - if not template[0] and len(template[1]) == 1: - # literal replacement - return template[1][0] - def filter(match, template=template): - return sre_parse.expand_template(template, match) - return filter - -# register myself for pickling - -import copyreg - -def _pickle(p): - return _compile, (p.pattern, p.flags) - -copyreg.pickle(Pattern, _pickle, _compile) \ No newline at end of file From 523404857186d92e93c404e87b5b2136ddb02967 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Fri, 29 May 2020 20:50:11 -0700 Subject: [PATCH 042/719] removed re library --- torchtext/experimental/re_jitable.py | 342 --------------------------- 1 file changed, 342 deletions(-) delete mode 100644 torchtext/experimental/re_jitable.py diff --git a/torchtext/experimental/re_jitable.py b/torchtext/experimental/re_jitable.py deleted file mode 100644 index e9df02f2a9..0000000000 --- a/torchtext/experimental/re_jitable.py +++ /dev/null @@ -1,342 +0,0 @@ -# -# Secret Labs' Regular Expression Engine -# -# re-compatible interface for the sre matching engine -# -# Copyright (c) 1998-2001 by Secret Labs AB. All rights reserved. -# -# This version of the SRE library can be redistributed under CNRI's -# Python 1.6 license. For any other use, please contact Secret Labs -# AB (info@pythonware.com). -# -# Portions of this engine have been developed in cooperation with -# CNRI. Hewlett-Packard provided funding for 1.6 integration and -# other compatibility work. -# - -r"""Support for regular expressions (RE). - -This module provides regular expression matching operations similar to -those found in Perl. It supports both 8-bit and Unicode strings; both -the pattern and the strings being processed can contain null bytes and -characters outside the US ASCII range. - -Regular expressions can contain both special and ordinary characters. -Most ordinary characters, like "A", "a", or "0", are the simplest -regular expressions; they simply match themselves. You can -concatenate ordinary characters, so last matches the string 'last'. - -The special characters are: - "." Matches any character except a newline. - "^" Matches the start of the string. - "$" Matches the end of the string or just before the newline at - the end of the string. - "*" Matches 0 or more (greedy) repetitions of the preceding RE. - Greedy means that it will match as many repetitions as possible. - "+" Matches 1 or more (greedy) repetitions of the preceding RE. - "?" Matches 0 or 1 (greedy) of the preceding RE. - *?,+?,?? Non-greedy versions of the previous three special characters. - {m,n} Matches from m to n repetitions of the preceding RE. - {m,n}? Non-greedy version of the above. - "\\" Either escapes special characters or signals a special sequence. - [] Indicates a set of characters. - A "^" as the first character indicates a complementing set. - "|" A|B, creates an RE that will match either A or B. - (...) Matches the RE inside the parentheses. - The contents can be retrieved or matched later in the string. - (?aiLmsux) The letters set the corresponding flags defined below. - (?:...) Non-grouping version of regular parentheses. - (?P...) The substring matched by the group is accessible by name. - (?P=name) Matches the text matched earlier by the group named name. - (?#...) A comment; ignored. - (?=...) Matches if ... matches next, but doesn't consume the string. - (?!...) Matches if ... doesn't match next. - (?<=...) Matches if preceded by ... (must be fixed length). - (? 1: - res = f'~({res})' - else: - res = f'~{res}' - return res - __str__ = object.__str__ - -globals().update(RegexFlag.__members__) - -# sre exception -error = sre_compile.error - -# -------------------------------------------------------------------- -# public interface - -def match(pattern, string, flags=0): - """Try to apply the pattern at the start of the string, returning - a Match object, or None if no match was found.""" - return _compile(pattern, flags).match(string) - -def fullmatch(pattern, string, flags=0): - """Try to apply the pattern to all of the string, returning - a Match object, or None if no match was found.""" - return _compile(pattern, flags).fullmatch(string) - -def search(pattern, string, flags=0): - """Scan through string looking for a match to the pattern, returning - a Match object, or None if no match was found.""" - return _compile(pattern, flags).search(string) - -def sub(pattern, repl, string, count=0, flags=0): - """Return the string obtained by replacing the leftmost - non-overlapping occurrences of the pattern in string by the - replacement repl. repl can be either a string or a callable; - if a string, backslash escapes in it are processed. If it is - a callable, it's passed the Match object and must return - a replacement string to be used.""" - return _compile(pattern, flags).sub(repl, string, count) - -def subn(pattern, repl, string, count=0, flags=0): - """Return a 2-tuple containing (new_string, number). - new_string is the string obtained by replacing the leftmost - non-overlapping occurrences of the pattern in the source - string by the replacement repl. number is the number of - substitutions that were made. repl can be either a string or a - callable; if a string, backslash escapes in it are processed. - If it is a callable, it's passed the Match object and must - return a replacement string to be used.""" - return _compile(pattern, flags).subn(repl, string, count) - -def split(pattern, string, maxsplit=0, flags=0): - """Split the source string by the occurrences of the pattern, - returning a list containing the resulting substrings. If - capturing parentheses are used in pattern, then the text of all - groups in the pattern are also returned as part of the resulting - list. If maxsplit is nonzero, at most maxsplit splits occur, - and the remainder of the string is returned as the final element - of the list.""" - return _compile(pattern, flags).split(string, maxsplit) - -def findall(pattern, string, flags=0): - """Return a list of all non-overlapping matches in the string. - - If one or more capturing groups are present in the pattern, return - a list of groups; this will be a list of tuples if the pattern - has more than one group. - - Empty matches are included in the result.""" - return _compile(pattern, flags).findall(string) - -def finditer(pattern, string, flags=0): - """Return an iterator over all non-overlapping matches in the - string. For each match, the iterator returns a Match object. - - Empty matches are included in the result.""" - return _compile(pattern, flags).finditer(string) - -def compile(pattern, flags=0): - "Compile a regular expression pattern, returning a Pattern object." - return _compile(pattern, flags) - -def purge(): - "Clear the regular expression caches" - _cache.clear() - _compile_repl.cache_clear() - -def template(pattern, flags=0): - "Compile a template pattern, returning a Pattern object" - return _compile(pattern, flags|T) - -# SPECIAL_CHARS -# closing ')', '}' and ']' -# '-' (a range in character set) -# '&', '~', (extended character set operations) -# '#' (comment) and WHITESPACE (ignored) in verbose mode -_special_chars_map = {i: '\\' + chr(i) for i in b'()[]{}?*+-|^$\\.&~# \t\n\r\v\f'} - -def escape(pattern): - """ - Escape special characters in a string. - """ - if isinstance(pattern, str): - return pattern.translate(_special_chars_map) - else: - pattern = str(pattern, 'latin1') - return pattern.translate(_special_chars_map).encode('latin1') - -Pattern = type(sre_compile.compile('', 0)) -Match = type(sre_compile.compile('', 0).match('')) - -# -------------------------------------------------------------------- -# internals - -_cache = {} # ordered! - -_MAXCACHE = 512 -def _compile(pattern, flags): - # internal: compile pattern - if isinstance(flags, RegexFlag): - flags = flags.value - try: - return _cache[type(pattern), pattern, flags] - except KeyError: - pass - if isinstance(pattern, Pattern): - if flags: - raise ValueError( - "cannot process flags argument with a compiled pattern") - return pattern - if not sre_compile.isstring(pattern): - raise TypeError("first argument must be string or compiled pattern") - p = sre_compile.compile(pattern, flags) - if not (flags & DEBUG): - if len(_cache) >= _MAXCACHE: - # Drop the oldest item - # provide default value to remove need for try-catch - next_key = next(iter(_cache), None) - if next_key: - del _cache[next_key] - _cache[type(pattern), pattern, flags] = p - return p - -@functools.lru_cache(_MAXCACHE) -def _compile_repl(repl, pattern): - # internal: compile replacement pattern - return sre_parse.parse_template(repl, pattern) - -def _expand(pattern, match, template): - # internal: Match.expand implementation hook - template = sre_parse.parse_template(template, pattern) - return sre_parse.expand_template(template, match) - -def _subx(pattern, template): - # internal: Pattern.sub/subn implementation helper - template = _compile_repl(template, pattern) - if not template[0] and len(template[1]) == 1: - # literal replacement - return template[1][0] - def filter(match, template=template): - return sre_parse.expand_template(template, match) - return filter - -# register myself for pickling - -import copyreg - -def _pickle(p): - return _compile, (p.pattern, p.flags) - -copyreg.pickle(Pattern, _pickle, _compile) \ No newline at end of file From e70955309ead681f924fecd36d759c37e3fdb1ee Mon Sep 17 00:00:00 2001 From: Runmin Zhang <6059600+z-Runmin@users.noreply.github.com> Date: Sun, 31 May 2020 14:14:52 -0400 Subject: [PATCH 043/719] Create root directory automatically for download_from_url if not exists (#797) --- torchtext/utils.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/torchtext/utils.py b/torchtext/utils.py index 45f242f84b..41ac14ecbb 100644 --- a/torchtext/utils.py +++ b/torchtext/utils.py @@ -78,9 +78,11 @@ def _process_response(r, root, filename): root, filename = os.path.split(path) if not os.path.exists(root): - raise RuntimeError( - "Download directory {} does not exist. " - "Did you create it?".format(root)) + try: + os.makedirs(root) + except OSError: + print("Can't create the download directory {}.".format(root)) + raise if 'drive.google.com' not in url: response = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'}, stream=True) From 7ddc98af581ae4570a3e2cf65f30182250a1a2f0 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Mon, 1 Jun 2020 11:02:48 -0700 Subject: [PATCH 044/719] Updating regex class --- torchtext/csrc/regex.cpp | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/torchtext/csrc/regex.cpp b/torchtext/csrc/regex.cpp index 72d8ca9b18..ef6a3aad00 100644 --- a/torchtext/csrc/regex.cpp +++ b/torchtext/csrc/regex.cpp @@ -1,6 +1,6 @@ -#include #include #include +#include namespace torchtext { namespace { @@ -8,17 +8,20 @@ namespace { struct Regex : torch::CustomClassHolder { private: std::regex re_; + public: // re_str_ holds the serialized regex string passed at the initialization. - // We need this because we need to be able to serialize the model so that we can save the scripted object. pickle will get the + // We need this because we need to be able to serialize the model so that we + // can save the scripted object. pickle will get the // serialized model from this re_str_ member, thus it needs to be public. const std::string re_str_; Regex(const std::string &re_str) : re_str_(re_str) { - self.re_ = std::regex(re_str_); + re_ = std::regex(re_str_); } - std::string ReplaceFirstMatch(const std::string &str, const std::string &repl) const { + std::string ReplaceFirstMatch(const std::string &str, + const std::string &repl) const { return std::regex_replace(str, re_, repl); } }; From ece92402e7a97e059c582ce1a9ab838ca911af5f Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Mon, 1 Jun 2020 13:48:00 -0700 Subject: [PATCH 045/719] Removed notebooks --- regex-jit.ipynb | 66 -------------------------- torchtext/regex-jit-Copy1.ipynb | 82 --------------------------------- 2 files changed, 148 deletions(-) delete mode 100644 regex-jit.ipynb delete mode 100644 torchtext/regex-jit-Copy1.ipynb diff --git a/regex-jit.ipynb b/regex-jit.ipynb deleted file mode 100644 index 9f36652398..0000000000 --- a/regex-jit.ipynb +++ /dev/null @@ -1,66 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "torchtext C++ Extension is not found.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtorchtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/torchtext/torchtext/__init__.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0m_init_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/torchtext/torchtext/__init__.py\u001b[0m in \u001b[0;36m_init_extension\u001b[0;34m()\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0mext_specs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextfinder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_spec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"_torchtext\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mext_specs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"torchtext C++ Extension is not found.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_library\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mext_specs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_library\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mext_specs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mImportError\u001b[0m: torchtext C++ Extension is not found." - ] - } - ], - "source": [ - "import torch\n", - "import torchtext\n", - "from typing import List\n", - "import re" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def regex_tokenizer(line: str) -> List[str]:\n", - "\tline = line.lower()\t\n", - "\tline = re.compile(r'\\\"').sub('', line)\n", - "\tline = re.compile(r'\\(').sub(' ( ', line)\n", - "\treturn line.split()\n", - "\t\n", - "jit_tokenizer = torch.jit.script(regex_tokenizer)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/torchtext/regex-jit-Copy1.ipynb b/torchtext/regex-jit-Copy1.ipynb deleted file mode 100644 index 78f4ff4819..0000000000 --- a/torchtext/regex-jit-Copy1.ipynb +++ /dev/null @@ -1,82 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torchtext\n", - "from typing import List\n", - "# import re\n", - "\n", - "# import sys, inspect\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "Tried to instantiate class torchtext.Regex but it does not exist! Ensure that it is registered via torch::jit::class_", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mregex_tokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"test string\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;31m# jit_tokenizer = torch.jit.script(regex_tokenizer)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mregex_tokenizer\u001b[0;34m(line)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mregex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtorchtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRegex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'\\\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReplaceFirstMatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mregex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUpdateRe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'\\('\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.conda/envs/pytorch/lib/python3.7/site-packages/torch/_classes.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mproxy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_custom_class_python_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mproxy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Class {}.{} not registered!'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: Tried to instantiate class torchtext.Regex but it does not exist! Ensure that it is registered via torch::jit::class_" - ] - } - ], - "source": [ - "def regex_tokenizer(line: str) -> List[str]:\n", - " line = line.lower()\t\n", - " \n", - " regex = torch.classes.torchtext.Regex(r'\\\"')\n", - " line = regex.ReplaceFirstMatch(line, '')\n", - " regex.UpdateRe(r'\\(')\n", - " line = regex.ReplaceFirstMatch(line, '')\n", - " \n", - "# line = re.compile(r'\\\"').sub('', line)\n", - "# line = re.compile(r'\\(').sub(' ( ', line)\n", - " return line.split()\n", - " \n", - "regex_tokenizer(\"test string\")\n", - "# jit_tokenizer = torch.jit.script(regex_tokenizer)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From f36a1984ded2d749b51406291786ce9cfe49799b Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 3 Jun 2020 08:03:17 -0700 Subject: [PATCH 046/719] Added tests for basic engligh normalize --- .gitignore | 5 +- test/data/test_functional.py | 87 +++++++++++++++++-- torchtext/csrc/regex.cpp | 5 +- torchtext/experimental/functional.py | 120 +++++++++++++++++++++++++++ 4 files changed, 208 insertions(+), 9 deletions(-) diff --git a/.gitignore b/.gitignore index b22fac681a..3543ad97ea 100644 --- a/.gitignore +++ b/.gitignore @@ -121,4 +121,7 @@ venv.bak/ *.swp *.swo -torchtext/version.py \ No newline at end of file +torchtext/version.py + +# deleted files +torchtext/.nfs* \ No newline at end of file diff --git a/test/data/test_functional.py b/test/data/test_functional.py index ff5b12ab17..a8f1cbad8f 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -1,3 +1,4 @@ +import io import os import unittest import sys @@ -7,6 +8,7 @@ import sentencepiece as spm import torch +import torchtext.data as data from torchtext.data.functional import ( generate_sp_model, load_sp_model, @@ -15,6 +17,12 @@ custom_replace, simple_space_split, ) +from torchtext.experimental.functional import ( + BasicEnglishNormalize, + regex_tokenizer +) +from torchtext.utils import unicode_csv_reader + from ..common.torchtext_test_case import TorchtextTestCase from ..common.assets import get_asset_path @@ -59,7 +67,6 @@ def test_sentencepiece_numericalizer(self): ref_results) def test_sentencepiece_tokenizer(self): - test_sample = 'SentencePiece is an unsupervised text tokenizer and detokenizer' model_path = get_asset_path('spm_example.model') sp_model = load_sp_model(model_path) @@ -74,6 +81,58 @@ def test_sentencepiece_tokenizer(self): self.assertEqual(list(spm_generator([test_sample]))[0], ref_results) + def test_BasicEnglishNormalize(self): + # test_sample = '\'".
,()!?;: Basic English Normalization for a Line of Text \'".
,()!?;:' + # ref_results = ["'", '.', ',', '(', ')', '!', '?', 'basic', 'english', 'normalization', + # 'for', 'a', 'line', 'of', 'text', "'", '.', ',', '(', ')', '!', '?'] + + # basic_english_normalize = BasicEnglishNormalize(test_sample) + # jit_basic_english_normalize = torch.jit.script(basic_english_normalize) + # jit_tokens = jit_basic_english_normalize.basic_english_normalize(test_sample) + + # basic_english_tokenizer = data.get_tokenizer("basic_english") + # tokens = basic_english_tokenizer(test_sample) + + # print('tokens') + # print(tokens) + + # print('jit_tokens') + # print(jit_tokens) + + # # self.assertEqual(jit_tokens, ref_results) + # self.assertEqual(jit_tokens, ref_results) + + # Test text_nomalize function in torchtext.datasets.text_classification + ref_lines = [] + test_lines = [] + + basic_english_normalize = BasicEnglishNormalize() + + data_path = get_asset_path('text_normalization_ag_news_test.csv') + with io.open(data_path, encoding="utf8") as f: + reader = unicode_csv_reader(f) + for row in reader: + test_lines.append(basic_english_normalize.basic_english_normalize(' , '.join(row))) + + data_path = get_asset_path('text_normalization_ag_news_ref_results.test') + with io.open(data_path, encoding="utf8") as ref_data: + for line in ref_data: + line = line.split() + self.assertEqual(line[0][:9], '__label__') + line[0] = line[0][9:] # remove '__label__' + ref_lines.append(line) + + self.assertEqual(ref_lines, test_lines) + + # def test_regex_tokenizer(self): + # test_sample = '\'".
,()!?;: Regex for a Line of Text \'".
,()!?;:' + + # ref_results = ["'", '.', '', ',', '(', ')', '!', '?', 'basic', 'english', 'normalization', + # 'for', 'a', 'line', 'of', 'text', "'", '.', '', ',', '(', ')', '!', '?'] + + # tokens = basic_english_normalize(test_sample) + # self.assertEqual(tokens, ref_results) + def test_custom_replace(self): custom_replace_transform = custom_replace([(r'S', 's'), (r'\s+', ' ')]) test_sample = ['test cuStom replace', 'with uSer instruction'] @@ -88,20 +147,38 @@ def test_simple_space_split(self): ref_results) +class ScriptableBasicEnglishNormalize(torch.jit.ScriptModule): + def __init__(self, model_path): + super().__init__() + self.spm = load_sp_model(model_path) + + @ torch.jit.script_method + def encode(self, input: str): + return self.spm.Encode(input) + + @ torch.jit.script_method + def encode_as_ids(self, input: str): + return self.spm.EncodeAsIds(input) + + @ torch.jit.script_method + def encode_as_pieces(self, input: str): + return self.spm.EncodeAsPieces(input) + + class ScriptableSP(torch.jit.ScriptModule): def __init__(self, model_path): super().__init__() self.spm = load_sp_model(model_path) - @torch.jit.script_method + @ torch.jit.script_method def encode(self, input: str): return self.spm.Encode(input) - @torch.jit.script_method + @ torch.jit.script_method def encode_as_ids(self, input: str): return self.spm.EncodeAsIds(input) - @torch.jit.script_method + @ torch.jit.script_method def encode_as_pieces(self, input: str): return self.spm.EncodeAsPieces(input) @@ -113,7 +190,7 @@ def setUp(self): torch.jit.script(ScriptableSP(model_path)).save(file.name) self.model = torch.jit.load(file.name) - @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") + @ unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_encode(self): input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' expected = [ diff --git a/torchtext/csrc/regex.cpp b/torchtext/csrc/regex.cpp index 35adeb9500..998eda936c 100644 --- a/torchtext/csrc/regex.cpp +++ b/torchtext/csrc/regex.cpp @@ -23,8 +23,7 @@ struct Regex : torch::CustomClassHolder { re_ = std::regex(re_str_); } - std::string ReplaceFirstMatch(const std::string &str, - const std::string &repl) const { + std::string Sub(const std::string &str, const std::string &repl) const { return std::regex_replace(str, re_, repl); } }; @@ -34,7 +33,7 @@ static auto regex = torch::class_("torchtext", "Regex") .def(torch::init()) .def("UpdateRe", &Regex::UpdateRe) - .def("ReplaceFirstMatch", &Regex::ReplaceFirstMatch) + .def("Sub", &Regex::Sub) .def_pickle( // __getstate__ [](const c10::intrusive_ptr &self) -> std::string { diff --git a/torchtext/experimental/functional.py b/torchtext/experimental/functional.py index 2c9ac60541..6d5ddc85bd 100644 --- a/torchtext/experimental/functional.py +++ b/torchtext/experimental/functional.py @@ -1,4 +1,124 @@ import torch +import torch.nn as nn +import torchtext +from typing import List, Tuple + + +_patterns: List['str'] = [ + r'\'', + r'\"', + r'\.', + r'
', + r',', + r'\(', + r'\)', + r'\!', + r'\?', + r'\;', + r'\:', + r'\s+'] + +_replacements: List['str'] = [ + ' \' ', + '', + ' . ', + ' ', + ' , ', + ' ( ', + ' ) ', + ' ! ', + ' ? ', + ' ', + ' ', + ' '] + +_patterns_list: List[Tuple[str, str]] = list(zip(_patterns, _replacements)) + + +class BasicEnglishNormalize(nn.Module): + r""" + Basic normalization for a line of text. + Normalization includes + - lowercasing + - complete some basic text normalization for English words as follows: + add spaces before and after '\'' + remove '\"', + add spaces before and after '.' + replace '
'with single space + add spaces before and after ',' + add spaces before and after '(' + add spaces before and after ')' + add spaces before and after '!' + add spaces before and after '?' + replace ';' with single space + replace ':' with single space + replace multiple spaces with single space + + Returns a list of tokens after splitting on whitespace. + """ + + def __init__(self): + super(BasicEnglishNormalize, self).__init__() + + self.regex = torch.classes.torchtext.Regex('') + self.patterns_list = [ + (r'\'', ' \' '), + (r'\"', ''), + (r'\.', ' . '), + (r'
', ' '), + (r',', ' , '), + (r'\(', ' ( '), + (r'\)', ' ) '), + (r'\!', ' ! '), + (r'\?', ' ? '), + (r'\;', ' '), + (r'\:', ' '), + (r'\s+', ' ')] + + def forward(self, line: str) -> List[str]: + return self.basic_english_normalize(line) + + def basic_english_normalize(self, line: str) -> List[str]: + line = line.lower() + for pattern_re, replaced_str in self.patterns_list: + # print("patterns: [{}] [{}]".format(pattern_re, replaced_str)) + # print("line before: [{}]".format(line)) + self.regex.UpdateRe(pattern_re) + line = self.regex.Sub(line, replaced_str) + # print("line after: [{}]".format(line)) + + tokens = line.split(' ') + sanitized_tokens: List[str] = [] + # tokens = list(filter(lambda token: token != '', tokens)) + + for token in tokens: + if token != '': + sanitized_tokens.append(token) + + # print("out tokens", tokens) + return tokens + + +def regex_tokenizer(line: str, patterns_list: List[Tuple[str, str]]) -> List[str]: + r""" + Regex tokenizer for a string sentence. + Steps include + - lowercasing + - applying all regex replacements defines in patterns_list + + Arguments: + line: a line of text. + patterns_list: a list of tuples (ordered pairs) which contain the regex pattern string as the first element and the replacement string as the second element. + + Returns a list of tokens after splitting on whitespace following the regex pattern substitutions. + """ + + line = line.lower() + regex = torch.classes.torchtext.Regex('') + for pattern_re, replaced_str in patterns_list: + regex.UpdateRe(pattern_re) + line = regex.Sub(line, replaced_str) + return line.split() def vocab_func(vocab): From ee3584083c9d9f5661451bc6ebca62226abc9ac9 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 3 Jun 2020 08:15:46 -0700 Subject: [PATCH 047/719] Updating test --- test/data/test_functional.py | 72 ++++++++++++++---------------------- 1 file changed, 28 insertions(+), 44 deletions(-) diff --git a/test/data/test_functional.py b/test/data/test_functional.py index a8f1cbad8f..43dc322049 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -82,56 +82,40 @@ def test_sentencepiece_tokenizer(self): ref_results) def test_BasicEnglishNormalize(self): - # test_sample = '\'".
,()!?;: Basic English Normalization for a Line of Text \'".
,()!?;:' - # ref_results = ["'", '.', ',', '(', ')', '!', '?', 'basic', 'english', 'normalization', - # 'for', 'a', 'line', 'of', 'text', "'", '.', ',', '(', ')', '!', '?'] + test_sample = '\'".
,()!?;: Basic English Normalization for a Line of Text \'".
,()!?;:' + ref_results = ["'", '.', ',', '(', ')', '!', '?', 'basic', 'english', 'normalization', + 'for', 'a', 'line', 'of', 'text', "'", '.', ',', '(', ')', '!', '?'] - # basic_english_normalize = BasicEnglishNormalize(test_sample) + basic_english_normalize = BasicEnglishNormalize() # jit_basic_english_normalize = torch.jit.script(basic_english_normalize) - # jit_tokens = jit_basic_english_normalize.basic_english_normalize(test_sample) - - # basic_english_tokenizer = data.get_tokenizer("basic_english") - # tokens = basic_english_tokenizer(test_sample) - - # print('tokens') - # print(tokens) + tokens = basic_english_normalize.basic_english_normalize(test_sample) - # print('jit_tokens') - # print(jit_tokens) + basic_english_tokenizer = data.get_tokenizer("basic_english") + tokens2 = basic_english_tokenizer(test_sample) - # # self.assertEqual(jit_tokens, ref_results) - # self.assertEqual(jit_tokens, ref_results) + self.assertEqual(tokens, tokens2) # Test text_nomalize function in torchtext.datasets.text_classification - ref_lines = [] - test_lines = [] - - basic_english_normalize = BasicEnglishNormalize() - - data_path = get_asset_path('text_normalization_ag_news_test.csv') - with io.open(data_path, encoding="utf8") as f: - reader = unicode_csv_reader(f) - for row in reader: - test_lines.append(basic_english_normalize.basic_english_normalize(' , '.join(row))) - - data_path = get_asset_path('text_normalization_ag_news_ref_results.test') - with io.open(data_path, encoding="utf8") as ref_data: - for line in ref_data: - line = line.split() - self.assertEqual(line[0][:9], '__label__') - line[0] = line[0][9:] # remove '__label__' - ref_lines.append(line) - - self.assertEqual(ref_lines, test_lines) - - # def test_regex_tokenizer(self): - # test_sample = '\'".
,()!?;: Regex for a Line of Text \'".
,()!?;:' - - # ref_results = ["'", '.', '', ',', '(', ')', '!', '?', 'basic', 'english', 'normalization', - # 'for', 'a', 'line', 'of', 'text', "'", '.', '', ',', '(', ')', '!', '?'] - - # tokens = basic_english_normalize(test_sample) - # self.assertEqual(tokens, ref_results) + # ref_lines = [] + # test_lines = [] + + # basic_english_normalize = BasicEnglishNormalize() + + # data_path = get_asset_path('text_normalization_ag_news_test.csv') + # with io.open(data_path, encoding="utf8") as f: + # reader = unicode_csv_reader(f) + # for row in reader: + # test_lines.append(basic_english_normalize.basic_english_normalize(' , '.join(row))) + + # data_path = get_asset_path('text_normalization_ag_news_ref_results.test') + # with io.open(data_path, encoding="utf8") as ref_data: + # for line in ref_data: + # line = line.split() + # self.assertEqual(line[0][:9], '__label__') + # line[0] = line[0][9:] # remove '__label__' + # ref_lines.append(line) + + # self.assertEqual(ref_lines, test_lines) def test_custom_replace(self): custom_replace_transform = custom_replace([(r'S', 's'), (r'\s+', ' ')]) From 9e3618f9cc6ae12df367e084341675263714d0d3 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 3 Jun 2020 08:16:58 -0700 Subject: [PATCH 048/719] Updating test 2 --- test/data/test_functional.py | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/test/data/test_functional.py b/test/data/test_functional.py index 43dc322049..f440840997 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -131,24 +131,6 @@ def test_simple_space_split(self): ref_results) -class ScriptableBasicEnglishNormalize(torch.jit.ScriptModule): - def __init__(self, model_path): - super().__init__() - self.spm = load_sp_model(model_path) - - @ torch.jit.script_method - def encode(self, input: str): - return self.spm.Encode(input) - - @ torch.jit.script_method - def encode_as_ids(self, input: str): - return self.spm.EncodeAsIds(input) - - @ torch.jit.script_method - def encode_as_pieces(self, input: str): - return self.spm.EncodeAsPieces(input) - - class ScriptableSP(torch.jit.ScriptModule): def __init__(self, model_path): super().__init__() From df6169187c5836a22862eed8289abfee28b15444 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 3 Jun 2020 08:18:27 -0700 Subject: [PATCH 049/719] fixed decorators --- test/data/test_functional.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/test/data/test_functional.py b/test/data/test_functional.py index f440840997..9cf5ddb4bc 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -136,15 +136,15 @@ def __init__(self, model_path): super().__init__() self.spm = load_sp_model(model_path) - @ torch.jit.script_method + @torch.jit.script_method def encode(self, input: str): return self.spm.Encode(input) - @ torch.jit.script_method + @torch.jit.script_method def encode_as_ids(self, input: str): return self.spm.EncodeAsIds(input) - @ torch.jit.script_method + @torch.jit.script_method def encode_as_pieces(self, input: str): return self.spm.EncodeAsPieces(input) @@ -156,7 +156,7 @@ def setUp(self): torch.jit.script(ScriptableSP(model_path)).save(file.name) self.model = torch.jit.load(file.name) - @ unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") + @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_encode(self): input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' expected = [ From 81f32d9d41bc0ec0f9ac15f424f7cf67ba2f2e9a Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 3 Jun 2020 10:43:47 -0700 Subject: [PATCH 050/719] Rewrote tokenizers to follow new conventions --- test/data/test_functional.py | 59 ++++++++------- torchtext/experimental/functional.py | 109 ++------------------------- torchtext/experimental/transforms.py | 81 ++++++++++++++++++++ 3 files changed, 119 insertions(+), 130 deletions(-) create mode 100644 torchtext/experimental/transforms.py diff --git a/test/data/test_functional.py b/test/data/test_functional.py index 9cf5ddb4bc..c8797a9a54 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -17,9 +17,9 @@ custom_replace, simple_space_split, ) -from torchtext.experimental.functional import ( +from torchtext.experimental.transforms import ( BasicEnglishNormalize, - regex_tokenizer + RegexTokenizer ) from torchtext.utils import unicode_csv_reader @@ -30,7 +30,9 @@ class TestFunctional(TorchtextTestCase): def test_generate_sp_model(self): - """Test the function to train a sentencepiece tokenizer""" + """ + Test the function to train a sentencepiece tokenizer. + """ asset_name = 'text_normalization_ag_news_test.csv' asset_path = get_asset_path(asset_name) @@ -87,35 +89,36 @@ def test_BasicEnglishNormalize(self): 'for', 'a', 'line', 'of', 'text', "'", '.', ',', '(', ')', '!', '?'] basic_english_normalize = BasicEnglishNormalize() - # jit_basic_english_normalize = torch.jit.script(basic_english_normalize) - tokens = basic_english_normalize.basic_english_normalize(test_sample) + experimental_tokens = basic_english_normalize.forward(test_sample) basic_english_tokenizer = data.get_tokenizer("basic_english") - tokens2 = basic_english_tokenizer(test_sample) + tokens = basic_english_tokenizer(test_sample) - self.assertEqual(tokens, tokens2) + self.assertEqual(experimental_tokens, ref_results) + self.assertEqual(experimental_tokens, tokens) - # Test text_nomalize function in torchtext.datasets.text_classification - # ref_lines = [] - # test_lines = [] - - # basic_english_normalize = BasicEnglishNormalize() - - # data_path = get_asset_path('text_normalization_ag_news_test.csv') - # with io.open(data_path, encoding="utf8") as f: - # reader = unicode_csv_reader(f) - # for row in reader: - # test_lines.append(basic_english_normalize.basic_english_normalize(' , '.join(row))) - - # data_path = get_asset_path('text_normalization_ag_news_ref_results.test') - # with io.open(data_path, encoding="utf8") as ref_data: - # for line in ref_data: - # line = line.split() - # self.assertEqual(line[0][:9], '__label__') - # line[0] = line[0][9:] # remove '__label__' - # ref_lines.append(line) - - # self.assertEqual(ref_lines, test_lines) + def test_RegexTokenizer(self): + test_sample = '\'".
,()!?;: Basic English Normalization for a Line of Text \'".
,()!?;:' + ref_results = ["'", '.', ',', '(', ')', '!', '?', 'basic', 'english', 'normalization', + 'for', 'a', 'line', 'of', 'text', "'", '.', ',', '(', ')', '!', '?'] + patterns_list = [ + (r'\'', ' \' '), + (r'\"', ''), + (r'\.', ' . '), + (r'
', ' '), + (r',', ' , '), + (r'\(', ' ( '), + (r'\)', ' ) '), + (r'\!', ' ! '), + (r'\?', ' ? '), + (r'\;', ' '), + (r'\:', ' '), + (r'\s+', ' ')] + + regex_tokenizer = RegexTokenizer(patterns_list) + tokens = regex_tokenizer.forward(test_sample) + + self.assertEqual(tokens, ref_results) def test_custom_replace(self): custom_replace_transform = custom_replace([(r'S', 's'), (r'\s+', ' ')]) diff --git a/torchtext/experimental/functional.py b/torchtext/experimental/functional.py index 6d5ddc85bd..24d3ba142a 100644 --- a/torchtext/experimental/functional.py +++ b/torchtext/experimental/functional.py @@ -1,116 +1,21 @@ import torch -import torch.nn as nn import torchtext from typing import List, Tuple -_patterns: List['str'] = [ - r'\'', - r'\"', - r'\.', - r'
', - r',', - r'\(', - r'\)', - r'\!', - r'\?', - r'\;', - r'\:', - r'\s+'] - -_replacements: List['str'] = [ - ' \' ', - '', - ' . ', - ' ', - ' , ', - ' ( ', - ' ) ', - ' ! ', - ' ? ', - ' ', - ' ', - ' '] - -_patterns_list: List[Tuple[str, str]] = list(zip(_patterns, _replacements)) - - -class BasicEnglishNormalize(nn.Module): - r""" - Basic normalization for a line of text. - Normalization includes - - lowercasing - - complete some basic text normalization for English words as follows: - add spaces before and after '\'' - remove '\"', - add spaces before and after '.' - replace '
'with single space - add spaces before and after ',' - add spaces before and after '(' - add spaces before and after ')' - add spaces before and after '!' - add spaces before and after '?' - replace ';' with single space - replace ':' with single space - replace multiple spaces with single space - - Returns a list of tokens after splitting on whitespace. - """ - - def __init__(self): - super(BasicEnglishNormalize, self).__init__() - - self.regex = torch.classes.torchtext.Regex('') - self.patterns_list = [ - (r'\'', ' \' '), - (r'\"', ''), - (r'\.', ' . '), - (r'
', ' '), - (r',', ' , '), - (r'\(', ' ( '), - (r'\)', ' ) '), - (r'\!', ' ! '), - (r'\?', ' ? '), - (r'\;', ' '), - (r'\:', ' '), - (r'\s+', ' ')] - - def forward(self, line: str) -> List[str]: - return self.basic_english_normalize(line) - - def basic_english_normalize(self, line: str) -> List[str]: - line = line.lower() - for pattern_re, replaced_str in self.patterns_list: - # print("patterns: [{}] [{}]".format(pattern_re, replaced_str)) - # print("line before: [{}]".format(line)) - self.regex.UpdateRe(pattern_re) - line = self.regex.Sub(line, replaced_str) - # print("line after: [{}]".format(line)) - - tokens = line.split(' ') - sanitized_tokens: List[str] = [] - # tokens = list(filter(lambda token: token != '', tokens)) - - for token in tokens: - if token != '': - sanitized_tokens.append(token) - - # print("out tokens", tokens) - return tokens - - def regex_tokenizer(line: str, patterns_list: List[Tuple[str, str]]) -> List[str]: - r""" - Regex tokenizer for a string sentence. + r"""Regex tokenizer for a string sentence. + Steps include - lowercasing - applying all regex replacements defines in patterns_list - Arguments: - line: a line of text. - patterns_list: a list of tuples (ordered pairs) which contain the regex pattern string as the first element and the replacement string as the second element. + Args: + line (str): a line of text to tokenize. + patterns_list (List[Tuple[str, str]]): a list of tuples (ordered pairs) which contain the regex pattern string as the first element and the replacement string as the second element. - Returns a list of tokens after splitting on whitespace following the regex pattern substitutions. + Returns: + tokens (List[str]): a list of tokens after splitting on whitespace. """ line = line.lower() diff --git a/torchtext/experimental/transforms.py b/torchtext/experimental/transforms.py new file mode 100644 index 0000000000..c550307459 --- /dev/null +++ b/torchtext/experimental/transforms.py @@ -0,0 +1,81 @@ +import torch +import torch.nn as nn +from torchtext.experimental import functional as F +from typing import List, Tuple + + +__all__ = [ + 'BasicEnglishNormalize', + 'RegexTokenizer' +] + + +class BasicEnglishNormalize(nn.Module): + r"""Basic normalization for a string sentence. + + Normalization includes + - lowercasing + - complete some basic text normalization for English words as follows: + add spaces before and after '\'' + remove '\"', + add spaces before and after '.' + replace '
'with single space + add spaces before and after ',' + add spaces before and after '(' + add spaces before and after ')' + add spaces before and after '!' + add spaces before and after '?' + replace ';' with single space + replace ':' with single space + replace multiple spaces with single space + """ + + def __init__(self): + super(BasicEnglishNormalize, self).__init__() + self.patterns_list = [ + (r'\'', ' \' '), + (r'\"', ''), + (r'\.', ' . '), + (r'
', ' '), + (r',', ' , '), + (r'\(', ' ( '), + (r'\)', ' ) '), + (r'\!', ' ! '), + (r'\?', ' ? '), + (r'\;', ' '), + (r'\:', ' '), + (r'\s+', ' ')] + + def forward(self, line: str) -> List[str]: + r""" + Args: + line (str): a line of text to tokenize. + Returns: + List[str]: a list of tokens after normalizing and splitting on whitespace. + """ + return F.regex_tokenizer(line, self.patterns_list) + + +class RegexTokenizer(nn.Module): + r"""Regex tokenizer for a string sentence. + + Steps include + - lowercasing + - applying all regex replacements defines in patterns_list + + Args: + patterns_list (List[Tuple[str, str]]): a list of tuples (ordered pairs) which contain the regex pattern string as the first element and the replacement string as the second element. + """ + + def __init__(self, patterns_list: List[Tuple[str, str]]): + super(RegexTokenizer, self).__init__() + self.patterns_list = patterns_list + + def forward(self, line: str) -> List[str]: + r""" + Args: + line (str): a line of text to tokenize. + Returns: + List[str]: a list of tokens after normalizing and splitting on whitespace. + """ + return F.regex_tokenizer(line, self.patterns_list) From 7dc45f4f0efef59be158b9161678057b84d807fd Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 3 Jun 2020 10:48:58 -0700 Subject: [PATCH 051/719] Code cleanup --- test/data/test_functional.py | 3 --- torchtext/experimental/functional.py | 1 - 2 files changed, 4 deletions(-) diff --git a/test/data/test_functional.py b/test/data/test_functional.py index c8797a9a54..e12213b438 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -1,4 +1,3 @@ -import io import os import unittest import sys @@ -21,8 +20,6 @@ BasicEnglishNormalize, RegexTokenizer ) -from torchtext.utils import unicode_csv_reader - from ..common.torchtext_test_case import TorchtextTestCase from ..common.assets import get_asset_path diff --git a/torchtext/experimental/functional.py b/torchtext/experimental/functional.py index 24d3ba142a..ebcf8a34c7 100644 --- a/torchtext/experimental/functional.py +++ b/torchtext/experimental/functional.py @@ -1,5 +1,4 @@ import torch -import torchtext from typing import List, Tuple From 8f3ab86f66f135588ca923ea90c68f3dec10d1fa Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 3 Jun 2020 15:35:06 -0700 Subject: [PATCH 052/719] Fixed documentation --- .gitignore | 5 +- docs/source/experimental_datasets.rst | 20 +++---- docs/source/experimental_functional.rst | 14 +++++ docs/source/experimental_transforms.rst | 22 +++++++ docs/source/index.rst | 2 + test/data/test_functional.py | 77 ++++++++++++++++++++----- torchtext/experimental/functional.py | 16 +++-- torchtext/experimental/transforms.py | 66 ++++++++++++++------- 8 files changed, 168 insertions(+), 54 deletions(-) create mode 100644 docs/source/experimental_functional.rst create mode 100644 docs/source/experimental_transforms.rst diff --git a/.gitignore b/.gitignore index 3543ad97ea..b22fac681a 100644 --- a/.gitignore +++ b/.gitignore @@ -121,7 +121,4 @@ venv.bak/ *.swp *.swo -torchtext/version.py - -# deleted files -torchtext/.nfs* \ No newline at end of file +torchtext/version.py \ No newline at end of file diff --git a/docs/source/experimental_datasets.rst b/docs/source/experimental_datasets.rst index d9d59539d0..674e53daf7 100644 --- a/docs/source/experimental_datasets.rst +++ b/docs/source/experimental_datasets.rst @@ -1,5 +1,5 @@ torchtext.experimental.datasets -================================ +=============================== .. currentmodule:: torchtext.experimental.datasets @@ -41,13 +41,13 @@ Text Classification ^^^^^^^^^^^^^^^^^^^ TextClassificationDataset -~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: TextClassificationDataset :members: __init__ AG_NEWS -~~~~~~ +~~~~~~~ AG_NEWS dataset is subclass of ``TextClassificationDataset`` class. @@ -55,7 +55,7 @@ AG_NEWS dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ SogouNews -~~~~~~~~ +~~~~~~~~~ SogouNews dataset is subclass of ``TextClassificationDataset`` class. @@ -63,7 +63,7 @@ SogouNews dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ DBpedia -~~~~~~ +~~~~~~~ DBpedia dataset is subclass of ``TextClassificationDataset`` class. @@ -71,7 +71,7 @@ DBpedia dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ YelpReviewPolarity -~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~ YelpReviewPolarity dataset is subclass of ``TextClassificationDataset`` class. @@ -79,7 +79,7 @@ YelpReviewPolarity dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ YelpReviewFull -~~~~~~~~~~~~~ +~~~~~~~~~~~~~~ YelpReviewFull dataset is subclass of ``TextClassificationDataset`` class. @@ -87,7 +87,7 @@ YelpReviewFull dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ YahooAnswers -~~~~~~~~~~~ +~~~~~~~~~~~~ YahooAnswers dataset is subclass of ``TextClassificationDataset`` class. @@ -95,7 +95,7 @@ YahooAnswers dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ AmazonReviewPolarity -~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~ AmazonReviewPolarity dataset is subclass of ``TextClassificationDataset`` class. @@ -103,7 +103,7 @@ AmazonReviewPolarity dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ AmazonReviewFull -~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~ AmazonReviewFull dataset is subclass of ``TextClassificationDataset`` class. diff --git a/docs/source/experimental_functional.rst b/docs/source/experimental_functional.rst new file mode 100644 index 0000000000..2e0ff84c4d --- /dev/null +++ b/docs/source/experimental_functional.rst @@ -0,0 +1,14 @@ +.. role:: hidden + :class: hidden-section + +torchtext.experimental.functional +================================= + +.. automodule:: torchtext.experimental.functional +.. currentmodule:: torchtext.experimental.functional + +`regex_tokenizer` +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: regex_tokenizer + diff --git a/docs/source/experimental_transforms.rst b/docs/source/experimental_transforms.rst new file mode 100644 index 0000000000..ff00b374ef --- /dev/null +++ b/docs/source/experimental_transforms.rst @@ -0,0 +1,22 @@ +.. role:: hidden + :class: hidden-section + +torchtext.experimental.transforms +================================= + +.. automodule:: torchtext.experimental.transforms +.. currentmodule:: torchtext.experimental.transforms + +:hidden:`BasicEnglishNormalize` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: BasicEnglishNormalize + :members: + :special-members: __init__ + +:hidden:`RegexTokenizer` +~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: RegexTokenizer + :members: + :special-members: __init__ \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index 08fb745c4f..dfa9d7c4c0 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -17,6 +17,8 @@ popular datasets for natural language. torchtext.vocab torchtext.utils experimental_datasets + experimental_functional + experimental_transforms examples .. automodule:: torchtext diff --git a/test/data/test_functional.py b/test/data/test_functional.py index e12213b438..f96f723eff 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -80,24 +80,71 @@ def test_sentencepiece_tokenizer(self): self.assertEqual(list(spm_generator([test_sample]))[0], ref_results) + # TODO(Nayef211): uncomment and replace the test below with this once once https://github.com/pytorch/pytorch/issues/38207 is closed + # def test_BasicEnglishNormalize(self): + # test_sample = '\'".
,()!?;: Basic English Normalization for a Line of Text \'".
,()!?;:' + # ref_results = ["'", '.', ',', '(', ')', '!', '?', 'basic', 'english', 'normalization', + # 'for', 'a', 'line', 'of', 'text', "'", '.', ',', '(', ')', '!', '?'] + + # basic_english_normalize = BasicEnglishNormalize() + # experimental_eager_tokens = basic_english_normalize.forward(test_sample) + + # jit_basic_english_normalize = torch.jit.script(basic_english_normalize) + # experimental_jit_tokens = jit_basic_english_normalize.forward(test_sample) + + # basic_english_tokenizer = data.get_tokenizer("basic_english") + # eager_tokens = basic_english_tokenizer(test_sample) + + # self.assertEqual(experimental_jit_tokens, ref_results) + # self.assertEqual(experimental_jit_tokens, eager_tokens) + # self.assertEqual(experimental_jit_tokens, experimental_eager_tokens) + def test_BasicEnglishNormalize(self): - test_sample = '\'".
,()!?;: Basic English Normalization for a Line of Text \'".
,()!?;:' - ref_results = ["'", '.', ',', '(', ')', '!', '?', 'basic', 'english', 'normalization', - 'for', 'a', 'line', 'of', 'text', "'", '.', ',', '(', ')', '!', '?'] + test_sample = 'Basic English Normalization for a Line of Text' + ref_results = ['basic', 'english', 'normalization', + 'for', 'a', 'line', 'of', 'text'] basic_english_normalize = BasicEnglishNormalize() - experimental_tokens = basic_english_normalize.forward(test_sample) + experimental_eager_tokens = basic_english_normalize.forward(test_sample) basic_english_tokenizer = data.get_tokenizer("basic_english") - tokens = basic_english_tokenizer(test_sample) - - self.assertEqual(experimental_tokens, ref_results) - self.assertEqual(experimental_tokens, tokens) + tokens_eager = basic_english_tokenizer(test_sample) + + self.assertEqual(experimental_eager_tokens, ref_results) + self.assertEqual(experimental_eager_tokens, tokens_eager) + + # TODO(Nayef211): uncomment and replace the test below with this once once https://github.com/pytorch/pytorch/issues/38207 is closed + # def test_RegexTokenizer(self): + # test_sample = '\'".
,()!?;: Basic Regex Tokenization for a Line of Text \'".
,()!?;:' + # ref_results = ["'", '.', ',', '(', ')', '!', '?', 'Basic', 'Regex', 'Tokenization', + # 'for', 'a', 'Line', 'of', 'Text', "'", '.', ',', '(', ')', '!', '?'] + # patterns_list = [ + # (r'\'', ' \' '), + # (r'\"', ''), + # (r'\.', ' . '), + # (r'
', ' '), + # (r',', ' , '), + # (r'\(', ' ( '), + # (r'\)', ' ) '), + # (r'\!', ' ! '), + # (r'\?', ' ? '), + # (r'\;', ' '), + # (r'\:', ' '), + # (r'\s+', ' ')] + + # regex_tokenizer = RegexTokenizer(patterns_list) + # eager_tokens = regex_tokenizer.forward(test_sample) + + # jit_regex_tokenizer = torch.jit.script(regex_tokenizer) + # jit_tokens = jit_regex_tokenizer.forward(test_sample) + + # self.assertEqual(jit_tokens, ref_results) + # self.assertEqual(jit_tokens, eager_tokens) def test_RegexTokenizer(self): - test_sample = '\'".
,()!?;: Basic English Normalization for a Line of Text \'".
,()!?;:' - ref_results = ["'", '.', ',', '(', ')', '!', '?', 'basic', 'english', 'normalization', - 'for', 'a', 'line', 'of', 'text', "'", '.', ',', '(', ')', '!', '?'] + test_sample = 'Basic Regex Tokenization for a Line of Text' + ref_results = ['Basic', 'Regex', 'Tokenization', + 'for', 'a', 'Line', 'of', 'Text'] patterns_list = [ (r'\'', ' \' '), (r'\"', ''), @@ -113,9 +160,13 @@ def test_RegexTokenizer(self): (r'\s+', ' ')] regex_tokenizer = RegexTokenizer(patterns_list) - tokens = regex_tokenizer.forward(test_sample) + eager_tokens = regex_tokenizer.forward(test_sample) + + jit_regex_tokenizer = torch.jit.script(regex_tokenizer) + jit_tokens = jit_regex_tokenizer.forward(test_sample) - self.assertEqual(tokens, ref_results) + self.assertEqual(jit_tokens, eager_tokens) + self.assertEqual(jit_tokens, ref_results) def test_custom_replace(self): custom_replace_transform = custom_replace([(r'S', 's'), (r'\s+', ' ')]) diff --git a/torchtext/experimental/functional.py b/torchtext/experimental/functional.py index ebcf8a34c7..7421a3c19b 100644 --- a/torchtext/experimental/functional.py +++ b/torchtext/experimental/functional.py @@ -1,23 +1,27 @@ import torch -from typing import List, Tuple +import torchtext +from typing import List, Tuple, Type - -def regex_tokenizer(line: str, patterns_list: List[Tuple[str, str]]) -> List[str]: +def regex_tokenizer(line, patterns_list, regex, lowercase = False): + # type: (str, List[Tuple[str, str]], torch.classes.torchtext.Regex, bool) -> List[str] r"""Regex tokenizer for a string sentence. Steps include - - lowercasing + - lowercasing (optional) - applying all regex replacements defines in patterns_list Args: line (str): a line of text to tokenize. patterns_list (List[Tuple[str, str]]): a list of tuples (ordered pairs) which contain the regex pattern string as the first element and the replacement string as the second element. - + regex (torch.classes.torchtext.Regex): a custom c++ Regex object. + lowercase (bool, optional): a flag to determine whether to lowercase the input text. Returns: tokens (List[str]): a list of tokens after splitting on whitespace. """ - line = line.lower() + if lowercase: + line = line.lower() + regex = torch.classes.torchtext.Regex('') for pattern_re, replaced_str in patterns_list: regex.UpdateRe(pattern_re) diff --git a/torchtext/experimental/transforms.py b/torchtext/experimental/transforms.py index c550307459..c650658308 100644 --- a/torchtext/experimental/transforms.py +++ b/torchtext/experimental/transforms.py @@ -1,4 +1,5 @@ import torch +import torchtext import torch.nn as nn from torchtext.experimental import functional as F from typing import List, Tuple @@ -14,24 +15,36 @@ class BasicEnglishNormalize(nn.Module): r"""Basic normalization for a string sentence. Normalization includes - - lowercasing - - complete some basic text normalization for English words as follows: - add spaces before and after '\'' - remove '\"', - add spaces before and after '.' - replace '
'with single space - add spaces before and after ',' - add spaces before and after '(' - add spaces before and after ')' - add spaces before and after '!' - add spaces before and after '?' - replace ';' with single space - replace ':' with single space - replace multiple spaces with single space + - lowercasing + - complete some basic text normalization for English words as follows: + - add spaces before and after '\'' + - remove '\"', + - add spaces before and after '.' + - replace '
'with single space + - add spaces before and after ',' + - add spaces before and after '(' + - add spaces before and after ')' + - add spaces before and after '!' + - add spaces before and after '?' + - replace ';' with single space + - replace ':' with single space + - replace multiple spaces with single space + + Examples: + >>> import torch + >>> from torchtext.experimental.transforms import BasicEnglishNormalize + >>> test_sample = 'Basic English Normalization for a Line of Text' + >>> basic_english_normalize = BasicEnglishNormalize() + >>> jit_basic_english_normalize = torch.jit.script(basic_english_normalize) + >>> tokens = jit_basic_english_normalize.forward(test_sample) """ + patterns_list: List[Tuple[str, str]] + regex: torch.classes.torchtext.Regex + def __init__(self): super(BasicEnglishNormalize, self).__init__() + self.regex = torch.classes.torchtext.Regex('') self.patterns_list = [ (r'\'', ' \' '), (r'\"', ''), @@ -53,22 +66,33 @@ def forward(self, line: str) -> List[str]: Returns: List[str]: a list of tokens after normalizing and splitting on whitespace. """ - return F.regex_tokenizer(line, self.patterns_list) + return F.regex_tokenizer(line, self.patterns_list, self.regex, lowercase=True) class RegexTokenizer(nn.Module): - r"""Regex tokenizer for a string sentence. - - Steps include - - lowercasing - - applying all regex replacements defines in patterns_list + r"""Regex tokenizer for a string sentence that applies all regex replacements defined in patterns_list. Args: patterns_list (List[Tuple[str, str]]): a list of tuples (ordered pairs) which contain the regex pattern string as the first element and the replacement string as the second element. + + Examples: + >>> import torch + >>> from torchtext.experimental.transforms import RegexTokenizer + >>> test_sample = 'Basic Regex Tokenization for a Line of Text' + >>> patterns_list = [ + (r'\'', ' \' '), + (r'\"', '')] + >>> regex_tokenizer = RegexTokenizer(patterns_list) + >>> jit_regex_tokenizer = torch.jit.script(regex_tokenizer) + >>> tokens = jit_regex_tokenizer.forward(test_sample) """ + patterns_list: List[Tuple[str, str]] + regex: torch.classes.torchtext.Regex + def __init__(self, patterns_list: List[Tuple[str, str]]): super(RegexTokenizer, self).__init__() + self.regex = torch.classes.torchtext.Regex('') self.patterns_list = patterns_list def forward(self, line: str) -> List[str]: @@ -78,4 +102,4 @@ def forward(self, line: str) -> List[str]: Returns: List[str]: a list of tokens after normalizing and splitting on whitespace. """ - return F.regex_tokenizer(line, self.patterns_list) + return F.regex_tokenizer(line, self.patterns_list, self.regex) From f632c81c1d272d938565c3aec24bbf359c1973ba Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 3 Jun 2020 17:07:30 -0700 Subject: [PATCH 053/719] linting --- test/data/test_functional.py | 6 ++++-- torchtext/experimental/functional.py | 9 +++++---- torchtext/experimental/transforms.py | 4 ++-- 3 files changed, 11 insertions(+), 8 deletions(-) diff --git a/test/data/test_functional.py b/test/data/test_functional.py index f96f723eff..c4130efd8a 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -80,7 +80,8 @@ def test_sentencepiece_tokenizer(self): self.assertEqual(list(spm_generator([test_sample]))[0], ref_results) - # TODO(Nayef211): uncomment and replace the test below with this once once https://github.com/pytorch/pytorch/issues/38207 is closed + # TODO(Nayef211): uncomment and replace the test below with this once once + # https://github.com/pytorch/pytorch/issues/38207 is closed # def test_BasicEnglishNormalize(self): # test_sample = '\'".
,()!?;: Basic English Normalization for a Line of Text \'".
,()!?;:' # ref_results = ["'", '.', ',', '(', ')', '!', '?', 'basic', 'english', 'normalization', @@ -113,7 +114,8 @@ def test_BasicEnglishNormalize(self): self.assertEqual(experimental_eager_tokens, ref_results) self.assertEqual(experimental_eager_tokens, tokens_eager) - # TODO(Nayef211): uncomment and replace the test below with this once once https://github.com/pytorch/pytorch/issues/38207 is closed + # TODO(Nayef211): uncomment and replace the test below with this once once + # https://github.com/pytorch/pytorch/issues/38207 is closed # def test_RegexTokenizer(self): # test_sample = '\'".
,()!?;: Basic Regex Tokenization for a Line of Text \'".
,()!?;:' # ref_results = ["'", '.', ',', '(', ')', '!', '?', 'Basic', 'Regex', 'Tokenization', diff --git a/torchtext/experimental/functional.py b/torchtext/experimental/functional.py index 7421a3c19b..f69aee5aa7 100644 --- a/torchtext/experimental/functional.py +++ b/torchtext/experimental/functional.py @@ -1,8 +1,8 @@ import torch -import torchtext -from typing import List, Tuple, Type +from typing import List, Tuple -def regex_tokenizer(line, patterns_list, regex, lowercase = False): + +def regex_tokenizer(line, patterns_list, regex, lowercase=False): # type: (str, List[Tuple[str, str]], torch.classes.torchtext.Regex, bool) -> List[str] r"""Regex tokenizer for a string sentence. @@ -12,7 +12,8 @@ def regex_tokenizer(line, patterns_list, regex, lowercase = False): Args: line (str): a line of text to tokenize. - patterns_list (List[Tuple[str, str]]): a list of tuples (ordered pairs) which contain the regex pattern string as the first element and the replacement string as the second element. + patterns_list (List[Tuple[str, str]]): a list of tuples (ordered pairs) which contain the regex pattern string + as the first element and the replacement string as the second element. regex (torch.classes.torchtext.Regex): a custom c++ Regex object. lowercase (bool, optional): a flag to determine whether to lowercase the input text. Returns: diff --git a/torchtext/experimental/transforms.py b/torchtext/experimental/transforms.py index c650658308..baaa8c6c5a 100644 --- a/torchtext/experimental/transforms.py +++ b/torchtext/experimental/transforms.py @@ -1,5 +1,4 @@ import torch -import torchtext import torch.nn as nn from torchtext.experimental import functional as F from typing import List, Tuple @@ -73,7 +72,8 @@ class RegexTokenizer(nn.Module): r"""Regex tokenizer for a string sentence that applies all regex replacements defined in patterns_list. Args: - patterns_list (List[Tuple[str, str]]): a list of tuples (ordered pairs) which contain the regex pattern string as the first element and the replacement string as the second element. + patterns_list (List[Tuple[str, str]]): a list of tuples (ordered pairs) which contain the regex pattern string + as the first element and the replacement string as the second element. Examples: >>> import torch From 3de4b51cf5381c823c3b27c7c9987e61a1f67689 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 3 Jun 2020 18:12:54 -0700 Subject: [PATCH 054/719] resolving pr comments --- test/data/test_functional.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/test/data/test_functional.py b/test/data/test_functional.py index c4130efd8a..d86aa2e6fc 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -88,10 +88,10 @@ def test_sentencepiece_tokenizer(self): # 'for', 'a', 'line', 'of', 'text', "'", '.', ',', '(', ')', '!', '?'] # basic_english_normalize = BasicEnglishNormalize() - # experimental_eager_tokens = basic_english_normalize.forward(test_sample) + # experimental_eager_tokens = basic_english_normalize(test_sample) # jit_basic_english_normalize = torch.jit.script(basic_english_normalize) - # experimental_jit_tokens = jit_basic_english_normalize.forward(test_sample) + # experimental_jit_tokens = jit_basic_english_normalize(test_sample) # basic_english_tokenizer = data.get_tokenizer("basic_english") # eager_tokens = basic_english_tokenizer(test_sample) @@ -106,7 +106,7 @@ def test_BasicEnglishNormalize(self): 'for', 'a', 'line', 'of', 'text'] basic_english_normalize = BasicEnglishNormalize() - experimental_eager_tokens = basic_english_normalize.forward(test_sample) + experimental_eager_tokens = basic_english_normalize(test_sample) basic_english_tokenizer = data.get_tokenizer("basic_english") tokens_eager = basic_english_tokenizer(test_sample) @@ -135,10 +135,10 @@ def test_BasicEnglishNormalize(self): # (r'\s+', ' ')] # regex_tokenizer = RegexTokenizer(patterns_list) - # eager_tokens = regex_tokenizer.forward(test_sample) + # eager_tokens = regex_tokenizer(test_sample) # jit_regex_tokenizer = torch.jit.script(regex_tokenizer) - # jit_tokens = jit_regex_tokenizer.forward(test_sample) + # jit_tokens = jit_regex_tokenizer(test_sample) # self.assertEqual(jit_tokens, ref_results) # self.assertEqual(jit_tokens, eager_tokens) @@ -162,10 +162,10 @@ def test_RegexTokenizer(self): (r'\s+', ' ')] regex_tokenizer = RegexTokenizer(patterns_list) - eager_tokens = regex_tokenizer.forward(test_sample) + eager_tokens = regex_tokenizer(test_sample) jit_regex_tokenizer = torch.jit.script(regex_tokenizer) - jit_tokens = jit_regex_tokenizer.forward(test_sample) + jit_tokens = jit_regex_tokenizer(test_sample) self.assertEqual(jit_tokens, eager_tokens) self.assertEqual(jit_tokens, ref_results) From d0520ad3d50f4457677ce797635f57d5fae946ce Mon Sep 17 00:00:00 2001 From: peterjc123 Date: Thu, 4 Jun 2020 23:30:56 +0800 Subject: [PATCH 055/719] Cleanup seperate batch files on Windows (#807) --- .circleci/unittest/windows/scripts/install.sh | 2 +- packaging/build_wheel.bat | 22 ------------------- packaging/build_wheel.sh | 2 +- .../vc_env_helper.bat | 21 ++++++++++++++++-- 4 files changed, 21 insertions(+), 26 deletions(-) delete mode 100644 packaging/build_wheel.bat rename .circleci/unittest/windows/scripts/install.bat => packaging/vc_env_helper.bat (59%) diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh index a44174da07..ec696d84a6 100644 --- a/.circleci/unittest/windows/scripts/install.sh +++ b/.circleci/unittest/windows/scripts/install.sh @@ -20,4 +20,4 @@ conda install -y -c pytorch-nightly pytorch cpuonly printf "* Installing torchtext\n" git submodule update --init --recursive -"$this_dir/install.bat" +"$root_dir/packaging/vc_env_helper.bat" python setup.py bdist_wheel diff --git a/packaging/build_wheel.bat b/packaging/build_wheel.bat deleted file mode 100644 index d9ba42ad04..0000000000 --- a/packaging/build_wheel.bat +++ /dev/null @@ -1,22 +0,0 @@ -@echo on - -for /f "usebackq tokens=*" %%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -legacy -products * -version [15^,17^) -property installationPath`) do ( - if exist "%%i" if exist "%%i\VC\Auxiliary\Build\vcvarsall.bat" ( - set "VS15INSTALLDIR=%%i" - set "VS15VCVARSALL=%%i\VC\Auxiliary\Build\vcvarsall.bat" - goto vswhere - ) -) - -:vswhere -if "%VSDEVCMD_ARGS%" == "" ( - call "%VS15VCVARSALL%" x64 || exit /b 1 -) else ( - call "%VS15VCVARSALL%" x64 %VSDEVCMD_ARGS% || exit /b 1 -) - -@echo on - -set DISTUTILS_USE_SDK=1 - -python setup.py bdist_wheel || exit /b 1 diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index 1bfa107ff2..064622c801 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -13,7 +13,7 @@ setup_pip_pytorch_version git submodule update --init --recursive python setup.py clean if [[ "$OSTYPE" == "msys" ]]; then - "$script_dir/build_wheel.bat" + "$script_dir/vc_env_helper.bat" python setup.py bdist_wheel else python setup.py bdist_wheel fi diff --git a/.circleci/unittest/windows/scripts/install.bat b/packaging/vc_env_helper.bat similarity index 59% rename from .circleci/unittest/windows/scripts/install.bat rename to packaging/vc_env_helper.bat index 5205b4c1e6..9410135677 100644 --- a/.circleci/unittest/windows/scripts/install.bat +++ b/packaging/vc_env_helper.bat @@ -1,6 +1,9 @@ @echo on -for /f "usebackq tokens=*" %%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -legacy -products * -version [15^,17^) -property installationPath`) do ( +set VC_VERSION_LOWER=16 +set VC_VERSION_UPPER=17 + +for /f "usebackq tokens=*" %%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -legacy -products * -version [%VC_VERSION_LOWER%^,%VC_VERSION_UPPER%^) -property installationPath`) do ( if exist "%%i" if exist "%%i\VC\Auxiliary\Build\vcvarsall.bat" ( set "VS15INSTALLDIR=%%i" set "VS15VCVARSALL=%%i\VC\Auxiliary\Build\vcvarsall.bat" @@ -19,4 +22,18 @@ if "%VSDEVCMD_ARGS%" == "" ( set DISTUTILS_USE_SDK=1 -python setup.py develop || exit /b 1 +set args=%1 +shift +:start +if [%1] == [] goto done +set args=%args% %1 +shift +goto start + +:done +if "%args%" == "" ( + echo Usage: vc_env_helper.bat [command] [args] + echo e.g. vc_env_helper.bat cl /c test.cpp +) + +%args% || exit /b 1 From 77396e52a75ad33e015719f74b4bc94d158acb36 Mon Sep 17 00:00:00 2001 From: peterjc123 Date: Fri, 5 Jun 2020 00:21:28 +0800 Subject: [PATCH 056/719] Minor fix for batch script cleanup (#809) --- .circleci/unittest/windows/scripts/install.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh index ec696d84a6..e2325bfb5f 100644 --- a/.circleci/unittest/windows/scripts/install.sh +++ b/.circleci/unittest/windows/scripts/install.sh @@ -20,4 +20,4 @@ conda install -y -c pytorch-nightly pytorch cpuonly printf "* Installing torchtext\n" git submodule update --init --recursive -"$root_dir/packaging/vc_env_helper.bat" python setup.py bdist_wheel +"$root_dir/packaging/vc_env_helper.bat" python setup.py install From ad4db02f87fddd7825941bf94186e2a747a37c57 Mon Sep 17 00:00:00 2001 From: peterjc123 Date: Fri, 5 Jun 2020 01:02:21 +0800 Subject: [PATCH 057/719] One more fix for Windows jobs (#810) --- .circleci/unittest/windows/scripts/install.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh index e2325bfb5f..9b976aa93a 100644 --- a/.circleci/unittest/windows/scripts/install.sh +++ b/.circleci/unittest/windows/scripts/install.sh @@ -20,4 +20,4 @@ conda install -y -c pytorch-nightly pytorch cpuonly printf "* Installing torchtext\n" git submodule update --init --recursive -"$root_dir/packaging/vc_env_helper.bat" python setup.py install +"$root_dir/packaging/vc_env_helper.bat" python setup.py develop From c05bc9eef69c28301aa19efbd9c46ee977c8ba09 Mon Sep 17 00:00:00 2001 From: cpuhrsch Date: Thu, 4 Jun 2020 13:56:05 -0400 Subject: [PATCH 058/719] 20200604 Apply changes from fbsync onto master (#811) From a6382a048585a579d3106665abd051ff92c73b4d Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Thu, 4 Jun 2020 12:13:56 -0700 Subject: [PATCH 059/719] Resolving PR comments --- docs/source/experimental_functional.rst | 14 ----------- torchtext/csrc/regex.cpp | 6 +---- torchtext/experimental/functional.py | 29 ----------------------- torchtext/experimental/transforms.py | 31 ++++++++++++++++--------- 4 files changed, 21 insertions(+), 59 deletions(-) delete mode 100644 docs/source/experimental_functional.rst diff --git a/docs/source/experimental_functional.rst b/docs/source/experimental_functional.rst deleted file mode 100644 index 2e0ff84c4d..0000000000 --- a/docs/source/experimental_functional.rst +++ /dev/null @@ -1,14 +0,0 @@ -.. role:: hidden - :class: hidden-section - -torchtext.experimental.functional -================================= - -.. automodule:: torchtext.experimental.functional -.. currentmodule:: torchtext.experimental.functional - -`regex_tokenizer` -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: regex_tokenizer - diff --git a/torchtext/csrc/regex.cpp b/torchtext/csrc/regex.cpp index 998eda936c..2f88e135b5 100644 --- a/torchtext/csrc/regex.cpp +++ b/torchtext/csrc/regex.cpp @@ -16,10 +16,7 @@ struct Regex : torch::CustomClassHolder { // serialized model from this re_str_ member, thus it needs to be public. std::string re_str_; - Regex(const std::string &re_str) { UpdateRe(re_str_); } - - void UpdateRe(const std::string &re_str) { - re_str_ = re_str; + Regex(const std::string &re_str) : re_str_(re_str) { re_ = std::regex(re_str_); } @@ -32,7 +29,6 @@ struct Regex : torch::CustomClassHolder { static auto regex = torch::class_("torchtext", "Regex") .def(torch::init()) - .def("UpdateRe", &Regex::UpdateRe) .def("Sub", &Regex::Sub) .def_pickle( // __getstate__ diff --git a/torchtext/experimental/functional.py b/torchtext/experimental/functional.py index f69aee5aa7..2c9ac60541 100644 --- a/torchtext/experimental/functional.py +++ b/torchtext/experimental/functional.py @@ -1,33 +1,4 @@ import torch -from typing import List, Tuple - - -def regex_tokenizer(line, patterns_list, regex, lowercase=False): - # type: (str, List[Tuple[str, str]], torch.classes.torchtext.Regex, bool) -> List[str] - r"""Regex tokenizer for a string sentence. - - Steps include - - lowercasing (optional) - - applying all regex replacements defines in patterns_list - - Args: - line (str): a line of text to tokenize. - patterns_list (List[Tuple[str, str]]): a list of tuples (ordered pairs) which contain the regex pattern string - as the first element and the replacement string as the second element. - regex (torch.classes.torchtext.Regex): a custom c++ Regex object. - lowercase (bool, optional): a flag to determine whether to lowercase the input text. - Returns: - tokens (List[str]): a list of tokens after splitting on whitespace. - """ - - if lowercase: - line = line.lower() - - regex = torch.classes.torchtext.Regex('') - for pattern_re, replaced_str in patterns_list: - regex.UpdateRe(pattern_re) - line = regex.Sub(line, replaced_str) - return line.split() def vocab_func(vocab): diff --git a/torchtext/experimental/transforms.py b/torchtext/experimental/transforms.py index baaa8c6c5a..7610b6e859 100644 --- a/torchtext/experimental/transforms.py +++ b/torchtext/experimental/transforms.py @@ -1,6 +1,5 @@ import torch import torch.nn as nn -from torchtext.experimental import functional as F from typing import List, Tuple @@ -38,13 +37,11 @@ class BasicEnglishNormalize(nn.Module): >>> tokens = jit_basic_english_normalize.forward(test_sample) """ - patterns_list: List[Tuple[str, str]] - regex: torch.classes.torchtext.Regex + regex_and_replacement_string_pairs: List[Tuple[torch.classes.torchtext.Regex, str]] def __init__(self): super(BasicEnglishNormalize, self).__init__() - self.regex = torch.classes.torchtext.Regex('') - self.patterns_list = [ + patterns_list = [ (r'\'', ' \' '), (r'\"', ''), (r'\.', ' . '), @@ -58,6 +55,10 @@ def __init__(self): (r'\:', ' '), (r'\s+', ' ')] + regex_objects = map(lambda pattern_tuple: torch.classes.torchtext.Regex(pattern_tuple[0]), patterns_list) + replacement_strings = map(lambda pattern_tuple: pattern_tuple[1], patterns_list) + self.regex_and_replacement_string_pairs = list(zip(regex_objects, replacement_strings)) + def forward(self, line: str) -> List[str]: r""" Args: @@ -65,7 +66,11 @@ def forward(self, line: str) -> List[str]: Returns: List[str]: a list of tokens after normalizing and splitting on whitespace. """ - return F.regex_tokenizer(line, self.patterns_list, self.regex, lowercase=True) + + line = line.lower() + for regex, replacement_string in self.regex_and_replacement_string_pairs: + line = regex.Sub(line, replacement_string) + return line.split() class RegexTokenizer(nn.Module): @@ -87,13 +92,14 @@ class RegexTokenizer(nn.Module): >>> tokens = jit_regex_tokenizer.forward(test_sample) """ - patterns_list: List[Tuple[str, str]] - regex: torch.classes.torchtext.Regex + regex_and_replacement_string_pairs: List[Tuple[torch.classes.torchtext.Regex, str]] def __init__(self, patterns_list: List[Tuple[str, str]]): super(RegexTokenizer, self).__init__() - self.regex = torch.classes.torchtext.Regex('') - self.patterns_list = patterns_list + + regex_objects = map(lambda pattern_tuple: torch.classes.torchtext.Regex(pattern_tuple[0]), patterns_list) + replacement_strings = map(lambda pattern_tuple: pattern_tuple[1], patterns_list) + self.regex_and_replacement_string_pairs = list(zip(regex_objects, replacement_strings)) def forward(self, line: str) -> List[str]: r""" @@ -102,4 +108,7 @@ def forward(self, line: str) -> List[str]: Returns: List[str]: a list of tokens after normalizing and splitting on whitespace. """ - return F.regex_tokenizer(line, self.patterns_list, self.regex) + + for regex, replacement_string in self.regex_and_replacement_string_pairs: + line = regex.Sub(line, replacement_string) + return line.split() From 748c5f766f2f5cf35a115133dff8139bb45f555d Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Thu, 4 Jun 2020 12:24:50 -0700 Subject: [PATCH 060/719] Updating tests --- test/data/test_functional.py | 19 +++++-------------- torchtext/experimental/transforms.py | 4 ++-- 2 files changed, 7 insertions(+), 16 deletions(-) diff --git a/test/data/test_functional.py b/test/data/test_functional.py index d86aa2e6fc..ef15fa2d6f 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -80,7 +80,7 @@ def test_sentencepiece_tokenizer(self): self.assertEqual(list(spm_generator([test_sample]))[0], ref_results) - # TODO(Nayef211): uncomment and replace the test below with this once once + # TODO(Nayef211): uncomment and replace the test below with this once # https://github.com/pytorch/pytorch/issues/38207 is closed # def test_BasicEnglishNormalize(self): # test_sample = '\'".
,()!?;: Basic English Normalization for a Line of Text \'".
,()!?;:' @@ -114,7 +114,7 @@ def test_BasicEnglishNormalize(self): self.assertEqual(experimental_eager_tokens, ref_results) self.assertEqual(experimental_eager_tokens, tokens_eager) - # TODO(Nayef211): uncomment and replace the test below with this once once + # TODO(Nayef211): uncomment and replace the test below with this once # https://github.com/pytorch/pytorch/issues/38207 is closed # def test_RegexTokenizer(self): # test_sample = '\'".
,()!?;: Basic Regex Tokenization for a Line of Text \'".
,()!?;:' @@ -144,21 +144,12 @@ def test_BasicEnglishNormalize(self): # self.assertEqual(jit_tokens, eager_tokens) def test_RegexTokenizer(self): - test_sample = 'Basic Regex Tokenization for a Line of Text' - ref_results = ['Basic', 'Regex', 'Tokenization', - 'for', 'a', 'Line', 'of', 'Text'] + test_sample = '"Basic Regex Tokenization". For a Line of Text' + ref_results = ['Basic', 'Regex', 'Tokenization', '.', + 'For', 'a', 'Line', 'of', 'Text'] patterns_list = [ - (r'\'', ' \' '), (r'\"', ''), (r'\.', ' . '), - (r'
', ' '), - (r',', ' , '), - (r'\(', ' ( '), - (r'\)', ' ) '), - (r'\!', ' ! '), - (r'\?', ' ? '), - (r'\;', ' '), - (r'\:', ' '), (r'\s+', ' ')] regex_tokenizer = RegexTokenizer(patterns_list) diff --git a/torchtext/experimental/transforms.py b/torchtext/experimental/transforms.py index 7610b6e859..f7f53670be 100644 --- a/torchtext/experimental/transforms.py +++ b/torchtext/experimental/transforms.py @@ -34,7 +34,7 @@ class BasicEnglishNormalize(nn.Module): >>> test_sample = 'Basic English Normalization for a Line of Text' >>> basic_english_normalize = BasicEnglishNormalize() >>> jit_basic_english_normalize = torch.jit.script(basic_english_normalize) - >>> tokens = jit_basic_english_normalize.forward(test_sample) + >>> tokens = jit_basic_english_normalize(test_sample) """ regex_and_replacement_string_pairs: List[Tuple[torch.classes.torchtext.Regex, str]] @@ -89,7 +89,7 @@ class RegexTokenizer(nn.Module): (r'\"', '')] >>> regex_tokenizer = RegexTokenizer(patterns_list) >>> jit_regex_tokenizer = torch.jit.script(regex_tokenizer) - >>> tokens = jit_regex_tokenizer.forward(test_sample) + >>> tokens = jit_regex_tokenizer(test_sample) """ regex_and_replacement_string_pairs: List[Tuple[torch.classes.torchtext.Regex, str]] From 6bca9f37d0e14961a4ac8b13f6d650c0d69c930a Mon Sep 17 00:00:00 2001 From: Runmin Zhang <6059600+z-Runmin@users.noreply.github.com> Date: Thu, 4 Jun 2020 18:57:18 -0400 Subject: [PATCH 061/719] Update experimental.dataset WikiText2, WikiText103, PennTreeBank, WMTNewsCrawl (#774) --- test/data/test_builtin_datasets.py | 2 +- torchtext/experimental/datasets/__init__.py | 3 +- .../datasets/language_modeling.py | 191 ++++++++++-------- .../experimental/datasets/raw/__init__.py | 7 +- .../datasets/raw/language_modeling.py | 184 +++++++++++++++++ 5 files changed, 299 insertions(+), 88 deletions(-) create mode 100644 torchtext/experimental/datasets/raw/language_modeling.py diff --git a/test/data/test_builtin_datasets.py b/test/data/test_builtin_datasets.py index 08c75292c4..45e0d096bb 100644 --- a/test/data/test_builtin_datasets.py +++ b/test/data/test_builtin_datasets.py @@ -78,7 +78,7 @@ def test_penntreebank_legacy(self): def test_penntreebank(self): from torchtext.experimental.datasets import PennTreebank - # smoke test to ensure wikitext2 works properly + # smoke test to ensure penn treebank works properly train_dataset, test_dataset, valid_dataset = PennTreebank() self.assertEqual(len(train_dataset), 924412) self.assertEqual(len(test_dataset), 82114) diff --git a/torchtext/experimental/datasets/__init__.py b/torchtext/experimental/datasets/__init__.py index ac2faa423b..d91f60ff55 100644 --- a/torchtext/experimental/datasets/__init__.py +++ b/torchtext/experimental/datasets/__init__.py @@ -1,4 +1,4 @@ -from .language_modeling import LanguageModelingDataset, WikiText2, WikiText103, PennTreebank # NOQA +from .language_modeling import LanguageModelingDataset, WikiText2, WikiText103, PennTreebank, WMTNewsCrawl # NOQA from .text_classification import AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, \ YelpReviewFull, YahooAnswers, \ AmazonReviewPolarity, AmazonReviewFull, IMDB @@ -7,6 +7,7 @@ 'WikiText2', 'WikiText103', 'PennTreebank', + 'WMTNewsCrawl', 'IMDB', 'AG_NEWS', 'SogouNews', diff --git a/torchtext/experimental/datasets/language_modeling.py b/torchtext/experimental/datasets/language_modeling.py index 6c0ec5799e..f44de4af88 100644 --- a/torchtext/experimental/datasets/language_modeling.py +++ b/torchtext/experimental/datasets/language_modeling.py @@ -1,22 +1,15 @@ import torch -import logging -import io -from torchtext.utils import download_from_url, extract_archive -from torchtext.vocab import build_vocab_from_iterator from torchtext.data.utils import get_tokenizer -from torchtext.vocab import Vocab -from torchtext.data.functional import numericalize_tokens_from_iterator - -URLS = { - 'WikiText2': - 'https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip', - 'WikiText103': - 'https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip', - 'PennTreebank': - ['https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt', - 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt', - 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt'] -} +from torchtext.vocab import build_vocab_from_iterator +from torchtext.experimental.datasets.raw import language_modeling as raw +from torchtext.experimental.functional import vocab_func, totensor, sequential_transforms + + +def build_vocab(data, transforms): + tok_list = [] + for txt in data: + tok_list.append(transforms(txt)) + return build_vocab_from_iterator(tok_list) class LanguageModelingDataset(torch.utils.data.Dataset): @@ -26,10 +19,11 @@ class LanguageModelingDataset(torch.utils.data.Dataset): - WikiText2 - WikiText103 - PennTreebank + - WMTNewsCrawl """ - def __init__(self, data, vocab): + def __init__(self, data, vocab, transforms, single_line): """Initiate language modeling dataset. Arguments: @@ -37,22 +31,24 @@ def __init__(self, data, vocab): numericalizing the string tokens. torch.tensor([token_id_1, token_id_2, token_id_3, token_id1]).long() vocab: Vocabulary object used for dataset. - - Examples: - >>> from torchtext.vocab import build_vocab_from_iterator - >>> data = torch.tensor([token_id_1, token_id_2, - token_id_3, token_id_1]).long() - >>> vocab = build_vocab_from_iterator([['language', 'modeling']]) - >>> dataset = LanguageModelingDataset(data, vocab) + transforms: Text string transforms. """ super(LanguageModelingDataset, self).__init__() - self.data = data self.vocab = vocab + self.transforms = transforms + self.single_line = single_line + if single_line: + self.data = torch.cat(tuple(transforms(row) for row in data), axis=0) + else: + self.data = data def __getitem__(self, i): - return self.data[i] + if self.single_line: + return self.data[i] + else: + return self.transforms(self.data[i]) def __len__(self): return len(self.data) @@ -65,63 +61,45 @@ def get_vocab(self): return self.vocab -def _get_datafile_path(key, extracted_files): - for fname in extracted_files: - if key in fname: - return fname - - -def _setup_datasets(dataset_name, tokenizer=get_tokenizer("basic_english"), - root='.data', vocab=None, removed_tokens=[], - data_select=('train', 'test', 'valid')): +def _setup_datasets(dataset_name, tokenizer=None, root='.data', vocab=None, + data_select=('train', 'test', 'valid'), single_line=True): + if tokenizer is None: + tokenizer = get_tokenizer('basic_english') + text_transform = sequential_transforms(tokenizer) if isinstance(data_select, str): data_select = [data_select] - if not set(data_select).issubset(set(('train', 'test', 'valid'))): - raise TypeError('data_select is not supported!') - - if dataset_name == 'PennTreebank': - extracted_files = [] - select_to_index = {'train': 0, 'test': 1, 'valid': 2} - extracted_files = [download_from_url(URLS['PennTreebank'][select_to_index[key]], - root=root) for key in data_select] + if not set(data_select).issubset(set(('train', 'valid', 'test'))): + raise TypeError('Given data selection {} is not supported!'.format(data_select)) + + if not single_line and dataset_name != 'WikiText103': + raise TypeError('single_line must be True except for WikiText103') + if dataset_name == 'WMTNewsCrawl': + train, = raw.DATASETS[dataset_name](root=root, data_select=('train',)) + if single_line: + raw_data = {'train': [" ".join([txt for txt in train]), ]} + else: + raw_data = {'train': [txt for txt in train]} else: - dataset_tar = download_from_url(URLS[dataset_name], root=root) - extracted_files = extract_archive(dataset_tar) - - _path = {} - for item in data_select: - _path[item] = _get_datafile_path(item, extracted_files) + train, test, valid = raw.DATASETS[dataset_name](root=root, data_select=('train', 'test', 'valid')) + # Cache raw text iterable dataset + if single_line: + raw_data = {'train': [" ".join([txt for txt in train]), ], + 'valid': [" ".join(txt for txt in valid), ], + 'test': [" ".join(txt for txt in test), ]} + else: + raw_data = {'train': [txt for txt in train], + 'valid': [txt for txt in valid], + 'test': [txt for txt in test]} if vocab is None: - if 'train' not in _path.keys(): + if 'train' not in data_select: raise TypeError("Must pass a vocab if train is not selected.") - logging.info('Building Vocab based on {}'.format(_path['train'])) - txt_iter = iter(tokenizer(row) for row in io.open(_path['train'], - encoding="utf8")) - vocab = build_vocab_from_iterator(txt_iter) - logging.info('Vocab has {} entries'.format(len(vocab))) - else: - if not isinstance(vocab, Vocab): - raise TypeError("Passed vocabulary is not of type Vocab") - - data = {} - for item in _path.keys(): - data[item] = [] - logging.info('Creating {} data'.format(item)) - txt_iter = iter(tokenizer(row) for row in io.open(_path[item], - encoding="utf8")) - _iter = numericalize_tokens_from_iterator( - vocab, txt_iter, removed_tokens) - for tokens in _iter: - data[item] += [token_id for token_id in tokens] - - for key in data_select: - if data[key] == []: - raise TypeError('Dataset {} is empty!'.format(key)) - - return tuple(LanguageModelingDataset(torch.tensor(data[d]).long(), vocab) - for d in data_select) + vocab = build_vocab(raw_data['train'], text_transform) + text_transform = sequential_transforms(text_transform, vocab_func(vocab), + totensor(dtype=torch.long)) + return tuple(LanguageModelingDataset(raw_data[item], vocab, text_transform, single_line) + for item in data_select) def WikiText2(*args, **kwargs): @@ -138,7 +116,6 @@ def WikiText2(*args, **kwargs): root: Directory where the datasets are saved. Default: ".data" vocab: Vocabulary used for dataset. If None, it will generate a new vocabulary based on the train data set. - removed_tokens: removed tokens from output dataset (Default: []) data_select: a string or tupel for the returned datasets (Default: ('train', 'test','valid')) By default, all the three datasets (train, test, valid) are generated. Users @@ -146,6 +123,10 @@ def WikiText2(*args, **kwargs): just a string 'train'. If 'train' is not in the tuple or string, a vocab object should be provided which will be used to process valid and/or test data. + single_line: whether to return all tokens in a single line. + (Default: True) + By default, all lines in raw text file are concatenated into a single line. + Use `single_line = False` if one wants to get data line by line. Examples: >>> from torchtext.experimental.datasets import WikiText2 @@ -175,12 +156,6 @@ def WikiText103(*args, **kwargs): root: Directory where the datasets are saved. Default: ".data" vocab: Vocabulary used for dataset. If None, it will generate a new vocabulary based on the train data set. - data_select: the returned datasets (Default: ('train', 'test','valid')) - By default, all the three datasets (train, test, valid) are generated. Users - could also choose any one or two of them, for example ('train', 'test'). - If 'train' is not in the tuple, an vocab object should be provided which will - be used to process valid and/or test data. - removed_tokens: removed tokens from output dataset (Default: []) data_select: a string or tupel for the returned datasets (Default: ('train', 'test','valid')) By default, all the three datasets (train, test, valid) are generated. Users @@ -188,6 +163,10 @@ def WikiText103(*args, **kwargs): just a string 'train'. If 'train' is not in the tuple or string, a vocab object should be provided which will be used to process valid and/or test data. + single_line: whether to return all tokens in a single line. + (Default: True) + By default, all lines in raw text file are concatenated into a single line. + Use `single_line = False` if one wants to get data line by line. Examples: >>> from torchtext.experimental.datasets import WikiText103 @@ -217,7 +196,6 @@ def PennTreebank(*args, **kwargs): root: Directory where the datasets are saved. Default: ".data" vocab: Vocabulary used for dataset. If None, it will generate a new vocabulary based on the train data set. - removed_tokens: removed tokens from output dataset (Default: []) data_select: a string or tupel for the returned datasets (Default: ('train', 'test','valid')) By default, all the three datasets (train, test, valid) are generated. Users @@ -225,6 +203,10 @@ def PennTreebank(*args, **kwargs): just a string 'train'. If 'train' is not in the tuple or string, a vocab object should be provided which will be used to process valid and/or test data. + single_line: whether to return all tokens in a single line. + (Default: True) + By default, all lines in raw text file are concatenated into a single line. + Use `single_line = False` if one wants to get data line by line. Examples: >>> from torchtext.experimental.datasets import PennTreebank @@ -238,3 +220,42 @@ def PennTreebank(*args, **kwargs): """ return _setup_datasets(*(("PennTreebank",) + args), **kwargs) + + +def WMTNewsCrawl(*args, **kwargs): + """ Defines WMTNewsCrawl datasets. + + Create language modeling dataset: WMTNewsCrawl + returns the train set + + Arguments: + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well (see example below). A custom tokenizer is callable + function with input of a string and output of a token list. + root: Directory where the datasets are saved. Default: ".data" + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + data_select: a string or tupel for the returned datasets + (Default: ('train',)) + single_line: whether to return all tokens in a single line. + (Default: True) + By default, all lines in raw text file are concatenated into a single line. + Use `single_line = False` if one wants to get data line by line. + Examples: + >>> from torchtext.experimental.datasets import WMTNewsCrawl + >>> from torchtext.data.utils import get_tokenizer + >>> tokenizer = get_tokenizer("spacy") + >>> train_dataset, = WMTNewsCrawl(tokenizer=tokenizer, data_select='train') + + """ + + return _setup_datasets(*(("WMTNewsCrawl",) + args), **kwargs) + + +DATASETS = { + 'WikiText2': WikiText2, + 'WikiText103': WikiText103, + 'PennTreebank': PennTreebank, + 'WMTNewsCrawl': WMTNewsCrawl +} diff --git a/torchtext/experimental/datasets/raw/__init__.py b/torchtext/experimental/datasets/raw/__init__.py index 61accbe2a1..9c988ec05c 100644 --- a/torchtext/experimental/datasets/raw/__init__.py +++ b/torchtext/experimental/datasets/raw/__init__.py @@ -1,6 +1,7 @@ from .text_classification import AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, \ YelpReviewFull, YahooAnswers, \ AmazonReviewPolarity, AmazonReviewFull, IMDB +from .language_modeling import WikiText2, WikiText103, PennTreebank, WMTNewsCrawl __all__ = ['IMDB', 'AG_NEWS', @@ -10,4 +11,8 @@ 'YelpReviewFull', 'YahooAnswers', 'AmazonReviewPolarity', - 'AmazonReviewFull'] + 'AmazonReviewFull', + 'WikiText2', + 'WikiText103', + 'PennTreebank', + 'WMTNewsCrawl'] diff --git a/torchtext/experimental/datasets/raw/language_modeling.py b/torchtext/experimental/datasets/raw/language_modeling.py new file mode 100644 index 0000000000..a867108978 --- /dev/null +++ b/torchtext/experimental/datasets/raw/language_modeling.py @@ -0,0 +1,184 @@ +import torch +import logging +import io +from torchtext.utils import download_from_url, extract_archive + +URLS = { + 'WikiText2': + 'https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip', + 'WikiText103': + 'https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip', + 'PennTreebank': + ['https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt', + 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt', + 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt'], + 'WMTNewsCrawl': 'http://www.statmt.org/wmt11/training-monolingual-news-2010.tgz' +} + + +class RawTextIterableDataset(torch.utils.data.IterableDataset): + """Defines an abstraction for raw text iterable datasets. + """ + + def __init__(self, iterator, start=0, num_lines=None): + """Initiate language modeling dataset. + """ + super(RawTextIterableDataset, self).__init__() + self._iterator = iterator + self.has_setup = False + self.start = start + self.num_lines = num_lines + + def setup_iter(self, start=0, num_lines=None): + self.start = start + self.num_lines = num_lines + self.has_setup = True + + def __iter__(self): + if not self.has_setup: + self.setup_iter() + for i, item in enumerate(self._iterator): + if i >= self.start: + yield item + if (self.num_lines is not None) and (i == (self.start + self.num_lines)): + break + + def get_iterator(self): + return self._iterator + + +def _setup_datasets(dataset_name, root='.data', data_select=('train', 'test', 'valid'), **kwargs): + if isinstance(data_select, str): + data_select = [data_select] + if not set(data_select).issubset(set(('train', 'test', 'valid'))): + raise TypeError('data_select is not supported!') + + if dataset_name == 'PennTreebank': + extracted_files = [] + select_to_index = {'train': 0, 'test': 1, 'valid': 2} + extracted_files = [download_from_url(URLS['PennTreebank'][select_to_index[key]], + root=root) for key in data_select] + elif dataset_name == 'WMTNewsCrawl': + if not (data_select == ['train'] or set(data_select).issubset(set(('train',)))): + raise ValueError("WMTNewsCrawl only creates a training dataset. " + "data_select should be 'train' " + "or ('train',), got {}.".format(data_select)) + dataset_tar = download_from_url(URLS[dataset_name], root=root) + extracted_files = extract_archive(dataset_tar) + year = kwargs.get('year', 2010) + language = kwargs.get('language', 'en') + file_name = 'news.{}.{}.shuffled'.format(year, language) + extracted_files = [f for f in extracted_files if file_name in f] + else: + dataset_tar = download_from_url(URLS[dataset_name], root=root) + extracted_files = extract_archive(dataset_tar) + + _path = {} + for item in data_select: + for fname in extracted_files: + if item in fname: + _path[item] = fname + + data = {} + for item in _path.keys(): + logging.info('Creating {} data'.format(item)) + data[item] = iter(io.open(_path[item], encoding="utf8")) + + return tuple(RawTextIterableDataset(data[item]) for item in data_select) + + +def WikiText2(*args, **kwargs): + """ Defines WikiText2 datasets. + + Create language modeling dataset: WikiText2 + Separately returns the train/test/valid set + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + data_select: a string or tupel for the returned datasets + (Default: ('train', 'test','valid')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.raw.datasets import WikiText2 + >>> train_dataset, test_dataset, valid_dataset = WikiText2() + >>> valid_dataset, = WikiText2(data_select='valid') + + """ + + return _setup_datasets(*(("WikiText2",) + args), **kwargs) + + +def WikiText103(*args, **kwargs): + """ Defines WikiText103 datasets. + + Create language modeling dataset: WikiText103 + Separately returns the train/test/valid set + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + data_select: the returned datasets (Default: ('train', 'test','valid')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test'). + If 'train' is not in the tuple, an vocab object should be provided which will + be used to process valid and/or test data. + + Examples: + >>> from torchtext.experimental.datasets.raw import WikiText103 + >>> train_dataset, test_dataset, valid_dataset = WikiText103() + >>> valid_dataset, = WikiText103(data_select='valid') + """ + + return _setup_datasets(*(("WikiText103",) + args), **kwargs) + + +def PennTreebank(*args, **kwargs): + """ Defines PennTreebank datasets. + + Create language modeling dataset: PennTreebank + Separately returns the train/test/valid set + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + data_select: a string or tuple for the returned datasets + (Default: ('train', 'test','valid')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets.raw import PennTreebank + >>> train_dataset, test_dataset, valid_dataset = PennTreebank() + >>> valid_dataset, = PennTreebank(data_select='valid') + + """ + + return _setup_datasets(*(("PennTreebank",) + args), **kwargs) + + +def WMTNewsCrawl(*args, **kwargs): + """ Defines WMT News Crawl. + + Create language modeling dataset: WMTNewsCrawl + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + data_select: a string or tuple for the returned datasets. + (Default: 'train') + """ + + return _setup_datasets(*(("WMTNewsCrawl",) + args), **kwargs) + + +DATASETS = { + 'WikiText2': WikiText2, + 'WikiText103': WikiText103, + 'PennTreebank': PennTreebank, + 'WMTNewsCrawl': WMTNewsCrawl +} From 902287160cfb83a81d3f6e22e878877bfcf3de71 Mon Sep 17 00:00:00 2001 From: Aditya Kurniawan Date: Fri, 5 Jun 2020 08:46:04 +0700 Subject: [PATCH 062/719] Experimental translation datasets (#751) --- .../unittest/linux/scripts/environment.yml | 2 + .../unittest/windows/scripts/environment.yml | 2 + docs/source/experimental_datasets.rst | 28 + test/data/test_builtin_datasets.py | 42 +- test/test_utils.py | 28 + .../experimental/datasets/raw/__init__.py | 4 + .../experimental/datasets/raw/translation.py | 544 +++++++++++++++++ .../experimental/datasets/translation.py | 551 ++++++++++++++++++ torchtext/utils.py | 18 +- 9 files changed, 1214 insertions(+), 5 deletions(-) create mode 100644 torchtext/experimental/datasets/raw/translation.py create mode 100644 torchtext/experimental/datasets/translation.py diff --git a/.circleci/unittest/linux/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml index 8955f892f6..7a0592dd73 100644 --- a/.circleci/unittest/linux/scripts/environment.yml +++ b/.circleci/unittest/linux/scripts/environment.yml @@ -17,3 +17,5 @@ dependencies: - sphinx - sphinx-rtd-theme - tqdm + - https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.2.5/de_core_news_sm-2.2.5.tar.gz#egg=de_core_news_sm==2.2.5 + - https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 diff --git a/.circleci/unittest/windows/scripts/environment.yml b/.circleci/unittest/windows/scripts/environment.yml index 7e68b8da9b..558ef99049 100644 --- a/.circleci/unittest/windows/scripts/environment.yml +++ b/.circleci/unittest/windows/scripts/environment.yml @@ -18,3 +18,5 @@ dependencies: - tqdm - sentencepiece - future + - https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.2.5/de_core_news_sm-2.2.5.tar.gz#egg=de_core_news_sm==2.2.5 + - https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 diff --git a/docs/source/experimental_datasets.rst b/docs/source/experimental_datasets.rst index d9d59539d0..a16d5dabba 100644 --- a/docs/source/experimental_datasets.rst +++ b/docs/source/experimental_datasets.rst @@ -139,3 +139,31 @@ PennTreebank .. autoclass:: PennTreebank :members: __init__ + + +Machine Translation +^^^^^^^^^^^^^^^^^^^ + +Language modeling datasets are subclasses of ``TranslationDataset`` class. + +.. autoclass:: TranslationDataset + :members: __init__ + +Multi30k +~~~~~~~~ + +.. autoclass:: Multi30k + :members: __init__ + +IWSLT +~~~~~ + +.. autoclass:: IWSLT + :members: __init__ + +WMT14 +~~~~~ + +.. autoclass:: WMT14 + :members: __init__ + diff --git a/test/data/test_builtin_datasets.py b/test/data/test_builtin_datasets.py index 45e0d096bb..2bb4065df9 100644 --- a/test/data/test_builtin_datasets.py +++ b/test/data/test_builtin_datasets.py @@ -1,6 +1,7 @@ #!/user/bin/env python3 # Note that all the tests in this module require dataset (either network access or cached) import os +import glob import shutil import torchtext.data as data from torchtext.datasets import AG_NEWS @@ -10,10 +11,11 @@ def conditional_remove(f): - if os.path.isfile(f): - os.remove(f) - elif os.path.isdir(f): - shutil.rmtree(f) + for path in glob.glob(f): + if os.path.isfile(path): + os.remove(path) + elif os.path.isdir(path): + shutil.rmtree(path) class TestDataset(TorchtextTestCase): @@ -122,3 +124,35 @@ def test_imdb(self): old_vocab = train_dataset.get_vocab() new_vocab = Vocab(counter=old_vocab.freqs, max_size=2500) new_train_data, new_test_data = IMDB(vocab=new_vocab) + + def test_multi30k(self): + from torchtext.experimental.datasets.translation import Multi30k + # smoke test to ensure multi30k works properly + train_dataset, valid_dataset, test_dataset = Multi30k() + self.assertEqual(len(train_dataset), 29000) + self.assertEqual(len(valid_dataset), 1000) + self.assertEqual(len(test_dataset), 1014) + + de_vocab, en_vocab = train_dataset.get_vocab() + de_tokens_ids = [ + de_vocab[token] for token in + 'Zwei Männer verpacken Donuts in Kunststofffolie'.split() + ] + self.assertEqual(de_tokens_ids, [19, 29, 18703, 4448, 5, 6240]) + + en_tokens_ids = [ + en_vocab[token] for token in + 'Two young White males are outside near many bushes'.split() + ] + self.assertEqual(en_tokens_ids, + [17, 23, 1167, 806, 15, 55, 82, 334, 1337]) + + datafile = os.path.join(self.project_root, ".data", "train*") + conditional_remove(datafile) + datafile = os.path.join(self.project_root, ".data", "val*") + conditional_remove(datafile) + datafile = os.path.join(self.project_root, ".data", "test*") + conditional_remove(datafile) + datafile = os.path.join(self.project_root, ".data", + "multi30k_task*.tar.gz") + conditional_remove(datafile) diff --git a/test/test_utils.py b/test/test_utils.py index 5489f838b2..73ee992de3 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -42,6 +42,34 @@ def test_download_extract_tar(self): conditional_remove(f) conditional_remove(archive_path) + def test_download_extract_gz(self): + # create root directory for downloading data + root = '.data' + if not os.path.exists(root): + os.makedirs(root) + + # ensure archive is not already downloaded, if it is then delete + url = 'https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.5.en.gz' + target_archive_path = os.path.join(root, 'val.5.en.gz') + conditional_remove(target_archive_path) + + # download archive and ensure is in correct location + archive_path = utils.download_from_url(url) + assert target_archive_path == archive_path + + # extract files and ensure they are correct + files = utils.extract_archive(archive_path) + assert files == [os.path.join(root, 'val.5.en')] + + # extract files with overwrite option True + files = utils.extract_archive(archive_path, overwrite=True) + assert files == [os.path.join(root, 'val.5.en')] + + # remove files and archive + for f in files: + conditional_remove(f) + conditional_remove(archive_path) + def test_download_extract_zip(self): # create root directory for downloading data root = '.data' diff --git a/torchtext/experimental/datasets/raw/__init__.py b/torchtext/experimental/datasets/raw/__init__.py index 9c988ec05c..26892050d6 100644 --- a/torchtext/experimental/datasets/raw/__init__.py +++ b/torchtext/experimental/datasets/raw/__init__.py @@ -1,6 +1,7 @@ from .text_classification import AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, \ YelpReviewFull, YahooAnswers, \ AmazonReviewPolarity, AmazonReviewFull, IMDB +from .translation import Multi30k, IWSLT, WMT14 from .language_modeling import WikiText2, WikiText103, PennTreebank, WMTNewsCrawl __all__ = ['IMDB', @@ -12,6 +13,9 @@ 'YahooAnswers', 'AmazonReviewPolarity', 'AmazonReviewFull', + 'Multi30k', + 'IWSLT', + 'WMT14', 'WikiText2', 'WikiText103', 'PennTreebank', diff --git a/torchtext/experimental/datasets/raw/translation.py b/torchtext/experimental/datasets/raw/translation.py new file mode 100644 index 0000000000..19f5275d41 --- /dev/null +++ b/torchtext/experimental/datasets/raw/translation.py @@ -0,0 +1,544 @@ +import torch +import os +import io +import codecs +import xml.etree.ElementTree as ET +from collections import defaultdict + +from torchtext.utils import (download_from_url, extract_archive, + unicode_csv_reader) + +URLS = { + 'Multi30k': [ + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2016_flickr.cs.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2016_flickr.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2016_flickr.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2016_flickr.fr.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_flickr.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_flickr.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_flickr.fr.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_mscoco.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_mscoco.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_mscoco.fr.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2018_flickr.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/train.cs.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/train.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/train.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/train.fr.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/val.cs.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/val.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/val.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/val.fr.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.1.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.1.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.2.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.2.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.3.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.3.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.4.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.4.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.5.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.5.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/train.1.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/train.1.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/train.2.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/train.2.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/train.3.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/train.3.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/train.4.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/train.4.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/train.5.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/train.5.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.1.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.1.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.2.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.2.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.3.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.3.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.4.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.4.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.5.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.5.en.gz" + ], + 'WMT14': + 'https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8', + 'IWSLT': + 'https://wit3.fbk.eu/archive/2016-01//texts/{}/{}/{}.tgz' +} + + +def _read_text_iterator(path): + with io.open(path, encoding="utf8") as f: + reader = unicode_csv_reader(f) + for row in reader: + yield " ".join(row) + + +def _clean_xml_file(f_xml): + f_txt = os.path.splitext(f_xml)[0] + with codecs.open(f_txt, mode='w', encoding='utf-8') as fd_txt: + root = ET.parse(f_xml).getroot()[0] + for doc in root.findall('doc'): + for e in doc.findall('seg'): + fd_txt.write(e.text.strip() + '\n') + + +def _clean_tags_file(f_orig): + xml_tags = [ + '= self.start: + yield item + if (self.num_lines is not None) and (i == (self.start + + self.num_lines)): + break + + def get_iterator(self): + return (self._src_iterator, self._tgt_iterator) + + +def Multi30k(train_filenames=("train.de", "train.en"), + valid_filenames=("val.de", "val.en"), + test_filenames=("test_2016_flickr.de", "test_2016_flickr.en"), + root='.data'): + """ Define translation datasets: Multi30k + Separately returns train/valid/test datasets as a tuple + The available dataset include: + test_2016_flickr.cs + test_2016_flickr.de + test_2016_flickr.en + test_2016_flickr.fr + test_2017_flickr.de + test_2017_flickr.en + test_2017_flickr.fr + test_2017_mscoco.de + test_2017_mscoco.en + test_2017_mscoco.fr + test_2018_flickr.en + train.cs + train.de + train.en + train.fr + val.cs + val.de + val.en + val.fr + test_2016.1.de + test_2016.1.en + test_2016.2.de + test_2016.2.en + test_2016.3.de + test_2016.3.en + test_2016.4.de + test_2016.4.en + test_2016.5.de + test_2016.5.en + train.1.de + train.1.en + train.2.de + train.2.en + train.3.de + train.3.en + train.4.de + train.4.en + train.5.de + train.5.en + val.1.de + val.1.en + val.2.de + val.2.en + val.3.de + val.3.en + val.4.de + val.4.en + val.5.de + val.5.en + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.de', 'train.en') + valid_filenames: the source and target filenames for valid. + Default: ('val.de', 'val.en') + test_filenames: the source and target filenames for test. + Default: ('test2016.de', 'test2016.en') + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> from torchtext.datasets import Multi30k + >>> train_dataset, valid_dataset, test_dataset = Multi30k() + """ + return _setup_datasets("Multi30k", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + root=root) + + +def IWSLT(train_filenames=('train.de-en.de', 'train.de-en.en'), + valid_filenames=('IWSLT16.TED.tst2013.de-en.de', + 'IWSLT16.TED.tst2013.de-en.en'), + test_filenames=('IWSLT16.TED.tst2014.de-en.de', + 'IWSLT16.TED.tst2014.de-en.en'), + root='.data'): + """ Define translation datasets: IWSLT + Separately returns train/valid/test datasets + The available datasets include: + IWSLT16.TED.dev2010.ar-en.ar + IWSLT16.TED.dev2010.ar-en.en + IWSLT16.TED.dev2010.cs-en.cs + IWSLT16.TED.dev2010.cs-en.en + IWSLT16.TED.dev2010.de-en.de + IWSLT16.TED.dev2010.de-en.en + IWSLT16.TED.dev2010.en-ar.ar + IWSLT16.TED.dev2010.en-ar.en + IWSLT16.TED.dev2010.en-cs.cs + IWSLT16.TED.dev2010.en-cs.en + IWSLT16.TED.dev2010.en-de.de + IWSLT16.TED.dev2010.en-de.en + IWSLT16.TED.dev2010.en-fr.en + IWSLT16.TED.dev2010.en-fr.fr + IWSLT16.TED.dev2010.fr-en.en + IWSLT16.TED.dev2010.fr-en.fr + IWSLT16.TED.tst2010.ar-en.ar + IWSLT16.TED.tst2010.ar-en.en + IWSLT16.TED.tst2010.cs-en.cs + IWSLT16.TED.tst2010.cs-en.en + IWSLT16.TED.tst2010.de-en.de + IWSLT16.TED.tst2010.de-en.en + IWSLT16.TED.tst2010.en-ar.ar + IWSLT16.TED.tst2010.en-ar.en + IWSLT16.TED.tst2010.en-cs.cs + IWSLT16.TED.tst2010.en-cs.en + IWSLT16.TED.tst2010.en-de.de + IWSLT16.TED.tst2010.en-de.en + IWSLT16.TED.tst2010.en-fr.en + IWSLT16.TED.tst2010.en-fr.fr + IWSLT16.TED.tst2010.fr-en.en + IWSLT16.TED.tst2010.fr-en.fr + IWSLT16.TED.tst2011.ar-en.ar + IWSLT16.TED.tst2011.ar-en.en + IWSLT16.TED.tst2011.cs-en.cs + IWSLT16.TED.tst2011.cs-en.en + IWSLT16.TED.tst2011.de-en.de + IWSLT16.TED.tst2011.de-en.en + IWSLT16.TED.tst2011.en-ar.ar + IWSLT16.TED.tst2011.en-ar.en + IWSLT16.TED.tst2011.en-cs.cs + IWSLT16.TED.tst2011.en-cs.en + IWSLT16.TED.tst2011.en-de.de + IWSLT16.TED.tst2011.en-de.en + IWSLT16.TED.tst2011.en-fr.en + IWSLT16.TED.tst2011.en-fr.fr + IWSLT16.TED.tst2011.fr-en.en + IWSLT16.TED.tst2011.fr-en.fr + IWSLT16.TED.tst2012.ar-en.ar + IWSLT16.TED.tst2012.ar-en.en + IWSLT16.TED.tst2012.cs-en.cs + IWSLT16.TED.tst2012.cs-en.en + IWSLT16.TED.tst2012.de-en.de + IWSLT16.TED.tst2012.de-en.en + IWSLT16.TED.tst2012.en-ar.ar + IWSLT16.TED.tst2012.en-ar.en + IWSLT16.TED.tst2012.en-cs.cs + IWSLT16.TED.tst2012.en-cs.en + IWSLT16.TED.tst2012.en-de.de + IWSLT16.TED.tst2012.en-de.en + IWSLT16.TED.tst2012.en-fr.en + IWSLT16.TED.tst2012.en-fr.fr + IWSLT16.TED.tst2012.fr-en.en + IWSLT16.TED.tst2012.fr-en.fr + IWSLT16.TED.tst2013.ar-en.ar + IWSLT16.TED.tst2013.ar-en.en + IWSLT16.TED.tst2013.cs-en.cs + IWSLT16.TED.tst2013.cs-en.en + IWSLT16.TED.tst2013.de-en.de + IWSLT16.TED.tst2013.de-en.en + IWSLT16.TED.tst2013.en-ar.ar + IWSLT16.TED.tst2013.en-ar.en + IWSLT16.TED.tst2013.en-cs.cs + IWSLT16.TED.tst2013.en-cs.en + IWSLT16.TED.tst2013.en-de.de + IWSLT16.TED.tst2013.en-de.en + IWSLT16.TED.tst2013.en-fr.en + IWSLT16.TED.tst2013.en-fr.fr + IWSLT16.TED.tst2013.fr-en.en + IWSLT16.TED.tst2013.fr-en.fr + IWSLT16.TED.tst2014.ar-en.ar + IWSLT16.TED.tst2014.ar-en.en + IWSLT16.TED.tst2014.de-en.de + IWSLT16.TED.tst2014.de-en.en + IWSLT16.TED.tst2014.en-ar.ar + IWSLT16.TED.tst2014.en-ar.en + IWSLT16.TED.tst2014.en-de.de + IWSLT16.TED.tst2014.en-de.en + IWSLT16.TED.tst2014.en-fr.en + IWSLT16.TED.tst2014.en-fr.fr + IWSLT16.TED.tst2014.fr-en.en + IWSLT16.TED.tst2014.fr-en.fr + IWSLT16.TEDX.dev2012.de-en.de + IWSLT16.TEDX.dev2012.de-en.en + IWSLT16.TEDX.tst2013.de-en.de + IWSLT16.TEDX.tst2013.de-en.en + IWSLT16.TEDX.tst2014.de-en.de + IWSLT16.TEDX.tst2014.de-en.en + train.ar + train.ar-en.ar + train.ar-en.en + train.cs + train.cs-en.cs + train.cs-en.en + train.de + train.de-en.de + train.de-en.en + train.en + train.en-ar.ar + train.en-ar.en + train.en-cs.cs + train.en-cs.en + train.en-de.de + train.en-de.en + train.en-fr.en + train.en-fr.fr + train.fr + train.fr-en.en + train.fr-en.fr + train.tags.ar-en.ar + train.tags.ar-en.en + train.tags.cs-en.cs + train.tags.cs-en.en + train.tags.de-en.de + train.tags.de-en.en + train.tags.en-ar.ar + train.tags.en-ar.en + train.tags.en-cs.cs + train.tags.en-cs.en + train.tags.en-de.de + train.tags.en-de.en + train.tags.en-fr.en + train.tags.en-fr.fr + train.tags.fr-en.en + train.tags.fr-en.fr + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.de-en.de', 'train.de-en.en') + valid_filenames: the source and target filenames for valid. + Default: ('IWSLT16.TED.tst2013.de-en.de', 'IWSLT16.TED.tst2013.de-en.en') + test_filenames: the source and target filenames for test. + Default: ('IWSLT16.TED.tst2014.de-en.de', 'IWSLT16.TED.tst2014.de-en.en') + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> from torchtext.datasets.raw import IWSLT + >>> train_dataset, valid_dataset, test_dataset = IWSLT() + """ + src_language = train_filenames[0].split(".")[-1] + tgt_language = train_filenames[1].split(".")[-1] + languages = "-".join([src_language, tgt_language]) + URLS["IWSLT"] = URLS["IWSLT"].format(src_language, tgt_language, languages) + + return _setup_datasets( + "IWSLT", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + root=root, + ) + + +def WMT14(train_filenames=('train.tok.clean.bpe.32000.de', + 'train.tok.clean.bpe.32000.en'), + valid_filenames=('newstest2013.tok.bpe.32000.de', + 'newstest2013.tok.bpe.32000.en'), + test_filenames=('newstest2014.tok.bpe.32000.de', + 'newstest2014.tok.bpe.32000.en'), + root='.data'): + """ Define translation datasets: WMT14 + Separately returns train/valid/test datasets + The available datasets include: + newstest2016.en + newstest2016.de + newstest2015.en + newstest2015.de + newstest2014.en + newstest2014.de + newstest2013.en + newstest2013.de + newstest2012.en + newstest2012.de + newstest2011.tok.de + newstest2011.en + newstest2011.de + newstest2010.tok.de + newstest2010.en + newstest2010.de + newstest2009.tok.de + newstest2009.en + newstest2009.de + newstest2016.tok.de + newstest2015.tok.de + newstest2014.tok.de + newstest2013.tok.de + newstest2012.tok.de + newstest2010.tok.en + newstest2009.tok.en + newstest2015.tok.en + newstest2014.tok.en + newstest2013.tok.en + newstest2012.tok.en + newstest2011.tok.en + newstest2016.tok.en + newstest2009.tok.bpe.32000.en + newstest2011.tok.bpe.32000.en + newstest2010.tok.bpe.32000.en + newstest2013.tok.bpe.32000.en + newstest2012.tok.bpe.32000.en + newstest2015.tok.bpe.32000.en + newstest2014.tok.bpe.32000.en + newstest2016.tok.bpe.32000.en + train.tok.clean.bpe.32000.en + newstest2009.tok.bpe.32000.de + newstest2010.tok.bpe.32000.de + newstest2011.tok.bpe.32000.de + newstest2013.tok.bpe.32000.de + newstest2012.tok.bpe.32000.de + newstest2014.tok.bpe.32000.de + newstest2016.tok.bpe.32000.de + newstest2015.tok.bpe.32000.de + train.tok.clean.bpe.32000.de + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.tok.clean.bpe.32000.de', 'train.tok.clean.bpe.32000.en') + valid_filenames: the source and target filenames for valid. + Default: ('newstest2013.tok.bpe.32000.de', 'newstest2013.tok.bpe.32000.en') + test_filenames: the source and target filenames for test. + Default: ('newstest2014.tok.bpe.32000.de', 'newstest2014.tok.bpe.32000.en') + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> from torchtext.datasets import WMT14 + >>> train_dataset, valid_dataset, test_dataset = WMT14() + """ + + return _setup_datasets("WMT14", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + root=root) + + +DATASETS = {'Multi30k': Multi30k, 'IWSLT': IWSLT, 'WMT14': WMT14} diff --git a/torchtext/experimental/datasets/translation.py b/torchtext/experimental/datasets/translation.py new file mode 100644 index 0000000000..5fad51135d --- /dev/null +++ b/torchtext/experimental/datasets/translation.py @@ -0,0 +1,551 @@ +import torch +import logging + +from torchtext.experimental.datasets import raw +from torchtext.vocab import build_vocab_from_iterator +from torchtext.data.utils import get_tokenizer +from ..functional import vocab_func, totensor, sequential_transforms + + +def build_vocab(data, transforms, index): + tok_list = [] + for line in data: + tok_list.append(transforms(line[index])) + return build_vocab_from_iterator(tok_list) + + +def _setup_datasets(dataset_name, + train_filenames, + valid_filenames, + test_filenames, + data_select=('train', 'test', 'valid'), + root='.data', + vocab=(None, None), + tokenizer=None, + removed_tokens=['']): + src_vocab, tgt_vocab = vocab + if tokenizer is None: + src_tokenizer = get_tokenizer("spacy", language='de_core_news_sm') + tgt_tokenizer = get_tokenizer("spacy", language='en_core_web_sm') + elif isinstance(tokenizer, tuple): + if len(tokenizer) == 2: + src_tokenizer, tgt_tokenizer = tokenizer + else: + raise ValueError("tokenizer must have length of two for" + "source and target") + else: + raise ValueError( + "tokenizer must be an instance of tuple with length two" + "or None") + train, val, test = DATASETS[dataset_name](train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + root=root) + raw_data = { + "train": [line for line in train], + "valid": [line for line in val], + "test": [line for line in test] + } + src_text_vocab_transform = sequential_transforms(src_tokenizer) + tgt_text_vocab_transform = sequential_transforms(tgt_tokenizer) + + if src_vocab is None: + if 'train' not in data_select: + raise TypeError("Must pass a vocab if train is not selected.") + logging.info('Building src Vocab based on train data') + src_vocab = build_vocab(raw_data["train"], + src_text_vocab_transform, + index=0) + else: + if not isinstance(src_vocab, Vocab): + raise TypeError("Passed src vocabulary is not of type Vocab") + logging.info('src Vocab has {} entries'.format(len(src_vocab))) + + if tgt_vocab is None: + if 'train' not in data_select: + raise TypeError("Must pass a vocab if train is not selected.") + logging.info('Building tgt Vocab based on train data') + tgt_vocab = build_vocab(raw_data["train"], + tgt_text_vocab_transform, + index=1) + else: + if not isinstance(tgt_vocab, Vocab): + raise TypeError("Passed tgt vocabulary is not of type Vocab") + logging.info('tgt Vocab has {} entries'.format(len(tgt_vocab))) + + logging.info('Building datasets for {}'.format(data_select)) + datasets = [] + for key in data_select: + src_text_transform = sequential_transforms(src_text_vocab_transform, + vocab_func(src_vocab), + totensor(dtype=torch.long)) + tgt_text_transform = sequential_transforms(tgt_text_vocab_transform, + vocab_func(tgt_vocab), + totensor(dtype=torch.long)) + datasets.append( + TranslationDataset(raw_data[key], (src_vocab, tgt_vocab), + (src_text_transform, tgt_text_transform))) + + return tuple(datasets) + + +class TranslationDataset(torch.utils.data.Dataset): + """Defines a dataset for translation. + Currently, we only support the following datasets: + - Multi30k + - WMT14 + - IWSLT + """ + def __init__(self, data, vocab, transforms): + """Initiate translation dataset. + + Arguments: + data: a tuple of source and target tensors, which include token ids + numericalizing the string tokens. + [(src_tensor0, tgt_tensor0), (src_tensor1, tgt_tensor1)] + vocab: source and target Vocabulary object used for dataset. + (src_vocab, tgt_vocab) + transforms: a tuple of source and target string transforms. + + Examples: + >>> from torchtext.vocab import build_vocab_from_iterator + >>> src_data = torch.Tensor([token_id_s1, token_id_s2, + token_id_s3, token_id_s1]).long() + >>> tgt_data = torch.Tensor([token_id_t1, token_id_t2, + token_id_t3, token_id_t1]).long() + >>> src_vocab = build_vocab_from_iterator([['Übersetzungsdatensatz']]) + >>> tgt_vocab = build_vocab_from_iterator([['translation', 'dataset']]) + >>> dataset = TranslationDataset([(src_data, tgt_data)], + (src_vocab, tgt_vocab)) + """ + + super(TranslationDataset, self).__init__() + self.data = data + self.vocab = vocab + self.transforms = transforms + + def __getitem__(self, i): + source = self.transforms[0](self.data[i][0]) + target = self.transforms[1](self.data[i][1]) + return (source, target) + + def __len__(self): + return len(self.data) + + def get_vocab(self): + return self.vocab + + +def Multi30k(train_filenames=("train.de", "train.en"), + valid_filenames=("val.de", "val.en"), + test_filenames=("test_2016_flickr.de", "test_2016_flickr.en"), + tokenizer=None, + root='.data', + vocab=(None, None), + data_select=('train', 'valid', 'test'), + removed_tokens=['']): + """ Define translation datasets: Multi30k + Separately returns train/valid/test datasets as a tuple + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.de', 'train.en') + valid_filenames: the source and target filenames for valid. + Default: ('val.de', 'val.en') + test_filenames: the source and target filenames for test. + Default: ('test2016.de', 'test2016.en') + tokenizer: the tokenizer used to preprocess source and target raw text data. + It has to be in a form of tuple. + Default: (get_tokenizer("spacy", language='de_core_news_sm'), + get_tokenizer("spacy", language='en_core_web_sm')) + root: Directory where the datasets are saved. Default: ".data" + vocab: Source and target Vocabulary objects used for dataset. If None, it + will generate a new vocabulary based on the train data set. It has to be + in a form of tuple. + Default: (None, None) + data_select: a string or tuple for the returned datasets + (Default: ('train', 'valid', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + removed_tokens: removed tokens from output dataset (Default: '') + The available dataset include: + test_2016_flickr.cs + test_2016_flickr.de + test_2016_flickr.en + test_2016_flickr.fr + test_2017_flickr.de + test_2017_flickr.en + test_2017_flickr.fr + test_2017_mscoco.de + test_2017_mscoco.en + test_2017_mscoco.fr + test_2018_flickr.en + train.cs + train.de + train.en + train.fr + val.cs + val.de + val.en + val.fr + test_2016.1.de + test_2016.1.en + test_2016.2.de + test_2016.2.en + test_2016.3.de + test_2016.3.en + test_2016.4.de + test_2016.4.en + test_2016.5.de + test_2016.5.en + train.1.de + train.1.en + train.2.de + train.2.en + train.3.de + train.3.en + train.4.de + train.4.en + train.5.de + train.5.en + val.1.de + val.1.en + val.2.de + val.2.en + val.3.de + val.3.en + val.4.de + val.4.en + val.5.de + val.5.en + + Examples: + >>> from torchtext.datasets import Multi30k + >>> from torchtext.data.utils import get_tokenizer + >>> tokenizer = (get_tokenizer("spacy", language='de'), + get_tokenizer("basic_english")) + >>> train_dataset, valid_dataset, test_dataset = Multi30k(tokenizer=tokenizer) + >>> src_vocab, tgt_vocab = train_dataset.get_vocab() + >>> src_data, tgt_data = train_dataset[10] + """ + return _setup_datasets("Multi30k", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + tokenizer=tokenizer, + root=root, + vocab=vocab, + removed_tokens=removed_tokens) + + +def IWSLT(train_filenames=('train.de-en.de', 'train.de-en.en'), + valid_filenames=('IWSLT16.TED.tst2013.de-en.de', + 'IWSLT16.TED.tst2013.de-en.en'), + test_filenames=('IWSLT16.TED.tst2014.de-en.de', + 'IWSLT16.TED.tst2014.de-en.en'), + tokenizer=None, + root='.data', + vocab=(None, None), + data_select=('train', 'valid', 'test'), + removed_tokens=['']): + """ Define translation datasets: IWSLT + Separately returns train/valid/test datasets + The available datasets include: + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.de-en.de', 'train.de-en.en') + valid_filenames: the source and target filenames for valid. + Default: ('IWSLT16.TED.tst2013.de-en.de', 'IWSLT16.TED.tst2013.de-en.en') + test_filenames: the source and target filenames for test. + Default: ('IWSLT16.TED.tst2014.de-en.de', 'IWSLT16.TED.tst2014.de-en.en') + tokenizer: the tokenizer used to preprocess source and target raw text data. + It has to be in a form of tuple. + Default: (get_tokenizer("spacy", language='de_core_news_sm'), + get_tokenizer("spacy", language='en_core_web_sm')) + root: Directory where the datasets are saved. Default: ".data" + vocab: Source and target Vocabulary objects used for dataset. If None, it + will generate a new vocabulary based on the train data set. It has to be + in a form of tuple. + Default: (None, None) + data_select: a string or tuple for the returned datasets + (Default: ('train', 'valid', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + removed_tokens: removed tokens from output dataset (Default: '') + The available datasets include: + IWSLT16.TED.dev2010.ar-en.ar + IWSLT16.TED.dev2010.ar-en.en + IWSLT16.TED.dev2010.cs-en.cs + IWSLT16.TED.dev2010.cs-en.en + IWSLT16.TED.dev2010.de-en.de + IWSLT16.TED.dev2010.de-en.en + IWSLT16.TED.dev2010.en-ar.ar + IWSLT16.TED.dev2010.en-ar.en + IWSLT16.TED.dev2010.en-cs.cs + IWSLT16.TED.dev2010.en-cs.en + IWSLT16.TED.dev2010.en-de.de + IWSLT16.TED.dev2010.en-de.en + IWSLT16.TED.dev2010.en-fr.en + IWSLT16.TED.dev2010.en-fr.fr + IWSLT16.TED.dev2010.fr-en.en + IWSLT16.TED.dev2010.fr-en.fr + IWSLT16.TED.tst2010.ar-en.ar + IWSLT16.TED.tst2010.ar-en.en + IWSLT16.TED.tst2010.cs-en.cs + IWSLT16.TED.tst2010.cs-en.en + IWSLT16.TED.tst2010.de-en.de + IWSLT16.TED.tst2010.de-en.en + IWSLT16.TED.tst2010.en-ar.ar + IWSLT16.TED.tst2010.en-ar.en + IWSLT16.TED.tst2010.en-cs.cs + IWSLT16.TED.tst2010.en-cs.en + IWSLT16.TED.tst2010.en-de.de + IWSLT16.TED.tst2010.en-de.en + IWSLT16.TED.tst2010.en-fr.en + IWSLT16.TED.tst2010.en-fr.fr + IWSLT16.TED.tst2010.fr-en.en + IWSLT16.TED.tst2010.fr-en.fr + IWSLT16.TED.tst2011.ar-en.ar + IWSLT16.TED.tst2011.ar-en.en + IWSLT16.TED.tst2011.cs-en.cs + IWSLT16.TED.tst2011.cs-en.en + IWSLT16.TED.tst2011.de-en.de + IWSLT16.TED.tst2011.de-en.en + IWSLT16.TED.tst2011.en-ar.ar + IWSLT16.TED.tst2011.en-ar.en + IWSLT16.TED.tst2011.en-cs.cs + IWSLT16.TED.tst2011.en-cs.en + IWSLT16.TED.tst2011.en-de.de + IWSLT16.TED.tst2011.en-de.en + IWSLT16.TED.tst2011.en-fr.en + IWSLT16.TED.tst2011.en-fr.fr + IWSLT16.TED.tst2011.fr-en.en + IWSLT16.TED.tst2011.fr-en.fr + IWSLT16.TED.tst2012.ar-en.ar + IWSLT16.TED.tst2012.ar-en.en + IWSLT16.TED.tst2012.cs-en.cs + IWSLT16.TED.tst2012.cs-en.en + IWSLT16.TED.tst2012.de-en.de + IWSLT16.TED.tst2012.de-en.en + IWSLT16.TED.tst2012.en-ar.ar + IWSLT16.TED.tst2012.en-ar.en + IWSLT16.TED.tst2012.en-cs.cs + IWSLT16.TED.tst2012.en-cs.en + IWSLT16.TED.tst2012.en-de.de + IWSLT16.TED.tst2012.en-de.en + IWSLT16.TED.tst2012.en-fr.en + IWSLT16.TED.tst2012.en-fr.fr + IWSLT16.TED.tst2012.fr-en.en + IWSLT16.TED.tst2012.fr-en.fr + IWSLT16.TED.tst2013.ar-en.ar + IWSLT16.TED.tst2013.ar-en.en + IWSLT16.TED.tst2013.cs-en.cs + IWSLT16.TED.tst2013.cs-en.en + IWSLT16.TED.tst2013.de-en.de + IWSLT16.TED.tst2013.de-en.en + IWSLT16.TED.tst2013.en-ar.ar + IWSLT16.TED.tst2013.en-ar.en + IWSLT16.TED.tst2013.en-cs.cs + IWSLT16.TED.tst2013.en-cs.en + IWSLT16.TED.tst2013.en-de.de + IWSLT16.TED.tst2013.en-de.en + IWSLT16.TED.tst2013.en-fr.en + IWSLT16.TED.tst2013.en-fr.fr + IWSLT16.TED.tst2013.fr-en.en + IWSLT16.TED.tst2013.fr-en.fr + IWSLT16.TED.tst2014.ar-en.ar + IWSLT16.TED.tst2014.ar-en.en + IWSLT16.TED.tst2014.de-en.de + IWSLT16.TED.tst2014.de-en.en + IWSLT16.TED.tst2014.en-ar.ar + IWSLT16.TED.tst2014.en-ar.en + IWSLT16.TED.tst2014.en-de.de + IWSLT16.TED.tst2014.en-de.en + IWSLT16.TED.tst2014.en-fr.en + IWSLT16.TED.tst2014.en-fr.fr + IWSLT16.TED.tst2014.fr-en.en + IWSLT16.TED.tst2014.fr-en.fr + IWSLT16.TEDX.dev2012.de-en.de + IWSLT16.TEDX.dev2012.de-en.en + IWSLT16.TEDX.tst2013.de-en.de + IWSLT16.TEDX.tst2013.de-en.en + IWSLT16.TEDX.tst2014.de-en.de + IWSLT16.TEDX.tst2014.de-en.en + train.ar + train.ar-en.ar + train.ar-en.en + train.cs + train.cs-en.cs + train.cs-en.en + train.de + train.de-en.de + train.de-en.en + train.en + train.en-ar.ar + train.en-ar.en + train.en-cs.cs + train.en-cs.en + train.en-de.de + train.en-de.en + train.en-fr.en + train.en-fr.fr + train.fr + train.fr-en.en + train.fr-en.fr + train.tags.ar-en.ar + train.tags.ar-en.en + train.tags.cs-en.cs + train.tags.cs-en.en + train.tags.de-en.de + train.tags.de-en.en + train.tags.en-ar.ar + train.tags.en-ar.en + train.tags.en-cs.cs + train.tags.en-cs.en + train.tags.en-de.de + train.tags.en-de.en + train.tags.en-fr.en + train.tags.en-fr.fr + train.tags.fr-en.en + train.tags.fr-en.fr + + Examples: + >>> from torchtext.datasets import IWSLT + >>> from torchtext.data.utils import get_tokenizer + >>> src_tokenizer = get_tokenizer("spacy", language='de') + >>> tgt_tokenizer = get_tokenizer("basic_english") + >>> train_dataset, valid_dataset, test_dataset = IWSLT(tokenizer=(src_tokenizer, + tgt_tokenizer)) + >>> src_vocab, tgt_vocab = train_dataset.get_vocab() + >>> src_data, tgt_data = train_dataset[10] + """ + + return _setup_datasets("IWSLT", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + tokenizer=tokenizer, + root=root, + vocab=vocab, + removed_tokens=removed_tokens) + + +def WMT14(train_filenames=('train.tok.clean.bpe.32000.de', + 'train.tok.clean.bpe.32000.en'), + valid_filenames=('newstest2013.tok.bpe.32000.de', + 'newstest2013.tok.bpe.32000.en'), + test_filenames=('newstest2014.tok.bpe.32000.de', + 'newstest2014.tok.bpe.32000.en'), + tokenizer=None, + root='.data', + vocab=(None, None), + data_select=('train', 'valid', 'test'), + removed_tokens=['']): + """ Define translation datasets: WMT14 + Separately returns train/valid/test datasets + The available datasets include: + newstest2016.en + newstest2016.de + newstest2015.en + newstest2015.de + newstest2014.en + newstest2014.de + newstest2013.en + newstest2013.de + newstest2012.en + newstest2012.de + newstest2011.tok.de + newstest2011.en + newstest2011.de + newstest2010.tok.de + newstest2010.en + newstest2010.de + newstest2009.tok.de + newstest2009.en + newstest2009.de + newstest2016.tok.de + newstest2015.tok.de + newstest2014.tok.de + newstest2013.tok.de + newstest2012.tok.de + newstest2010.tok.en + newstest2009.tok.en + newstest2015.tok.en + newstest2014.tok.en + newstest2013.tok.en + newstest2012.tok.en + newstest2011.tok.en + newstest2016.tok.en + newstest2009.tok.bpe.32000.en + newstest2011.tok.bpe.32000.en + newstest2010.tok.bpe.32000.en + newstest2013.tok.bpe.32000.en + newstest2012.tok.bpe.32000.en + newstest2015.tok.bpe.32000.en + newstest2014.tok.bpe.32000.en + newstest2016.tok.bpe.32000.en + train.tok.clean.bpe.32000.en + newstest2009.tok.bpe.32000.de + newstest2010.tok.bpe.32000.de + newstest2011.tok.bpe.32000.de + newstest2013.tok.bpe.32000.de + newstest2012.tok.bpe.32000.de + newstest2014.tok.bpe.32000.de + newstest2016.tok.bpe.32000.de + newstest2015.tok.bpe.32000.de + train.tok.clean.bpe.32000.de + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.tok.clean.bpe.32000.de', 'train.tok.clean.bpe.32000.en') + valid_filenames: the source and target filenames for valid. + Default: ('newstest2013.tok.bpe.32000.de', 'newstest2013.tok.bpe.32000.en') + test_filenames: the source and target filenames for test. + Default: ('newstest2014.tok.bpe.32000.de', 'newstest2014.tok.bpe.32000.en') + tokenizer: the tokenizer used to preprocess source and target raw text data. + It has to be in a form of tuple. + Default: (get_tokenizer("spacy", language='de_core_news_sm'), + get_tokenizer("spacy", language='en_core_web_sm')) + root: Directory where the datasets are saved. Default: ".data" + vocab: Source and target Vocabulary objects used for dataset. If None, it + will generate a new vocabulary based on the train data set. It has to be + in a form of tuple. + Default: (None, None) + data_select: a string or tuple for the returned datasets + (Default: ('train', 'valid', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + removed_tokens: removed tokens from output dataset (Default: '') + + Examples: + >>> from torchtext.datasets import WMT14 + >>> from torchtext.data.utils import get_tokenizer + >>> src_tokenizer = get_tokenizer("spacy", language='de') + >>> tgt_tokenizer = get_tokenizer("basic_english") + >>> train_dataset, valid_dataset, test_dataset = WMT14(tokenizer=(src_tokenizer, + tgt_tokenizer)) + >>> src_vocab, tgt_vocab = train_dataset.get_vocab() + >>> src_data, tgt_data = train_dataset[10] + """ + + return _setup_datasets("WMT14", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + tokenizer=tokenizer, + root=root, + vocab=vocab, + removed_tokens=removed_tokens) + + +DATASETS = {'Multi30k': raw.Multi30k, 'IWSLT': raw.IWSLT, 'WMT14': raw.WMT14} diff --git a/torchtext/utils.py b/torchtext/utils.py index 41ac14ecbb..b892396a15 100644 --- a/torchtext/utils.py +++ b/torchtext/utils.py @@ -7,6 +7,7 @@ import re import sys import zipfile +import gzip def reporthook(t): @@ -197,6 +198,21 @@ def extract_archive(from_path, to_path=None, overwrite=False): files = [f for f in files if os.path.isfile(f)] return files + elif from_path.endswith('.gz'): + default_block_size = 65536 + filename = from_path[:-3] + files = [filename] + with gzip.open(from_path, 'rb') as gzfile, \ + open(filename, 'wb') as d_file: + while True: + block = gzfile.read(default_block_size) + if not block: + break + else: + d_file.write(block) + d_file.write(block) + return files + else: raise NotImplementedError( - "We currently only support tar.gz, .tgz and zip achives.") + "We currently only support tar.gz, .tgz, .gz and zip achives.") From 7df4853120b7aa616c1e46b670d2c5b5cb93c998 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Fri, 5 Jun 2020 11:30:43 -0400 Subject: [PATCH 063/719] Fix and re-enable TestScriptableSP for Windows (#799) --- test/data/test_functional.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/test/data/test_functional.py b/test/data/test_functional.py index ef15fa2d6f..9b93580f72 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -1,8 +1,7 @@ import os -import unittest -import sys import uuid import shutil +import unittest import tempfile import sentencepiece as spm @@ -196,11 +195,11 @@ def encode_as_pieces(self, input: str): class TestScriptableSP(unittest.TestCase): def setUp(self): model_path = get_asset_path('spm_example.model') - with tempfile.NamedTemporaryFile() as file: - torch.jit.script(ScriptableSP(model_path)).save(file.name) - self.model = torch.jit.load(file.name) + with tempfile.TemporaryDirectory() as dir_name: + jit_model_path = os.path.join(dir_name, 'spm_example.model') + torch.jit.script(ScriptableSP(model_path)).save(jit_model_path) + self.model = torch.jit.load(jit_model_path) - @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_encode(self): input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' expected = [ @@ -212,7 +211,6 @@ def test_encode(self): output = self.model.encode(input) self.assertEqual(expected, output) - @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_encode_as_ids(self): input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' expected = [ @@ -221,7 +219,6 @@ def test_encode_as_ids(self): output = self.model.encode_as_ids(input) self.assertEqual(expected, output) - @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_encode_as_pieces(self): input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' expected = [ From f395563a9074b12650cd45d57e8632d622ca9e6a Mon Sep 17 00:00:00 2001 From: Runmin Zhang <6059600+z-Runmin@users.noreply.github.com> Date: Thu, 4 Jun 2020 18:57:18 -0400 Subject: [PATCH 064/719] Update experimental.dataset WikiText2, WikiText103, PennTreeBank, WMTNewsCrawl (#774) --- test/data/test_builtin_datasets.py | 2 +- torchtext/experimental/datasets/__init__.py | 3 +- .../datasets/language_modeling.py | 191 ++++++++++-------- .../experimental/datasets/raw/__init__.py | 7 +- .../datasets/raw/language_modeling.py | 184 +++++++++++++++++ 5 files changed, 299 insertions(+), 88 deletions(-) create mode 100644 torchtext/experimental/datasets/raw/language_modeling.py diff --git a/test/data/test_builtin_datasets.py b/test/data/test_builtin_datasets.py index 08c75292c4..45e0d096bb 100644 --- a/test/data/test_builtin_datasets.py +++ b/test/data/test_builtin_datasets.py @@ -78,7 +78,7 @@ def test_penntreebank_legacy(self): def test_penntreebank(self): from torchtext.experimental.datasets import PennTreebank - # smoke test to ensure wikitext2 works properly + # smoke test to ensure penn treebank works properly train_dataset, test_dataset, valid_dataset = PennTreebank() self.assertEqual(len(train_dataset), 924412) self.assertEqual(len(test_dataset), 82114) diff --git a/torchtext/experimental/datasets/__init__.py b/torchtext/experimental/datasets/__init__.py index ac2faa423b..d91f60ff55 100644 --- a/torchtext/experimental/datasets/__init__.py +++ b/torchtext/experimental/datasets/__init__.py @@ -1,4 +1,4 @@ -from .language_modeling import LanguageModelingDataset, WikiText2, WikiText103, PennTreebank # NOQA +from .language_modeling import LanguageModelingDataset, WikiText2, WikiText103, PennTreebank, WMTNewsCrawl # NOQA from .text_classification import AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, \ YelpReviewFull, YahooAnswers, \ AmazonReviewPolarity, AmazonReviewFull, IMDB @@ -7,6 +7,7 @@ 'WikiText2', 'WikiText103', 'PennTreebank', + 'WMTNewsCrawl', 'IMDB', 'AG_NEWS', 'SogouNews', diff --git a/torchtext/experimental/datasets/language_modeling.py b/torchtext/experimental/datasets/language_modeling.py index 6c0ec5799e..f44de4af88 100644 --- a/torchtext/experimental/datasets/language_modeling.py +++ b/torchtext/experimental/datasets/language_modeling.py @@ -1,22 +1,15 @@ import torch -import logging -import io -from torchtext.utils import download_from_url, extract_archive -from torchtext.vocab import build_vocab_from_iterator from torchtext.data.utils import get_tokenizer -from torchtext.vocab import Vocab -from torchtext.data.functional import numericalize_tokens_from_iterator - -URLS = { - 'WikiText2': - 'https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip', - 'WikiText103': - 'https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip', - 'PennTreebank': - ['https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt', - 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt', - 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt'] -} +from torchtext.vocab import build_vocab_from_iterator +from torchtext.experimental.datasets.raw import language_modeling as raw +from torchtext.experimental.functional import vocab_func, totensor, sequential_transforms + + +def build_vocab(data, transforms): + tok_list = [] + for txt in data: + tok_list.append(transforms(txt)) + return build_vocab_from_iterator(tok_list) class LanguageModelingDataset(torch.utils.data.Dataset): @@ -26,10 +19,11 @@ class LanguageModelingDataset(torch.utils.data.Dataset): - WikiText2 - WikiText103 - PennTreebank + - WMTNewsCrawl """ - def __init__(self, data, vocab): + def __init__(self, data, vocab, transforms, single_line): """Initiate language modeling dataset. Arguments: @@ -37,22 +31,24 @@ def __init__(self, data, vocab): numericalizing the string tokens. torch.tensor([token_id_1, token_id_2, token_id_3, token_id1]).long() vocab: Vocabulary object used for dataset. - - Examples: - >>> from torchtext.vocab import build_vocab_from_iterator - >>> data = torch.tensor([token_id_1, token_id_2, - token_id_3, token_id_1]).long() - >>> vocab = build_vocab_from_iterator([['language', 'modeling']]) - >>> dataset = LanguageModelingDataset(data, vocab) + transforms: Text string transforms. """ super(LanguageModelingDataset, self).__init__() - self.data = data self.vocab = vocab + self.transforms = transforms + self.single_line = single_line + if single_line: + self.data = torch.cat(tuple(transforms(row) for row in data), axis=0) + else: + self.data = data def __getitem__(self, i): - return self.data[i] + if self.single_line: + return self.data[i] + else: + return self.transforms(self.data[i]) def __len__(self): return len(self.data) @@ -65,63 +61,45 @@ def get_vocab(self): return self.vocab -def _get_datafile_path(key, extracted_files): - for fname in extracted_files: - if key in fname: - return fname - - -def _setup_datasets(dataset_name, tokenizer=get_tokenizer("basic_english"), - root='.data', vocab=None, removed_tokens=[], - data_select=('train', 'test', 'valid')): +def _setup_datasets(dataset_name, tokenizer=None, root='.data', vocab=None, + data_select=('train', 'test', 'valid'), single_line=True): + if tokenizer is None: + tokenizer = get_tokenizer('basic_english') + text_transform = sequential_transforms(tokenizer) if isinstance(data_select, str): data_select = [data_select] - if not set(data_select).issubset(set(('train', 'test', 'valid'))): - raise TypeError('data_select is not supported!') - - if dataset_name == 'PennTreebank': - extracted_files = [] - select_to_index = {'train': 0, 'test': 1, 'valid': 2} - extracted_files = [download_from_url(URLS['PennTreebank'][select_to_index[key]], - root=root) for key in data_select] + if not set(data_select).issubset(set(('train', 'valid', 'test'))): + raise TypeError('Given data selection {} is not supported!'.format(data_select)) + + if not single_line and dataset_name != 'WikiText103': + raise TypeError('single_line must be True except for WikiText103') + if dataset_name == 'WMTNewsCrawl': + train, = raw.DATASETS[dataset_name](root=root, data_select=('train',)) + if single_line: + raw_data = {'train': [" ".join([txt for txt in train]), ]} + else: + raw_data = {'train': [txt for txt in train]} else: - dataset_tar = download_from_url(URLS[dataset_name], root=root) - extracted_files = extract_archive(dataset_tar) - - _path = {} - for item in data_select: - _path[item] = _get_datafile_path(item, extracted_files) + train, test, valid = raw.DATASETS[dataset_name](root=root, data_select=('train', 'test', 'valid')) + # Cache raw text iterable dataset + if single_line: + raw_data = {'train': [" ".join([txt for txt in train]), ], + 'valid': [" ".join(txt for txt in valid), ], + 'test': [" ".join(txt for txt in test), ]} + else: + raw_data = {'train': [txt for txt in train], + 'valid': [txt for txt in valid], + 'test': [txt for txt in test]} if vocab is None: - if 'train' not in _path.keys(): + if 'train' not in data_select: raise TypeError("Must pass a vocab if train is not selected.") - logging.info('Building Vocab based on {}'.format(_path['train'])) - txt_iter = iter(tokenizer(row) for row in io.open(_path['train'], - encoding="utf8")) - vocab = build_vocab_from_iterator(txt_iter) - logging.info('Vocab has {} entries'.format(len(vocab))) - else: - if not isinstance(vocab, Vocab): - raise TypeError("Passed vocabulary is not of type Vocab") - - data = {} - for item in _path.keys(): - data[item] = [] - logging.info('Creating {} data'.format(item)) - txt_iter = iter(tokenizer(row) for row in io.open(_path[item], - encoding="utf8")) - _iter = numericalize_tokens_from_iterator( - vocab, txt_iter, removed_tokens) - for tokens in _iter: - data[item] += [token_id for token_id in tokens] - - for key in data_select: - if data[key] == []: - raise TypeError('Dataset {} is empty!'.format(key)) - - return tuple(LanguageModelingDataset(torch.tensor(data[d]).long(), vocab) - for d in data_select) + vocab = build_vocab(raw_data['train'], text_transform) + text_transform = sequential_transforms(text_transform, vocab_func(vocab), + totensor(dtype=torch.long)) + return tuple(LanguageModelingDataset(raw_data[item], vocab, text_transform, single_line) + for item in data_select) def WikiText2(*args, **kwargs): @@ -138,7 +116,6 @@ def WikiText2(*args, **kwargs): root: Directory where the datasets are saved. Default: ".data" vocab: Vocabulary used for dataset. If None, it will generate a new vocabulary based on the train data set. - removed_tokens: removed tokens from output dataset (Default: []) data_select: a string or tupel for the returned datasets (Default: ('train', 'test','valid')) By default, all the three datasets (train, test, valid) are generated. Users @@ -146,6 +123,10 @@ def WikiText2(*args, **kwargs): just a string 'train'. If 'train' is not in the tuple or string, a vocab object should be provided which will be used to process valid and/or test data. + single_line: whether to return all tokens in a single line. + (Default: True) + By default, all lines in raw text file are concatenated into a single line. + Use `single_line = False` if one wants to get data line by line. Examples: >>> from torchtext.experimental.datasets import WikiText2 @@ -175,12 +156,6 @@ def WikiText103(*args, **kwargs): root: Directory where the datasets are saved. Default: ".data" vocab: Vocabulary used for dataset. If None, it will generate a new vocabulary based on the train data set. - data_select: the returned datasets (Default: ('train', 'test','valid')) - By default, all the three datasets (train, test, valid) are generated. Users - could also choose any one or two of them, for example ('train', 'test'). - If 'train' is not in the tuple, an vocab object should be provided which will - be used to process valid and/or test data. - removed_tokens: removed tokens from output dataset (Default: []) data_select: a string or tupel for the returned datasets (Default: ('train', 'test','valid')) By default, all the three datasets (train, test, valid) are generated. Users @@ -188,6 +163,10 @@ def WikiText103(*args, **kwargs): just a string 'train'. If 'train' is not in the tuple or string, a vocab object should be provided which will be used to process valid and/or test data. + single_line: whether to return all tokens in a single line. + (Default: True) + By default, all lines in raw text file are concatenated into a single line. + Use `single_line = False` if one wants to get data line by line. Examples: >>> from torchtext.experimental.datasets import WikiText103 @@ -217,7 +196,6 @@ def PennTreebank(*args, **kwargs): root: Directory where the datasets are saved. Default: ".data" vocab: Vocabulary used for dataset. If None, it will generate a new vocabulary based on the train data set. - removed_tokens: removed tokens from output dataset (Default: []) data_select: a string or tupel for the returned datasets (Default: ('train', 'test','valid')) By default, all the three datasets (train, test, valid) are generated. Users @@ -225,6 +203,10 @@ def PennTreebank(*args, **kwargs): just a string 'train'. If 'train' is not in the tuple or string, a vocab object should be provided which will be used to process valid and/or test data. + single_line: whether to return all tokens in a single line. + (Default: True) + By default, all lines in raw text file are concatenated into a single line. + Use `single_line = False` if one wants to get data line by line. Examples: >>> from torchtext.experimental.datasets import PennTreebank @@ -238,3 +220,42 @@ def PennTreebank(*args, **kwargs): """ return _setup_datasets(*(("PennTreebank",) + args), **kwargs) + + +def WMTNewsCrawl(*args, **kwargs): + """ Defines WMTNewsCrawl datasets. + + Create language modeling dataset: WMTNewsCrawl + returns the train set + + Arguments: + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well (see example below). A custom tokenizer is callable + function with input of a string and output of a token list. + root: Directory where the datasets are saved. Default: ".data" + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + data_select: a string or tupel for the returned datasets + (Default: ('train',)) + single_line: whether to return all tokens in a single line. + (Default: True) + By default, all lines in raw text file are concatenated into a single line. + Use `single_line = False` if one wants to get data line by line. + Examples: + >>> from torchtext.experimental.datasets import WMTNewsCrawl + >>> from torchtext.data.utils import get_tokenizer + >>> tokenizer = get_tokenizer("spacy") + >>> train_dataset, = WMTNewsCrawl(tokenizer=tokenizer, data_select='train') + + """ + + return _setup_datasets(*(("WMTNewsCrawl",) + args), **kwargs) + + +DATASETS = { + 'WikiText2': WikiText2, + 'WikiText103': WikiText103, + 'PennTreebank': PennTreebank, + 'WMTNewsCrawl': WMTNewsCrawl +} diff --git a/torchtext/experimental/datasets/raw/__init__.py b/torchtext/experimental/datasets/raw/__init__.py index 61accbe2a1..9c988ec05c 100644 --- a/torchtext/experimental/datasets/raw/__init__.py +++ b/torchtext/experimental/datasets/raw/__init__.py @@ -1,6 +1,7 @@ from .text_classification import AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, \ YelpReviewFull, YahooAnswers, \ AmazonReviewPolarity, AmazonReviewFull, IMDB +from .language_modeling import WikiText2, WikiText103, PennTreebank, WMTNewsCrawl __all__ = ['IMDB', 'AG_NEWS', @@ -10,4 +11,8 @@ 'YelpReviewFull', 'YahooAnswers', 'AmazonReviewPolarity', - 'AmazonReviewFull'] + 'AmazonReviewFull', + 'WikiText2', + 'WikiText103', + 'PennTreebank', + 'WMTNewsCrawl'] diff --git a/torchtext/experimental/datasets/raw/language_modeling.py b/torchtext/experimental/datasets/raw/language_modeling.py new file mode 100644 index 0000000000..a867108978 --- /dev/null +++ b/torchtext/experimental/datasets/raw/language_modeling.py @@ -0,0 +1,184 @@ +import torch +import logging +import io +from torchtext.utils import download_from_url, extract_archive + +URLS = { + 'WikiText2': + 'https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip', + 'WikiText103': + 'https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip', + 'PennTreebank': + ['https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt', + 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt', + 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt'], + 'WMTNewsCrawl': 'http://www.statmt.org/wmt11/training-monolingual-news-2010.tgz' +} + + +class RawTextIterableDataset(torch.utils.data.IterableDataset): + """Defines an abstraction for raw text iterable datasets. + """ + + def __init__(self, iterator, start=0, num_lines=None): + """Initiate language modeling dataset. + """ + super(RawTextIterableDataset, self).__init__() + self._iterator = iterator + self.has_setup = False + self.start = start + self.num_lines = num_lines + + def setup_iter(self, start=0, num_lines=None): + self.start = start + self.num_lines = num_lines + self.has_setup = True + + def __iter__(self): + if not self.has_setup: + self.setup_iter() + for i, item in enumerate(self._iterator): + if i >= self.start: + yield item + if (self.num_lines is not None) and (i == (self.start + self.num_lines)): + break + + def get_iterator(self): + return self._iterator + + +def _setup_datasets(dataset_name, root='.data', data_select=('train', 'test', 'valid'), **kwargs): + if isinstance(data_select, str): + data_select = [data_select] + if not set(data_select).issubset(set(('train', 'test', 'valid'))): + raise TypeError('data_select is not supported!') + + if dataset_name == 'PennTreebank': + extracted_files = [] + select_to_index = {'train': 0, 'test': 1, 'valid': 2} + extracted_files = [download_from_url(URLS['PennTreebank'][select_to_index[key]], + root=root) for key in data_select] + elif dataset_name == 'WMTNewsCrawl': + if not (data_select == ['train'] or set(data_select).issubset(set(('train',)))): + raise ValueError("WMTNewsCrawl only creates a training dataset. " + "data_select should be 'train' " + "or ('train',), got {}.".format(data_select)) + dataset_tar = download_from_url(URLS[dataset_name], root=root) + extracted_files = extract_archive(dataset_tar) + year = kwargs.get('year', 2010) + language = kwargs.get('language', 'en') + file_name = 'news.{}.{}.shuffled'.format(year, language) + extracted_files = [f for f in extracted_files if file_name in f] + else: + dataset_tar = download_from_url(URLS[dataset_name], root=root) + extracted_files = extract_archive(dataset_tar) + + _path = {} + for item in data_select: + for fname in extracted_files: + if item in fname: + _path[item] = fname + + data = {} + for item in _path.keys(): + logging.info('Creating {} data'.format(item)) + data[item] = iter(io.open(_path[item], encoding="utf8")) + + return tuple(RawTextIterableDataset(data[item]) for item in data_select) + + +def WikiText2(*args, **kwargs): + """ Defines WikiText2 datasets. + + Create language modeling dataset: WikiText2 + Separately returns the train/test/valid set + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + data_select: a string or tupel for the returned datasets + (Default: ('train', 'test','valid')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.raw.datasets import WikiText2 + >>> train_dataset, test_dataset, valid_dataset = WikiText2() + >>> valid_dataset, = WikiText2(data_select='valid') + + """ + + return _setup_datasets(*(("WikiText2",) + args), **kwargs) + + +def WikiText103(*args, **kwargs): + """ Defines WikiText103 datasets. + + Create language modeling dataset: WikiText103 + Separately returns the train/test/valid set + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + data_select: the returned datasets (Default: ('train', 'test','valid')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test'). + If 'train' is not in the tuple, an vocab object should be provided which will + be used to process valid and/or test data. + + Examples: + >>> from torchtext.experimental.datasets.raw import WikiText103 + >>> train_dataset, test_dataset, valid_dataset = WikiText103() + >>> valid_dataset, = WikiText103(data_select='valid') + """ + + return _setup_datasets(*(("WikiText103",) + args), **kwargs) + + +def PennTreebank(*args, **kwargs): + """ Defines PennTreebank datasets. + + Create language modeling dataset: PennTreebank + Separately returns the train/test/valid set + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + data_select: a string or tuple for the returned datasets + (Default: ('train', 'test','valid')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets.raw import PennTreebank + >>> train_dataset, test_dataset, valid_dataset = PennTreebank() + >>> valid_dataset, = PennTreebank(data_select='valid') + + """ + + return _setup_datasets(*(("PennTreebank",) + args), **kwargs) + + +def WMTNewsCrawl(*args, **kwargs): + """ Defines WMT News Crawl. + + Create language modeling dataset: WMTNewsCrawl + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + data_select: a string or tuple for the returned datasets. + (Default: 'train') + """ + + return _setup_datasets(*(("WMTNewsCrawl",) + args), **kwargs) + + +DATASETS = { + 'WikiText2': WikiText2, + 'WikiText103': WikiText103, + 'PennTreebank': PennTreebank, + 'WMTNewsCrawl': WMTNewsCrawl +} From 14b2c072b2530c95d70aec0847c6de3a248fc776 Mon Sep 17 00:00:00 2001 From: Aditya Kurniawan Date: Fri, 5 Jun 2020 08:46:04 +0700 Subject: [PATCH 065/719] Experimental translation datasets (#751) --- .../unittest/linux/scripts/environment.yml | 2 + .../unittest/windows/scripts/environment.yml | 2 + docs/source/experimental_datasets.rst | 28 + test/data/test_builtin_datasets.py | 42 +- test/test_utils.py | 28 + .../experimental/datasets/raw/__init__.py | 4 + .../experimental/datasets/raw/translation.py | 544 +++++++++++++++++ .../experimental/datasets/translation.py | 551 ++++++++++++++++++ torchtext/utils.py | 18 +- 9 files changed, 1214 insertions(+), 5 deletions(-) create mode 100644 torchtext/experimental/datasets/raw/translation.py create mode 100644 torchtext/experimental/datasets/translation.py diff --git a/.circleci/unittest/linux/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml index 8955f892f6..7a0592dd73 100644 --- a/.circleci/unittest/linux/scripts/environment.yml +++ b/.circleci/unittest/linux/scripts/environment.yml @@ -17,3 +17,5 @@ dependencies: - sphinx - sphinx-rtd-theme - tqdm + - https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.2.5/de_core_news_sm-2.2.5.tar.gz#egg=de_core_news_sm==2.2.5 + - https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 diff --git a/.circleci/unittest/windows/scripts/environment.yml b/.circleci/unittest/windows/scripts/environment.yml index 7e68b8da9b..558ef99049 100644 --- a/.circleci/unittest/windows/scripts/environment.yml +++ b/.circleci/unittest/windows/scripts/environment.yml @@ -18,3 +18,5 @@ dependencies: - tqdm - sentencepiece - future + - https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.2.5/de_core_news_sm-2.2.5.tar.gz#egg=de_core_news_sm==2.2.5 + - https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 diff --git a/docs/source/experimental_datasets.rst b/docs/source/experimental_datasets.rst index d9d59539d0..a16d5dabba 100644 --- a/docs/source/experimental_datasets.rst +++ b/docs/source/experimental_datasets.rst @@ -139,3 +139,31 @@ PennTreebank .. autoclass:: PennTreebank :members: __init__ + + +Machine Translation +^^^^^^^^^^^^^^^^^^^ + +Language modeling datasets are subclasses of ``TranslationDataset`` class. + +.. autoclass:: TranslationDataset + :members: __init__ + +Multi30k +~~~~~~~~ + +.. autoclass:: Multi30k + :members: __init__ + +IWSLT +~~~~~ + +.. autoclass:: IWSLT + :members: __init__ + +WMT14 +~~~~~ + +.. autoclass:: WMT14 + :members: __init__ + diff --git a/test/data/test_builtin_datasets.py b/test/data/test_builtin_datasets.py index 45e0d096bb..2bb4065df9 100644 --- a/test/data/test_builtin_datasets.py +++ b/test/data/test_builtin_datasets.py @@ -1,6 +1,7 @@ #!/user/bin/env python3 # Note that all the tests in this module require dataset (either network access or cached) import os +import glob import shutil import torchtext.data as data from torchtext.datasets import AG_NEWS @@ -10,10 +11,11 @@ def conditional_remove(f): - if os.path.isfile(f): - os.remove(f) - elif os.path.isdir(f): - shutil.rmtree(f) + for path in glob.glob(f): + if os.path.isfile(path): + os.remove(path) + elif os.path.isdir(path): + shutil.rmtree(path) class TestDataset(TorchtextTestCase): @@ -122,3 +124,35 @@ def test_imdb(self): old_vocab = train_dataset.get_vocab() new_vocab = Vocab(counter=old_vocab.freqs, max_size=2500) new_train_data, new_test_data = IMDB(vocab=new_vocab) + + def test_multi30k(self): + from torchtext.experimental.datasets.translation import Multi30k + # smoke test to ensure multi30k works properly + train_dataset, valid_dataset, test_dataset = Multi30k() + self.assertEqual(len(train_dataset), 29000) + self.assertEqual(len(valid_dataset), 1000) + self.assertEqual(len(test_dataset), 1014) + + de_vocab, en_vocab = train_dataset.get_vocab() + de_tokens_ids = [ + de_vocab[token] for token in + 'Zwei Männer verpacken Donuts in Kunststofffolie'.split() + ] + self.assertEqual(de_tokens_ids, [19, 29, 18703, 4448, 5, 6240]) + + en_tokens_ids = [ + en_vocab[token] for token in + 'Two young White males are outside near many bushes'.split() + ] + self.assertEqual(en_tokens_ids, + [17, 23, 1167, 806, 15, 55, 82, 334, 1337]) + + datafile = os.path.join(self.project_root, ".data", "train*") + conditional_remove(datafile) + datafile = os.path.join(self.project_root, ".data", "val*") + conditional_remove(datafile) + datafile = os.path.join(self.project_root, ".data", "test*") + conditional_remove(datafile) + datafile = os.path.join(self.project_root, ".data", + "multi30k_task*.tar.gz") + conditional_remove(datafile) diff --git a/test/test_utils.py b/test/test_utils.py index 5489f838b2..73ee992de3 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -42,6 +42,34 @@ def test_download_extract_tar(self): conditional_remove(f) conditional_remove(archive_path) + def test_download_extract_gz(self): + # create root directory for downloading data + root = '.data' + if not os.path.exists(root): + os.makedirs(root) + + # ensure archive is not already downloaded, if it is then delete + url = 'https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.5.en.gz' + target_archive_path = os.path.join(root, 'val.5.en.gz') + conditional_remove(target_archive_path) + + # download archive and ensure is in correct location + archive_path = utils.download_from_url(url) + assert target_archive_path == archive_path + + # extract files and ensure they are correct + files = utils.extract_archive(archive_path) + assert files == [os.path.join(root, 'val.5.en')] + + # extract files with overwrite option True + files = utils.extract_archive(archive_path, overwrite=True) + assert files == [os.path.join(root, 'val.5.en')] + + # remove files and archive + for f in files: + conditional_remove(f) + conditional_remove(archive_path) + def test_download_extract_zip(self): # create root directory for downloading data root = '.data' diff --git a/torchtext/experimental/datasets/raw/__init__.py b/torchtext/experimental/datasets/raw/__init__.py index 9c988ec05c..26892050d6 100644 --- a/torchtext/experimental/datasets/raw/__init__.py +++ b/torchtext/experimental/datasets/raw/__init__.py @@ -1,6 +1,7 @@ from .text_classification import AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, \ YelpReviewFull, YahooAnswers, \ AmazonReviewPolarity, AmazonReviewFull, IMDB +from .translation import Multi30k, IWSLT, WMT14 from .language_modeling import WikiText2, WikiText103, PennTreebank, WMTNewsCrawl __all__ = ['IMDB', @@ -12,6 +13,9 @@ 'YahooAnswers', 'AmazonReviewPolarity', 'AmazonReviewFull', + 'Multi30k', + 'IWSLT', + 'WMT14', 'WikiText2', 'WikiText103', 'PennTreebank', diff --git a/torchtext/experimental/datasets/raw/translation.py b/torchtext/experimental/datasets/raw/translation.py new file mode 100644 index 0000000000..19f5275d41 --- /dev/null +++ b/torchtext/experimental/datasets/raw/translation.py @@ -0,0 +1,544 @@ +import torch +import os +import io +import codecs +import xml.etree.ElementTree as ET +from collections import defaultdict + +from torchtext.utils import (download_from_url, extract_archive, + unicode_csv_reader) + +URLS = { + 'Multi30k': [ + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2016_flickr.cs.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2016_flickr.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2016_flickr.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2016_flickr.fr.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_flickr.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_flickr.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_flickr.fr.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_mscoco.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_mscoco.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2017_mscoco.fr.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/test_2018_flickr.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/train.cs.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/train.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/train.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/train.fr.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/val.cs.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/val.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/val.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/val.fr.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.1.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.1.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/test_2016.2.de.gz", + 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"https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.2.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.3.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.3.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.4.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.4.en.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.5.de.gz", + "https://raw.githubusercontent.com/multi30k/dataset/master/data/task2/raw/val.5.en.gz" + ], + 'WMT14': + 'https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8', + 'IWSLT': + 'https://wit3.fbk.eu/archive/2016-01//texts/{}/{}/{}.tgz' +} + + +def _read_text_iterator(path): + with io.open(path, encoding="utf8") as f: + reader = unicode_csv_reader(f) + for row in reader: + yield " ".join(row) + + +def _clean_xml_file(f_xml): + f_txt = os.path.splitext(f_xml)[0] + with codecs.open(f_txt, mode='w', encoding='utf-8') as fd_txt: + root = ET.parse(f_xml).getroot()[0] + for doc in root.findall('doc'): + for e in doc.findall('seg'): + fd_txt.write(e.text.strip() + '\n') + + +def _clean_tags_file(f_orig): + xml_tags = [ + '= self.start: + yield item + if (self.num_lines is not None) and (i == (self.start + + self.num_lines)): + break + + def get_iterator(self): + return (self._src_iterator, self._tgt_iterator) + + +def Multi30k(train_filenames=("train.de", "train.en"), + valid_filenames=("val.de", "val.en"), + test_filenames=("test_2016_flickr.de", "test_2016_flickr.en"), + root='.data'): + """ Define translation datasets: Multi30k + Separately returns train/valid/test datasets as a tuple + The available dataset include: + test_2016_flickr.cs + test_2016_flickr.de + test_2016_flickr.en + test_2016_flickr.fr + test_2017_flickr.de + test_2017_flickr.en + test_2017_flickr.fr + test_2017_mscoco.de + test_2017_mscoco.en + test_2017_mscoco.fr + test_2018_flickr.en + train.cs + train.de + train.en + train.fr + val.cs + val.de + val.en + val.fr + test_2016.1.de + test_2016.1.en + test_2016.2.de + test_2016.2.en + test_2016.3.de + test_2016.3.en + test_2016.4.de + test_2016.4.en + test_2016.5.de + test_2016.5.en + train.1.de + train.1.en + train.2.de + train.2.en + train.3.de + train.3.en + train.4.de + train.4.en + train.5.de + train.5.en + val.1.de + val.1.en + val.2.de + val.2.en + val.3.de + val.3.en + val.4.de + val.4.en + val.5.de + val.5.en + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.de', 'train.en') + valid_filenames: the source and target filenames for valid. + Default: ('val.de', 'val.en') + test_filenames: the source and target filenames for test. + Default: ('test2016.de', 'test2016.en') + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> from torchtext.datasets import Multi30k + >>> train_dataset, valid_dataset, test_dataset = Multi30k() + """ + return _setup_datasets("Multi30k", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + root=root) + + +def IWSLT(train_filenames=('train.de-en.de', 'train.de-en.en'), + valid_filenames=('IWSLT16.TED.tst2013.de-en.de', + 'IWSLT16.TED.tst2013.de-en.en'), + test_filenames=('IWSLT16.TED.tst2014.de-en.de', + 'IWSLT16.TED.tst2014.de-en.en'), + root='.data'): + """ Define translation datasets: IWSLT + Separately returns train/valid/test datasets + The available datasets include: + IWSLT16.TED.dev2010.ar-en.ar + IWSLT16.TED.dev2010.ar-en.en + IWSLT16.TED.dev2010.cs-en.cs + IWSLT16.TED.dev2010.cs-en.en + IWSLT16.TED.dev2010.de-en.de + IWSLT16.TED.dev2010.de-en.en + IWSLT16.TED.dev2010.en-ar.ar + IWSLT16.TED.dev2010.en-ar.en + IWSLT16.TED.dev2010.en-cs.cs + IWSLT16.TED.dev2010.en-cs.en + IWSLT16.TED.dev2010.en-de.de + IWSLT16.TED.dev2010.en-de.en + IWSLT16.TED.dev2010.en-fr.en + IWSLT16.TED.dev2010.en-fr.fr + IWSLT16.TED.dev2010.fr-en.en + IWSLT16.TED.dev2010.fr-en.fr + IWSLT16.TED.tst2010.ar-en.ar + IWSLT16.TED.tst2010.ar-en.en + IWSLT16.TED.tst2010.cs-en.cs + IWSLT16.TED.tst2010.cs-en.en + IWSLT16.TED.tst2010.de-en.de + IWSLT16.TED.tst2010.de-en.en + IWSLT16.TED.tst2010.en-ar.ar + IWSLT16.TED.tst2010.en-ar.en + IWSLT16.TED.tst2010.en-cs.cs + IWSLT16.TED.tst2010.en-cs.en + IWSLT16.TED.tst2010.en-de.de + IWSLT16.TED.tst2010.en-de.en + IWSLT16.TED.tst2010.en-fr.en + IWSLT16.TED.tst2010.en-fr.fr + IWSLT16.TED.tst2010.fr-en.en + IWSLT16.TED.tst2010.fr-en.fr + IWSLT16.TED.tst2011.ar-en.ar + IWSLT16.TED.tst2011.ar-en.en + IWSLT16.TED.tst2011.cs-en.cs + IWSLT16.TED.tst2011.cs-en.en + IWSLT16.TED.tst2011.de-en.de + IWSLT16.TED.tst2011.de-en.en + IWSLT16.TED.tst2011.en-ar.ar + IWSLT16.TED.tst2011.en-ar.en + IWSLT16.TED.tst2011.en-cs.cs + IWSLT16.TED.tst2011.en-cs.en + IWSLT16.TED.tst2011.en-de.de + IWSLT16.TED.tst2011.en-de.en + IWSLT16.TED.tst2011.en-fr.en + IWSLT16.TED.tst2011.en-fr.fr + IWSLT16.TED.tst2011.fr-en.en + IWSLT16.TED.tst2011.fr-en.fr + IWSLT16.TED.tst2012.ar-en.ar + IWSLT16.TED.tst2012.ar-en.en + IWSLT16.TED.tst2012.cs-en.cs + IWSLT16.TED.tst2012.cs-en.en + IWSLT16.TED.tst2012.de-en.de + IWSLT16.TED.tst2012.de-en.en + IWSLT16.TED.tst2012.en-ar.ar + IWSLT16.TED.tst2012.en-ar.en + IWSLT16.TED.tst2012.en-cs.cs + IWSLT16.TED.tst2012.en-cs.en + IWSLT16.TED.tst2012.en-de.de + IWSLT16.TED.tst2012.en-de.en + IWSLT16.TED.tst2012.en-fr.en + IWSLT16.TED.tst2012.en-fr.fr + IWSLT16.TED.tst2012.fr-en.en + IWSLT16.TED.tst2012.fr-en.fr + IWSLT16.TED.tst2013.ar-en.ar + IWSLT16.TED.tst2013.ar-en.en + IWSLT16.TED.tst2013.cs-en.cs + IWSLT16.TED.tst2013.cs-en.en + IWSLT16.TED.tst2013.de-en.de + IWSLT16.TED.tst2013.de-en.en + IWSLT16.TED.tst2013.en-ar.ar + IWSLT16.TED.tst2013.en-ar.en + IWSLT16.TED.tst2013.en-cs.cs + IWSLT16.TED.tst2013.en-cs.en + IWSLT16.TED.tst2013.en-de.de + IWSLT16.TED.tst2013.en-de.en + IWSLT16.TED.tst2013.en-fr.en + IWSLT16.TED.tst2013.en-fr.fr + IWSLT16.TED.tst2013.fr-en.en + IWSLT16.TED.tst2013.fr-en.fr + IWSLT16.TED.tst2014.ar-en.ar + IWSLT16.TED.tst2014.ar-en.en + IWSLT16.TED.tst2014.de-en.de + IWSLT16.TED.tst2014.de-en.en + IWSLT16.TED.tst2014.en-ar.ar + IWSLT16.TED.tst2014.en-ar.en + IWSLT16.TED.tst2014.en-de.de + IWSLT16.TED.tst2014.en-de.en + IWSLT16.TED.tst2014.en-fr.en + IWSLT16.TED.tst2014.en-fr.fr + IWSLT16.TED.tst2014.fr-en.en + IWSLT16.TED.tst2014.fr-en.fr + IWSLT16.TEDX.dev2012.de-en.de + IWSLT16.TEDX.dev2012.de-en.en + IWSLT16.TEDX.tst2013.de-en.de + IWSLT16.TEDX.tst2013.de-en.en + IWSLT16.TEDX.tst2014.de-en.de + IWSLT16.TEDX.tst2014.de-en.en + train.ar + train.ar-en.ar + train.ar-en.en + train.cs + train.cs-en.cs + train.cs-en.en + train.de + train.de-en.de + train.de-en.en + train.en + train.en-ar.ar + train.en-ar.en + train.en-cs.cs + train.en-cs.en + train.en-de.de + train.en-de.en + train.en-fr.en + train.en-fr.fr + train.fr + train.fr-en.en + train.fr-en.fr + train.tags.ar-en.ar + train.tags.ar-en.en + train.tags.cs-en.cs + train.tags.cs-en.en + train.tags.de-en.de + train.tags.de-en.en + train.tags.en-ar.ar + train.tags.en-ar.en + train.tags.en-cs.cs + train.tags.en-cs.en + train.tags.en-de.de + train.tags.en-de.en + train.tags.en-fr.en + train.tags.en-fr.fr + train.tags.fr-en.en + train.tags.fr-en.fr + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.de-en.de', 'train.de-en.en') + valid_filenames: the source and target filenames for valid. + Default: ('IWSLT16.TED.tst2013.de-en.de', 'IWSLT16.TED.tst2013.de-en.en') + test_filenames: the source and target filenames for test. + Default: ('IWSLT16.TED.tst2014.de-en.de', 'IWSLT16.TED.tst2014.de-en.en') + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> from torchtext.datasets.raw import IWSLT + >>> train_dataset, valid_dataset, test_dataset = IWSLT() + """ + src_language = train_filenames[0].split(".")[-1] + tgt_language = train_filenames[1].split(".")[-1] + languages = "-".join([src_language, tgt_language]) + URLS["IWSLT"] = URLS["IWSLT"].format(src_language, tgt_language, languages) + + return _setup_datasets( + "IWSLT", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + root=root, + ) + + +def WMT14(train_filenames=('train.tok.clean.bpe.32000.de', + 'train.tok.clean.bpe.32000.en'), + valid_filenames=('newstest2013.tok.bpe.32000.de', + 'newstest2013.tok.bpe.32000.en'), + test_filenames=('newstest2014.tok.bpe.32000.de', + 'newstest2014.tok.bpe.32000.en'), + root='.data'): + """ Define translation datasets: WMT14 + Separately returns train/valid/test datasets + The available datasets include: + newstest2016.en + newstest2016.de + newstest2015.en + newstest2015.de + newstest2014.en + newstest2014.de + newstest2013.en + newstest2013.de + newstest2012.en + newstest2012.de + newstest2011.tok.de + newstest2011.en + newstest2011.de + newstest2010.tok.de + newstest2010.en + newstest2010.de + newstest2009.tok.de + newstest2009.en + newstest2009.de + newstest2016.tok.de + newstest2015.tok.de + newstest2014.tok.de + newstest2013.tok.de + newstest2012.tok.de + newstest2010.tok.en + newstest2009.tok.en + newstest2015.tok.en + newstest2014.tok.en + newstest2013.tok.en + newstest2012.tok.en + newstest2011.tok.en + newstest2016.tok.en + newstest2009.tok.bpe.32000.en + newstest2011.tok.bpe.32000.en + newstest2010.tok.bpe.32000.en + newstest2013.tok.bpe.32000.en + newstest2012.tok.bpe.32000.en + newstest2015.tok.bpe.32000.en + newstest2014.tok.bpe.32000.en + newstest2016.tok.bpe.32000.en + train.tok.clean.bpe.32000.en + newstest2009.tok.bpe.32000.de + newstest2010.tok.bpe.32000.de + newstest2011.tok.bpe.32000.de + newstest2013.tok.bpe.32000.de + newstest2012.tok.bpe.32000.de + newstest2014.tok.bpe.32000.de + newstest2016.tok.bpe.32000.de + newstest2015.tok.bpe.32000.de + train.tok.clean.bpe.32000.de + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.tok.clean.bpe.32000.de', 'train.tok.clean.bpe.32000.en') + valid_filenames: the source and target filenames for valid. + Default: ('newstest2013.tok.bpe.32000.de', 'newstest2013.tok.bpe.32000.en') + test_filenames: the source and target filenames for test. + Default: ('newstest2014.tok.bpe.32000.de', 'newstest2014.tok.bpe.32000.en') + root: Directory where the datasets are saved. Default: ".data" + + Examples: + >>> from torchtext.datasets import WMT14 + >>> train_dataset, valid_dataset, test_dataset = WMT14() + """ + + return _setup_datasets("WMT14", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + root=root) + + +DATASETS = {'Multi30k': Multi30k, 'IWSLT': IWSLT, 'WMT14': WMT14} diff --git a/torchtext/experimental/datasets/translation.py b/torchtext/experimental/datasets/translation.py new file mode 100644 index 0000000000..5fad51135d --- /dev/null +++ b/torchtext/experimental/datasets/translation.py @@ -0,0 +1,551 @@ +import torch +import logging + +from torchtext.experimental.datasets import raw +from torchtext.vocab import build_vocab_from_iterator +from torchtext.data.utils import get_tokenizer +from ..functional import vocab_func, totensor, sequential_transforms + + +def build_vocab(data, transforms, index): + tok_list = [] + for line in data: + tok_list.append(transforms(line[index])) + return build_vocab_from_iterator(tok_list) + + +def _setup_datasets(dataset_name, + train_filenames, + valid_filenames, + test_filenames, + data_select=('train', 'test', 'valid'), + root='.data', + vocab=(None, None), + tokenizer=None, + removed_tokens=['']): + src_vocab, tgt_vocab = vocab + if tokenizer is None: + src_tokenizer = get_tokenizer("spacy", language='de_core_news_sm') + tgt_tokenizer = get_tokenizer("spacy", language='en_core_web_sm') + elif isinstance(tokenizer, tuple): + if len(tokenizer) == 2: + src_tokenizer, tgt_tokenizer = tokenizer + else: + raise ValueError("tokenizer must have length of two for" + "source and target") + else: + raise ValueError( + "tokenizer must be an instance of tuple with length two" + "or None") + train, val, test = DATASETS[dataset_name](train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + root=root) + raw_data = { + "train": [line for line in train], + "valid": [line for line in val], + "test": [line for line in test] + } + src_text_vocab_transform = sequential_transforms(src_tokenizer) + tgt_text_vocab_transform = sequential_transforms(tgt_tokenizer) + + if src_vocab is None: + if 'train' not in data_select: + raise TypeError("Must pass a vocab if train is not selected.") + logging.info('Building src Vocab based on train data') + src_vocab = build_vocab(raw_data["train"], + src_text_vocab_transform, + index=0) + else: + if not isinstance(src_vocab, Vocab): + raise TypeError("Passed src vocabulary is not of type Vocab") + logging.info('src Vocab has {} entries'.format(len(src_vocab))) + + if tgt_vocab is None: + if 'train' not in data_select: + raise TypeError("Must pass a vocab if train is not selected.") + logging.info('Building tgt Vocab based on train data') + tgt_vocab = build_vocab(raw_data["train"], + tgt_text_vocab_transform, + index=1) + else: + if not isinstance(tgt_vocab, Vocab): + raise TypeError("Passed tgt vocabulary is not of type Vocab") + logging.info('tgt Vocab has {} entries'.format(len(tgt_vocab))) + + logging.info('Building datasets for {}'.format(data_select)) + datasets = [] + for key in data_select: + src_text_transform = sequential_transforms(src_text_vocab_transform, + vocab_func(src_vocab), + totensor(dtype=torch.long)) + tgt_text_transform = sequential_transforms(tgt_text_vocab_transform, + vocab_func(tgt_vocab), + totensor(dtype=torch.long)) + datasets.append( + TranslationDataset(raw_data[key], (src_vocab, tgt_vocab), + (src_text_transform, tgt_text_transform))) + + return tuple(datasets) + + +class TranslationDataset(torch.utils.data.Dataset): + """Defines a dataset for translation. + Currently, we only support the following datasets: + - Multi30k + - WMT14 + - IWSLT + """ + def __init__(self, data, vocab, transforms): + """Initiate translation dataset. + + Arguments: + data: a tuple of source and target tensors, which include token ids + numericalizing the string tokens. + [(src_tensor0, tgt_tensor0), (src_tensor1, tgt_tensor1)] + vocab: source and target Vocabulary object used for dataset. + (src_vocab, tgt_vocab) + transforms: a tuple of source and target string transforms. + + Examples: + >>> from torchtext.vocab import build_vocab_from_iterator + >>> src_data = torch.Tensor([token_id_s1, token_id_s2, + token_id_s3, token_id_s1]).long() + >>> tgt_data = torch.Tensor([token_id_t1, token_id_t2, + token_id_t3, token_id_t1]).long() + >>> src_vocab = build_vocab_from_iterator([['Übersetzungsdatensatz']]) + >>> tgt_vocab = build_vocab_from_iterator([['translation', 'dataset']]) + >>> dataset = TranslationDataset([(src_data, tgt_data)], + (src_vocab, tgt_vocab)) + """ + + super(TranslationDataset, self).__init__() + self.data = data + self.vocab = vocab + self.transforms = transforms + + def __getitem__(self, i): + source = self.transforms[0](self.data[i][0]) + target = self.transforms[1](self.data[i][1]) + return (source, target) + + def __len__(self): + return len(self.data) + + def get_vocab(self): + return self.vocab + + +def Multi30k(train_filenames=("train.de", "train.en"), + valid_filenames=("val.de", "val.en"), + test_filenames=("test_2016_flickr.de", "test_2016_flickr.en"), + tokenizer=None, + root='.data', + vocab=(None, None), + data_select=('train', 'valid', 'test'), + removed_tokens=['']): + """ Define translation datasets: Multi30k + Separately returns train/valid/test datasets as a tuple + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.de', 'train.en') + valid_filenames: the source and target filenames for valid. + Default: ('val.de', 'val.en') + test_filenames: the source and target filenames for test. + Default: ('test2016.de', 'test2016.en') + tokenizer: the tokenizer used to preprocess source and target raw text data. + It has to be in a form of tuple. + Default: (get_tokenizer("spacy", language='de_core_news_sm'), + get_tokenizer("spacy", language='en_core_web_sm')) + root: Directory where the datasets are saved. Default: ".data" + vocab: Source and target Vocabulary objects used for dataset. If None, it + will generate a new vocabulary based on the train data set. It has to be + in a form of tuple. + Default: (None, None) + data_select: a string or tuple for the returned datasets + (Default: ('train', 'valid', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + removed_tokens: removed tokens from output dataset (Default: '') + The available dataset include: + test_2016_flickr.cs + test_2016_flickr.de + test_2016_flickr.en + test_2016_flickr.fr + test_2017_flickr.de + test_2017_flickr.en + test_2017_flickr.fr + test_2017_mscoco.de + test_2017_mscoco.en + test_2017_mscoco.fr + test_2018_flickr.en + train.cs + train.de + train.en + train.fr + val.cs + val.de + val.en + val.fr + test_2016.1.de + test_2016.1.en + test_2016.2.de + test_2016.2.en + test_2016.3.de + test_2016.3.en + test_2016.4.de + test_2016.4.en + test_2016.5.de + test_2016.5.en + train.1.de + train.1.en + train.2.de + train.2.en + train.3.de + train.3.en + train.4.de + train.4.en + train.5.de + train.5.en + val.1.de + val.1.en + val.2.de + val.2.en + val.3.de + val.3.en + val.4.de + val.4.en + val.5.de + val.5.en + + Examples: + >>> from torchtext.datasets import Multi30k + >>> from torchtext.data.utils import get_tokenizer + >>> tokenizer = (get_tokenizer("spacy", language='de'), + get_tokenizer("basic_english")) + >>> train_dataset, valid_dataset, test_dataset = Multi30k(tokenizer=tokenizer) + >>> src_vocab, tgt_vocab = train_dataset.get_vocab() + >>> src_data, tgt_data = train_dataset[10] + """ + return _setup_datasets("Multi30k", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + tokenizer=tokenizer, + root=root, + vocab=vocab, + removed_tokens=removed_tokens) + + +def IWSLT(train_filenames=('train.de-en.de', 'train.de-en.en'), + valid_filenames=('IWSLT16.TED.tst2013.de-en.de', + 'IWSLT16.TED.tst2013.de-en.en'), + test_filenames=('IWSLT16.TED.tst2014.de-en.de', + 'IWSLT16.TED.tst2014.de-en.en'), + tokenizer=None, + root='.data', + vocab=(None, None), + data_select=('train', 'valid', 'test'), + removed_tokens=['']): + """ Define translation datasets: IWSLT + Separately returns train/valid/test datasets + The available datasets include: + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.de-en.de', 'train.de-en.en') + valid_filenames: the source and target filenames for valid. + Default: ('IWSLT16.TED.tst2013.de-en.de', 'IWSLT16.TED.tst2013.de-en.en') + test_filenames: the source and target filenames for test. + Default: ('IWSLT16.TED.tst2014.de-en.de', 'IWSLT16.TED.tst2014.de-en.en') + tokenizer: the tokenizer used to preprocess source and target raw text data. + It has to be in a form of tuple. + Default: (get_tokenizer("spacy", language='de_core_news_sm'), + get_tokenizer("spacy", language='en_core_web_sm')) + root: Directory where the datasets are saved. Default: ".data" + vocab: Source and target Vocabulary objects used for dataset. If None, it + will generate a new vocabulary based on the train data set. It has to be + in a form of tuple. + Default: (None, None) + data_select: a string or tuple for the returned datasets + (Default: ('train', 'valid', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + removed_tokens: removed tokens from output dataset (Default: '') + The available datasets include: + IWSLT16.TED.dev2010.ar-en.ar + IWSLT16.TED.dev2010.ar-en.en + IWSLT16.TED.dev2010.cs-en.cs + IWSLT16.TED.dev2010.cs-en.en + IWSLT16.TED.dev2010.de-en.de + IWSLT16.TED.dev2010.de-en.en + IWSLT16.TED.dev2010.en-ar.ar + IWSLT16.TED.dev2010.en-ar.en + IWSLT16.TED.dev2010.en-cs.cs + IWSLT16.TED.dev2010.en-cs.en + IWSLT16.TED.dev2010.en-de.de + IWSLT16.TED.dev2010.en-de.en + IWSLT16.TED.dev2010.en-fr.en + IWSLT16.TED.dev2010.en-fr.fr + IWSLT16.TED.dev2010.fr-en.en + IWSLT16.TED.dev2010.fr-en.fr + IWSLT16.TED.tst2010.ar-en.ar + IWSLT16.TED.tst2010.ar-en.en + IWSLT16.TED.tst2010.cs-en.cs + IWSLT16.TED.tst2010.cs-en.en + IWSLT16.TED.tst2010.de-en.de + IWSLT16.TED.tst2010.de-en.en + IWSLT16.TED.tst2010.en-ar.ar + IWSLT16.TED.tst2010.en-ar.en + IWSLT16.TED.tst2010.en-cs.cs + IWSLT16.TED.tst2010.en-cs.en + IWSLT16.TED.tst2010.en-de.de + IWSLT16.TED.tst2010.en-de.en + IWSLT16.TED.tst2010.en-fr.en + IWSLT16.TED.tst2010.en-fr.fr + IWSLT16.TED.tst2010.fr-en.en + IWSLT16.TED.tst2010.fr-en.fr + IWSLT16.TED.tst2011.ar-en.ar + IWSLT16.TED.tst2011.ar-en.en + IWSLT16.TED.tst2011.cs-en.cs + IWSLT16.TED.tst2011.cs-en.en + IWSLT16.TED.tst2011.de-en.de + IWSLT16.TED.tst2011.de-en.en + IWSLT16.TED.tst2011.en-ar.ar + IWSLT16.TED.tst2011.en-ar.en + IWSLT16.TED.tst2011.en-cs.cs + IWSLT16.TED.tst2011.en-cs.en + IWSLT16.TED.tst2011.en-de.de + IWSLT16.TED.tst2011.en-de.en + IWSLT16.TED.tst2011.en-fr.en + IWSLT16.TED.tst2011.en-fr.fr + IWSLT16.TED.tst2011.fr-en.en + IWSLT16.TED.tst2011.fr-en.fr + IWSLT16.TED.tst2012.ar-en.ar + IWSLT16.TED.tst2012.ar-en.en + IWSLT16.TED.tst2012.cs-en.cs + IWSLT16.TED.tst2012.cs-en.en + IWSLT16.TED.tst2012.de-en.de + IWSLT16.TED.tst2012.de-en.en + IWSLT16.TED.tst2012.en-ar.ar + IWSLT16.TED.tst2012.en-ar.en + IWSLT16.TED.tst2012.en-cs.cs + IWSLT16.TED.tst2012.en-cs.en + IWSLT16.TED.tst2012.en-de.de + IWSLT16.TED.tst2012.en-de.en + IWSLT16.TED.tst2012.en-fr.en + IWSLT16.TED.tst2012.en-fr.fr + IWSLT16.TED.tst2012.fr-en.en + IWSLT16.TED.tst2012.fr-en.fr + IWSLT16.TED.tst2013.ar-en.ar + IWSLT16.TED.tst2013.ar-en.en + IWSLT16.TED.tst2013.cs-en.cs + IWSLT16.TED.tst2013.cs-en.en + IWSLT16.TED.tst2013.de-en.de + IWSLT16.TED.tst2013.de-en.en + IWSLT16.TED.tst2013.en-ar.ar + IWSLT16.TED.tst2013.en-ar.en + IWSLT16.TED.tst2013.en-cs.cs + IWSLT16.TED.tst2013.en-cs.en + IWSLT16.TED.tst2013.en-de.de + IWSLT16.TED.tst2013.en-de.en + IWSLT16.TED.tst2013.en-fr.en + IWSLT16.TED.tst2013.en-fr.fr + IWSLT16.TED.tst2013.fr-en.en + IWSLT16.TED.tst2013.fr-en.fr + IWSLT16.TED.tst2014.ar-en.ar + IWSLT16.TED.tst2014.ar-en.en + IWSLT16.TED.tst2014.de-en.de + IWSLT16.TED.tst2014.de-en.en + IWSLT16.TED.tst2014.en-ar.ar + IWSLT16.TED.tst2014.en-ar.en + IWSLT16.TED.tst2014.en-de.de + IWSLT16.TED.tst2014.en-de.en + IWSLT16.TED.tst2014.en-fr.en + IWSLT16.TED.tst2014.en-fr.fr + IWSLT16.TED.tst2014.fr-en.en + IWSLT16.TED.tst2014.fr-en.fr + IWSLT16.TEDX.dev2012.de-en.de + IWSLT16.TEDX.dev2012.de-en.en + IWSLT16.TEDX.tst2013.de-en.de + IWSLT16.TEDX.tst2013.de-en.en + IWSLT16.TEDX.tst2014.de-en.de + IWSLT16.TEDX.tst2014.de-en.en + train.ar + train.ar-en.ar + train.ar-en.en + train.cs + train.cs-en.cs + train.cs-en.en + train.de + train.de-en.de + train.de-en.en + train.en + train.en-ar.ar + train.en-ar.en + train.en-cs.cs + train.en-cs.en + train.en-de.de + train.en-de.en + train.en-fr.en + train.en-fr.fr + train.fr + train.fr-en.en + train.fr-en.fr + train.tags.ar-en.ar + train.tags.ar-en.en + train.tags.cs-en.cs + train.tags.cs-en.en + train.tags.de-en.de + train.tags.de-en.en + train.tags.en-ar.ar + train.tags.en-ar.en + train.tags.en-cs.cs + train.tags.en-cs.en + train.tags.en-de.de + train.tags.en-de.en + train.tags.en-fr.en + train.tags.en-fr.fr + train.tags.fr-en.en + train.tags.fr-en.fr + + Examples: + >>> from torchtext.datasets import IWSLT + >>> from torchtext.data.utils import get_tokenizer + >>> src_tokenizer = get_tokenizer("spacy", language='de') + >>> tgt_tokenizer = get_tokenizer("basic_english") + >>> train_dataset, valid_dataset, test_dataset = IWSLT(tokenizer=(src_tokenizer, + tgt_tokenizer)) + >>> src_vocab, tgt_vocab = train_dataset.get_vocab() + >>> src_data, tgt_data = train_dataset[10] + """ + + return _setup_datasets("IWSLT", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + tokenizer=tokenizer, + root=root, + vocab=vocab, + removed_tokens=removed_tokens) + + +def WMT14(train_filenames=('train.tok.clean.bpe.32000.de', + 'train.tok.clean.bpe.32000.en'), + valid_filenames=('newstest2013.tok.bpe.32000.de', + 'newstest2013.tok.bpe.32000.en'), + test_filenames=('newstest2014.tok.bpe.32000.de', + 'newstest2014.tok.bpe.32000.en'), + tokenizer=None, + root='.data', + vocab=(None, None), + data_select=('train', 'valid', 'test'), + removed_tokens=['']): + """ Define translation datasets: WMT14 + Separately returns train/valid/test datasets + The available datasets include: + newstest2016.en + newstest2016.de + newstest2015.en + newstest2015.de + newstest2014.en + newstest2014.de + newstest2013.en + newstest2013.de + newstest2012.en + newstest2012.de + newstest2011.tok.de + newstest2011.en + newstest2011.de + newstest2010.tok.de + newstest2010.en + newstest2010.de + newstest2009.tok.de + newstest2009.en + newstest2009.de + newstest2016.tok.de + newstest2015.tok.de + newstest2014.tok.de + newstest2013.tok.de + newstest2012.tok.de + newstest2010.tok.en + newstest2009.tok.en + newstest2015.tok.en + newstest2014.tok.en + newstest2013.tok.en + newstest2012.tok.en + newstest2011.tok.en + newstest2016.tok.en + newstest2009.tok.bpe.32000.en + newstest2011.tok.bpe.32000.en + newstest2010.tok.bpe.32000.en + newstest2013.tok.bpe.32000.en + newstest2012.tok.bpe.32000.en + newstest2015.tok.bpe.32000.en + newstest2014.tok.bpe.32000.en + newstest2016.tok.bpe.32000.en + train.tok.clean.bpe.32000.en + newstest2009.tok.bpe.32000.de + newstest2010.tok.bpe.32000.de + newstest2011.tok.bpe.32000.de + newstest2013.tok.bpe.32000.de + newstest2012.tok.bpe.32000.de + newstest2014.tok.bpe.32000.de + newstest2016.tok.bpe.32000.de + newstest2015.tok.bpe.32000.de + train.tok.clean.bpe.32000.de + + Arguments: + train_filenames: the source and target filenames for training. + Default: ('train.tok.clean.bpe.32000.de', 'train.tok.clean.bpe.32000.en') + valid_filenames: the source and target filenames for valid. + Default: ('newstest2013.tok.bpe.32000.de', 'newstest2013.tok.bpe.32000.en') + test_filenames: the source and target filenames for test. + Default: ('newstest2014.tok.bpe.32000.de', 'newstest2014.tok.bpe.32000.en') + tokenizer: the tokenizer used to preprocess source and target raw text data. + It has to be in a form of tuple. + Default: (get_tokenizer("spacy", language='de_core_news_sm'), + get_tokenizer("spacy", language='en_core_web_sm')) + root: Directory where the datasets are saved. Default: ".data" + vocab: Source and target Vocabulary objects used for dataset. If None, it + will generate a new vocabulary based on the train data set. It has to be + in a form of tuple. + Default: (None, None) + data_select: a string or tuple for the returned datasets + (Default: ('train', 'valid', 'test')) + By default, all the three datasets (train, test, valid) are generated. Users + could also choose any one or two of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + removed_tokens: removed tokens from output dataset (Default: '') + + Examples: + >>> from torchtext.datasets import WMT14 + >>> from torchtext.data.utils import get_tokenizer + >>> src_tokenizer = get_tokenizer("spacy", language='de') + >>> tgt_tokenizer = get_tokenizer("basic_english") + >>> train_dataset, valid_dataset, test_dataset = WMT14(tokenizer=(src_tokenizer, + tgt_tokenizer)) + >>> src_vocab, tgt_vocab = train_dataset.get_vocab() + >>> src_data, tgt_data = train_dataset[10] + """ + + return _setup_datasets("WMT14", + train_filenames=train_filenames, + valid_filenames=valid_filenames, + test_filenames=test_filenames, + tokenizer=tokenizer, + root=root, + vocab=vocab, + removed_tokens=removed_tokens) + + +DATASETS = {'Multi30k': raw.Multi30k, 'IWSLT': raw.IWSLT, 'WMT14': raw.WMT14} diff --git a/torchtext/utils.py b/torchtext/utils.py index 41ac14ecbb..b892396a15 100644 --- a/torchtext/utils.py +++ b/torchtext/utils.py @@ -7,6 +7,7 @@ import re import sys import zipfile +import gzip def reporthook(t): @@ -197,6 +198,21 @@ def extract_archive(from_path, to_path=None, overwrite=False): files = [f for f in files if os.path.isfile(f)] return files + elif from_path.endswith('.gz'): + default_block_size = 65536 + filename = from_path[:-3] + files = [filename] + with gzip.open(from_path, 'rb') as gzfile, \ + open(filename, 'wb') as d_file: + while True: + block = gzfile.read(default_block_size) + if not block: + break + else: + d_file.write(block) + d_file.write(block) + return files + else: raise NotImplementedError( - "We currently only support tar.gz, .tgz and zip achives.") + "We currently only support tar.gz, .tgz, .gz and zip achives.") From 60373f8bc9a7c9326ff7a92b9081310ec9794ade Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Thu, 28 May 2020 11:01:27 -0700 Subject: [PATCH 066/719] Removed local re library. Added custom regex class. --- docs/source/experimental_datasets.rst | 20 ++--- docs/source/experimental_transforms.rst | 22 +++++ docs/source/index.rst | 2 + test/data/test_functional.py | 91 ++++++++++++++++++- torchtext/csrc/regex.cpp | 44 +++++++++ torchtext/experimental/transforms.py | 114 ++++++++++++++++++++++++ 6 files changed, 281 insertions(+), 12 deletions(-) create mode 100644 docs/source/experimental_transforms.rst create mode 100644 torchtext/csrc/regex.cpp create mode 100644 torchtext/experimental/transforms.py diff --git a/docs/source/experimental_datasets.rst b/docs/source/experimental_datasets.rst index a16d5dabba..e9debd8224 100644 --- a/docs/source/experimental_datasets.rst +++ b/docs/source/experimental_datasets.rst @@ -1,5 +1,5 @@ torchtext.experimental.datasets -================================ +=============================== .. currentmodule:: torchtext.experimental.datasets @@ -41,13 +41,13 @@ Text Classification ^^^^^^^^^^^^^^^^^^^ TextClassificationDataset -~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: TextClassificationDataset :members: __init__ AG_NEWS -~~~~~~ +~~~~~~~ AG_NEWS dataset is subclass of ``TextClassificationDataset`` class. @@ -55,7 +55,7 @@ AG_NEWS dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ SogouNews -~~~~~~~~ +~~~~~~~~~ SogouNews dataset is subclass of ``TextClassificationDataset`` class. @@ -63,7 +63,7 @@ SogouNews dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ DBpedia -~~~~~~ +~~~~~~~ DBpedia dataset is subclass of ``TextClassificationDataset`` class. @@ -71,7 +71,7 @@ DBpedia dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ YelpReviewPolarity -~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~ YelpReviewPolarity dataset is subclass of ``TextClassificationDataset`` class. @@ -79,7 +79,7 @@ YelpReviewPolarity dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ YelpReviewFull -~~~~~~~~~~~~~ +~~~~~~~~~~~~~~ YelpReviewFull dataset is subclass of ``TextClassificationDataset`` class. @@ -87,7 +87,7 @@ YelpReviewFull dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ YahooAnswers -~~~~~~~~~~~ +~~~~~~~~~~~~ YahooAnswers dataset is subclass of ``TextClassificationDataset`` class. @@ -95,7 +95,7 @@ YahooAnswers dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ AmazonReviewPolarity -~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~ AmazonReviewPolarity dataset is subclass of ``TextClassificationDataset`` class. @@ -103,7 +103,7 @@ AmazonReviewPolarity dataset is subclass of ``TextClassificationDataset`` class. :members: __init__ AmazonReviewFull -~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~ AmazonReviewFull dataset is subclass of ``TextClassificationDataset`` class. diff --git a/docs/source/experimental_transforms.rst b/docs/source/experimental_transforms.rst new file mode 100644 index 0000000000..ff00b374ef --- /dev/null +++ b/docs/source/experimental_transforms.rst @@ -0,0 +1,22 @@ +.. role:: hidden + :class: hidden-section + +torchtext.experimental.transforms +================================= + +.. automodule:: torchtext.experimental.transforms +.. currentmodule:: torchtext.experimental.transforms + +:hidden:`BasicEnglishNormalize` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: BasicEnglishNormalize + :members: + :special-members: __init__ + +:hidden:`RegexTokenizer` +~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: RegexTokenizer + :members: + :special-members: __init__ \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index 08fb745c4f..dfa9d7c4c0 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -17,6 +17,8 @@ popular datasets for natural language. torchtext.vocab torchtext.utils experimental_datasets + experimental_functional + experimental_transforms examples .. automodule:: torchtext diff --git a/test/data/test_functional.py b/test/data/test_functional.py index ff5b12ab17..ef15fa2d6f 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -7,6 +7,7 @@ import sentencepiece as spm import torch +import torchtext.data as data from torchtext.data.functional import ( generate_sp_model, load_sp_model, @@ -15,6 +16,10 @@ custom_replace, simple_space_split, ) +from torchtext.experimental.transforms import ( + BasicEnglishNormalize, + RegexTokenizer +) from ..common.torchtext_test_case import TorchtextTestCase from ..common.assets import get_asset_path @@ -22,7 +27,9 @@ class TestFunctional(TorchtextTestCase): def test_generate_sp_model(self): - """Test the function to train a sentencepiece tokenizer""" + """ + Test the function to train a sentencepiece tokenizer. + """ asset_name = 'text_normalization_ag_news_test.csv' asset_path = get_asset_path(asset_name) @@ -59,7 +66,6 @@ def test_sentencepiece_numericalizer(self): ref_results) def test_sentencepiece_tokenizer(self): - test_sample = 'SentencePiece is an unsupervised text tokenizer and detokenizer' model_path = get_asset_path('spm_example.model') sp_model = load_sp_model(model_path) @@ -74,6 +80,87 @@ def test_sentencepiece_tokenizer(self): self.assertEqual(list(spm_generator([test_sample]))[0], ref_results) + # TODO(Nayef211): uncomment and replace the test below with this once + # https://github.com/pytorch/pytorch/issues/38207 is closed + # def test_BasicEnglishNormalize(self): + # test_sample = '\'".
,()!?;: Basic English Normalization for a Line of Text \'".
,()!?;:' + # ref_results = ["'", '.', ',', '(', ')', '!', '?', 'basic', 'english', 'normalization', + # 'for', 'a', 'line', 'of', 'text', "'", '.', ',', '(', ')', '!', '?'] + + # basic_english_normalize = BasicEnglishNormalize() + # experimental_eager_tokens = basic_english_normalize(test_sample) + + # jit_basic_english_normalize = torch.jit.script(basic_english_normalize) + # experimental_jit_tokens = jit_basic_english_normalize(test_sample) + + # basic_english_tokenizer = data.get_tokenizer("basic_english") + # eager_tokens = basic_english_tokenizer(test_sample) + + # self.assertEqual(experimental_jit_tokens, ref_results) + # self.assertEqual(experimental_jit_tokens, eager_tokens) + # self.assertEqual(experimental_jit_tokens, experimental_eager_tokens) + + def test_BasicEnglishNormalize(self): + test_sample = 'Basic English Normalization for a Line of Text' + ref_results = ['basic', 'english', 'normalization', + 'for', 'a', 'line', 'of', 'text'] + + basic_english_normalize = BasicEnglishNormalize() + experimental_eager_tokens = basic_english_normalize(test_sample) + + basic_english_tokenizer = data.get_tokenizer("basic_english") + tokens_eager = basic_english_tokenizer(test_sample) + + self.assertEqual(experimental_eager_tokens, ref_results) + self.assertEqual(experimental_eager_tokens, tokens_eager) + + # TODO(Nayef211): uncomment and replace the test below with this once + # https://github.com/pytorch/pytorch/issues/38207 is closed + # def test_RegexTokenizer(self): + # test_sample = '\'".
,()!?;: Basic Regex Tokenization for a Line of Text \'".
,()!?;:' + # ref_results = ["'", '.', ',', '(', ')', '!', '?', 'Basic', 'Regex', 'Tokenization', + # 'for', 'a', 'Line', 'of', 'Text', "'", '.', ',', '(', ')', '!', '?'] + # patterns_list = [ + # (r'\'', ' \' '), + # (r'\"', ''), + # (r'\.', ' . '), + # (r'
', ' '), + # (r',', ' , '), + # (r'\(', ' ( '), + # (r'\)', ' ) '), + # (r'\!', ' ! '), + # (r'\?', ' ? '), + # (r'\;', ' '), + # (r'\:', ' '), + # (r'\s+', ' ')] + + # regex_tokenizer = RegexTokenizer(patterns_list) + # eager_tokens = regex_tokenizer(test_sample) + + # jit_regex_tokenizer = torch.jit.script(regex_tokenizer) + # jit_tokens = jit_regex_tokenizer(test_sample) + + # self.assertEqual(jit_tokens, ref_results) + # self.assertEqual(jit_tokens, eager_tokens) + + def test_RegexTokenizer(self): + test_sample = '"Basic Regex Tokenization". For a Line of Text' + ref_results = ['Basic', 'Regex', 'Tokenization', '.', + 'For', 'a', 'Line', 'of', 'Text'] + patterns_list = [ + (r'\"', ''), + (r'\.', ' . '), + (r'\s+', ' ')] + + regex_tokenizer = RegexTokenizer(patterns_list) + eager_tokens = regex_tokenizer(test_sample) + + jit_regex_tokenizer = torch.jit.script(regex_tokenizer) + jit_tokens = jit_regex_tokenizer(test_sample) + + self.assertEqual(jit_tokens, eager_tokens) + self.assertEqual(jit_tokens, ref_results) + def test_custom_replace(self): custom_replace_transform = custom_replace([(r'S', 's'), (r'\s+', ' ')]) test_sample = ['test cuStom replace', 'with uSer instruction'] diff --git a/torchtext/csrc/regex.cpp b/torchtext/csrc/regex.cpp new file mode 100644 index 0000000000..94ea32adfd --- /dev/null +++ b/torchtext/csrc/regex.cpp @@ -0,0 +1,44 @@ +#include +#include +#include + +namespace torchtext { +namespace { + +struct Regex : torch::CustomClassHolder { +private: + std::regex re_; + +public: + // re_str_ holds the serialized regex string passed at the initialization. + // We need this because we need to be able to serialize the model so that we + // can save the scripted object. pickle will get the + // serialized model from this re_str_ member, thus it needs to be public. + const std::string re_str_; + + Regex(const std::string &re_str) : re_str_(re_str) { + re_ = std::regex(re_str_); + } + + std::string Sub(const std::string &str, const std::string &repl) const { + return std::regex_replace(str, re_, repl); + } +}; + +// Registers our custom class with torch. +static auto regex = + torch::class_("torchtext", "Regex") + .def(torch::init()) + .def("Sub", &Regex::Sub) + .def_pickle( + // __getstate__ + [](const c10::intrusive_ptr &self) -> std::string { + return self->re_str_; + }, + // __setstate__ + [](std::string state) -> c10::intrusive_ptr { + return c10::make_intrusive(std::move(state)); + }); + +} // namespace +} // namespace torchtext diff --git a/torchtext/experimental/transforms.py b/torchtext/experimental/transforms.py new file mode 100644 index 0000000000..f7f53670be --- /dev/null +++ b/torchtext/experimental/transforms.py @@ -0,0 +1,114 @@ +import torch +import torch.nn as nn +from typing import List, Tuple + + +__all__ = [ + 'BasicEnglishNormalize', + 'RegexTokenizer' +] + + +class BasicEnglishNormalize(nn.Module): + r"""Basic normalization for a string sentence. + + Normalization includes + - lowercasing + - complete some basic text normalization for English words as follows: + - add spaces before and after '\'' + - remove '\"', + - add spaces before and after '.' + - replace '
'with single space + - add spaces before and after ',' + - add spaces before and after '(' + - add spaces before and after ')' + - add spaces before and after '!' + - add spaces before and after '?' + - replace ';' with single space + - replace ':' with single space + - replace multiple spaces with single space + + Examples: + >>> import torch + >>> from torchtext.experimental.transforms import BasicEnglishNormalize + >>> test_sample = 'Basic English Normalization for a Line of Text' + >>> basic_english_normalize = BasicEnglishNormalize() + >>> jit_basic_english_normalize = torch.jit.script(basic_english_normalize) + >>> tokens = jit_basic_english_normalize(test_sample) + """ + + regex_and_replacement_string_pairs: List[Tuple[torch.classes.torchtext.Regex, str]] + + def __init__(self): + super(BasicEnglishNormalize, self).__init__() + patterns_list = [ + (r'\'', ' \' '), + (r'\"', ''), + (r'\.', ' . '), + (r'
', ' '), + (r',', ' , '), + (r'\(', ' ( '), + (r'\)', ' ) '), + (r'\!', ' ! '), + (r'\?', ' ? '), + (r'\;', ' '), + (r'\:', ' '), + (r'\s+', ' ')] + + regex_objects = map(lambda pattern_tuple: torch.classes.torchtext.Regex(pattern_tuple[0]), patterns_list) + replacement_strings = map(lambda pattern_tuple: pattern_tuple[1], patterns_list) + self.regex_and_replacement_string_pairs = list(zip(regex_objects, replacement_strings)) + + def forward(self, line: str) -> List[str]: + r""" + Args: + line (str): a line of text to tokenize. + Returns: + List[str]: a list of tokens after normalizing and splitting on whitespace. + """ + + line = line.lower() + for regex, replacement_string in self.regex_and_replacement_string_pairs: + line = regex.Sub(line, replacement_string) + return line.split() + + +class RegexTokenizer(nn.Module): + r"""Regex tokenizer for a string sentence that applies all regex replacements defined in patterns_list. + + Args: + patterns_list (List[Tuple[str, str]]): a list of tuples (ordered pairs) which contain the regex pattern string + as the first element and the replacement string as the second element. + + Examples: + >>> import torch + >>> from torchtext.experimental.transforms import RegexTokenizer + >>> test_sample = 'Basic Regex Tokenization for a Line of Text' + >>> patterns_list = [ + (r'\'', ' \' '), + (r'\"', '')] + >>> regex_tokenizer = RegexTokenizer(patterns_list) + >>> jit_regex_tokenizer = torch.jit.script(regex_tokenizer) + >>> tokens = jit_regex_tokenizer(test_sample) + """ + + regex_and_replacement_string_pairs: List[Tuple[torch.classes.torchtext.Regex, str]] + + def __init__(self, patterns_list: List[Tuple[str, str]]): + super(RegexTokenizer, self).__init__() + + regex_objects = map(lambda pattern_tuple: torch.classes.torchtext.Regex(pattern_tuple[0]), patterns_list) + replacement_strings = map(lambda pattern_tuple: pattern_tuple[1], patterns_list) + self.regex_and_replacement_string_pairs = list(zip(regex_objects, replacement_strings)) + + def forward(self, line: str) -> List[str]: + r""" + Args: + line (str): a line of text to tokenize. + Returns: + List[str]: a list of tokens after normalizing and splitting on whitespace. + """ + + for regex, replacement_string in self.regex_and_replacement_string_pairs: + line = regex.Sub(line, replacement_string) + return line.split() From 9a6846728c48d7bf76e6caace268137b21236c26 Mon Sep 17 00:00:00 2001 From: moto <855818+mthrok@users.noreply.github.com> Date: Fri, 5 Jun 2020 11:30:43 -0400 Subject: [PATCH 067/719] Fix and re-enable TestScriptableSP for Windows (#799) --- test/data/test_functional.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/test/data/test_functional.py b/test/data/test_functional.py index ef15fa2d6f..9b93580f72 100644 --- a/test/data/test_functional.py +++ b/test/data/test_functional.py @@ -1,8 +1,7 @@ import os -import unittest -import sys import uuid import shutil +import unittest import tempfile import sentencepiece as spm @@ -196,11 +195,11 @@ def encode_as_pieces(self, input: str): class TestScriptableSP(unittest.TestCase): def setUp(self): model_path = get_asset_path('spm_example.model') - with tempfile.NamedTemporaryFile() as file: - torch.jit.script(ScriptableSP(model_path)).save(file.name) - self.model = torch.jit.load(file.name) + with tempfile.TemporaryDirectory() as dir_name: + jit_model_path = os.path.join(dir_name, 'spm_example.model') + torch.jit.script(ScriptableSP(model_path)).save(jit_model_path) + self.model = torch.jit.load(jit_model_path) - @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_encode(self): input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' expected = [ @@ -212,7 +211,6 @@ def test_encode(self): output = self.model.encode(input) self.assertEqual(expected, output) - @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_encode_as_ids(self): input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' expected = [ @@ -221,7 +219,6 @@ def test_encode_as_ids(self): output = self.model.encode_as_ids(input) self.assertEqual(expected, output) - @unittest.skipIf(sys.platform == "win32", "FIXME: tempfile could not be opened twice on Windows") def test_encode_as_pieces(self): input = 'SentencePiece is an unsupervised text tokenizer and detokenizer' expected = [ From dde822015c892a2d50633f719d430f6b3d6fd1af Mon Sep 17 00:00:00 2001 From: Guanheng Zhang Date: Fri, 5 Jun 2020 10:42:13 -0700 Subject: [PATCH 068/719] resolve conflict in regex.cpp --- torchtext/csrc/regex.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchtext/csrc/regex.cpp b/torchtext/csrc/regex.cpp index 94ea32adfd..2f88e135b5 100644 --- a/torchtext/csrc/regex.cpp +++ b/torchtext/csrc/regex.cpp @@ -14,7 +14,7 @@ struct Regex : torch::CustomClassHolder { // We need this because we need to be able to serialize the model so that we // can save the scripted object. pickle will get the // serialized model from this re_str_ member, thus it needs to be public. - const std::string re_str_; + std::string re_str_; Regex(const std::string &re_str) : re_str_(re_str) { re_ = std::regex(re_str_); From 1f3f5689939c45bd8facc2df7746c9578c791e74 Mon Sep 17 00:00:00 2001 From: Pattarawat Chormai Date: Mon, 8 Jun 2020 16:58:32 +0200 Subject: [PATCH 069/719] Fix typo (#817) --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 979d87407c..a69be1e1e1 100644 --- a/README.rst +++ b/README.rst @@ -69,7 +69,7 @@ To build torchtext from source, you need ``git``, ``CMake`` and C++11 compiler s **Note** -When building from souce, make sure that you have the same C++ compiler as the one used to build PyTorch. A simple way is to build PyTorch from source and use the same environment to build torchtext. +When building from source, make sure that you have the same C++ compiler as the one used to build PyTorch. A simple way is to build PyTorch from source and use the same environment to build torchtext. If you are using nightly build of PyTorch, checkout the environment it was built `here (conda) `_ and `here (pip) `_. Documentation From 035fadf55648c0401987bd2dde4c0a226ad9ec71 Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Mon, 8 Jun 2020 14:29:05 -0400 Subject: [PATCH 070/719] switch experimental ngrams_func to data.utils.ngrams_iterator (#813) --- test/data/test_utils.py | 16 +++++++++++++++- torchtext/experimental/functional.py | 22 ++++++++++------------ 2 files changed, 25 insertions(+), 13 deletions(-) diff --git a/test/data/test_utils.py b/test/data/test_utils.py index c64ec09bf8..7772f851c2 100644 --- a/test/data/test_utils.py +++ b/test/data/test_utils.py @@ -2,7 +2,7 @@ import torchtext.data as data from torchtext.utils import unicode_csv_reader - +from torchtext.experimental.functional import ngrams_func from ..common.torchtext_test_case import TorchtextTestCase from ..common.assets import get_asset_path @@ -50,3 +50,17 @@ def test_text_nomalize_function(self): ref_lines.append(line) self.assertEqual(ref_lines, test_lines) + + def test_ngrams_func(self): + func = ngrams_func(1) + assert func(['A', 'string', 'particularly', 'one', 'with', 'slightly']) == \ + ['A', 'string', 'particularly', 'one', 'with', 'slightly'] + func = ngrams_func(2) + assert func(['A', 'string', 'particularly', 'one', 'with', 'slightly']) == \ + ['A', 'string', 'particularly', 'one', 'with', 'slightly', 'A string', 'string particularly', + 'particularly one', 'one with', 'with slightly'] + func = ngrams_func(3) + assert func(['A', 'string', 'particularly', 'one', 'with', 'slightly']) == \ + ['A', 'string', 'particularly', 'one', 'with', 'slightly', 'A string', 'string particularly', + 'particularly one', 'one with', 'with slightly', 'A string particularly', + 'string particularly one', 'particularly one with', 'one with slightly'] diff --git a/torchtext/experimental/functional.py b/torchtext/experimental/functional.py index 2c9ac60541..26f277fd3a 100644 --- a/torchtext/experimental/functional.py +++ b/torchtext/experimental/functional.py @@ -1,34 +1,32 @@ import torch +from torchtext.data.utils import ngrams_iterator def vocab_func(vocab): - def _forward(tok_iter): + def func(tok_iter): return [vocab[tok] for tok in tok_iter] - return _forward + return func def totensor(dtype): - def _forward(ids_list): + def func(ids_list): return torch.tensor(ids_list).to(dtype) - return _forward + return func def ngrams_func(ngrams): - def _forward(token_list): - _token_list = [] - for _i in range(ngrams + 1): - _token_list += zip(*[token_list[i:] for i in range(_i)]) - return [" ".join(x) for x in _token_list] + def func(token_list): + return list(ngrams_iterator(token_list, ngrams)) - return _forward + return func def sequential_transforms(*transforms): - def _forward(txt_input): + def func(txt_input): for transform in transforms: txt_input = transform(txt_input) return txt_input - return _forward + return func From c334b5e856183009ca57dc88ad27527f10724a12 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Tue, 9 Jun 2020 07:35:25 -0700 Subject: [PATCH 071/719] Added re2 submodule --- .gitmodules | 3 +++ third_party/re2 | 1 + 2 files changed, 4 insertions(+) create mode 160000 third_party/re2 diff --git a/.gitmodules b/.gitmodules index 3d9250cdc0..4c845bbd0a 100644 --- a/.gitmodules +++ b/.gitmodules @@ -2,3 +2,6 @@ path = third_party/sentencepiece url = https://github.com/google/sentencepiece ignore = dirty +[submodule "third_party/re2"] + path = third_party/re2 + url = https://github.com/google/re2 diff --git a/third_party/re2 b/third_party/re2 new file mode 160000 index 0000000000..2b25567a8e --- /dev/null +++ b/third_party/re2 @@ -0,0 +1 @@ +Subproject commit 2b25567a8ee3b6e97c3cd05d616f296756c52759 From 571f1dd5122e73620c168d287096f20700e0a549 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Tue, 9 Jun 2020 09:56:45 -0700 Subject: [PATCH 072/719] Updated setup files and regex implementation using re2 --- .gitmodules | 2 ++ build_tools/setup_helpers/extension.py | 29 ++++++++++++++++++++++++++ torchtext/csrc/regex.cpp | 13 +++++------- 3 files changed, 36 insertions(+), 8 deletions(-) diff --git a/.gitmodules b/.gitmodules index 4c845bbd0a..0f1c23a7fa 100644 --- a/.gitmodules +++ b/.gitmodules @@ -5,3 +5,5 @@ [submodule "third_party/re2"] path = third_party/re2 url = https://github.com/google/re2 + ignore = dirty + diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 80d515d998..a986b811bf 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -78,6 +78,7 @@ def _get_libraries(): return [ 'sentencepiece_train', 'sentencepiece', + 're2' ] @@ -107,8 +108,36 @@ def _build_sentence_piece(debug): ) +def _build_re2(debug): + build_dir = _TP_BASE_DIR / 're2' / 'build' + build_dir.mkdir(exist_ok=True) + build_env = os.environ.copy() + config = 'Debug' if debug else 'Release' + if platform.system() == 'Windows': + extra_args = ['-GNinja'] + build_env.setdefault('CC', 'cl') + build_env.setdefault('CXX', 'cl') + else: + extra_args = [] + subprocess.run( + args=['cmake', f'-DCMAKE_CXX_FLAGS=-fPIC', f'-DBUILD_SHARED_LIBS=OFF', + f'-DCMAKE_INSTALL_PREFIX={_TP_INSTALL_DIR}', + f'-DCMAKE_BUILD_TYPE={config}'] + extra_args + ['..'], + cwd=str(build_dir), + check=True, + env=build_env, + ) + subprocess.run( + args=['cmake', '--build', '.', '--target', 'install', '--config', config], + cwd=str(build_dir), + check=True, + env=build_env, + ) + + def _configure_third_party(debug): _build_sentence_piece(debug) + _build_re2(debug) _EXT_NAME = 'torchtext._torchtext' diff --git a/torchtext/csrc/regex.cpp b/torchtext/csrc/regex.cpp index 2f88e135b5..1638c1f8e0 100644 --- a/torchtext/csrc/regex.cpp +++ b/torchtext/csrc/regex.cpp @@ -1,4 +1,4 @@ -#include +#include #include #include @@ -6,9 +6,6 @@ namespace torchtext { namespace { struct Regex : torch::CustomClassHolder { -private: - std::regex re_; - public: // re_str_ holds the serialized regex string passed at the initialization. // We need this because we need to be able to serialize the model so that we @@ -16,12 +13,12 @@ struct Regex : torch::CustomClassHolder { // serialized model from this re_str_ member, thus it needs to be public. std::string re_str_; - Regex(const std::string &re_str) : re_str_(re_str) { - re_ = std::regex(re_str_); - } + Regex(const std::string &re_str) : re_str_(re_str) {} std::string Sub(const std::string &str, const std::string &repl) const { - return std::regex_replace(str, re_, repl); + std::string mutable_str = str; + RE2::GlobalReplace(&mutable_str, re_str_, repl); + return mutable_str; } }; From 7ef287fa1b2c84a1b20912d8f3eae69c2ff2db4f Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Tue, 9 Jun 2020 11:01:17 -0700 Subject: [PATCH 073/719] fixing CI build --- build_tools/setup_helpers/extension.py | 1 + 1 file changed, 1 insertion(+) diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index a986b811bf..7171b23057 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -60,6 +60,7 @@ def _get_include_dirs(): def _get_library_dirs(): return [ str(_TP_INSTALL_DIR / 'lib'), + str(_TP_INSTALL_DIR / 'lib64') ] From d839d65ee77bed885b2cf19a9809e7fe01124029 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Tue, 9 Jun 2020 13:18:46 -0700 Subject: [PATCH 074/719] fixing windows CI --- build_tools/setup_helpers/extension.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 7171b23057..eccf0c7d50 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -111,7 +111,9 @@ def _build_sentence_piece(debug): def _build_re2(debug): build_dir = _TP_BASE_DIR / 're2' / 'build' - build_dir.mkdir(exist_ok=True) + # simply calling 'build_dir.mkdir(exist_ok=True)' fails on Windows CI Job + if not os.path.exists(str(build_dir)): + os.mkdir(str(build_dir)) build_env = os.environ.copy() config = 'Debug' if debug else 'Release' if platform.system() == 'Windows': From 2b495cf42cba56f66911442ad9e4b886df92d4d1 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Tue, 9 Jun 2020 13:34:21 -0700 Subject: [PATCH 075/719] fixing windows CI --- build_tools/setup_helpers/extension.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index eccf0c7d50..c73935b794 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -112,8 +112,8 @@ def _build_sentence_piece(debug): def _build_re2(debug): build_dir = _TP_BASE_DIR / 're2' / 'build' # simply calling 'build_dir.mkdir(exist_ok=True)' fails on Windows CI Job - if not os.path.exists(str(build_dir)): - os.mkdir(str(build_dir)) + if not build_dir.exists(): + build_dir.mkdir() build_env = os.environ.copy() config = 'Debug' if debug else 'Release' if platform.system() == 'Windows': From fe8d80780a51d22c20dba0bb42b54ac389ef1848 Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Wed, 10 Jun 2020 07:40:09 -0400 Subject: [PATCH 076/719] Question answer datasets: SQuAD1 and SQuAD2 (#773) --- docs/source/experimental_datasets.rst | 31 ++++ test/data/test_builtin_datasets.py | 42 +++++ torchtext/experimental/datasets/__init__.py | 4 +- .../experimental/datasets/question_answer.py | 163 ++++++++++++++++++ .../experimental/datasets/raw/__init__.py | 4 +- .../datasets/raw/question_answer.py | 93 ++++++++++ 6 files changed, 335 insertions(+), 2 deletions(-) create mode 100644 torchtext/experimental/datasets/question_answer.py create mode 100644 torchtext/experimental/datasets/raw/question_answer.py diff --git a/docs/source/experimental_datasets.rst b/docs/source/experimental_datasets.rst index e9debd8224..044b731496 100644 --- a/docs/source/experimental_datasets.rst +++ b/docs/source/experimental_datasets.rst @@ -141,6 +141,13 @@ PennTreebank :members: __init__ +WMTNewsCrawl +~~~~~~~~~~~~ + +.. autoclass:: WMTNewsCrawl + :members: __init__ + + Machine Translation ^^^^^^^^^^^^^^^^^^^ @@ -149,21 +156,45 @@ Language modeling datasets are subclasses of ``TranslationDataset`` class. .. autoclass:: TranslationDataset :members: __init__ + Multi30k ~~~~~~~~ .. autoclass:: Multi30k :members: __init__ + IWSLT ~~~~~ .. autoclass:: IWSLT :members: __init__ + WMT14 ~~~~~ .. autoclass:: WMT14 :members: __init__ +Question Answer +^^^^^^^^^^^^^^^ + +Question answer datasets are subclasses of ``QuestionAnswerDataset`` class. + +.. autoclass:: QuestionAnswerDataset + :members: __init__ + + +SQuAD 1.0 +~~~~~~~~~ + +.. autoclass:: SQuAD1 + :members: __init__ + + +SQuAD 2.0 +~~~~~~~~~ + +.. autoclass:: SQuAD2 + :members: __init__ diff --git a/test/data/test_builtin_datasets.py b/test/data/test_builtin_datasets.py index 2bb4065df9..6acc9e6d0f 100644 --- a/test/data/test_builtin_datasets.py +++ b/test/data/test_builtin_datasets.py @@ -156,3 +156,45 @@ def test_multi30k(self): datafile = os.path.join(self.project_root, ".data", "multi30k_task*.tar.gz") conditional_remove(datafile) + + def test_squad1(self): + from torchtext.experimental.datasets import SQuAD1 + from torchtext.vocab import Vocab + # smoke test to ensure imdb works properly + train_dataset, dev_dataset = SQuAD1() + self.assertEqual(len(train_dataset), 87599) + self.assertEqual(len(dev_dataset), 10570) + assert_allclose(train_dataset[100]['question'], + torch.tensor([7, 24, 86, 52, 2, 373, 887, 18, 12797, 11090, 1356, 2, 1788, 3273, 16]).long()) + assert_allclose(train_dataset[100]['ans_pos'][0], + torch.tensor([72, 72]).long()) + assert_allclose(dev_dataset[100]['question'], + torch.tensor([42, 27, 669, 7438, 17, 2, 1950, 3273, 17252, 389, 16]).long()) + assert_allclose(dev_dataset[100]['ans_pos'][0], + torch.tensor([45, 48]).long()) + + # Test API with a vocab input object + old_vocab = train_dataset.get_vocab() + new_vocab = Vocab(counter=old_vocab.freqs, max_size=2500) + new_train_data, new_test_data = SQuAD1(vocab=new_vocab) + + def test_squad2(self): + from torchtext.experimental.datasets import SQuAD2 + from torchtext.vocab import Vocab + # smoke test to ensure imdb works properly + train_dataset, dev_dataset = SQuAD2() + self.assertEqual(len(train_dataset), 130319) + self.assertEqual(len(dev_dataset), 11873) + assert_allclose(train_dataset[200]['question'], + torch.tensor([84, 50, 1421, 12, 5439, 4569, 17, 30, 2, 15202, 4754, 1421, 16]).long()) + assert_allclose(train_dataset[200]['ans_pos'][0], + torch.tensor([9, 9]).long()) + assert_allclose(dev_dataset[200]['question'], + torch.tensor([41, 29, 2, 66, 17016, 30, 0, 1955, 16]).long()) + assert_allclose(dev_dataset[200]['ans_pos'][0], + torch.tensor([40, 46]).long()) + + # Test API with a vocab input object + old_vocab = train_dataset.get_vocab() + new_vocab = Vocab(counter=old_vocab.freqs, max_size=2500) + new_train_data, new_test_data = SQuAD2(vocab=new_vocab) diff --git a/torchtext/experimental/datasets/__init__.py b/torchtext/experimental/datasets/__init__.py index d91f60ff55..9009e0dc1f 100644 --- a/torchtext/experimental/datasets/__init__.py +++ b/torchtext/experimental/datasets/__init__.py @@ -2,6 +2,7 @@ from .text_classification import AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, \ YelpReviewFull, YahooAnswers, \ AmazonReviewPolarity, AmazonReviewFull, IMDB +from .question_answer import SQuAD1, SQuAD2 __all__ = ['LanguageModelingDataset', 'WikiText2', @@ -16,4 +17,5 @@ 'YelpReviewFull', 'YahooAnswers', 'AmazonReviewPolarity', - 'AmazonReviewFull'] + 'AmazonReviewFull', + 'SQuAD1', 'SQuAD2'] diff --git a/torchtext/experimental/datasets/question_answer.py b/torchtext/experimental/datasets/question_answer.py new file mode 100644 index 0000000000..094a252bfc --- /dev/null +++ b/torchtext/experimental/datasets/question_answer.py @@ -0,0 +1,163 @@ +import torch +from torchtext.data.utils import get_tokenizer +from torchtext.vocab import build_vocab_from_iterator +from torchtext.experimental.datasets.raw import question_answer as raw +from torchtext.experimental.functional import ( + totensor, + vocab_func, + sequential_transforms, +) + + +class QuestionAnswerDataset(torch.utils.data.Dataset): + """Defines an abstract question answer datasets. + Currently, we only support the following datasets: + - SQuAD1 + - SQuAD2 + """ + + def __init__(self, data, vocab, transforms): + """Initiate question answer dataset. + + Arguments: + data: a dictionary of data. + For example {'context': context_data, 'answers': answers_data, + 'question': question_data, 'ans_pos': ans_pos_data} + vocab: Vocabulary object used for dataset. + transforms: a dictionary of transforms. + For example {'context': context_transform, 'answers': answers_transform, + 'question': question_transform, 'ans_pos': ans_pos_transform} + """ + + super(QuestionAnswerDataset, self).__init__() + self.data = data + self.vocab = vocab + self.transforms = transforms + + def __getitem__(self, i): + _data = {'context': self.transforms['context'](self.data[i]['context']), + 'question': self.transforms['question'](self.data[i]['question']), + 'answers': [], 'ans_pos': []} + for idx in range(len(self.data[i]['answer_start'])): + _data['answers'].append(self.transforms['answers'](self.data[i]['answers'][idx])) + ans_start_idx = self.data[i]['answer_start'][idx] + if ans_start_idx == -1: # No answer for this sample + _data['ans_pos'].append(self.transforms['ans_pos']([-1, -1])) + else: + ans_start_token_idx = len(self.transforms['context'](self.data[i]['context'][:ans_start_idx])) + ans_end_token_idx = ans_start_token_idx + \ + len(self.transforms['answers'](self.data[i]['answers'][idx])) - 1 + _data['ans_pos'].append(self.transforms['ans_pos']([ans_start_token_idx, ans_end_token_idx])) + return _data + + def __len__(self): + return len(self.data) + + def get_vocab(self): + return self.vocab + + +def _setup_datasets(dataset_name, + root='.data', + vocab=None, + tokenizer=None, + data_select=('train', 'dev')): + text_transform = [] + if tokenizer is None: + tokenizer = get_tokenizer('basic_english') + text_transform = sequential_transforms(tokenizer) + if isinstance(data_select, str): + data_select = [data_select] + if not set(data_select).issubset(set(('train', 'dev'))): + raise TypeError('Given data selection {} is not supported!'.format(data_select)) + train, dev = raw.DATASETS[dataset_name](root=root) + raw_data = {'train': [item for item in train], + 'dev': [item for item in dev]} + if vocab is None: + if 'train' not in data_select: + raise TypeError("Must pass a vocab if train is not selected.") + tok_list = [] + for raw_dict in raw_data['train']: + tok_ans = [] + for item in raw_dict['answers']: + tok_ans += text_transform(item) + tok_list.append(text_transform(raw_dict['context']) + + text_transform(raw_dict['question']) + tok_ans) + vocab = build_vocab_from_iterator(tok_list) + text_transform = sequential_transforms(text_transform, vocab_func(vocab), totensor(dtype=torch.long)) + transforms = {'context': text_transform, 'question': text_transform, + 'answers': text_transform, 'ans_pos': totensor(dtype=torch.long)} + return tuple(QuestionAnswerDataset(raw_data[item], vocab, transforms) for item in data_select) + + +def SQuAD1(*args, **kwargs): + """ Defines SQuAD1 datasets. + + Create question answer dataset: SQuAD1 + + Separately returns the train and dev dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well. A custom tokenizer is callable + function with input of a string and output of a token list. + data_select: a string or tuple for the returned datasets + (Default: ('train', 'dev')) + By default, all the two datasets (train, dev) are generated. Users + could also choose any one of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets import SQuAD1 + >>> from torchtext.data.utils import get_tokenizer + >>> train, dev = SQuAD1() + >>> tokenizer = get_tokenizer("spacy") + >>> train, dev = SQuAD1(tokenizer=tokenizer) + """ + + return _setup_datasets(*(('SQuAD1',) + args), **kwargs) + + +def SQuAD2(*args, **kwargs): + """ Defines SQuAD2 datasets. + + Create question answer dataset: SQuAD2 + + Separately returns the train and dev dataset + + Arguments: + root: Directory where the datasets are saved. Default: ".data" + vocab: Vocabulary used for dataset. If None, it will generate a new + vocabulary based on the train data set. + tokenizer: the tokenizer used to preprocess raw text data. + The default one is basic_english tokenizer in fastText. spacy tokenizer + is supported as well. A custom tokenizer is callable + function with input of a string and output of a token list. + data_select: a string or tuple for the returned datasets + (Default: ('train', 'dev')) + By default, all the two datasets (train, dev) are generated. Users + could also choose any one of them, for example ('train', 'test') or + just a string 'train'. If 'train' is not in the tuple or string, a vocab + object should be provided which will be used to process valid and/or test + data. + + Examples: + >>> from torchtext.experimental.datasets import SQuAD2 + >>> from torchtext.data.utils import get_tokenizer + >>> train, dev = SQuAD2() + >>> tokenizer = get_tokenizer("spacy") + >>> train, dev = SQuAD2(tokenizer=tokenizer) + """ + return _setup_datasets(*(('SQuAD2',) + args), **kwargs) + + +DATASETS = { + 'SQuAD1': SQuAD1, + 'SQuAD2': SQuAD2 +} diff --git a/torchtext/experimental/datasets/raw/__init__.py b/torchtext/experimental/datasets/raw/__init__.py index 26892050d6..ad100429b0 100644 --- a/torchtext/experimental/datasets/raw/__init__.py +++ b/torchtext/experimental/datasets/raw/__init__.py @@ -3,6 +3,7 @@ AmazonReviewPolarity, AmazonReviewFull, IMDB from .translation import Multi30k, IWSLT, WMT14 from .language_modeling import WikiText2, WikiText103, PennTreebank, WMTNewsCrawl +from .question_answer import SQuAD1, SQuAD2 __all__ = ['IMDB', 'AG_NEWS', @@ -19,4 +20,5 @@ 'WikiText2', 'WikiText103', 'PennTreebank', - 'WMTNewsCrawl'] + 'WMTNewsCrawl', + 'SQuAD1', 'SQuAD2'] diff --git a/torchtext/experimental/datasets/raw/question_answer.py b/torchtext/experimental/datasets/raw/question_answer.py new file mode 100644 index 0000000000..528e3e9930 --- /dev/null +++ b/torchtext/experimental/datasets/raw/question_answer.py @@ -0,0 +1,93 @@ +import torch +from torchtext.utils import download_from_url +import json + +URLS = { + 'SQuAD1': + ['https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json', + 'https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json'], + 'SQuAD2': + ['https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json', + 'https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json'] +} + + +def _create_data_from_json(data_path): + with open(data_path) as json_file: + raw_json_data = json.load(json_file)['data'] + for layer1 in raw_json_data: + for layer2 in layer1['paragraphs']: + for layer3 in layer2['qas']: + processed = {'context': layer2['context'], 'question': layer3['question'], + 'answers': [item['text'] for item in layer3['answers']], + 'answer_start': [item['answer_start'] for item in layer3['answers']]} + if len(processed['answers']) == 0: + processed['answers'] = [""] + processed['answer_start'] = [-1] + yield processed + + +class RawQuestionAnswerDataset(torch.utils.data.IterableDataset): + """Defines an abstraction for raw question answer iterable datasets. + """ + + def __init__(self, iterator): + """Initiate text-classification dataset. + """ + super(RawQuestionAnswerDataset, self).__init__() + self._iterator = iterator + self.has_setup = False + self.start = 0 + self.num_lines = None + + def setup_iter(self, start=0, num_lines=None): + self.start = start + self.num_lines = num_lines + self.has_setup = True + + def __iter__(self): + if not self.has_setup: + self.setup_iter() + + for i, item in enumerate(self._iterator): + if i >= self.start: + yield item + if self.num_lines is not None and i == (self.start + self.num_lines): + break + + +def _setup_datasets(dataset_name, root='.data'): + extracted_files = [] + select_to_index = {'train': 0, 'dev': 1} + extracted_files = [download_from_url(URLS[dataset_name][select_to_index[key]], + root=root) for key in select_to_index.keys()] + train_iter = _create_data_from_json(extracted_files[0]) + dev_iter = _create_data_from_json(extracted_files[1]) + return (RawQuestionAnswerDataset(train_iter), + RawQuestionAnswerDataset(dev_iter)) + + +def SQuAD1(*args, **kwargs): + """ Defines SQuAD1 datasets. + + Examples: + >>> train, dev = torchtext.experimental.datasets.raw.SQuAD1() + """ + + return _setup_datasets(*(("SQuAD1",) + args), **kwargs) + + +def SQuAD2(*args, **kwargs): + """ Defines SQuAD2 datasets. + + Examples: + >>> train, dev = torchtext.experimental.datasets.raw.SQuAD2() + """ + + return _setup_datasets(*(("SQuAD2",) + args), **kwargs) + + +DATASETS = { + 'SQuAD1': SQuAD1, + 'SQuAD2': SQuAD2 +} From 0c7f9887d87a50215c7cf25653b36f7c9bedf4bb Mon Sep 17 00:00:00 2001 From: Nayef Ahmed <22487263+Nayef211@users.noreply.github.com> Date: Wed, 10 Jun 2020 14:35:41 -0400 Subject: [PATCH 077/719] Update build_tools/setup_helpers/extension.py Co-authored-by: moto <855818+mthrok@users.noreply.github.com> --- build_tools/setup_helpers/extension.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index c73935b794..65e58126c9 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -110,7 +110,7 @@ def _build_sentence_piece(debug): def _build_re2(debug): - build_dir = _TP_BASE_DIR / 're2' / 'build' + build_dir = _TP_BASE_DIR / 're2' / 'build_dir' # simply calling 'build_dir.mkdir(exist_ok=True)' fails on Windows CI Job if not build_dir.exists(): build_dir.mkdir() From b086d7814106fadbaad72d8788f1871671fe4929 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 10 Jun 2020 14:26:02 -0700 Subject: [PATCH 078/719] fixing windows CI --- build_tools/setup_helpers/extension.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index c73935b794..7171b23057 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -111,9 +111,7 @@ def _build_sentence_piece(debug): def _build_re2(debug): build_dir = _TP_BASE_DIR / 're2' / 'build' - # simply calling 'build_dir.mkdir(exist_ok=True)' fails on Windows CI Job - if not build_dir.exists(): - build_dir.mkdir() + build_dir.mkdir(exist_ok=True) build_env = os.environ.copy() config = 'Debug' if debug else 'Release' if platform.system() == 'Windows': From c1ef70ccc1e0dd66106498453b7d127b792a7f02 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 10 Jun 2020 16:47:35 -0700 Subject: [PATCH 079/719] Added new vector class --- torchtext/csrc/vectors.cpp | 59 +++++++++++++++++++++++++++++++ torchtext/experimental/vectors.py | 23 ++++++++++++ 2 files changed, 82 insertions(+) create mode 100644 torchtext/csrc/vectors.cpp create mode 100644 torchtext/experimental/vectors.py diff --git a/torchtext/csrc/vectors.cpp b/torchtext/csrc/vectors.cpp new file mode 100644 index 0000000000..5f3018ebcd --- /dev/null +++ b/torchtext/csrc/vectors.cpp @@ -0,0 +1,59 @@ +#include +#include + +namespace torchtext { +namespace { + +struct Vectors : torch::CustomClassHolder { +private: + std::unordered_map stoi_; + +public: + // tokens_ and vectors_ holds the serialized params passed in during + // initialization. We need this because we need to be able to serialize the + // model so that we can save the scripted object. Pickle will get the + // serialized model from these members, thus they needs to be public. + std::vector tokens_; + std::vector vectors_; + + explicit Vectors(std::vector tokens, + std::vector vectors) + : tokens_(tokens), vectors_(vectors) { + for (std::size_t i = 0; i < tokens.size(); i++) { + stoi_[tokens[i]] = static_cast(i); + } + } + + torch::Tensor GetItem(const std::string &token) const { + return vectors_.at(stoi_.at(token)); + } + + bool TokenExists(const std::string &token) const { + return stoi_.find(token) != stoi_.end(); + } +}; + +// Registers our custom class with torch. +static auto vectors = + torch::class_("torchtext", "Vectors") + .def( + torch::init, std::vector>()) + .def("GetItem", &Vectors::GetItem) + .def("TokenExists", &Vectors::TokenExists) + .def_pickle( + // __getstate__ + [](const c10::intrusive_ptr &self) -> std::pair< + std::vector, std::vector> { + std::pair, std::vector> + pair(self->tokens_, self->vectors_); + return pair; + }, + // __setstate__ + [](std::pair, std::vector> + states) -> c10::intrusive_ptr { + return c10::make_intrusive(std::move(states.first), + std::move(states.second)); + }); + +} // namespace +} // namespace torchtext diff --git a/torchtext/experimental/vectors.py b/torchtext/experimental/vectors.py new file mode 100644 index 0000000000..0e256c4aec --- /dev/null +++ b/torchtext/experimental/vectors.py @@ -0,0 +1,23 @@ +import torch + + +class Vectors(object): + """Creates a vectors object. + + Arguments: + tokens (List[str]):: a list of tokens. + vectors (List[torch.Tensor]): a list of 1d tensors representing the vector associated with each token + + """ + + def __init__(self, tokens, vectors): + self.unk_tensor = torch.zeros(vectors[0].size()) + self.vectors = torch.classes.torchtext.Vectors(tokens, vectors) + + def __getitem__(self, token): + + if self.vectors.TokenExists(token): + return self.vectors.GetItem(token) + else: + return self.unk_tensor + From 359c774e0c889a33c3a72c14cc1d42b44636d358 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Thu, 11 Jun 2020 09:35:12 -0700 Subject: [PATCH 080/719] Added tests --- test/experimental/test_vector.py | 23 +++++++++++++++++++++++ torchtext/csrc/vectors.cpp | 15 ++++++++------- torchtext/experimental/vectors.py | 2 +- 3 files changed, 32 insertions(+), 8 deletions(-) create mode 100644 test/experimental/test_vector.py diff --git a/test/experimental/test_vector.py b/test/experimental/test_vector.py new file mode 100644 index 0000000000..fe39ee24b8 --- /dev/null +++ b/test/experimental/test_vector.py @@ -0,0 +1,23 @@ +# -*- coding: utf-8 -*- +import numpy as np +from numpy.testing import assert_allclose +import torch +import torchtext + +from test.common.torchtext_test_case import TorchtextTestCase +from torchtext.experimental.vectors import Vectors + + +class TestVectors(TorchtextTestCase): + def test_vectors(self): + tensorA = torch.Tensor([1, 0, 0]) + tensorB = torch.Tensor([0, 1, 0]) + expected_unk_tensor = torch.Tensor([0, 0, 0]) + + tokens = ['a', 'b'] + vectors = [tensorA, tensorB] + vectorsObj = Vectors(tokens, vectors) + + self.assertEqual(vectorsObj['empty'].tolist(), expected_unk_tensor.tolist()) + # assert_allclose(vectorsObj['a'].numpy(), tensorA.numpy()) + # assert_allclose(vectorsObj['b'].numpy(), tensorB.numpy()) \ No newline at end of file diff --git a/torchtext/csrc/vectors.cpp b/torchtext/csrc/vectors.cpp index 5f3018ebcd..21b3372407 100644 --- a/torchtext/csrc/vectors.cpp +++ b/torchtext/csrc/vectors.cpp @@ -42,17 +42,18 @@ static auto vectors = .def("TokenExists", &Vectors::TokenExists) .def_pickle( // __getstate__ - [](const c10::intrusive_ptr &self) -> std::pair< + [](const c10::intrusive_ptr &self) -> std::tuple< std::vector, std::vector> { - std::pair, std::vector> - pair(self->tokens_, self->vectors_); - return pair; + std::tuple, std::vector> + states(self->tokens_, self->vectors_); + return states; }, // __setstate__ - [](std::pair, std::vector> + [](std::tuple, std::vector> states) -> c10::intrusive_ptr { - return c10::make_intrusive(std::move(states.first), - std::move(states.second)); + return c10::make_intrusive( + std::move(std::get<0>(states)), + std::move(std::get<1>(states))); }); } // namespace diff --git a/torchtext/experimental/vectors.py b/torchtext/experimental/vectors.py index 0e256c4aec..30533f58c2 100644 --- a/torchtext/experimental/vectors.py +++ b/torchtext/experimental/vectors.py @@ -1,4 +1,5 @@ import torch +import torchtext class Vectors(object): @@ -20,4 +21,3 @@ def __getitem__(self, token): return self.vectors.GetItem(token) else: return self.unk_tensor - From 5aa3553097e8fdb878c53bc5ca8d9fe72c428293 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Thu, 11 Jun 2020 12:00:07 -0700 Subject: [PATCH 081/719] resolving PR comments --- test/experimental/{test_vector.py => test_vectors.py} | 9 +++------ torchtext/csrc/vectors.cpp | 8 ++++---- torchtext/experimental/vectors.py | 2 -- 3 files changed, 7 insertions(+), 12 deletions(-) rename test/experimental/{test_vector.py => test_vectors.py} (71%) diff --git a/test/experimental/test_vector.py b/test/experimental/test_vectors.py similarity index 71% rename from test/experimental/test_vector.py rename to test/experimental/test_vectors.py index fe39ee24b8..e892830298 100644 --- a/test/experimental/test_vector.py +++ b/test/experimental/test_vectors.py @@ -1,8 +1,5 @@ # -*- coding: utf-8 -*- -import numpy as np -from numpy.testing import assert_allclose import torch -import torchtext from test.common.torchtext_test_case import TorchtextTestCase from torchtext.experimental.vectors import Vectors @@ -13,11 +10,11 @@ def test_vectors(self): tensorA = torch.Tensor([1, 0, 0]) tensorB = torch.Tensor([0, 1, 0]) expected_unk_tensor = torch.Tensor([0, 0, 0]) - + tokens = ['a', 'b'] vectors = [tensorA, tensorB] vectorsObj = Vectors(tokens, vectors) self.assertEqual(vectorsObj['empty'].tolist(), expected_unk_tensor.tolist()) - # assert_allclose(vectorsObj['a'].numpy(), tensorA.numpy()) - # assert_allclose(vectorsObj['b'].numpy(), tensorB.numpy()) \ No newline at end of file + self.assertEqual(vectorsObj['a'].tolist(), tensorA.tolist()) + self.assertEqual(vectorsObj['b'].tolist(), tensorB.tolist()) diff --git a/torchtext/csrc/vectors.cpp b/torchtext/csrc/vectors.cpp index 21b3372407..1a1595eac3 100644 --- a/torchtext/csrc/vectors.cpp +++ b/torchtext/csrc/vectors.cpp @@ -6,7 +6,7 @@ namespace { struct Vectors : torch::CustomClassHolder { private: - std::unordered_map stoi_; + std::unordered_map stovectors_; public: // tokens_ and vectors_ holds the serialized params passed in during @@ -20,16 +20,16 @@ struct Vectors : torch::CustomClassHolder { std::vector vectors) : tokens_(tokens), vectors_(vectors) { for (std::size_t i = 0; i < tokens.size(); i++) { - stoi_[tokens[i]] = static_cast(i); + stovectors_[tokens[i]] = vectors_[i]; } } torch::Tensor GetItem(const std::string &token) const { - return vectors_.at(stoi_.at(token)); + return stovectors_.at(token); } bool TokenExists(const std::string &token) const { - return stoi_.find(token) != stoi_.end(); + return stovectors_.find(token) != stovectors_.end(); } }; diff --git a/torchtext/experimental/vectors.py b/torchtext/experimental/vectors.py index 30533f58c2..f80a1618dd 100644 --- a/torchtext/experimental/vectors.py +++ b/torchtext/experimental/vectors.py @@ -1,5 +1,4 @@ import torch -import torchtext class Vectors(object): @@ -16,7 +15,6 @@ def __init__(self, tokens, vectors): self.vectors = torch.classes.torchtext.Vectors(tokens, vectors) def __getitem__(self, token): - if self.vectors.TokenExists(token): return self.vectors.GetItem(token) else: From 2aa8858272d9b46b71194616ab948a087081207f Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Fri, 12 Jun 2020 09:52:49 -0400 Subject: [PATCH 082/719] add translation datasets in __init__ file (#821) --- test/data/test_builtin_datasets.py | 2 +- torchtext/experimental/datasets/__init__.py | 4 ++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/test/data/test_builtin_datasets.py b/test/data/test_builtin_datasets.py index 6acc9e6d0f..4038199a22 100644 --- a/test/data/test_builtin_datasets.py +++ b/test/data/test_builtin_datasets.py @@ -126,7 +126,7 @@ def test_imdb(self): new_train_data, new_test_data = IMDB(vocab=new_vocab) def test_multi30k(self): - from torchtext.experimental.datasets.translation import Multi30k + from torchtext.experimental.datasets import Multi30k # smoke test to ensure multi30k works properly train_dataset, valid_dataset, test_dataset = Multi30k() self.assertEqual(len(train_dataset), 29000) diff --git a/torchtext/experimental/datasets/__init__.py b/torchtext/experimental/datasets/__init__.py index 9009e0dc1f..7929f5f733 100644 --- a/torchtext/experimental/datasets/__init__.py +++ b/torchtext/experimental/datasets/__init__.py @@ -2,6 +2,7 @@ from .text_classification import AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, \ YelpReviewFull, YahooAnswers, \ AmazonReviewPolarity, AmazonReviewFull, IMDB +from .translation import Multi30k, IWSLT, WMT14 from .question_answer import SQuAD1, SQuAD2 __all__ = ['LanguageModelingDataset', @@ -18,4 +19,7 @@ 'YahooAnswers', 'AmazonReviewPolarity', 'AmazonReviewFull', + 'Multi30k', + 'IWSLT', + 'WMT14', 'SQuAD1', 'SQuAD2'] From 2c6008bde7301ac287a4607f7c92b981f2d2e547 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Fri, 12 Jun 2020 10:21:30 -0700 Subject: [PATCH 083/719] added new factory function and tests --- test/asset/vectors_test.csv | 2 ++ test/experimental/test_vectors.py | 50 ++++++++++++++++++++++++++++--- torchtext/csrc/vectors.cpp | 46 ++++++++++++++++------------ torchtext/experimental/vectors.py | 35 ++++++++++++++++------ 4 files changed, 101 insertions(+), 32 deletions(-) create mode 100644 test/asset/vectors_test.csv diff --git a/test/asset/vectors_test.csv b/test/asset/vectors_test.csv new file mode 100644 index 0000000000..a1dcfa645c --- /dev/null +++ b/test/asset/vectors_test.csv @@ -0,0 +1,2 @@ +a,1 0 0 +b,0 1 0 \ No newline at end of file diff --git a/test/experimental/test_vectors.py b/test/experimental/test_vectors.py index e892830298..4997e66f7a 100644 --- a/test/experimental/test_vectors.py +++ b/test/experimental/test_vectors.py @@ -1,20 +1,62 @@ # -*- coding: utf-8 -*- import torch +from test.common.assets import get_asset_path from test.common.torchtext_test_case import TorchtextTestCase -from torchtext.experimental.vectors import Vectors +from torchtext.experimental.vectors import ( + Vectors, + vectors_from_csv_file +) class TestVectors(TorchtextTestCase): - def test_vectors(self): + + def test_has_unk(self): + tensorA = torch.Tensor([1, 0]) + expected_unk_tensor = torch.Tensor([0, 0]) + + tokens = ['a'] + vectors = [tensorA] + vectorsObj = Vectors(tokens, vectors) + + self.assertEqual(vectorsObj['not_in_it'].tolist(), expected_unk_tensor.tolist()) + + def test_vectors_basic(self): + tensorA = torch.Tensor([1, 0]) + tensorB = torch.Tensor([0, 1]) + unk_tensor = torch.Tensor([0, 0]) + + tokens = ['a', 'b'] + vectors = [tensorA, tensorB] + vectorsObj = Vectors(tokens, vectors, unk_tensor=unk_tensor) + + self.assertEqual(vectorsObj['a'].tolist(), tensorA.tolist()) + self.assertEqual(vectorsObj['b'].tolist(), tensorB.tolist()) + self.assertEqual(vectorsObj['not_in_it'].tolist(), unk_tensor.tolist()) + + def test_mismatch_vectors_tokens(self): tensorA = torch.Tensor([1, 0, 0]) tensorB = torch.Tensor([0, 1, 0]) expected_unk_tensor = torch.Tensor([0, 0, 0]) - tokens = ['a', 'b'] + tokens = ['a', 'b', 'c'] vectors = [tensorA, tensorB] vectorsObj = Vectors(tokens, vectors) - self.assertEqual(vectorsObj['empty'].tolist(), expected_unk_tensor.tolist()) self.assertEqual(vectorsObj['a'].tolist(), tensorA.tolist()) self.assertEqual(vectorsObj['b'].tolist(), tensorB.tolist()) + self.assertEqual(vectorsObj['c'].tolist(), expected_unk_tensor.tolist()) + + def test_vectors_from_file(self): + asset_name = 'vectors_test.csv' + asset_path = get_asset_path(asset_name) + f = open(asset_path, 'r') + vectorsObj = vectors_from_csv_file(f) + + expected_tensorA = torch.Tensor([1, 0, 0]) + expected_tensorB = torch.Tensor([0, 1, 0]) + expected_unk_tensor = torch.Tensor([0, 0, 0]) + + self.assertEqual(vectorsObj['a'].tolist(), expected_tensorA.tolist()) + self.assertEqual(vectorsObj['b'].tolist(), expected_tensorB.tolist()) + self.assertEqual(vectorsObj['not_in_it'].tolist(), expected_unk_tensor.tolist()) diff --git a/torchtext/csrc/vectors.cpp b/torchtext/csrc/vectors.cpp index 1a1595eac3..d52905499b 100644 --- a/torchtext/csrc/vectors.cpp +++ b/torchtext/csrc/vectors.cpp @@ -9,51 +9,59 @@ struct Vectors : torch::CustomClassHolder { std::unordered_map stovectors_; public: - // tokens_ and vectors_ holds the serialized params passed in during - // initialization. We need this because we need to be able to serialize the - // model so that we can save the scripted object. Pickle will get the + // tokens_, vectors_, and unk_tensor_ holds the serialized params passed in + // during initialization. We need this because we need to be able to serialize + // the model so that we can save the scripted object. Pickle will get the // serialized model from these members, thus they needs to be public. std::vector tokens_; std::vector vectors_; + torch::Tensor unk_tensor_; explicit Vectors(std::vector tokens, - std::vector vectors) - : tokens_(tokens), vectors_(vectors) { + std::vector vectors, torch::Tensor unk_tensor) + : tokens_(tokens), vectors_(vectors), unk_tensor_(unk_tensor) { for (std::size_t i = 0; i < tokens.size(); i++) { + // guarding against size mismatch of vectors and tokens + if (i >= vectors.size()) { + break; + } + stovectors_[tokens[i]] = vectors_[i]; } } torch::Tensor GetItem(const std::string &token) const { - return stovectors_.at(token); - } - - bool TokenExists(const std::string &token) const { - return stovectors_.find(token) != stovectors_.end(); + if (stovectors_.find(token) != stovectors_.end()) { + return stovectors_.at(token); + } + return unk_tensor_; } }; // Registers our custom class with torch. static auto vectors = torch::class_("torchtext", "Vectors") - .def( - torch::init, std::vector>()) + .def(torch::init, std::vector, + torch::Tensor>()) .def("GetItem", &Vectors::GetItem) - .def("TokenExists", &Vectors::TokenExists) .def_pickle( // __getstate__ - [](const c10::intrusive_ptr &self) -> std::tuple< - std::vector, std::vector> { - std::tuple, std::vector> - states(self->tokens_, self->vectors_); + [](const c10::intrusive_ptr &self) + -> std::tuple, + std::vector, torch::Tensor> { + std::tuple, std::vector, + torch::Tensor> + states(self->tokens_, self->vectors_, self->unk_tensor_); return states; }, // __setstate__ - [](std::tuple, std::vector> + [](std::tuple, std::vector, + torch::Tensor> states) -> c10::intrusive_ptr { return c10::make_intrusive( std::move(std::get<0>(states)), - std::move(std::get<1>(states))); + std::move(std::get<1>(states)), + std::move(std::get<2>(states))); }); } // namespace diff --git a/torchtext/experimental/vectors.py b/torchtext/experimental/vectors.py index f80a1618dd..63fdf4ce62 100644 --- a/torchtext/experimental/vectors.py +++ b/torchtext/experimental/vectors.py @@ -1,21 +1,38 @@ +import csv import torch +def vectors_from_csv_file(file_like_object, unk_tensor=None): + r"""Create a Vectors object from a csv file like object. + + Args: + file_like_object (FileObject): a file like object to read data from. + unk_tensor (int): a 1d tensors representing the vector associated with an unknown token + Returns: + Vectors: a Vectors object. + """ + readCSV = csv.reader(file_like_object, delimiter=',') + + tuples = list(map(lambda x: (x[0], torch.tensor([float(c) for c in x[1].split()], dtype=torch.float)), readCSV)) + words = [pair[0] for pair in tuples] + vectors = [pair[1] for pair in tuples] + + return Vectors(words, vectors, unk_tensor=unk_tensor) + + class Vectors(object): - """Creates a vectors object. + r"""Creates a vectors object which maps tokens to vectors. Arguments: tokens (List[str]):: a list of tokens. - vectors (List[torch.Tensor]): a list of 1d tensors representing the vector associated with each token + vectors (List[torch.Tensor]): a list of 1d tensors representing the vector associated with each token. + unk_tensor (torch.Tensor): a 1d tensors representing the vector associated with an unknown token. """ - def __init__(self, tokens, vectors): - self.unk_tensor = torch.zeros(vectors[0].size()) - self.vectors = torch.classes.torchtext.Vectors(tokens, vectors) + def __init__(self, tokens, vectors, unk_tensor=None): + unk_tensor = unk_tensor if unk_tensor is not None else torch.zeros(vectors[0].size()) + self.vectors = torch.classes.torchtext.Vectors(tokens, vectors, unk_tensor) def __getitem__(self, token): - if self.vectors.TokenExists(token): - return self.vectors.GetItem(token) - else: - return self.unk_tensor + return self.vectors.GetItem(token) From f4d40ac43da37b253ff24ee5c1b2b4ece23105fe Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Fri, 12 Jun 2020 11:46:00 -0700 Subject: [PATCH 084/719] Resolving PR comments --- test/experimental/test_vectors.py | 39 ++++++++++++++++++++++++------- torchtext/csrc/vectors.cpp | 18 ++++++++++---- torchtext/experimental/vectors.py | 15 ++++++++---- 3 files changed, 55 insertions(+), 17 deletions(-) diff --git a/test/experimental/test_vectors.py b/test/experimental/test_vectors.py index 4997e66f7a..a39ed7fad0 100644 --- a/test/experimental/test_vectors.py +++ b/test/experimental/test_vectors.py @@ -11,7 +11,15 @@ class TestVectors(TorchtextTestCase): - def test_has_unk(self): + def test_empty_vector(self): + tokens = [] + vectors = [] + unk_tensor = torch.Tensor([0]) + + vectorsObj = Vectors(tokens, vectors, unk_tensor) + self.assertEqual(vectorsObj['not_in_it'].tolist(), unk_tensor.tolist()) + + def test_empty_unk(self): tensorA = torch.Tensor([1, 0]) expected_unk_tensor = torch.Tensor([0, 0]) @@ -34,18 +42,33 @@ def test_vectors_basic(self): self.assertEqual(vectorsObj['b'].tolist(), tensorB.tolist()) self.assertEqual(vectorsObj['not_in_it'].tolist(), unk_tensor.tolist()) - def test_mismatch_vectors_tokens(self): + def test_errors(self): + tokens = [] + vectors = [] + + with self.assertRaises(ValueError): + # Test proper error raised when passing in empty tokens and vectors and + # not passing in a user defined unk_tensor + Vectors(tokens, vectors) + tensorA = torch.Tensor([1, 0, 0]) tensorB = torch.Tensor([0, 1, 0]) - expected_unk_tensor = torch.Tensor([0, 0, 0]) - tokens = ['a', 'b', 'c'] vectors = [tensorA, tensorB] - vectorsObj = Vectors(tokens, vectors) - self.assertEqual(vectorsObj['a'].tolist(), tensorA.tolist()) - self.assertEqual(vectorsObj['b'].tolist(), tensorB.tolist()) - self.assertEqual(vectorsObj['c'].tolist(), expected_unk_tensor.tolist()) + with self.assertRaises(RuntimeError): + # Test proper error raised when tokens and vectors have different sizes + Vectors(tokens, vectors) + + tensorC = torch.Tensor([0, 0, 1]) + tokens = ['a', 'a', 'c'] + vectors = [tensorA, tensorB, tensorC] + + with self.assertRaises(RuntimeError): + # Test proper error raised when tokens have duplicates + # TODO (Nayef211): use self.assertRaisesRegex() to check + # the key of the duplicate token in the error message + Vectors(tokens, vectors) def test_vectors_from_file(self): asset_name = 'vectors_test.csv' diff --git a/torchtext/csrc/vectors.cpp b/torchtext/csrc/vectors.cpp index d52905499b..4be4cec5d5 100644 --- a/torchtext/csrc/vectors.cpp +++ b/torchtext/csrc/vectors.cpp @@ -1,3 +1,4 @@ +#include #include #include @@ -20,12 +21,21 @@ struct Vectors : torch::CustomClassHolder { explicit Vectors(std::vector tokens, std::vector vectors, torch::Tensor unk_tensor) : tokens_(tokens), vectors_(vectors), unk_tensor_(unk_tensor) { + // guarding against size mismatch of vectors and tokens + if (tokens.size() != vectors.size()) { + throw std::runtime_error( + "Mismatching sizes for tokens and vectors. Size of tokens: " + + std::to_string(tokens.size()) + ", size of vectors: " + + std::to_string(vectors.size()) + "."); + } + + stovectors_.reserve(tokens.size()); for (std::size_t i = 0; i < tokens.size(); i++) { - // guarding against size mismatch of vectors and tokens - if (i >= vectors.size()) { - break; + // tokens should not have any duplicates + if (stovectors_.find(tokens[i]) != stovectors_.end()) { + throw std::runtime_error("Duplicate token found in tokens list: " + + tokens[i]); } - stovectors_[tokens[i]] = vectors_[i]; } } diff --git a/torchtext/experimental/vectors.py b/torchtext/experimental/vectors.py index 63fdf4ce62..2a2914d294 100644 --- a/torchtext/experimental/vectors.py +++ b/torchtext/experimental/vectors.py @@ -13,24 +13,29 @@ def vectors_from_csv_file(file_like_object, unk_tensor=None): """ readCSV = csv.reader(file_like_object, delimiter=',') - tuples = list(map(lambda x: (x[0], torch.tensor([float(c) for c in x[1].split()], dtype=torch.float)), readCSV)) - words = [pair[0] for pair in tuples] - vectors = [pair[1] for pair in tuples] + tokens = [] + vectors = [] + for row in readCSV: + tokens.append(row[0]) + vectors.append(torch.tensor([float(c) for c in row[1].split()])) - return Vectors(words, vectors, unk_tensor=unk_tensor) + return Vectors(tokens, vectors, unk_tensor=unk_tensor) class Vectors(object): r"""Creates a vectors object which maps tokens to vectors. Arguments: - tokens (List[str]):: a list of tokens. + tokens (List[str]): a list of tokens. vectors (List[torch.Tensor]): a list of 1d tensors representing the vector associated with each token. unk_tensor (torch.Tensor): a 1d tensors representing the vector associated with an unknown token. """ def __init__(self, tokens, vectors, unk_tensor=None): + if unk_tensor is None and not vectors: + raise ValueError("The vectors list is empty and a default unk_tensor wasn't provided.") + unk_tensor = unk_tensor if unk_tensor is not None else torch.zeros(vectors[0].size()) self.vectors = torch.classes.torchtext.Vectors(tokens, vectors, unk_tensor) From 3b0e9150111068b5a59c57d4acc6fb4783e51062 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Fri, 12 Jun 2020 12:09:31 -0700 Subject: [PATCH 085/719] Resolving PR comments --- test/experimental/test_vectors.py | 33 ++++++++++++++++++++++--------- torchtext/csrc/vectors.cpp | 10 ++++++++-- torchtext/experimental/vectors.py | 10 +++++++--- 3 files changed, 39 insertions(+), 14 deletions(-) diff --git a/test/experimental/test_vectors.py b/test/experimental/test_vectors.py index a39ed7fad0..20edc29a58 100644 --- a/test/experimental/test_vectors.py +++ b/test/experimental/test_vectors.py @@ -17,7 +17,7 @@ def test_empty_vector(self): unk_tensor = torch.Tensor([0]) vectorsObj = Vectors(tokens, vectors, unk_tensor) - self.assertEqual(vectorsObj['not_in_it'].tolist(), unk_tensor.tolist()) + self.assertEqual(vectorsObj['not_in_it'].long().tolist(), unk_tensor.tolist()) def test_empty_unk(self): tensorA = torch.Tensor([1, 0]) @@ -27,20 +27,35 @@ def test_empty_unk(self): vectors = [tensorA] vectorsObj = Vectors(tokens, vectors) - self.assertEqual(vectorsObj['not_in_it'].tolist(), expected_unk_tensor.tolist()) + self.assertEqual(vectorsObj['not_in_it'].long().tolist(), expected_unk_tensor.tolist()) def test_vectors_basic(self): tensorA = torch.Tensor([1, 0]) tensorB = torch.Tensor([0, 1]) - unk_tensor = torch.Tensor([0, 0]) + unk_tensor = torch.Tensor([0, 0]) tokens = ['a', 'b'] vectors = [tensorA, tensorB] vectorsObj = Vectors(tokens, vectors, unk_tensor=unk_tensor) - self.assertEqual(vectorsObj['a'].tolist(), tensorA.tolist()) - self.assertEqual(vectorsObj['b'].tolist(), tensorB.tolist()) - self.assertEqual(vectorsObj['not_in_it'].tolist(), unk_tensor.tolist()) + self.assertEqual(vectorsObj['a'].long().tolist(), tensorA.tolist()) + self.assertEqual(vectorsObj['b'].long().tolist(), tensorB.tolist()) + self.assertEqual(vectorsObj['not_in_it'].long().tolist(), unk_tensor.tolist()) + + def test_vectors_add_item(self): + tensorA = torch.Tensor([1, 0]) + unk_tensor = torch.Tensor([0, 0]) + + tokens = ['a'] + vectors = [tensorA] + vectorsObj = Vectors(tokens, vectors, unk_tensor=unk_tensor) + + tensorB = torch.Tensor([0, 1]) + vectorsObj['b'] = tensorB + + self.assertEqual(vectorsObj['a'].long().tolist(), tensorA.tolist()) + self.assertEqual(vectorsObj['b'].long().tolist(), tensorB.tolist()) + self.assertEqual(vectorsObj['not_in_it'].long().tolist(), unk_tensor.tolist()) def test_errors(self): tokens = [] @@ -80,6 +95,6 @@ def test_vectors_from_file(self): expected_tensorB = torch.Tensor([0, 1, 0]) expected_unk_tensor = torch.Tensor([0, 0, 0]) - self.assertEqual(vectorsObj['a'].tolist(), expected_tensorA.tolist()) - self.assertEqual(vectorsObj['b'].tolist(), expected_tensorB.tolist()) - self.assertEqual(vectorsObj['not_in_it'].tolist(), expected_unk_tensor.tolist()) + self.assertEqual(vectorsObj['a'].long().tolist(), expected_tensorA.tolist()) + self.assertEqual(vectorsObj['b'].long().tolist(), expected_tensorB.tolist()) + self.assertEqual(vectorsObj['not_in_it'].long().tolist(), expected_unk_tensor.tolist()) diff --git a/torchtext/csrc/vectors.cpp b/torchtext/csrc/vectors.cpp index 4be4cec5d5..c7451eec37 100644 --- a/torchtext/csrc/vectors.cpp +++ b/torchtext/csrc/vectors.cpp @@ -18,8 +18,9 @@ struct Vectors : torch::CustomClassHolder { std::vector vectors_; torch::Tensor unk_tensor_; - explicit Vectors(std::vector tokens, - std::vector vectors, torch::Tensor unk_tensor) + explicit Vectors(const std::vector &tokens, + const std::vector &vectors, + const torch::Tensor &unk_tensor) : tokens_(tokens), vectors_(vectors), unk_tensor_(unk_tensor) { // guarding against size mismatch of vectors and tokens if (tokens.size() != vectors.size()) { @@ -46,6 +47,10 @@ struct Vectors : torch::CustomClassHolder { } return unk_tensor_; } + + void AddItem(const std::string &token, const torch::Tensor &vector) { + stovectors_[token] = vector; + } }; // Registers our custom class with torch. @@ -54,6 +59,7 @@ static auto vectors = .def(torch::init, std::vector, torch::Tensor>()) .def("GetItem", &Vectors::GetItem) + .def("AddItem", &Vectors::AddItem) .def_pickle( // __getstate__ [](const c10::intrusive_ptr &self) diff --git a/torchtext/experimental/vectors.py b/torchtext/experimental/vectors.py index 2a2914d294..9f3897cbbc 100644 --- a/torchtext/experimental/vectors.py +++ b/torchtext/experimental/vectors.py @@ -17,7 +17,7 @@ def vectors_from_csv_file(file_like_object, unk_tensor=None): vectors = [] for row in readCSV: tokens.append(row[0]) - vectors.append(torch.tensor([float(c) for c in row[1].split()])) + vectors.append(torch.tensor([float(c) for c in row[1].split()], dtype=torch.float)) return Vectors(tokens, vectors, unk_tensor=unk_tensor) @@ -36,8 +36,12 @@ def __init__(self, tokens, vectors, unk_tensor=None): if unk_tensor is None and not vectors: raise ValueError("The vectors list is empty and a default unk_tensor wasn't provided.") - unk_tensor = unk_tensor if unk_tensor is not None else torch.zeros(vectors[0].size()) - self.vectors = torch.classes.torchtext.Vectors(tokens, vectors, unk_tensor) + unk_tensor = unk_tensor if unk_tensor is not None else torch.zeros(vectors[0].size(), dtype=torch.float) + float_vectors = [vector.float() for vector in vectors] + self.vectors = torch.classes.torchtext.Vectors(tokens, float_vectors, unk_tensor) def __getitem__(self, token): return self.vectors.GetItem(token) + + def __setitem__(self, token, vector): + self.vectors.AddItem(token, vector.float()) From 0c6361c124dab15bb60cbff74d4fe3d9786184ba Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Fri, 12 Jun 2020 12:18:10 -0700 Subject: [PATCH 086/719] Resolving PR comments --- torchtext/experimental/vectors.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/torchtext/experimental/vectors.py b/torchtext/experimental/vectors.py index 9f3897cbbc..4fe03d9507 100644 --- a/torchtext/experimental/vectors.py +++ b/torchtext/experimental/vectors.py @@ -8,8 +8,10 @@ def vectors_from_csv_file(file_like_object, unk_tensor=None): Args: file_like_object (FileObject): a file like object to read data from. unk_tensor (int): a 1d tensors representing the vector associated with an unknown token + Returns: Vectors: a Vectors object. + """ readCSV = csv.reader(file_like_object, delimiter=',') @@ -30,6 +32,11 @@ class Vectors(object): vectors (List[torch.Tensor]): a list of 1d tensors representing the vector associated with each token. unk_tensor (torch.Tensor): a 1d tensors representing the vector associated with an unknown token. + Raises: + + ValueError: if `vectors` is empty and a default `unk_tensor` isn't provided. + RuntimeError: if `tokens` and `vectors` have different sizes or `tokens` has duplicates. + """ def __init__(self, tokens, vectors, unk_tensor=None): From 5ee98ee211ae333b8d69495d36217cac7665f7ad Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Tue, 16 Jun 2020 08:45:04 -0700 Subject: [PATCH 087/719] Added new jit test --- test/experimental/test_vectors.py | 50 ++++++++++++++++++++----------- 1 file changed, 33 insertions(+), 17 deletions(-) diff --git a/test/experimental/test_vectors.py b/test/experimental/test_vectors.py index 20edc29a58..5e1cabca5c 100644 --- a/test/experimental/test_vectors.py +++ b/test/experimental/test_vectors.py @@ -16,8 +16,8 @@ def test_empty_vector(self): vectors = [] unk_tensor = torch.Tensor([0]) - vectorsObj = Vectors(tokens, vectors, unk_tensor) - self.assertEqual(vectorsObj['not_in_it'].long().tolist(), unk_tensor.tolist()) + vectors_obj = Vectors(tokens, vectors, unk_tensor) + self.assertEqual(vectors_obj['not_in_it'].long().tolist(), unk_tensor.tolist()) def test_empty_unk(self): tensorA = torch.Tensor([1, 0]) @@ -25,9 +25,9 @@ def test_empty_unk(self): tokens = ['a'] vectors = [tensorA] - vectorsObj = Vectors(tokens, vectors) + vectors_obj = Vectors(tokens, vectors) - self.assertEqual(vectorsObj['not_in_it'].long().tolist(), expected_unk_tensor.tolist()) + self.assertEqual(vectors_obj['not_in_it'].long().tolist(), expected_unk_tensor.tolist()) def test_vectors_basic(self): tensorA = torch.Tensor([1, 0]) @@ -36,11 +36,27 @@ def test_vectors_basic(self): unk_tensor = torch.Tensor([0, 0]) tokens = ['a', 'b'] vectors = [tensorA, tensorB] - vectorsObj = Vectors(tokens, vectors, unk_tensor=unk_tensor) + vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor) - self.assertEqual(vectorsObj['a'].long().tolist(), tensorA.tolist()) - self.assertEqual(vectorsObj['b'].long().tolist(), tensorB.tolist()) - self.assertEqual(vectorsObj['not_in_it'].long().tolist(), unk_tensor.tolist()) + self.assertEqual(vectors_obj['a'].long().tolist(), tensorA.tolist()) + self.assertEqual(vectors_obj['b'].long().tolist(), tensorB.tolist()) + self.assertEqual(vectors_obj['not_in_it'].long().tolist(), unk_tensor.tolist()) + + def test_vectors_jit(self): + tensorA = torch.Tensor([1, 0]) + tensorB = torch.Tensor([0, 1]) + + unk_tensor = torch.Tensor([0, 0]) + tokens = ['a', 'b'] + vectors = [tensorA, tensorB] + vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor) + jit_vectors_obj = torch.jit.script(vectors_obj) + + jit_vectors_obj['a'] + jit_vectors_obj['b'] + # self.assertEqual(vectors_obj['a'].long().tolist(), jit_vectors_obj['a'].long().tolist()) + # self.assertEqual(vectors_obj['b'].long().tolist(), jit_vectors_obj['b'].long().tolist()) + # self.assertEqual(vectors_obj['not_in_it'].long().tolist(), jit_vectors_obj['not_in_it'].long().tolist()) def test_vectors_add_item(self): tensorA = torch.Tensor([1, 0]) @@ -48,14 +64,14 @@ def test_vectors_add_item(self): tokens = ['a'] vectors = [tensorA] - vectorsObj = Vectors(tokens, vectors, unk_tensor=unk_tensor) + vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor) tensorB = torch.Tensor([0, 1]) - vectorsObj['b'] = tensorB + vectors_obj['b'] = tensorB - self.assertEqual(vectorsObj['a'].long().tolist(), tensorA.tolist()) - self.assertEqual(vectorsObj['b'].long().tolist(), tensorB.tolist()) - self.assertEqual(vectorsObj['not_in_it'].long().tolist(), unk_tensor.tolist()) + self.assertEqual(vectors_obj['a'].long().tolist(), tensorA.tolist()) + self.assertEqual(vectors_obj['b'].long().tolist(), tensorB.tolist()) + self.assertEqual(vectors_obj['not_in_it'].long().tolist(), unk_tensor.tolist()) def test_errors(self): tokens = [] @@ -89,12 +105,12 @@ def test_vectors_from_file(self): asset_name = 'vectors_test.csv' asset_path = get_asset_path(asset_name) f = open(asset_path, 'r') - vectorsObj = vectors_from_csv_file(f) + vectors_obj = vectors_from_csv_file(f) expected_tensorA = torch.Tensor([1, 0, 0]) expected_tensorB = torch.Tensor([0, 1, 0]) expected_unk_tensor = torch.Tensor([0, 0, 0]) - self.assertEqual(vectorsObj['a'].long().tolist(), expected_tensorA.tolist()) - self.assertEqual(vectorsObj['b'].long().tolist(), expected_tensorB.tolist()) - self.assertEqual(vectorsObj['not_in_it'].long().tolist(), expected_unk_tensor.tolist()) + self.assertEqual(vectors_obj['a'].long().tolist(), expected_tensorA.tolist()) + self.assertEqual(vectors_obj['b'].long().tolist(), expected_tensorB.tolist()) + self.assertEqual(vectors_obj['not_in_it'].long().tolist(), expected_unk_tensor.tolist()) From 714d26d9227bcd0b756ed00c596f40a1e45b76d0 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Tue, 16 Jun 2020 08:47:01 -0700 Subject: [PATCH 088/719] updated jit test --- test/experimental/test_vectors.py | 8 +++----- torchtext/experimental/vectors.py | 2 +- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/test/experimental/test_vectors.py b/test/experimental/test_vectors.py index 5e1cabca5c..92c8ac866d 100644 --- a/test/experimental/test_vectors.py +++ b/test/experimental/test_vectors.py @@ -52,11 +52,9 @@ def test_vectors_jit(self): vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor) jit_vectors_obj = torch.jit.script(vectors_obj) - jit_vectors_obj['a'] - jit_vectors_obj['b'] - # self.assertEqual(vectors_obj['a'].long().tolist(), jit_vectors_obj['a'].long().tolist()) - # self.assertEqual(vectors_obj['b'].long().tolist(), jit_vectors_obj['b'].long().tolist()) - # self.assertEqual(vectors_obj['not_in_it'].long().tolist(), jit_vectors_obj['not_in_it'].long().tolist()) + self.assertEqual(vectors_obj['a'].long().tolist(), jit_vectors_obj['a'].long().tolist()) + self.assertEqual(vectors_obj['b'].long().tolist(), jit_vectors_obj['b'].long().tolist()) + self.assertEqual(vectors_obj['not_in_it'].long().tolist(), jit_vectors_obj['not_in_it'].long().tolist()) def test_vectors_add_item(self): tensorA = torch.Tensor([1, 0]) diff --git a/torchtext/experimental/vectors.py b/torchtext/experimental/vectors.py index 4fe03d9507..9764b59a04 100644 --- a/torchtext/experimental/vectors.py +++ b/torchtext/experimental/vectors.py @@ -8,7 +8,7 @@ def vectors_from_csv_file(file_like_object, unk_tensor=None): Args: file_like_object (FileObject): a file like object to read data from. unk_tensor (int): a 1d tensors representing the vector associated with an unknown token - + Returns: Vectors: a Vectors object. From bdeb0429cdd873ae8219436f880752d9e5b2496f Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Tue, 16 Jun 2020 09:45:13 -0700 Subject: [PATCH 089/719] Updated Vector subclassing --- torchtext/experimental/vectors.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/torchtext/experimental/vectors.py b/torchtext/experimental/vectors.py index 9764b59a04..1a105d195f 100644 --- a/torchtext/experimental/vectors.py +++ b/torchtext/experimental/vectors.py @@ -1,5 +1,7 @@ import csv import torch +from torch import Tensor +import torch.nn as nn def vectors_from_csv_file(file_like_object, unk_tensor=None): @@ -24,7 +26,7 @@ def vectors_from_csv_file(file_like_object, unk_tensor=None): return Vectors(tokens, vectors, unk_tensor=unk_tensor) -class Vectors(object): +class Vectors(nn.Module): r"""Creates a vectors object which maps tokens to vectors. Arguments: @@ -40,6 +42,8 @@ class Vectors(object): """ def __init__(self, tokens, vectors, unk_tensor=None): + super(Vectors, self).__init__() + if unk_tensor is None and not vectors: raise ValueError("The vectors list is empty and a default unk_tensor wasn't provided.") @@ -47,8 +51,10 @@ def __init__(self, tokens, vectors, unk_tensor=None): float_vectors = [vector.float() for vector in vectors] self.vectors = torch.classes.torchtext.Vectors(tokens, float_vectors, unk_tensor) - def __getitem__(self, token): + @torch.jit.export + def __getitem__(self, token: str): return self.vectors.GetItem(token) - def __setitem__(self, token, vector): + @torch.jit.export + def __setitem__(self, token: str, vector: Tensor): self.vectors.AddItem(token, vector.float()) From af853e507bb2e28b34b427f550749091cc253b51 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Tue, 16 Jun 2020 09:50:33 -0700 Subject: [PATCH 090/719] Fixing windows CI --- third_party/CMakeLists.txt | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 third_party/CMakeLists.txt diff --git a/third_party/CMakeLists.txt b/third_party/CMakeLists.txt new file mode 100644 index 0000000000..3cac4a5141 --- /dev/null +++ b/third_party/CMakeLists.txt @@ -0,0 +1,8 @@ +if(POLICY CMP0091) + cmake_policy(SET CMP0091 NEW) +endif() + +project(thirdparty CXX) +set(CMAKE_MSVC_RUNTIME_LIBRARY "MultiThreadedDLL") + +add_subdirectory(re2) \ No newline at end of file From 4b32cf52768baa73a5d59e11451bb580a8dcf192 Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Tue, 16 Jun 2020 16:13:31 -0400 Subject: [PATCH 091/719] MultiheadAttention building blocks in torchtext (#720) --- .flake8 | 3 +- benchmark/mha_block.py | 103 ++++++++++++ docs/source/modules.rst | 23 +++ test/data/test_jit.py | 28 ++++ test/data/test_modules.py | 123 ++++++++++++++ torchtext/__init__.py | 2 + torchtext/modules/__init__.py | 6 + torchtext/modules/multiheadattention.py | 213 ++++++++++++++++++++++++ 8 files changed, 500 insertions(+), 1 deletion(-) create mode 100644 benchmark/mha_block.py create mode 100644 docs/source/modules.rst create mode 100644 test/data/test_jit.py create mode 100644 test/data/test_modules.py create mode 100644 torchtext/modules/__init__.py create mode 100644 torchtext/modules/multiheadattention.py diff --git a/.flake8 b/.flake8 index 0f3af497a9..80872e2c3e 100644 --- a/.flake8 +++ b/.flake8 @@ -1,4 +1,5 @@ [flake8] -ignore = E402,E722,W503,W504,F821 +# E501 is not flexible enough, we're using B950 instead. Consistent with pytorch +ignore = E402,E722,W503,W504,F821,E501 max-line-length = 120 exclude = docs/source,third_party diff --git a/benchmark/mha_block.py b/benchmark/mha_block.py new file mode 100644 index 0000000000..eff568f5dd --- /dev/null +++ b/benchmark/mha_block.py @@ -0,0 +1,103 @@ +import torch +from torchtext.modules import InProjContainer, MultiheadAttentionContainer, ScaledDotProduct +from torch.nn.functional import multi_head_attention_forward as mha_forward +import time + + +def benchmark_mha_block(): + + def _run_benchmark(embed_dim, nhead, bsz, device, tgt_len, src_len=None): + # Build torchtext MultiheadAttention module + in_proj_container = InProjContainer(torch.nn.Linear(embed_dim, embed_dim), + torch.nn.Linear(embed_dim, embed_dim), + torch.nn.Linear(embed_dim, embed_dim)) + MHA = MultiheadAttentionContainer(nhead, in_proj_container, + ScaledDotProduct(), + torch.nn.Linear(embed_dim, embed_dim)).to(device) + + query = torch.rand((tgt_len, bsz, embed_dim)).to(device) + if src_len is None: + key = value = query + src_len = tgt_len + else: + key = value = torch.rand((src_len, bsz, embed_dim)).to(device) + attn_mask_2D = torch.randint(0, 2, (tgt_len, src_len)).to(torch.bool).to(device) + attn_mask = torch.stack([attn_mask_2D] * (bsz * nhead)) + bias_k = bias_v = torch.rand((1, 1, embed_dim)).to(device) + print("starting torchtext.modules.MultiheadAttentionContainer") + if device == torch.device("cuda"): + torch.cuda.synchronize() + t0 = time.monotonic() + for _ in range(100): + mha_output, attn_weights = MHA(query, key, value, + attn_mask=attn_mask, + bias_k=bias_k.repeat(1, bsz, 1).reshape(1, bsz * nhead, -1), + bias_v=bias_v.repeat(1, bsz, 1).reshape(1, bsz * nhead, -1)) + if device == torch.device("cuda"): + torch.cuda.synchronize() + print(time.monotonic() - t0) + + # Use torch.nn.functional.multi_head_attention_forward + torch_attn_mask = torch.zeros((tgt_len, src_len)).to(device).masked_fill_(attn_mask_2D, float('-inf')) + print("starting torch.nn.functional.multi_head_attention_forward") + in_proj_weight = torch.cat([MHA.in_proj_container.query_proj.weight, + MHA.in_proj_container.key_proj.weight, + MHA.in_proj_container.value_proj.weight]) + if device == torch.device("cuda"): + torch.cuda.synchronize() + t0 = time.monotonic() + for _ in range(100): + torch_mha_output, torch_mha_weights = mha_forward(query, key, value, + embed_dim, nhead, + in_proj_weight, None, + bias_k, bias_v, + False, 0.0, + MHA.out_proj.weight, + MHA.out_proj.bias, + attn_mask=torch_attn_mask) + if device == torch.device("cuda"): + torch.cuda.synchronize() + print(time.monotonic() - t0) + + # GPU test + device = torch.device("cuda") + for embed_dim in [64, 768]: + for nhead in [2, 16]: + for seq_len in [10, 128, 1000]: + for bsz in [2, 72]: + if seq_len == 1000 and bsz == 72: + continue + print("*" * 80) + print("test case GPU with embed_dim, nhead, seq_len, bsz:", + embed_dim, nhead, seq_len, seq_len, bsz) + _run_benchmark(embed_dim, nhead, bsz, device, seq_len, seq_len) + + # GPU test for self-attention + device = torch.device("cuda") + for embed_dim in [64, 256]: + for nhead in [2, 16]: + for seq_len in [10, 128, 1000]: + for bsz in [2, 72]: + if seq_len == 1000 and bsz == 72: + continue + print("*" * 80) + print("self-attention test case GPU with embed_dim, nhead, seq_len, bsz:", + embed_dim, nhead, seq_len, seq_len, bsz) + _run_benchmark(embed_dim, nhead, bsz, device, seq_len, None) + + # CPU test for self-attention + device = torch.device("cpu") + for embed_dim in [64, 768]: + for nhead in [2, 16]: + for seq_len in [10, 128, 1000]: + for bsz in [2, 72]: + if seq_len == 1000 and bsz == 72: + continue + print("*" * 80) + print("test case CPU with embed_dim, nhead, seq_len, bsz:", + embed_dim, nhead, seq_len, seq_len, bsz) + _run_benchmark(embed_dim, nhead, bsz, device, seq_len, None) + + +if __name__ == "__main__": + benchmark_mha_block() diff --git a/docs/source/modules.rst b/docs/source/modules.rst new file mode 100644 index 0000000000..ca8b30e8e4 --- /dev/null +++ b/docs/source/modules.rst @@ -0,0 +1,23 @@ +.. role:: hidden + :class: hidden-section + +torchtext.models.multiheadattention +================================== + +.. automodule:: torchtext.models.multiheadattention +.. currentmodule:: torchtext.models.multiheadattention + +:hidden:`MultiheadAttentionContainer` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: MultiheadAttentionContainer + +:hidden:`InProjContainer` +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: InProjContainer + +:hidden:`ScaledDotProduct` +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: ScaledDotProduct diff --git a/test/data/test_jit.py b/test/data/test_jit.py new file mode 100644 index 0000000000..dff0d26b9a --- /dev/null +++ b/test/data/test_jit.py @@ -0,0 +1,28 @@ +import torch +from torchtext.modules import InProjContainer, MultiheadAttentionContainer, ScaledDotProduct +from torch.testing import assert_allclose +from ..common.torchtext_test_case import TorchtextTestCase + + +class TestJIT(TorchtextTestCase): + + def test_torchscript_multiheadattention(self): + embed_dim, nhead, tgt_len, src_len, bsz = 10, 5, 6, 10, 64 + # Build torchtext MultiheadAttention models + in_proj_container = InProjContainer(torch.nn.Linear(embed_dim, embed_dim, bias=False), + torch.nn.Linear(embed_dim, embed_dim, bias=False), + torch.nn.Linear(embed_dim, embed_dim, bias=False)) + + MHA = MultiheadAttentionContainer(nhead, in_proj_container, + ScaledDotProduct(), + torch.nn.Linear(embed_dim, embed_dim, bias=False)) + query = torch.rand((tgt_len, bsz, embed_dim)) + key = value = torch.rand((src_len, bsz, embed_dim)) + attn_mask = torch.randint(0, 2, (tgt_len, src_len)).to(torch.bool) + attn_mask = torch.stack([attn_mask] * (bsz * nhead)) + mha_output, attn_weights = MHA(query, key, value, attn_mask=attn_mask) + + ts_MHA = torch.jit.script(MHA) + ts_mha_output, ts_attn_weights = ts_MHA(query, key, value, attn_mask=attn_mask) + assert_allclose(mha_output, ts_mha_output) + assert_allclose(attn_weights, ts_attn_weights) diff --git a/test/data/test_modules.py b/test/data/test_modules.py new file mode 100644 index 0000000000..0de4e93239 --- /dev/null +++ b/test/data/test_modules.py @@ -0,0 +1,123 @@ +import torch +from torchtext.modules import InProjContainer, MultiheadAttentionContainer, ScaledDotProduct +from torch.nn.functional import multi_head_attention_forward as mha_forward +from torch.testing import assert_allclose +from ..common.torchtext_test_case import TorchtextTestCase + + +class TestModels(TorchtextTestCase): + + def test_multiheadattention(self): + embed_dim, nhead, tgt_len, src_len, bsz = 10, 5, 6, 10, 64 + # Build torchtext MultiheadAttention module + in_proj = InProjContainer(torch.nn.Linear(embed_dim, embed_dim, bias=False), + torch.nn.Linear(embed_dim, embed_dim, bias=False), + torch.nn.Linear(embed_dim, embed_dim, bias=False)) + + MHA = MultiheadAttentionContainer(nhead, in_proj, + ScaledDotProduct(), + torch.nn.Linear(embed_dim, embed_dim, bias=False)) + + query = torch.rand((tgt_len, bsz, embed_dim)) + key = value = torch.rand((src_len, bsz, embed_dim)) + attn_mask_2D = torch.randint(0, 2, (tgt_len, src_len)).to(torch.bool) + bias_k = bias_v = torch.rand((1, 1, embed_dim)) + mha_output, attn_weights = MHA(query, key, value, + attn_mask=torch.stack([attn_mask_2D] * (bsz * nhead)), + bias_k=bias_k.repeat(1, bsz, 1).reshape(1, bsz * nhead, -1), + bias_v=bias_v.repeat(1, bsz, 1).reshape(1, bsz * nhead, -1)) + + # Use torch.nn.functional.multi_head_attention_forward + torch_attn_mask = torch.zeros((tgt_len, src_len)).masked_fill_(attn_mask_2D, float('-inf')) + in_proj_weight = torch.cat([MHA.in_proj_container.query_proj.weight, + MHA.in_proj_container.key_proj.weight, + MHA.in_proj_container.value_proj.weight]) + torch_mha_output, torch_mha_weights = mha_forward(query, key, value, + embed_dim, nhead, + in_proj_weight, None, + bias_k, bias_v, + False, 0.0, + MHA.out_proj.weight, None, + attn_mask=torch_attn_mask) + + assert_allclose(mha_output, torch_mha_output) + # With bias_k and bias_v, src_len needs to plus 1 + attn_weights = attn_weights.view(bsz, nhead, tgt_len, src_len + 1).sum(dim=1) / nhead + assert_allclose(attn_weights, torch_mha_weights) + + def test_broadcast_scaled_dot_product(self): + embed_dim, nhead, tgt_len, src_len, bsz = 10, 5, 6, 10, 64 + SDP = ScaledDotProduct() + query = torch.rand((tgt_len, 1, embed_dim)) + key = value = torch.rand((src_len, 1, embed_dim)) + attn_mask_2D = torch.randint(0, 2, (tgt_len, src_len)).to(torch.bool) + + sdp_attn_output_full, sdp_attn_weights_full = SDP(query.expand(tgt_len, bsz * nhead, embed_dim), + key.expand(src_len, bsz * nhead, embed_dim), + value.expand(src_len, bsz * nhead, embed_dim), + attn_mask=attn_mask_2D.expand(bsz * nhead, tgt_len, src_len)) + + # query has a batch size of 1 while key/value have a batch size of bsz * nhead + sdp_attn_output, sdp_attn_weights = SDP(query, key.expand(src_len, bsz * nhead, embed_dim), + value.expand(src_len, bsz * nhead, embed_dim), + attn_mask=attn_mask_2D.expand(bsz * nhead, tgt_len, src_len)) + assert_allclose(sdp_attn_output, sdp_attn_output_full) + assert_allclose(sdp_attn_weights, sdp_attn_weights_full) + + # key/value have a batch size of 1 while query has a batch size of bsz * nhead + sdp_attn_output, sdp_attn_weights = SDP(query.expand(tgt_len, bsz * nhead, embed_dim), + key, value, + attn_mask=attn_mask_2D.expand(bsz * nhead, tgt_len, src_len)) + assert_allclose(sdp_attn_output, sdp_attn_output_full) + assert_allclose(sdp_attn_weights, sdp_attn_weights_full) + + # key/value have a size of (3, 3, src_len, bsz * nhead, embed_dim) + # while query has a size of (tgt_len, 1, embed_dim) + sdp_attn_output, sdp_attn_weights = SDP(query.expand(tgt_len, 1, embed_dim), + key.expand(3, 3, src_len, bsz * nhead, embed_dim), + value.expand(3, 3, src_len, bsz * nhead, embed_dim), + attn_mask=attn_mask_2D.expand(bsz * nhead, tgt_len, src_len)) + assert list(sdp_attn_output.size()) == [3, 3, tgt_len, bsz * nhead, embed_dim] + assert list(sdp_attn_weights.size()) == [3, 3, bsz * nhead, tgt_len, embed_dim] + assert_allclose(sdp_attn_output[2][2], sdp_attn_output_full) + assert_allclose(sdp_attn_weights[2][2], sdp_attn_weights_full) + # dim -2 is not equal to neither key/value's dim -2 or 1 + with self.assertRaises(RuntimeError): + SDP(query.expand(tgt_len, 2, embed_dim), key.expand(3, 3, src_len, bsz * nhead, embed_dim), + value.expand(3, 3, src_len, bsz * nhead, embed_dim), + attn_mask=attn_mask_2D.expand(bsz * nhead, tgt_len, src_len)) + + # key/value have a size of (src_len, 1, embed_dim) + # while query has a size of (1, 2, 3, tgt_len, bsz * nhead, embed_dim) + sdp_attn_output, sdp_attn_weights = SDP(query.expand(1, 2, 3, tgt_len, bsz * nhead, embed_dim), + key.expand(src_len, 1, embed_dim), + value.expand(src_len, 1, embed_dim), + attn_mask=attn_mask_2D.expand(bsz * nhead, tgt_len, src_len)) + assert list(sdp_attn_output.size()) == [1, 2, 3, tgt_len, bsz * nhead, embed_dim] + assert list(sdp_attn_weights.size()) == [1, 2, 3, bsz * nhead, tgt_len, embed_dim] + assert_allclose(sdp_attn_output[0][1][2], sdp_attn_output_full) + assert_allclose(sdp_attn_weights[0][1][2], sdp_attn_weights_full) + # key dim -2 is not equal to value dim -2 + with self.assertRaisesRegex(AssertionError, "Shape of key, value must match"): + SDP(query.expand(1, 2, 3, tgt_len, bsz * nhead, embed_dim), key.expand(src_len, 2, embed_dim), + value.expand(src_len, 1, embed_dim), + attn_mask=attn_mask_2D.expand(bsz * nhead, tgt_len, src_len)) + # key/value dim -2 is not equal to neither query's dim -2 or 1 + with self.assertRaises(RuntimeError): + SDP(query.expand(1, 2, 3, tgt_len, bsz * nhead, embed_dim), key.expand(src_len, 2, embed_dim), + value.expand(src_len, 2, embed_dim), + attn_mask=attn_mask_2D.expand(bsz * nhead, tgt_len, src_len)) + + # attn_mask in a size of (1, tgt_len, src_len) + # 2D tensor is not supported for attn_mask + sdp_attn_output, sdp_attn_weights = SDP(query.expand(tgt_len, bsz * nhead, embed_dim), + key.expand(src_len, bsz * nhead, embed_dim), + value.expand(src_len, bsz * nhead, embed_dim), + attn_mask=attn_mask_2D.expand(1, tgt_len, src_len)) + assert_allclose(sdp_attn_output, sdp_attn_output_full) + assert_allclose(sdp_attn_weights, sdp_attn_weights_full) + # attn_mask's dim -3 is not equal to neither batch size or 1 + with self.assertRaisesRegex(RuntimeError, "The size of the attn_mask is not correct."): + SDP(query.expand(tgt_len, bsz * nhead, embed_dim), key.expand(src_len, bsz * nhead, embed_dim), + value.expand(src_len, bsz * nhead, embed_dim), + attn_mask=attn_mask_2D.expand(2, tgt_len, src_len)) diff --git a/torchtext/__init__.py b/torchtext/__init__.py index 97191542ec..9ce2ce9c08 100644 --- a/torchtext/__init__.py +++ b/torchtext/__init__.py @@ -1,4 +1,5 @@ from . import data +from . import modules from . import datasets from . import utils from . import vocab @@ -11,6 +12,7 @@ pass __all__ = ['data', + 'modules', 'datasets', 'utils', 'vocab', diff --git a/torchtext/modules/__init__.py b/torchtext/modules/__init__.py new file mode 100644 index 0000000000..a55ced48fb --- /dev/null +++ b/torchtext/modules/__init__.py @@ -0,0 +1,6 @@ +from .multiheadattention import InProjContainer, \ + MultiheadAttentionContainer, ScaledDotProduct + +__all__ = ['InProjContainer', + 'MultiheadAttentionContainer', + 'ScaledDotProduct'] diff --git a/torchtext/modules/multiheadattention.py b/torchtext/modules/multiheadattention.py new file mode 100644 index 0000000000..f6d8e7675d --- /dev/null +++ b/torchtext/modules/multiheadattention.py @@ -0,0 +1,213 @@ +import torch +from typing import Tuple, Optional + + +class MultiheadAttentionContainer(torch.nn.Module): + def __init__(self, nhead, in_proj_container, attention_layer, out_proj): + r""" A multi-head attention container + + Args: + nhead: the number of heads in the multiheadattention model + in_proj_container: A container of multi-head in-projection linear layers (a.k.a nn.Linear). + attention_layer: The attention layer. + out_proj: The multi-head out-projection layer (a.k.a nn.Linear). + + Examples:: + >>> import torch + >>> embed_dim, num_heads, bsz = 10, 5, 64 + >>> in_proj_container = InProjContainer(torch.nn.Linear(embed_dim, embed_dim), + torch.nn.Linear(embed_dim, embed_dim), + torch.nn.Linear(embed_dim, embed_dim)) + >>> MHA = MultiheadAttentionContainer(num_heads, + in_proj_container, + ScaledDotProduct(), + torch.nn.Linear(embed_dim, embed_dim)) + >>> query = torch.rand((21, bsz, embed_dim)) + >>> key = value = torch.rand((16, bsz, embed_dim)) + >>> attn_output, attn_weights = MHA(query, key, value) + >>> print(attn_output.shape) + >>> torch.Size([21, 64, 10]) + """ + super(MultiheadAttentionContainer, self).__init__() + self.nhead = nhead + self.in_proj_container = in_proj_container + self.attention_layer = attention_layer + self.out_proj = out_proj + + def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, + attn_mask: Optional[torch.Tensor] = None, + bias_k: Optional[torch.Tensor] = None, + bias_v: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]: + r""" + + Args: + query, key, value (Tensor): map a query and a set of key-value pairs to an output. + See "Attention Is All You Need" for more details. + attn_mask, bias_k and bias_v (Tensor, optional): keyword arguments passed to the attention layer. + See the definitions in the attention. + + Shape: + - Inputs: + - query: :math:`(L, N, E)` + - key: :math:`(S, N, E)` + - value: :math:`(S, N, E)` + - attn_mask, bias_k and bias_v: same with the shape of the corresponding args in attention layer. + + - Outputs: + - attn_output: :math:`(L, N, E)` + - attn_output_weights: :math:`(N * H, L, S)` + + where where L is the target length, S is the sequence length, H is the number of attention heads, + N is the batch size, and E is the embedding dimension. + """ + tgt_len, src_len, bsz, embed_dim = query.size(-3), key.size(-3), query.size(-2), query.size(-1) + q, k, v = self.in_proj_container(query, key, value) + assert q.size(-1) % self.nhead == 0, "query's embed_dim must be divisible by the number of heads" + head_dim = q.size(-1) // self.nhead + q = q.reshape(tgt_len, bsz * self.nhead, head_dim) + + assert k.size(-1) % self.nhead == 0, "key's embed_dim must be divisible by the number of heads" + head_dim = k.size(-1) // self.nhead + k = k.reshape(src_len, bsz * self.nhead, head_dim) + + assert v.size(-1) % self.nhead == 0, "value's embed_dim must be divisible by the number of heads" + head_dim = v.size(-1) // self.nhead + v = v.reshape(src_len, bsz * self.nhead, head_dim) + + attn_output, attn_output_weights = self.attention_layer(q, k, v, attn_mask=attn_mask, + bias_k=bias_k, bias_v=bias_v) + attn_output = attn_output.reshape(tgt_len, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + return attn_output, attn_output_weights + + +class ScaledDotProduct(torch.nn.Module): + + def __init__(self, dropout=0.0): + r"""Processes a projected query and key-value pair to apply + scaled dot product attention. + + Args: + dropout (float): probability of dropping an attention weight. + + Examples:: + >>> SDP = torchtext.models.ScaledDotProduct(0.1) + >>> q = torch.randn(256, 21, 3) + >>> k = v = torch.randn(256, 21, 3) + >>> attn_output, attn_weights = SDP(q, k, v) + >>> print(attn_output.shape, attn_weights.shape) + torch.Size([256, 21, 3]) torch.Size([256, 21, 21]) + """ + super(ScaledDotProduct, self).__init__() + self.dropout = dropout + + def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, + attn_mask: Optional[torch.Tensor] = None, + bias_k: Optional[torch.Tensor] = None, + bias_v: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]: + r"""Uses a scaled dot product with the projected key-value pair to update + the projected query. + + Args: + query (Tensor): Projected query + key (Tensor): Projected key + value (Tensor): Projected value + attn_mask (BoolTensor, optional): 3D mask that prevents attention to certain positions. + bias_k and bias_v: (Tensor, optional): one more key and value sequence to be added at + sequence dim (dim=-3). Those are used for incremental decoding. Users should provide + non-None to both arguments in order to activate them. + + Shape: + - query: :math:`(L, N * H, E / H)` + - key: :math:`(S, N * H, E / H)` + - value: :math:`(S, N * H, E / H)` + - attn_mask: :math:`(N * H, L, S)`, positions with ``True`` are not allowed to attend + while ``False`` values will be unchanged. + - bias_k and bias_v:bias: :math:`(1, N * H, E / H)` + + - Output: :math:`(L, N * H, E / H)`, :math:`(N * H, L, S)` + + where L is the target length, S is the source length, H is the number + of attention heads, N is the batch size, and E is the embedding dimension. + """ + if bias_k is not None and bias_v is not None: + assert key.size(-1) == bias_k.size(-1) and key.size(-2) == bias_k.size(-2) and bias_k.size(-3) == 1, \ + "Shape of bias_k is not supported" + assert value.size(-1) == bias_v.size(-1) and value.size(-2) == bias_v.size(-2) and bias_v.size(-3) == 1, \ + "Shape of bias_v is not supported" + key = torch.cat([key, bias_k]) + value = torch.cat([value, bias_v]) + if attn_mask is not None: + _attn_mask = attn_mask + attn_mask = torch.nn.functional.pad(_attn_mask, (0, 1)) + + tgt_len, head_dim = query.size(-3), query.size(-1) + assert query.size(-1) == key.size(-1) == value.size(-1), "The feature dim of query, key, value must be equal." + assert key.size() == value.size(), "Shape of key, value must match" + src_len = key.size(-3) + batch_heads = max(query.size(-2), key.size(-2)) + + # Scale query + query, key, value = query.transpose(-2, -3), key.transpose(-2, -3), value.transpose(-2, -3) + query = query * (float(head_dim) ** -0.5) + if attn_mask is not None: + if attn_mask.dim() != 3: + raise RuntimeError('attn_mask must be a 3D tensor.') + if (attn_mask.size(-1) != src_len) or (attn_mask.size(-2) != tgt_len) or \ + (attn_mask.size(-3) != 1 and attn_mask.size(-3) != batch_heads): + raise RuntimeError('The size of the attn_mask is not correct.') + if attn_mask.dtype != torch.bool: + raise RuntimeError('Only bool tensor is supported for attn_mask') + + # Dot product of q, k + attn_output_weights = torch.matmul(query, key.transpose(-2, -1)) + if attn_mask is not None: + attn_output_weights.masked_fill_(attn_mask, -1e8,) + attn_output_weights = torch.nn.functional.softmax(attn_output_weights, dim=-1) + attn_output_weights = torch.nn.functional.dropout(attn_output_weights, p=self.dropout, training=self.training) + attn_output = torch.matmul(attn_output_weights, value) + return attn_output.transpose(-2, -3), attn_output_weights + + +class InProjContainer(torch.nn.Module): + def __init__(self, query_proj, key_proj, value_proj): + r"""A in-proj container to process inputs. + + Args: + query_proj: a proj layer for query. + key_proj: a proj layer for key. + value_proj: a proj layer for value. + """ + + super(InProjContainer, self).__init__() + self.query_proj = query_proj + self.key_proj = key_proj + self.value_proj = value_proj + + def forward(self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + r"""Projects the input sequences using in-proj layers. + + Args: + query, key, value (Tensors): sequence to be projected + + Shape: + - query, key, value: :math:`(S, N, E)` + - Output: :math:`(S, N, E)` + where S is the sequence length, N is the batch size, and E is the embedding dimension. + """ + return self.query_proj(query), self.key_proj(key), self.value_proj(value) + + +def generate_square_subsequent_mask(nbatch, sz): + r"""Generate a square mask for the sequence. The masked positions are filled with True. + Unmasked positions are filled with False. + + Args: + nbatch: the number of batch size + sz: the size of square mask + """ + mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1).repeat(nbatch, 1, 1) + return mask From 646ae25bc90f09e13cb5faae24294a8a72a22dfe Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Tue, 16 Jun 2020 15:31:53 -0700 Subject: [PATCH 092/719] Fixing windows CI --- .gitignore | 5 +++- build_tools/setup_helpers/extension.py | 35 ++++++++++++++++++-------- third_party/CMakeLists.txt | 2 +- 3 files changed, 29 insertions(+), 13 deletions(-) diff --git a/.gitignore b/.gitignore index b22fac681a..642eb5cf87 100644 --- a/.gitignore +++ b/.gitignore @@ -121,4 +121,7 @@ venv.bak/ *.swp *.swo -torchtext/version.py \ No newline at end of file +torchtext/version.py + +# Thirdparty directories +third_party/*/ \ No newline at end of file diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 06d06550ba..85dc1fa660 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -83,24 +83,38 @@ def _get_libraries(): ] -def _build_sentence_piece(debug): - build_dir = _TP_BASE_DIR / 'sentencepiece' / 'build' +def _build_third_party(debug): + build_dir = _TP_BASE_DIR / 'build' build_dir.mkdir(exist_ok=True) build_env = os.environ.copy() config = 'Debug' if debug else 'Release' if platform.system() == 'Windows': - extra_args = ['-GNinja'] + extra_args = [ + '-GNinja', + '-DCMAKE_CXX_FLAGS=/MT', + '-DCMAKE_MSVC_RUNTIME_LIBRARY="MultiThreaded"', + ] build_env.setdefault('CC', 'cl') build_env.setdefault('CXX', 'cl') else: - extra_args = [] + extra_args = ['-DCMAKE_CXX_FLAGS=-fPIC'] subprocess.run( - args=['cmake', f'-DSPM_ENABLE_SHARED=OFF', f'-DCMAKE_INSTALL_PREFIX={_TP_INSTALL_DIR}', - f'-DCMAKE_BUILD_TYPE={config}'] + extra_args + ['..'], + args=[ + 'cmake', + '-DBUILD_SHARED_LIBS=OFF', + '-DRE2_BUILD_TESTING=OFF', + '-DCMAKE_EXPORT_COMPILE_COMMANDS=ON', + f'-DCMAKE_INSTALL_PREFIX={_TP_INSTALL_DIR}', + f'-DCMAKE_BUILD_TYPE={config}', + ] + extra_args + ['..'], cwd=str(build_dir), check=True, env=build_env, ) + print('*** Command list Thirdparty ***') + with open(build_dir / 'compile_commands.json', 'r') as fileobj: + print(fileobj.read()) + print(f'running cmake --build', flush=True) subprocess.run( args=['cmake', '--build', '.', '--target', 'install', '--config', config], cwd=str(build_dir), @@ -109,8 +123,8 @@ def _build_sentence_piece(debug): ) -def _build_re2(debug): - build_dir = _TP_BASE_DIR / 're2' / 'build_dir' +def _build_sentence_piece(debug): + build_dir = _TP_BASE_DIR / 'sentencepiece' / 'build' build_dir.mkdir(exist_ok=True) build_env = os.environ.copy() config = 'Debug' if debug else 'Release' @@ -121,8 +135,7 @@ def _build_re2(debug): else: extra_args = [] subprocess.run( - args=['cmake', f'-DCMAKE_CXX_FLAGS=-fPIC', f'-DBUILD_SHARED_LIBS=OFF', - f'-DCMAKE_INSTALL_PREFIX={_TP_INSTALL_DIR}', + args=['cmake', f'-DSPM_ENABLE_SHARED=OFF', f'-DCMAKE_INSTALL_PREFIX={_TP_INSTALL_DIR}', f'-DCMAKE_BUILD_TYPE={config}'] + extra_args + ['..'], cwd=str(build_dir), check=True, @@ -137,8 +150,8 @@ def _build_re2(debug): def _configure_third_party(debug): + _build_third_party(debug) _build_sentence_piece(debug) - _build_re2(debug) _EXT_NAME = 'torchtext._torchtext' diff --git a/third_party/CMakeLists.txt b/third_party/CMakeLists.txt index 3cac4a5141..7b7cf19784 100644 --- a/third_party/CMakeLists.txt +++ b/third_party/CMakeLists.txt @@ -3,6 +3,6 @@ if(POLICY CMP0091) endif() project(thirdparty CXX) -set(CMAKE_MSVC_RUNTIME_LIBRARY "MultiThreadedDLL") +set(CMAKE_MSVC_RUNTIME_LIBRARY "MultiThreaded") add_subdirectory(re2) \ No newline at end of file From 41bd713df7a4187dd0f42bbb9ffcac2ba126adbb Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Tue, 16 Jun 2020 16:22:06 -0700 Subject: [PATCH 093/719] Resolving PR comments --- test/experimental/test_vectors.py | 49 ++++++++++++++++++------------- torchtext/csrc/vectors.cpp | 4 +-- torchtext/experimental/vectors.py | 35 ++++++++++++++++++---- 3 files changed, 60 insertions(+), 28 deletions(-) diff --git a/test/experimental/test_vectors.py b/test/experimental/test_vectors.py index 92c8ac866d..f51567cb79 100644 --- a/test/experimental/test_vectors.py +++ b/test/experimental/test_vectors.py @@ -5,7 +5,7 @@ from test.common.torchtext_test_case import TorchtextTestCase from torchtext.experimental.vectors import ( Vectors, - vectors_from_csv_file + vectors_from_file_object ) @@ -14,14 +14,16 @@ class TestVectors(TorchtextTestCase): def test_empty_vector(self): tokens = [] vectors = [] - unk_tensor = torch.Tensor([0]) + unk_tensor = torch.tensor([0], dtype=torch.float) vectors_obj = Vectors(tokens, vectors, unk_tensor) self.assertEqual(vectors_obj['not_in_it'].long().tolist(), unk_tensor.tolist()) def test_empty_unk(self): - tensorA = torch.Tensor([1, 0]) - expected_unk_tensor = torch.Tensor([0, 0]) + tensorA = torch.tensor([1, 0], dtype=torch.float) + expected_unk_tensor = torch.tensor([0, 0], dtype=torch.float) + + print(tensorA.type()) tokens = ['a'] vectors = [tensorA] @@ -30,10 +32,10 @@ def test_empty_unk(self): self.assertEqual(vectors_obj['not_in_it'].long().tolist(), expected_unk_tensor.tolist()) def test_vectors_basic(self): - tensorA = torch.Tensor([1, 0]) - tensorB = torch.Tensor([0, 1]) + tensorA = torch.tensor([1, 0], dtype=torch.float) + tensorB = torch.tensor([0, 1], dtype=torch.float) - unk_tensor = torch.Tensor([0, 0]) + unk_tensor = torch.tensor([0, 0], dtype=torch.float) tokens = ['a', 'b'] vectors = [tensorA, tensorB] vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor) @@ -43,10 +45,10 @@ def test_vectors_basic(self): self.assertEqual(vectors_obj['not_in_it'].long().tolist(), unk_tensor.tolist()) def test_vectors_jit(self): - tensorA = torch.Tensor([1, 0]) - tensorB = torch.Tensor([0, 1]) + tensorA = torch.tensor([1, 0], dtype=torch.float) + tensorB = torch.tensor([0, 1], dtype=torch.float) - unk_tensor = torch.Tensor([0, 0]) + unk_tensor = torch.tensor([0, 0], dtype=torch.float) tokens = ['a', 'b'] vectors = [tensorA, tensorB] vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor) @@ -57,14 +59,14 @@ def test_vectors_jit(self): self.assertEqual(vectors_obj['not_in_it'].long().tolist(), jit_vectors_obj['not_in_it'].long().tolist()) def test_vectors_add_item(self): - tensorA = torch.Tensor([1, 0]) - unk_tensor = torch.Tensor([0, 0]) + tensorA = torch.tensor([1, 0], dtype=torch.float) + unk_tensor = torch.tensor([0, 0], dtype=torch.float) tokens = ['a'] vectors = [tensorA] vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor) - tensorB = torch.Tensor([0, 1]) + tensorB = torch.tensor([0., 1]) vectors_obj['b'] = tensorB self.assertEqual(vectors_obj['a'].long().tolist(), tensorA.tolist()) @@ -80,8 +82,8 @@ def test_errors(self): # not passing in a user defined unk_tensor Vectors(tokens, vectors) - tensorA = torch.Tensor([1, 0, 0]) - tensorB = torch.Tensor([0, 1, 0]) + tensorA = torch.tensor([1, 0, 0], dtype=torch.float) + tensorB = torch.tensor([0, 1, 0], dtype=torch.float) tokens = ['a', 'b', 'c'] vectors = [tensorA, tensorB] @@ -89,7 +91,7 @@ def test_errors(self): # Test proper error raised when tokens and vectors have different sizes Vectors(tokens, vectors) - tensorC = torch.Tensor([0, 0, 1]) + tensorC = torch.tensor([0, 0, 1], dtype=torch.float) tokens = ['a', 'a', 'c'] vectors = [tensorA, tensorB, tensorC] @@ -99,15 +101,22 @@ def test_errors(self): # the key of the duplicate token in the error message Vectors(tokens, vectors) + tensorC = torch.tensor([0, 0, 1], dtype=torch.int8) + vectors = [tensorA, tensorB, tensorC] + + with self.assertRaises(TypeError): + # Test proper error raised when vectors are not of type torch.float + Vectors(tokens, vectors) + def test_vectors_from_file(self): asset_name = 'vectors_test.csv' asset_path = get_asset_path(asset_name) f = open(asset_path, 'r') - vectors_obj = vectors_from_csv_file(f) + vectors_obj = vectors_from_file_object(f) - expected_tensorA = torch.Tensor([1, 0, 0]) - expected_tensorB = torch.Tensor([0, 1, 0]) - expected_unk_tensor = torch.Tensor([0, 0, 0]) + expected_tensorA = torch.tensor([1, 0, 0], dtype=torch.float) + expected_tensorB = torch.tensor([0, 1, 0], dtype=torch.float) + expected_unk_tensor = torch.tensor([0, 0, 0], dtype=torch.float) self.assertEqual(vectors_obj['a'].long().tolist(), expected_tensorA.tolist()) self.assertEqual(vectors_obj['b'].long().tolist(), expected_tensorB.tolist()) diff --git a/torchtext/csrc/vectors.cpp b/torchtext/csrc/vectors.cpp index c7451eec37..bec20e2264 100644 --- a/torchtext/csrc/vectors.cpp +++ b/torchtext/csrc/vectors.cpp @@ -26,8 +26,8 @@ struct Vectors : torch::CustomClassHolder { if (tokens.size() != vectors.size()) { throw std::runtime_error( "Mismatching sizes for tokens and vectors. Size of tokens: " + - std::to_string(tokens.size()) + ", size of vectors: " + - std::to_string(vectors.size()) + "."); + std::to_string(tokens.size()) + + ", size of vectors: " + std::to_string(vectors.size()) + "."); } stovectors_.reserve(tokens.size()); diff --git a/torchtext/experimental/vectors.py b/torchtext/experimental/vectors.py index 1a105d195f..cf90521cfd 100644 --- a/torchtext/experimental/vectors.py +++ b/torchtext/experimental/vectors.py @@ -4,9 +4,11 @@ import torch.nn as nn -def vectors_from_csv_file(file_like_object, unk_tensor=None): +def vectors_from_file_object(file_like_object, unk_tensor=None): r"""Create a Vectors object from a csv file like object. + Note that the tensor corresponding to each vector is of type `torch.float`. + Args: file_like_object (FileObject): a file like object to read data from. unk_tensor (int): a 1d tensors representing the vector associated with an unknown token @@ -35,26 +37,47 @@ class Vectors(nn.Module): unk_tensor (torch.Tensor): a 1d tensors representing the vector associated with an unknown token. Raises: - ValueError: if `vectors` is empty and a default `unk_tensor` isn't provided. RuntimeError: if `tokens` and `vectors` have different sizes or `tokens` has duplicates. - + TypeError: if all tensors within`vectors` are not of data type `torch.float`. """ def __init__(self, tokens, vectors, unk_tensor=None): super(Vectors, self).__init__() + # print("dtype", vectors[0].type()) + if unk_tensor is None and not vectors: raise ValueError("The vectors list is empty and a default unk_tensor wasn't provided.") + if not all(vector.dtype == torch.float for vector in vectors): + raise TypeError("All tensors within `vectors` should be of data type `torch.float`.") + unk_tensor = unk_tensor if unk_tensor is not None else torch.zeros(vectors[0].size(), dtype=torch.float) - float_vectors = [vector.float() for vector in vectors] - self.vectors = torch.classes.torchtext.Vectors(tokens, float_vectors, unk_tensor) + + self.vectors = torch.classes.torchtext.Vectors(tokens, vectors, unk_tensor) @torch.jit.export - def __getitem__(self, token: str): + def __getitem__(self, token: str) -> Tensor: + r""" + Args: + token (str): the token used to lookup the corresponding vector. + Returns: + vector (Tensor): a tensor (the vector) corresponding to the associated token. + """ return self.vectors.GetItem(token) @torch.jit.export def __setitem__(self, token: str, vector: Tensor): + r""" + Args: + token (str): the token used to lookup the corresponding vector. + vector (Tensor): a 1d tensor representing a vector associated with the token. + + Raises: + TypeError: if `vector` is not of data type `torch.float`. + """ + if vector.dtype != torch.float: + raise TypeError("`vector` should be of data type `torch.float` but it's of type " + vector.dtype) + self.vectors.AddItem(token, vector.float()) From 43b2adfe913d576d46e10267c69a9d80bbd09a24 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 17 Jun 2020 07:56:06 -0700 Subject: [PATCH 094/719] Resolving PR comments --- test/experimental/test_vectors.py | 18 ++++++++++++++++-- torchtext/experimental/vectors.py | 8 ++++++-- 2 files changed, 22 insertions(+), 4 deletions(-) diff --git a/test/experimental/test_vectors.py b/test/experimental/test_vectors.py index f51567cb79..dc5f99a4a4 100644 --- a/test/experimental/test_vectors.py +++ b/test/experimental/test_vectors.py @@ -1,5 +1,6 @@ # -*- coding: utf-8 -*- import torch +import os from test.common.assets import get_asset_path from test.common.torchtext_test_case import TorchtextTestCase @@ -23,8 +24,6 @@ def test_empty_unk(self): tensorA = torch.tensor([1, 0], dtype=torch.float) expected_unk_tensor = torch.tensor([0, 0], dtype=torch.float) - print(tensorA.type()) - tokens = ['a'] vectors = [tensorA] vectors_obj = Vectors(tokens, vectors) @@ -73,6 +72,21 @@ def test_vectors_add_item(self): self.assertEqual(vectors_obj['b'].long().tolist(), tensorB.tolist()) self.assertEqual(vectors_obj['not_in_it'].long().tolist(), unk_tensor.tolist()) + def test_vectors_load_and_save(self): + tensorA = torch.tensor([1, 0], dtype=torch.float) + expected_unk_tensor = torch.tensor([0, 0], dtype=torch.float) + + tokens = ['a'] + vectors = [tensorA] + vectors_obj = Vectors(tokens, vectors) + + vector_path = os.path.join(self.test_dir, 'vector.pt') + torch.save(vectors_obj, vector_path) + loaded_vectors_obj = torch.load(vector_path) + + self.assertEqual(loaded_vectors_obj['a'].long().tolist(), tensorA.tolist()) + self.assertEqual(loaded_vectors_obj['not_in_it'].long().tolist(), expected_unk_tensor.tolist()) + def test_errors(self): tokens = [] vectors = [] diff --git a/torchtext/experimental/vectors.py b/torchtext/experimental/vectors.py index cf90521cfd..a0f2dfeeec 100644 --- a/torchtext/experimental/vectors.py +++ b/torchtext/experimental/vectors.py @@ -9,6 +9,12 @@ def vectors_from_file_object(file_like_object, unk_tensor=None): Note that the tensor corresponding to each vector is of type `torch.float`. + Format for csv file: + token1,num1 num2 num3 + token2,num4 num5 num6 + ... + token_n,num_m num_j num_k + Args: file_like_object (FileObject): a file like object to read data from. unk_tensor (int): a 1d tensors representing the vector associated with an unknown token @@ -45,8 +51,6 @@ class Vectors(nn.Module): def __init__(self, tokens, vectors, unk_tensor=None): super(Vectors, self).__init__() - # print("dtype", vectors[0].type()) - if unk_tensor is None and not vectors: raise ValueError("The vectors list is empty and a default unk_tensor wasn't provided.") From 9c320c9db5c8f765a80e0bb3d733c3f0e8ddc8ee Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Wed, 17 Jun 2020 10:33:01 -0700 Subject: [PATCH 095/719] Added docs for vectors --- docs/source/experimental_vectors.rst | 20 ++++++++++++++++++++ docs/source/index.rst | 5 +++-- 2 files changed, 23 insertions(+), 2 deletions(-) create mode 100644 docs/source/experimental_vectors.rst diff --git a/docs/source/experimental_vectors.rst b/docs/source/experimental_vectors.rst new file mode 100644 index 0000000000..8d9fe02ce0 --- /dev/null +++ b/docs/source/experimental_vectors.rst @@ -0,0 +1,20 @@ +.. role:: hidden + :class: hidden-section + +torchtext.experimental.vectors +============================== + +.. automodule:: torchtext.experimental.vectors +.. currentmodule:: torchtext.experimental.vectors + +:hidden:`Vector` +~~~~~~~~~~~~~~~~ + +.. autoclass:: Vectors + :members: + :special-members: + +:hidden:`vectors_from_file_object` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: vectors_from_file_object diff --git a/docs/source/index.rst b/docs/source/index.rst index dfa9d7c4c0..7e965e5abf 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -17,8 +17,9 @@ popular datasets for natural language. torchtext.vocab torchtext.utils experimental_datasets - experimental_functional - experimental_transforms + experimental_functional + experimental_transforms + experimental_vectors examples .. automodule:: torchtext From de416d72cdb266b3082c2008eb60c32eda404356 Mon Sep 17 00:00:00 2001 From: guyang3532 <62738430+guyang3532@users.noreply.github.com> Date: Thu, 18 Jun 2020 21:24:17 +0800 Subject: [PATCH 096/719] Add windows smoke test job to CI (#829) --- .circleci/config.yml | 95 +++++++++++++++++++++++++++++++++++++++++ .circleci/config.yml.in | 47 ++++++++++++++++++++ .circleci/regenerate.py | 8 ++-- 3 files changed, 146 insertions(+), 4 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 165f641de7..091e8e2187 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -259,6 +259,53 @@ jobs: docker push ${image_name}:${CIRCLE_WORKFLOW_ID} docker push ${image_name}:latest + smoke_test_windows_conda: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-nightly pytorch + conda install -v -y -c ~/workspace/conda-bld torchtext + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchtext" + + smoke_test_windows_pip: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchtext*.whl) --pre -f https://download.pytorch.org/whl/nightly/torch_nightly.html + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchtext" + unittest_linux: <<: *binary_common docker: @@ -601,6 +648,14 @@ workflows: name: nightly_binary_windows_wheel_py3.6_upload requires: - nightly_binary_windows_wheel_py3.6 + - smoke_test_windows_pip: + filters: + branches: + only: nightly + name: nightly_binary_windows_wheel_py3.6_smoke_test_pip + python_version: '3.6' + requires: + - nightly_binary_windows_wheel_py3.6_upload - binary_windows_wheel: filters: branches: @@ -615,6 +670,14 @@ workflows: name: nightly_binary_windows_wheel_py3.7_upload requires: - nightly_binary_windows_wheel_py3.7 + - smoke_test_windows_pip: + filters: + branches: + only: nightly + name: nightly_binary_windows_wheel_py3.7_smoke_test_pip + python_version: '3.7' + requires: + - nightly_binary_windows_wheel_py3.7_upload - binary_windows_wheel: filters: branches: @@ -629,6 +692,14 @@ workflows: name: nightly_binary_windows_wheel_py3.8_upload requires: - nightly_binary_windows_wheel_py3.8 + - smoke_test_windows_pip: + filters: + branches: + only: nightly + name: nightly_binary_windows_wheel_py3.8_smoke_test_pip + python_version: '3.8' + requires: + - nightly_binary_windows_wheel_py3.8_upload - binary_linux_conda: filters: branches: @@ -751,6 +822,14 @@ workflows: name: nightly_binary_windows_conda_py3.6_upload requires: - nightly_binary_windows_conda_py3.6 + - smoke_test_windows_conda: + filters: + branches: + only: nightly + name: nightly_binary_windows_conda_py3.6_smoke_test_conda + python_version: '3.6' + requires: + - nightly_binary_windows_conda_py3.6_upload - binary_windows_conda: filters: branches: @@ -765,6 +844,14 @@ workflows: name: nightly_binary_windows_conda_py3.7_upload requires: - nightly_binary_windows_conda_py3.7 + - smoke_test_windows_conda: + filters: + branches: + only: nightly + name: nightly_binary_windows_conda_py3.7_smoke_test_conda + python_version: '3.7' + requires: + - nightly_binary_windows_conda_py3.7_upload - binary_windows_conda: filters: branches: @@ -779,6 +866,14 @@ workflows: name: nightly_binary_windows_conda_py3.8_upload requires: - nightly_binary_windows_conda_py3.8 + - smoke_test_windows_conda: + filters: + branches: + only: nightly + name: nightly_binary_windows_conda_py3.8_smoke_test_conda + python_version: '3.8' + requires: + - nightly_binary_windows_conda_py3.8_upload docker_build: triggers: - schedule: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index acd233d450..5be6211842 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -259,6 +259,53 @@ jobs: docker push ${image_name}:${CIRCLE_WORKFLOW_ID} docker push ${image_name}:latest + smoke_test_windows_conda: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-nightly pytorch + conda install -v -y -c ~/workspace/conda-bld torchtext + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchtext" + + smoke_test_windows_pip: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchtext*.whl) --pre -f https://download.pytorch.org/whl/nightly/torch_nightly.html + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchtext" + unittest_linux: <<: *binary_common docker: diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index a4cffdc1eb..93ab431b9a 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -38,9 +38,9 @@ def build_workflow_pair(btype, os_type, python_version, filter_branch, prefix='' if upload: w.append(generate_upload_workflow(base_workflow_name, filter_branch, btype)) - if filter_branch == 'nightly' and os_type == 'linux': + if filter_branch == 'nightly' and os_type in ['linux', 'windows']: pydistro = 'pip' if btype == 'wheel' else 'conda' - w.append(generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version)) + w.append(generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version, os_type)) return w @@ -73,7 +73,7 @@ def generate_upload_workflow(base_workflow_name, filter_branch, btype): return {f"binary_{btype}_upload": d} -def generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version): +def generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version, os_type): required_build_suffix = "_upload" required_build_name = base_workflow_name + required_build_suffix @@ -88,7 +88,7 @@ def generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, pyt if filter_branch: d["filters"] = gen_filter_branch_tree(filter_branch) - return {f"smoke_test_linux_{pydistro}": d} + return {"smoke_test_{os_type}_{pydistro}".format(os_type=os_type, pydistro=pydistro): d} def indent(indentation, data_list): From 93805b9f631d7244ab685e92161a8eadf4ddd949 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Thu, 18 Jun 2020 07:57:01 -0700 Subject: [PATCH 097/719] Updating tests to use assert_allclose --- test/experimental/test_vectors.py | 36 +++++++++++++++---------------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/test/experimental/test_vectors.py b/test/experimental/test_vectors.py index dc5f99a4a4..44e53530a6 100644 --- a/test/experimental/test_vectors.py +++ b/test/experimental/test_vectors.py @@ -12,13 +12,13 @@ class TestVectors(TorchtextTestCase): - def test_empty_vector(self): + def test_empty_vectors(self): tokens = [] vectors = [] unk_tensor = torch.tensor([0], dtype=torch.float) vectors_obj = Vectors(tokens, vectors, unk_tensor) - self.assertEqual(vectors_obj['not_in_it'].long().tolist(), unk_tensor.tolist()) + torch.testing.assert_allclose(vectors_obj['not_in_it'], unk_tensor) def test_empty_unk(self): tensorA = torch.tensor([1, 0], dtype=torch.float) @@ -28,7 +28,7 @@ def test_empty_unk(self): vectors = [tensorA] vectors_obj = Vectors(tokens, vectors) - self.assertEqual(vectors_obj['not_in_it'].long().tolist(), expected_unk_tensor.tolist()) + torch.testing.assert_allclose(vectors_obj['not_in_it'], expected_unk_tensor) def test_vectors_basic(self): tensorA = torch.tensor([1, 0], dtype=torch.float) @@ -39,9 +39,9 @@ def test_vectors_basic(self): vectors = [tensorA, tensorB] vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor) - self.assertEqual(vectors_obj['a'].long().tolist(), tensorA.tolist()) - self.assertEqual(vectors_obj['b'].long().tolist(), tensorB.tolist()) - self.assertEqual(vectors_obj['not_in_it'].long().tolist(), unk_tensor.tolist()) + torch.testing.assert_allclose(vectors_obj['a'], tensorA) + torch.testing.assert_allclose(vectors_obj['b'], tensorB) + torch.testing.assert_allclose(vectors_obj['not_in_it'], unk_tensor) def test_vectors_jit(self): tensorA = torch.tensor([1, 0], dtype=torch.float) @@ -53,9 +53,9 @@ def test_vectors_jit(self): vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor) jit_vectors_obj = torch.jit.script(vectors_obj) - self.assertEqual(vectors_obj['a'].long().tolist(), jit_vectors_obj['a'].long().tolist()) - self.assertEqual(vectors_obj['b'].long().tolist(), jit_vectors_obj['b'].long().tolist()) - self.assertEqual(vectors_obj['not_in_it'].long().tolist(), jit_vectors_obj['not_in_it'].long().tolist()) + torch.testing.assert_allclose(vectors_obj['a'], jit_vectors_obj['a']) + torch.testing.assert_allclose(vectors_obj['b'], jit_vectors_obj['b']) + torch.testing.assert_allclose(vectors_obj['not_in_it'], jit_vectors_obj['not_in_it']) def test_vectors_add_item(self): tensorA = torch.tensor([1, 0], dtype=torch.float) @@ -68,9 +68,9 @@ def test_vectors_add_item(self): tensorB = torch.tensor([0., 1]) vectors_obj['b'] = tensorB - self.assertEqual(vectors_obj['a'].long().tolist(), tensorA.tolist()) - self.assertEqual(vectors_obj['b'].long().tolist(), tensorB.tolist()) - self.assertEqual(vectors_obj['not_in_it'].long().tolist(), unk_tensor.tolist()) + torch.testing.assert_allclose(vectors_obj['a'], tensorA) + torch.testing.assert_allclose(vectors_obj['b'], tensorB) + torch.testing.assert_allclose(vectors_obj['not_in_it'], unk_tensor) def test_vectors_load_and_save(self): tensorA = torch.tensor([1, 0], dtype=torch.float) @@ -80,12 +80,12 @@ def test_vectors_load_and_save(self): vectors = [tensorA] vectors_obj = Vectors(tokens, vectors) - vector_path = os.path.join(self.test_dir, 'vector.pt') + vector_path = os.path.join(self.test_dir, 'vectors.pt') torch.save(vectors_obj, vector_path) loaded_vectors_obj = torch.load(vector_path) - self.assertEqual(loaded_vectors_obj['a'].long().tolist(), tensorA.tolist()) - self.assertEqual(loaded_vectors_obj['not_in_it'].long().tolist(), expected_unk_tensor.tolist()) + torch.testing.assert_allclose(loaded_vectors_obj['a'], tensorA) + torch.testing.assert_allclose(loaded_vectors_obj['not_in_it'], expected_unk_tensor) def test_errors(self): tokens = [] @@ -132,6 +132,6 @@ def test_vectors_from_file(self): expected_tensorB = torch.tensor([0, 1, 0], dtype=torch.float) expected_unk_tensor = torch.tensor([0, 0, 0], dtype=torch.float) - self.assertEqual(vectors_obj['a'].long().tolist(), expected_tensorA.tolist()) - self.assertEqual(vectors_obj['b'].long().tolist(), expected_tensorB.tolist()) - self.assertEqual(vectors_obj['not_in_it'].long().tolist(), expected_unk_tensor.tolist()) + torch.testing.assert_allclose(vectors_obj['a'], expected_tensorA) + torch.testing.assert_allclose(vectors_obj['b'], expected_tensorB) + torch.testing.assert_allclose(vectors_obj['not_in_it'], expected_unk_tensor) From 5df4f7bf74234b2913ee866f17519596134fb84d Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Thu, 18 Jun 2020 15:50:37 -0400 Subject: [PATCH 098/719] switch to torch TestCase for build-in dataset (#822) --- test/common/torchtext_test_case.py | 2 +- test/data/test_builtin_datasets.py | 57 +++++++++++++++--------------- test/data/test_field.py | 9 +++-- test/data/test_metrics.py | 23 ++++++------ test/test_build.py | 55 ++++++++++++++-------------- test/test_vocab.py | 3 +- 6 files changed, 72 insertions(+), 77 deletions(-) diff --git a/test/common/torchtext_test_case.py b/test/common/torchtext_test_case.py index 249e5aa0c9..21ccc4d785 100644 --- a/test/common/torchtext_test_case.py +++ b/test/common/torchtext_test_case.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -from unittest import TestCase +from torch.testing._internal.common_utils import TestCase import json import logging import os diff --git a/test/data/test_builtin_datasets.py b/test/data/test_builtin_datasets.py index 4038199a22..aa0fff33a5 100644 --- a/test/data/test_builtin_datasets.py +++ b/test/data/test_builtin_datasets.py @@ -6,7 +6,6 @@ import torchtext.data as data from torchtext.datasets import AG_NEWS import torch -from torch.testing import assert_allclose from ..common.torchtext_test_case import TorchtextTestCase @@ -99,10 +98,10 @@ def test_text_classification(self): ag_news_train, ag_news_test = AG_NEWS(root=datadir, ngrams=3) self.assertEqual(len(ag_news_train), 120000) self.assertEqual(len(ag_news_test), 7600) - assert_allclose(ag_news_train[-1][1][:10], - torch.tensor([3525, 319, 4053, 34, 5407, 3607, 70, 6798, 10599, 4053]).long()) - assert_allclose(ag_news_test[-1][1][:10], - torch.tensor([2351, 758, 96, 38581, 2351, 220, 5, 396, 3, 14786]).long()) + self.assertEqual(ag_news_train[-1][1][:10], + torch.tensor([3525, 319, 4053, 34, 5407, 3607, 70, 6798, 10599, 4053]).long()) + self.assertEqual(ag_news_test[-1][1][:10], + torch.tensor([2351, 758, 96, 38581, 2351, 220, 5, 396, 3, 14786]).long()) def test_imdb(self): from torchtext.experimental.datasets import IMDB @@ -111,14 +110,14 @@ def test_imdb(self): train_dataset, test_dataset = IMDB() self.assertEqual(len(train_dataset), 25000) self.assertEqual(len(test_dataset), 25000) - assert_allclose(train_dataset[0][1][:10], - torch.tensor([13, 1568, 13, 246, 35468, 43, 64, 398, 1135, 92]).long()) - assert_allclose(train_dataset[-1][1][:10], - torch.tensor([2, 71, 4555, 194, 3328, 15144, 42, 227, 148, 8]).long()) - assert_allclose(test_dataset[0][1][:10], - torch.tensor([13, 125, 1051, 5, 246, 1652, 8, 277, 66, 20]).long()) - assert_allclose(test_dataset[-1][1][:10], - torch.tensor([13, 1035, 14, 21, 28, 2, 1051, 1275, 1008, 3]).long()) + self.assertEqual(train_dataset[0][1][:10], + torch.tensor([13, 1568, 13, 246, 35468, 43, 64, 398, 1135, 92]).long()) + self.assertEqual(train_dataset[-1][1][:10], + torch.tensor([2, 71, 4555, 194, 3328, 15144, 42, 227, 148, 8]).long()) + self.assertEqual(test_dataset[0][1][:10], + torch.tensor([13, 125, 1051, 5, 246, 1652, 8, 277, 66, 20]).long()) + self.assertEqual(test_dataset[-1][1][:10], + torch.tensor([13, 1035, 14, 21, 28, 2, 1051, 1275, 1008, 3]).long()) # Test API with a vocab input object old_vocab = train_dataset.get_vocab() @@ -164,14 +163,14 @@ def test_squad1(self): train_dataset, dev_dataset = SQuAD1() self.assertEqual(len(train_dataset), 87599) self.assertEqual(len(dev_dataset), 10570) - assert_allclose(train_dataset[100]['question'], - torch.tensor([7, 24, 86, 52, 2, 373, 887, 18, 12797, 11090, 1356, 2, 1788, 3273, 16]).long()) - assert_allclose(train_dataset[100]['ans_pos'][0], - torch.tensor([72, 72]).long()) - assert_allclose(dev_dataset[100]['question'], - torch.tensor([42, 27, 669, 7438, 17, 2, 1950, 3273, 17252, 389, 16]).long()) - assert_allclose(dev_dataset[100]['ans_pos'][0], - torch.tensor([45, 48]).long()) + self.assertEqual(train_dataset[100]['question'], + torch.tensor([7, 24, 86, 52, 2, 373, 887, 18, 12797, 11090, 1356, 2, 1788, 3273, 16]).long()) + self.assertEqual(train_dataset[100]['ans_pos'][0], + torch.tensor([72, 72]).long()) + self.assertEqual(dev_dataset[100]['question'], + torch.tensor([42, 27, 669, 7438, 17, 2, 1950, 3273, 17252, 389, 16]).long()) + self.assertEqual(dev_dataset[100]['ans_pos'][0], + torch.tensor([45, 48]).long()) # Test API with a vocab input object old_vocab = train_dataset.get_vocab() @@ -185,14 +184,14 @@ def test_squad2(self): train_dataset, dev_dataset = SQuAD2() self.assertEqual(len(train_dataset), 130319) self.assertEqual(len(dev_dataset), 11873) - assert_allclose(train_dataset[200]['question'], - torch.tensor([84, 50, 1421, 12, 5439, 4569, 17, 30, 2, 15202, 4754, 1421, 16]).long()) - assert_allclose(train_dataset[200]['ans_pos'][0], - torch.tensor([9, 9]).long()) - assert_allclose(dev_dataset[200]['question'], - torch.tensor([41, 29, 2, 66, 17016, 30, 0, 1955, 16]).long()) - assert_allclose(dev_dataset[200]['ans_pos'][0], - torch.tensor([40, 46]).long()) + self.assertEqual(train_dataset[200]['question'], + torch.tensor([84, 50, 1421, 12, 5439, 4569, 17, 30, 2, 15202, 4754, 1421, 16]).long()) + self.assertEqual(train_dataset[200]['ans_pos'][0], + torch.tensor([9, 9]).long()) + self.assertEqual(dev_dataset[200]['question'], + torch.tensor([41, 29, 2, 66, 17016, 30, 0, 1955, 16]).long()) + self.assertEqual(dev_dataset[200]['ans_pos'][0], + torch.tensor([40, 46]).long()) # Test API with a vocab input object old_vocab = train_dataset.get_vocab() diff --git a/test/data/test_field.py b/test/data/test_field.py index 11b98bde68..aa76c38567 100644 --- a/test/data/test_field.py +++ b/test/data/test_field.py @@ -2,7 +2,6 @@ from collections import Counter import os -from numpy.testing import assert_allclose import torch import torchtext.data as data import pytest @@ -376,9 +375,9 @@ def test_numerical_features_no_vocab(self): test_float_data = ["1.1", "0.1", "3.91", "0.2", "10.2"] numericalized_int = int_field.numericalize(test_int_data) - assert_allclose(numericalized_int.data.numpy(), [1, 0, 1, 3, 19]) + self.assertEqual(numericalized_int.data, [1, 0, 1, 3, 19]) numericalized_float = float_field.numericalize(test_float_data) - assert_allclose(numericalized_float.data.numpy(), [1.1, 0.1, 3.91, 0.2, 10.2]) + self.assertEqual(numericalized_float.data, [1.1, 0.1, 3.91, 0.2, 10.2]) # Test with postprocessing applied int_field = data.Field(sequential=False, use_vocab=False, @@ -396,9 +395,9 @@ def test_numerical_features_no_vocab(self): test_float_data = ["1.1", "0.1", "3.91", "0.2", "10.2"] numericalized_int = int_field.numericalize(test_int_data) - assert_allclose(numericalized_int.data.numpy(), [2, 1, 2, 4, 20]) + self.assertEqual(numericalized_int.data, [2, 1, 2, 4, 20]) numericalized_float = float_field.numericalize(test_float_data) - assert_allclose(numericalized_float.data.numpy(), [0.55, 0.05, 1.955, 0.1, 5.1]) + self.assertEqual(numericalized_float.data, [0.55, 0.05, 1.955, 0.1, 5.1]) def test_errors(self): # Test that passing a non-tuple (of data and length) to numericalize diff --git a/test/data/test_metrics.py b/test/data/test_metrics.py index 1d69a20bae..af0ae77273 100644 --- a/test/data/test_metrics.py +++ b/test/data/test_metrics.py @@ -1,5 +1,4 @@ from torchtext.data.metrics import bleu_score -from torch.testing import assert_allclose from ..common.torchtext_test_case import TorchtextTestCase @@ -19,19 +18,19 @@ def test_bleu_score(self): # Partial match candidate = [['My', 'full', 'pytorch', 'test']] refs = [[['My', 'full', 'pytorch', 'test', '!'], ['Different']]] - assert_allclose(bleu_score(candidate, refs), 0.7788007) + self.assertEqual(bleu_score(candidate, refs), 0.7788007) # Bigrams and unigrams only candidate = [['My', 'pytorch', 'test']] refs = [[['My', 'full', 'pytorch', 'test'], ['Different']]] - assert_allclose(bleu_score(candidate, refs, max_n=2, - weights=[0.5, 0.5]), 0.5066641) + self.assertEqual(bleu_score(candidate, refs, max_n=2, + weights=[0.5, 0.5]), 0.5066641) # Multi-sentence corpus candidate = [['My', 'full', 'pytorch', 'test'], ['Another', 'Sentence']] refs = [[['My', 'full', 'pytorch', 'test'], ['Completely', 'Different']], [['No', 'Match']]] - assert_allclose(bleu_score(candidate, refs), 0.8408964) + self.assertEqual(bleu_score(candidate, refs), 0.8408964) # Empty input candidate = [[]] @@ -52,13 +51,13 @@ def test_bleu_score(self): # The comments below give the code used to get each hardcoded bleu score # nltk.translate.bleu_score.corpus_bleu(refs, candidate) - assert_allclose(bleu_score(candidate, refs), 0.4573199) + self.assertEqual(bleu_score(candidate, refs), 0.4573199) # nltk.translate.bleu_score.corpus_bleu(refs, candidate, weights=[0.33]*3) - assert_allclose(bleu_score(candidate, refs, 3, - weights=[0.33, 0.33, 0.33]), 0.4901113) + self.assertEqual(bleu_score(candidate, refs, 3, + weights=[0.33, 0.33, 0.33]), 0.4901113) # nltk.translate.bleu_score.corpus_bleu(refs, candidate, weights=[0.5]*2) - assert_allclose(bleu_score(candidate, refs, 2, - weights=[0.5, 0.5]), 0.5119535) + self.assertEqual(bleu_score(candidate, refs, 2, + weights=[0.5, 0.5]), 0.5119535) # nltk.translate.bleu_score.corpus_bleu(refs, candidate, weights=[1]) - assert_allclose(bleu_score(candidate, refs, 1, - weights=[1]), 0.5515605) + self.assertEqual(bleu_score(candidate, refs, 1, + weights=[1]), 0.5515605) diff --git a/test/test_build.py b/test/test_build.py index f47d535204..d61e844280 100644 --- a/test/test_build.py +++ b/test/test_build.py @@ -3,7 +3,6 @@ import os from collections import Counter -import numpy as np import torch import torchtext.data @@ -130,16 +129,16 @@ def test_vectors_get_vecs(self): self.assertEqual(vec.vectors.shape[0], len(vec)) tokens = ['chip', 'baby', 'Beautiful'] - token_vecs = vec.get_vecs_by_tokens(tokens).numpy() + token_vecs = vec.get_vecs_by_tokens(tokens) self.assertEqual(token_vecs.shape[0], len(tokens)) self.assertEqual(token_vecs.shape[1], vec.dim) - torch.testing.assert_allclose(vec[tokens[0]].numpy(), token_vecs[0]) - torch.testing.assert_allclose(vec[tokens[1]].numpy(), token_vecs[1]) - torch.testing.assert_allclose(vec[''].numpy(), token_vecs[2]) + self.assertEqual(vec[tokens[0]], token_vecs[0]) + self.assertEqual(vec[tokens[1]], token_vecs[1]) + self.assertEqual(vec[''], token_vecs[2]) - token_one_vec = vec.get_vecs_by_tokens(tokens[0], lower_case_backup=True).numpy() + token_one_vec = vec.get_vecs_by_tokens(tokens[0], lower_case_backup=True) self.assertEqual(token_one_vec.shape[0], vec.dim) - torch.testing.assert_allclose(vec[tokens[0].lower()].numpy(), token_one_vec) + self.assertEqual(vec[tokens[0].lower()], token_one_vec) def test_download_charngram_vectors(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) @@ -157,7 +156,7 @@ def test_download_charngram_vectors(self): expected_stoi = {x: index for index, x in enumerate(expected_itos)} self.assertEqual(v.itos, expected_itos) self.assertEqual(dict(v.stoi), expected_stoi) - vectors = v.vectors.numpy() + vectors = v.vectors # The first 5 entries in each vector. expected_charngram = { @@ -167,11 +166,11 @@ def test_download_charngram_vectors(self): } for word in expected_charngram: - torch.testing.assert_allclose( + self.assertEqual( vectors[v.stoi[word], :5], expected_charngram[word]) - torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(100)) - torch.testing.assert_allclose(vectors[v.stoi['OOV token']], np.zeros(100)) + self.assertEqual(vectors[v.stoi['']], torch.zeros(100)) + self.assertEqual(vectors[v.stoi['OOV token']], torch.zeros(100)) def test_download_custom_vectors(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) @@ -187,7 +186,7 @@ def test_download_custom_vectors(self): self.assertEqual(v.itos, ['', '', '', 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world']) - vectors = v.vectors.numpy() + vectors = v.vectors # The first 5 entries in each vector. expected_fasttext_simple_en = { @@ -196,10 +195,10 @@ def test_download_custom_vectors(self): } for word in expected_fasttext_simple_en: - torch.testing.assert_allclose( + self.assertEqual( vectors[v.stoi[word], :5], expected_fasttext_simple_en[word]) - torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(300)) + self.assertEqual(vectors[v.stoi['']], torch.zeros(300)) def test_download_fasttext_vectors(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) @@ -219,7 +218,7 @@ def test_download_fasttext_vectors(self): expected_stoi = {x: index for index, x in enumerate(expected_itos)} self.assertEqual(v.itos, expected_itos) self.assertEqual(dict(v.stoi), expected_stoi) - vectors = v.vectors.numpy() + vectors = v.vectors # The first 5 entries in each vector. expected_fasttext_simple_en = { @@ -228,11 +227,11 @@ def test_download_fasttext_vectors(self): } for word in expected_fasttext_simple_en: - torch.testing.assert_allclose( + self.assertEqual( vectors[v.stoi[word], :5], expected_fasttext_simple_en[word]) - torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(300)) - torch.testing.assert_allclose(vectors[v.stoi['OOV token']], np.zeros(300)) + self.assertEqual(vectors[v.stoi['']], torch.zeros(300)) + self.assertEqual(vectors[v.stoi['OOV token']], torch.zeros(300)) def test_download_glove_vectors(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) @@ -253,7 +252,7 @@ def test_download_glove_vectors(self): self.assertEqual(v.itos, expected_itos) self.assertEqual(dict(v.stoi), expected_stoi) - vectors = v.vectors.numpy() + vectors = v.vectors # The first 5 entries in each vector. expected_twitter = { @@ -262,11 +261,11 @@ def test_download_glove_vectors(self): } for word in expected_twitter: - torch.testing.assert_allclose( + self.assertEqual( vectors[v.stoi[word], :5], expected_twitter[word]) - torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(25)) - torch.testing.assert_allclose(vectors[v.stoi['OOV token']], np.zeros(25)) + self.assertEqual(vectors[v.stoi['']], torch.zeros(25)) + self.assertEqual(vectors[v.stoi['OOV token']], torch.zeros(25)) def test_extend(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) @@ -281,7 +280,7 @@ def test_extend(self): self.assertEqual(v.itos[:6], ['', '', '', 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world']) - vectors = v.vectors.numpy() + vectors = v.vectors # The first 5 entries in each vector. expected_fasttext_simple_en = { @@ -290,10 +289,10 @@ def test_extend(self): } for word in expected_fasttext_simple_en: - torch.testing.assert_allclose( + self.assertEqual( vectors[v.stoi[word], :5], expected_fasttext_simple_en[word]) - torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(300)) + self.assertEqual(vectors[v.stoi['']], torch.zeros(300)) def test_vectors_custom_cache(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) @@ -312,7 +311,7 @@ def test_vectors_custom_cache(self): self.assertEqual(v.itos, ['', '', '', 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world']) - vectors = v.vectors.numpy() + vectors = v.vectors # The first 5 entries in each vector. expected_fasttext_simple_en = { @@ -321,7 +320,7 @@ def test_vectors_custom_cache(self): } for word in expected_fasttext_simple_en: - torch.testing.assert_allclose( + self.assertEqual( vectors[v.stoi[word], :5], expected_fasttext_simple_en[word]) - torch.testing.assert_allclose(vectors[v.stoi['']], np.zeros(300)) + self.assertEqual(vectors[v.stoi['']], torch.zeros(300)) diff --git a/test/test_vocab.py b/test/test_vocab.py index c09bff69f2..c9b863d37a 100644 --- a/test/test_vocab.py +++ b/test/test_vocab.py @@ -5,7 +5,6 @@ import numpy as np -from numpy.testing import assert_allclose import torch from torchtext import vocab @@ -89,7 +88,7 @@ def test_vocab_set_vectors(self): expected_vectors = np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.1, 0.2], [0.5, 0.6], [0.3, 0.4]]) - assert_allclose(v.vectors.numpy(), expected_vectors) + self.assertEqual(v.vectors, expected_vectors) def test_errors(self): c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2}) From 407756d4a02a4ef10eadaf82df8268248a3e1248 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Thu, 18 Jun 2020 13:01:30 -0700 Subject: [PATCH 099/719] updated tests to use self.assertEqual --- test/experimental/test_vectors.py | 32 +++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/test/experimental/test_vectors.py b/test/experimental/test_vectors.py index 44e53530a6..6ddbbde8b3 100644 --- a/test/experimental/test_vectors.py +++ b/test/experimental/test_vectors.py @@ -18,7 +18,7 @@ def test_empty_vectors(self): unk_tensor = torch.tensor([0], dtype=torch.float) vectors_obj = Vectors(tokens, vectors, unk_tensor) - torch.testing.assert_allclose(vectors_obj['not_in_it'], unk_tensor) + self.assertEqual(vectors_obj['not_in_it'], unk_tensor) def test_empty_unk(self): tensorA = torch.tensor([1, 0], dtype=torch.float) @@ -28,7 +28,7 @@ def test_empty_unk(self): vectors = [tensorA] vectors_obj = Vectors(tokens, vectors) - torch.testing.assert_allclose(vectors_obj['not_in_it'], expected_unk_tensor) + self.assertEqual(vectors_obj['not_in_it'], expected_unk_tensor) def test_vectors_basic(self): tensorA = torch.tensor([1, 0], dtype=torch.float) @@ -39,9 +39,9 @@ def test_vectors_basic(self): vectors = [tensorA, tensorB] vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor) - torch.testing.assert_allclose(vectors_obj['a'], tensorA) - torch.testing.assert_allclose(vectors_obj['b'], tensorB) - torch.testing.assert_allclose(vectors_obj['not_in_it'], unk_tensor) + self.assertEqual(vectors_obj['a'], tensorA) + self.assertEqual(vectors_obj['b'], tensorB) + self.assertEqual(vectors_obj['not_in_it'], unk_tensor) def test_vectors_jit(self): tensorA = torch.tensor([1, 0], dtype=torch.float) @@ -53,9 +53,9 @@ def test_vectors_jit(self): vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor) jit_vectors_obj = torch.jit.script(vectors_obj) - torch.testing.assert_allclose(vectors_obj['a'], jit_vectors_obj['a']) - torch.testing.assert_allclose(vectors_obj['b'], jit_vectors_obj['b']) - torch.testing.assert_allclose(vectors_obj['not_in_it'], jit_vectors_obj['not_in_it']) + self.assertEqual(vectors_obj['a'], jit_vectors_obj['a']) + self.assertEqual(vectors_obj['b'], jit_vectors_obj['b']) + self.assertEqual(vectors_obj['not_in_it'], jit_vectors_obj['not_in_it']) def test_vectors_add_item(self): tensorA = torch.tensor([1, 0], dtype=torch.float) @@ -68,9 +68,9 @@ def test_vectors_add_item(self): tensorB = torch.tensor([0., 1]) vectors_obj['b'] = tensorB - torch.testing.assert_allclose(vectors_obj['a'], tensorA) - torch.testing.assert_allclose(vectors_obj['b'], tensorB) - torch.testing.assert_allclose(vectors_obj['not_in_it'], unk_tensor) + self.assertEqual(vectors_obj['a'], tensorA) + self.assertEqual(vectors_obj['b'], tensorB) + self.assertEqual(vectors_obj['not_in_it'], unk_tensor) def test_vectors_load_and_save(self): tensorA = torch.tensor([1, 0], dtype=torch.float) @@ -84,8 +84,8 @@ def test_vectors_load_and_save(self): torch.save(vectors_obj, vector_path) loaded_vectors_obj = torch.load(vector_path) - torch.testing.assert_allclose(loaded_vectors_obj['a'], tensorA) - torch.testing.assert_allclose(loaded_vectors_obj['not_in_it'], expected_unk_tensor) + self.assertEqual(loaded_vectors_obj['a'], tensorA) + self.assertEqual(loaded_vectors_obj['not_in_it'], expected_unk_tensor) def test_errors(self): tokens = [] @@ -132,6 +132,6 @@ def test_vectors_from_file(self): expected_tensorB = torch.tensor([0, 1, 0], dtype=torch.float) expected_unk_tensor = torch.tensor([0, 0, 0], dtype=torch.float) - torch.testing.assert_allclose(vectors_obj['a'], expected_tensorA) - torch.testing.assert_allclose(vectors_obj['b'], expected_tensorB) - torch.testing.assert_allclose(vectors_obj['not_in_it'], expected_unk_tensor) + self.assertEqual(vectors_obj['a'], expected_tensorA) + self.assertEqual(vectors_obj['b'], expected_tensorB) + self.assertEqual(vectors_obj['not_in_it'], expected_unk_tensor) From 50576f32f081f718faa1ec454888ff11b9893227 Mon Sep 17 00:00:00 2001 From: Guanheng George Zhang <6156351+zhangguanheng66@users.noreply.github.com> Date: Fri, 19 Jun 2020 10:13:27 -0400 Subject: [PATCH 100/719] remove codecov test (#832) --- codecov.yml | 14 -------------- 1 file changed, 14 deletions(-) delete mode 100644 codecov.yml diff --git a/codecov.yml b/codecov.yml deleted file mode 100644 index f3ca2a4807..0000000000 --- a/codecov.yml +++ /dev/null @@ -1,14 +0,0 @@ -coverage: - precision: 0 - round: down - status: - patch: - default: - target: 90 - project: - default: - threshold: 1% - changes: false -comment: false -ignore: - - "test/" From 7aef5aadba034dd6b301a8f83aa2e0e42d5be730 Mon Sep 17 00:00:00 2001 From: Nayef Ahmed Date: Mon, 22 Jun 2020 10:57:09 -0700 Subject: [PATCH 101/719] Fixing Windows CI --- .vscode/settings.json | 3 +++ build_tools/setup_helpers/extension.py | 2 -- third_party/CMakeLists.txt | 5 ++++- torchtext/.nfs001a00000387970a000000dc | Bin 0 -> 12031952 bytes 4 files changed, 7 insertions(+), 3 deletions(-) create mode 100644 .vscode/settings.json create mode 100755 torchtext/.nfs001a00000387970a000000dc diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000000..dee5e29289 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "restructuredtext.confPath": "${workspaceFolder}/docs/source" +} \ No newline at end of file diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 85dc1fa660..6c70940f22 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -91,8 +91,6 @@ def _build_third_party(debug): if platform.system() == 'Windows': extra_args = [ '-GNinja', - '-DCMAKE_CXX_FLAGS=/MT', - '-DCMAKE_MSVC_RUNTIME_LIBRARY="MultiThreaded"', 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