From 0e4cc050d63058969c53cce295dea99e90483c01 Mon Sep 17 00:00:00 2001 From: Sergey Mironov Date: Thu, 24 Oct 2019 18:15:55 +0300 Subject: [PATCH 001/179] Add support for resumable downloads for HTTP protocol. --- transformers/configuration_auto.py | 3 ++ transformers/configuration_utils.py | 7 ++++- transformers/file_utils.py | 47 ++++++++++++++++++++++------- transformers/modeling_auto.py | 8 +++++ transformers/modeling_tf_auto.py | 12 ++++++++ transformers/modeling_tf_utils.py | 8 ++++- transformers/modeling_utils.py | 8 ++++- transformers/tokenization_auto.py | 3 ++ transformers/tokenization_utils.py | 6 +++- 9 files changed, 87 insertions(+), 15 deletions(-) diff --git a/transformers/configuration_auto.py b/transformers/configuration_auto.py index edd21a670c..0e8eb03d03 100644 --- a/transformers/configuration_auto.py +++ b/transformers/configuration_auto.py @@ -92,6 +92,9 @@ class AutoConfig(object): force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. diff --git a/transformers/configuration_utils.py b/transformers/configuration_utils.py index cfa6502bcd..0f9d609b20 100644 --- a/transformers/configuration_utils.py +++ b/transformers/configuration_utils.py @@ -93,6 +93,9 @@ class PretrainedConfig(object): force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. @@ -119,6 +122,7 @@ class PretrainedConfig(object): """ cache_dir = kwargs.pop('cache_dir', None) force_download = kwargs.pop('force_download', False) + resume_download = kwargs.pop('resume_download', False) proxies = kwargs.pop('proxies', None) return_unused_kwargs = kwargs.pop('return_unused_kwargs', False) @@ -130,7 +134,8 @@ class PretrainedConfig(object): config_file = pretrained_model_name_or_path # redirect to the cache, if necessary try: - resolved_config_file = cached_path(config_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies) + resolved_config_file = cached_path(config_file, cache_dir=cache_dir, force_download=force_download, + proxies=proxies, resume_download=resume_download) except EnvironmentError: if pretrained_model_name_or_path in cls.pretrained_config_archive_map: msg = "Couldn't reach server at '{}' to download pretrained model configuration file.".format( diff --git a/transformers/file_utils.py b/transformers/file_utils.py index 27875212ff..24abd60781 100644 --- a/transformers/file_utils.py +++ b/transformers/file_utils.py @@ -22,6 +22,7 @@ from botocore.config import Config from botocore.exceptions import ClientError import requests from tqdm import tqdm +from contextlib import contextmanager logger = logging.getLogger(__name__) # pylint: disable=invalid-name @@ -152,7 +153,7 @@ def filename_to_url(filename, cache_dir=None): return url, etag -def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=None): +def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=None, resume_download=False): """ Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file and cache it, and @@ -161,6 +162,7 @@ def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=N Args: cache_dir: specify a cache directory to save the file to (overwrite the default cache dir). force_download: if True, re-dowload the file even if it's already cached in the cache dir. + resume_download: if True, resume the download if incompletly recieved file is found. """ if cache_dir is None: cache_dir = TRANSFORMERS_CACHE @@ -173,7 +175,9 @@ def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=N if parsed.scheme in ('http', 'https', 's3'): # URL, so get it from the cache (downloading if necessary) - return get_from_cache(url_or_filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies) + return get_from_cache(url_or_filename, cache_dir=cache_dir, + force_download=force_download, proxies=proxies, + resume_download=resume_download) elif os.path.exists(url_or_filename): # File, and it exists. return url_or_filename @@ -234,19 +238,22 @@ def s3_get(url, temp_file, proxies=None): s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file) -def http_get(url, temp_file, proxies=None): - req = requests.get(url, stream=True, proxies=proxies) - content_length = req.headers.get('Content-Length') - total = int(content_length) if content_length is not None else None - progress = tqdm(unit="B", total=total) - for chunk in req.iter_content(chunk_size=1024): +def http_get(url, temp_file, proxies=None, resume_size=0): + headers={'Range':'bytes=%d-'%(resume_size,)} if resume_size > 0 else None + response = requests.get(url, stream=True, proxies=proxies, headers=headers) + if response.status_code == 416: # Range not satisfiable + return + content_length = response.headers.get('Content-Length') + total = resume_size + int(content_length) if content_length is not None else None + progress = tqdm(unit="B", total=total, initial=resume_size) + for chunk in response.iter_content(chunk_size=1024): if chunk: # filter out keep-alive new chunks progress.update(len(chunk)) temp_file.write(chunk) progress.close() -def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag_timeout=10): +def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag_timeout=10, resume_download=False): """ Given a URL, look for the corresponding dataset in the local cache. If it's not there, download it. Then return the path to the cached file. @@ -289,17 +296,35 @@ def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag if matching_files: cache_path = os.path.join(cache_dir, matching_files[-1]) + if resume_download: + incomplete_path = cache_path + '.incomplete' + @contextmanager + def _resumable_file_manager(): + with open(incomplete_path,'a+b') as f: + yield f + os.remove(incomplete_path) + temp_file_manager = _resumable_file_manager + if os.path.exists(incomplete_path): + resume_size = os.stat(incomplete_path).st_size + else: + resume_size = 0 + else: + temp_file_manager = tempfile.NamedTemporaryFile + resume_size = 0 + if not os.path.exists(cache_path) or force_download: # Download to temporary file, then copy to cache dir once finished. # Otherwise you get corrupt cache entries if the download gets interrupted. - with tempfile.NamedTemporaryFile() as temp_file: + with temp_file_manager() as temp_file: logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name) # GET file object if url.startswith("s3://"): + if resume_download: + logger.warn('Warning: resumable downloads are not implemented for "s3://" urls') s3_get(url, temp_file, proxies=proxies) else: - http_get(url, temp_file, proxies=proxies) + http_get(url, temp_file, proxies=proxies, resume_size=resume_size) # we are copying the file before closing it, so flush to avoid truncation temp_file.flush() diff --git a/transformers/modeling_auto.py b/transformers/modeling_auto.py index d98110d4bd..ea5a539c8c 100644 --- a/transformers/modeling_auto.py +++ b/transformers/modeling_auto.py @@ -112,6 +112,9 @@ class AutoModel(object): force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. @@ -237,6 +240,8 @@ class AutoModelWithLMHead(object): force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. @@ -357,6 +362,9 @@ class AutoModelForSequenceClassification(object): force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. diff --git a/transformers/modeling_tf_auto.py b/transformers/modeling_tf_auto.py index df0ad6e401..cfe19ead2a 100644 --- a/transformers/modeling_tf_auto.py +++ b/transformers/modeling_tf_auto.py @@ -109,6 +109,9 @@ class TFAutoModel(object): force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. @@ -237,6 +240,9 @@ class TFAutoModelWithLMHead(object): force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. @@ -360,6 +366,9 @@ class TFAutoModelForSequenceClassification(object): force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. @@ -472,6 +481,9 @@ class TFAutoModelForQuestionAnswering(object): force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. diff --git a/transformers/modeling_tf_utils.py b/transformers/modeling_tf_utils.py index a96e2765fd..aed2a25643 100644 --- a/transformers/modeling_tf_utils.py +++ b/transformers/modeling_tf_utils.py @@ -176,6 +176,9 @@ class TFPreTrainedModel(tf.keras.Model): force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. @@ -201,6 +204,7 @@ class TFPreTrainedModel(tf.keras.Model): cache_dir = kwargs.pop('cache_dir', None) from_pt = kwargs.pop('from_pt', False) force_download = kwargs.pop('force_download', False) + resume_download = kwargs.pop('resume_download', False) proxies = kwargs.pop('proxies', None) # Load config @@ -209,6 +213,7 @@ class TFPreTrainedModel(tf.keras.Model): pretrained_model_name_or_path, *model_args, cache_dir=cache_dir, return_unused_kwargs=True, force_download=force_download, + resume_download=resume_download, **kwargs ) else: @@ -236,7 +241,8 @@ class TFPreTrainedModel(tf.keras.Model): # redirect to the cache, if necessary try: - resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies) + resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, + resume_download=resume_download, proxies=proxies) except EnvironmentError as e: if pretrained_model_name_or_path in cls.pretrained_model_archive_map: logger.error( diff --git a/transformers/modeling_utils.py b/transformers/modeling_utils.py index d082137d5d..db3ab93f8f 100644 --- a/transformers/modeling_utils.py +++ b/transformers/modeling_utils.py @@ -246,6 +246,9 @@ class PreTrainedModel(nn.Module): force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. @@ -275,6 +278,7 @@ class PreTrainedModel(nn.Module): cache_dir = kwargs.pop('cache_dir', None) from_tf = kwargs.pop('from_tf', False) force_download = kwargs.pop('force_download', False) + resume_download = kwargs.pop('resume_download', False) proxies = kwargs.pop('proxies', None) output_loading_info = kwargs.pop('output_loading_info', False) @@ -284,6 +288,7 @@ class PreTrainedModel(nn.Module): pretrained_model_name_or_path, *model_args, cache_dir=cache_dir, return_unused_kwargs=True, force_download=force_download, + resume_download=resume_download, **kwargs ) else: @@ -315,7 +320,8 @@ class PreTrainedModel(nn.Module): # redirect to the cache, if necessary try: - resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies) + resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, + proxies=proxies, resume_download=resume_download) except EnvironmentError: if pretrained_model_name_or_path in cls.pretrained_model_archive_map: msg = "Couldn't reach server at '{}' to download pretrained weights.".format( diff --git a/transformers/tokenization_auto.py b/transformers/tokenization_auto.py index ec056de17f..455f503a9d 100644 --- a/transformers/tokenization_auto.py +++ b/transformers/tokenization_auto.py @@ -87,6 +87,9 @@ class AutoTokenizer(object): force_download: (`optional`) boolean, default False: Force to (re-)download the vocabulary files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. diff --git a/transformers/tokenization_utils.py b/transformers/tokenization_utils.py index 5e5be872ef..b602ebf20a 100644 --- a/transformers/tokenization_utils.py +++ b/transformers/tokenization_utils.py @@ -251,6 +251,9 @@ class PreTrainedTokenizer(object): force_download: (`optional`) boolean, default False: Force to (re-)download the vocabulary files and override the cached versions if they exists. + resume_download: (`optional`) boolean, default False: + Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. + proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. @@ -286,6 +289,7 @@ class PreTrainedTokenizer(object): def _from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs): cache_dir = kwargs.pop('cache_dir', None) force_download = kwargs.pop('force_download', False) + resume_download = kwargs.pop('resume_download', False) proxies = kwargs.pop('proxies', None) s3_models = list(cls.max_model_input_sizes.keys()) @@ -352,7 +356,7 @@ class PreTrainedTokenizer(object): if file_path is None: resolved_vocab_files[file_id] = None else: - resolved_vocab_files[file_id] = cached_path(file_path, cache_dir=cache_dir, force_download=force_download, proxies=proxies) + resolved_vocab_files[file_id] = cached_path(file_path, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download) except EnvironmentError: if pretrained_model_name_or_path in s3_models: msg = "Couldn't reach server at '{}' to download vocabulary files." From 8d6b9d717c676560d291a7d2913b44e1cf226cd5 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Mon, 4 Nov 2019 17:07:51 +0100 Subject: [PATCH 002/179] fix #1532 and encode_plus --- .../tests/tokenization_tests_commons.py | 21 ++++-- transformers/tokenization_utils.py | 74 ++++++++++++++++--- 2 files changed, 77 insertions(+), 18 deletions(-) diff --git a/transformers/tests/tokenization_tests_commons.py b/transformers/tests/tokenization_tests_commons.py index a921696b77..cb137e8395 100644 --- a/transformers/tests/tokenization_tests_commons.py +++ b/transformers/tests/tokenization_tests_commons.py @@ -223,7 +223,11 @@ class CommonTestCases: sequence = tokenizer.encode(seq_0, add_special_tokens=False) num_added_tokens = tokenizer.num_added_tokens() total_length = len(sequence) + num_added_tokens - information = tokenizer.encode_plus(seq_0, max_length=total_length - 2, add_special_tokens=True, stride=stride) + information = tokenizer.encode_plus(seq_0, + max_length=total_length - 2, + add_special_tokens=True, + stride=stride, + return_overflowing_tokens=True) truncated_sequence = information["input_ids"] overflowing_tokens = information["overflowing_tokens"] @@ -250,10 +254,12 @@ class CommonTestCases: ) information = tokenizer.encode_plus(seq_0, seq_1, max_length=len(sequence) - 2, add_special_tokens=True, - stride=stride, truncation_strategy='only_second') + stride=stride, truncation_strategy='only_second', + return_overflowing_tokens=True) information_first_truncated = tokenizer.encode_plus(seq_0, seq_1, max_length=len(sequence) - 2, add_special_tokens=True, stride=stride, - truncation_strategy='only_first') + truncation_strategy='only_first', + return_overflowing_tokens=True) truncated_sequence = information["input_ids"] overflowing_tokens = information["overflowing_tokens"] @@ -285,7 +291,7 @@ class CommonTestCases: # Testing single inputs encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False) - encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True) + encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True, return_special_tokens_mask=True) encoded_sequence_w_special = encoded_sequence_dict["input_ids"] special_tokens_mask = encoded_sequence_dict["special_tokens_mask"] self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special)) @@ -297,7 +303,8 @@ class CommonTestCases: # Testing inputs pairs encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False) + tokenizer.encode(sequence_1, add_special_tokens=False) - encoded_sequence_dict = tokenizer.encode_plus(sequence_0, sequence_1, add_special_tokens=True) + encoded_sequence_dict = tokenizer.encode_plus(sequence_0, sequence_1, add_special_tokens=True, + return_special_tokens_mask=True) encoded_sequence_w_special = encoded_sequence_dict["input_ids"] special_tokens_mask = encoded_sequence_dict["special_tokens_mask"] self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special)) @@ -309,7 +316,9 @@ class CommonTestCases: # Testing with already existing special tokens if tokenizer.cls_token_id == tokenizer.unk_token_id and tokenizer.cls_token_id == tokenizer.unk_token_id: tokenizer.add_special_tokens({'cls_token': '', 'sep_token': ''}) - encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True) + encoded_sequence_dict = tokenizer.encode_plus(sequence_0, + add_special_tokens=True, + return_special_tokens_mask=True) encoded_sequence_w_special = encoded_sequence_dict["input_ids"] special_tokens_mask_orig = encoded_sequence_dict["special_tokens_mask"] special_tokens_mask = tokenizer.get_special_tokens_mask(encoded_sequence_w_special, already_has_special_tokens=True) diff --git a/transformers/tokenization_utils.py b/transformers/tokenization_utils.py index ac765165e2..f860755775 100644 --- a/transformers/tokenization_utils.py +++ b/transformers/tokenization_utils.py @@ -744,6 +744,9 @@ class PreTrainedTokenizer(object): stride=0, truncation_strategy='longest_first', return_tensors=None, + return_token_type_ids=True, + return_overflowing_tokens=False, + return_special_tokens_mask=False, **kwargs): """ Returns a dictionary containing the encoded sequence or sequence pair and additional informations: @@ -770,7 +773,30 @@ class PreTrainedTokenizer(object): - 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length) return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant or PyTorch torch.Tensor instead of a list of python integers. + return_token_type_ids: (optional) Set to False to avoid returning token_type_ids (default True). + return_overflowing_tokens: (optional) Set to True to return overflowing token information (default False). + return_special_tokens_mask: (optional) Set to True to return special tokens mask information (default False). **kwargs: passed to the `self.tokenize()` method + + Return: + A Dictionary of shape:: + + { + input_ids: list[int], + token_type_ids: list[int] if return_token_type_ids is True (default) + overflowing_tokens: list[int] if a ``max_length`` is specified and return_overflowing_tokens is True + num_truncated_tokens: int if a ``max_length`` is specified and return_overflowing_tokens is True + special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True`` and return_special_tokens_mask is True + } + + With the fields: + ``input_ids``: list of token ids to be fed to a model + ``token_type_ids``: list of token type ids to be fed to a model + + ``overflowing_tokens``: list of overflowing tokens if a max length is specified. + ``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified + ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added + tokens and 1 specifying sequence tokens. """ def get_input_ids(text): @@ -792,10 +818,17 @@ class PreTrainedTokenizer(object): add_special_tokens=add_special_tokens, stride=stride, truncation_strategy=truncation_strategy, - return_tensors=return_tensors) + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask) def prepare_for_model(self, ids, pair_ids=None, max_length=None, add_special_tokens=True, stride=0, - truncation_strategy='longest_first', return_tensors=None): + truncation_strategy='longest_first', + return_tensors=None, + return_token_type_ids=True, + return_overflowing_tokens=False, + return_special_tokens_mask=False): """ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, truncates @@ -820,21 +853,27 @@ class PreTrainedTokenizer(object): - 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length) return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant or PyTorch torch.Tensor instead of a list of python integers. + return_token_type_ids: (optional) Set to False to avoid returning token_type_ids (default True). + return_overflowing_tokens: (optional) Set to True to return overflowing token information (default False). + return_special_tokens_mask: (optional) Set to True to return special tokens mask information (default False). Return: A Dictionary of shape:: { input_ids: list[int], - overflowing_tokens: list[int] if a ``max_length`` is specified, else None - special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True`` + token_type_ids: list[int] if return_token_type_ids is True (default) + overflowing_tokens: list[int] if a ``max_length`` is specified and return_overflowing_tokens is True + num_truncated_tokens: int if a ``max_length`` is specified and return_overflowing_tokens is True + special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True`` and return_special_tokens_mask is True } With the fields: - ``input_ids``: list of tokens to be fed to a model + ``input_ids``: list of token ids to be fed to a model + ``token_type_ids``: list of token type ids to be fed to a model ``overflowing_tokens``: list of overflowing tokens if a max length is specified. - + ``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added tokens and 1 specifying sequence tokens. """ @@ -843,23 +882,31 @@ class PreTrainedTokenizer(object): len_pair_ids = len(pair_ids) if pair else 0 encoded_inputs = {} + + # Handle max sequence length total_len = len_ids + len_pair_ids + (self.num_added_tokens(pair=pair) if add_special_tokens else 0) if max_length and total_len > max_length: ids, pair_ids, overflowing_tokens = self.truncate_sequences(ids, pair_ids=pair_ids, num_tokens_to_remove=total_len-max_length, truncation_strategy=truncation_strategy, stride=stride) - encoded_inputs["overflowing_tokens"] = overflowing_tokens - encoded_inputs["num_truncated_tokens"] = total_len - max_length + if return_overflowing_tokens: + encoded_inputs["overflowing_tokens"] = overflowing_tokens + encoded_inputs["num_truncated_tokens"] = total_len - max_length + # Handle special_tokens if add_special_tokens: sequence = self.build_inputs_with_special_tokens(ids, pair_ids) token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids) - encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids) + special_tokens_mask = self.get_special_tokens_mask(ids, pair_ids) else: sequence = ids + pair_ids if pair else ids token_type_ids = [0] * len(ids) + ([1] * len(pair_ids) if pair else []) + special_tokens_mask = [0] * (len(ids) + (len(pair_ids) if pair else 0)) + if return_special_tokens_mask: + encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids) + # Prepare inputs as tensors if asked if return_tensors == 'tf' and is_tf_available(): sequence = tf.constant([sequence]) token_type_ids = tf.constant([token_type_ids]) @@ -870,12 +917,15 @@ class PreTrainedTokenizer(object): logger.warning("Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(return_tensors)) encoded_inputs["input_ids"] = sequence - encoded_inputs["token_type_ids"] = token_type_ids + if return_token_type_ids: + encoded_inputs["token_type_ids"] = token_type_ids if max_length and len(encoded_inputs["input_ids"]) > max_length: encoded_inputs["input_ids"] = encoded_inputs["input_ids"][:max_length] - encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"][:max_length] - encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"][:max_length] + if return_token_type_ids: + encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"][:max_length] + if return_special_tokens_mask: + encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"][:max_length] return encoded_inputs From 8df7dfd2a723465b0cca9f5e808a75e074482d02 Mon Sep 17 00:00:00 2001 From: Filip Povolny Date: Tue, 5 Nov 2019 11:09:16 +0100 Subject: [PATCH 003/179] Make dummy inputs a local variable in TFPreTrainedModel. --- transformers/modeling_tf_utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/transformers/modeling_tf_utils.py b/transformers/modeling_tf_utils.py index a96e2765fd..110a590f55 100644 --- a/transformers/modeling_tf_utils.py +++ b/transformers/modeling_tf_utils.py @@ -51,7 +51,6 @@ class TFPreTrainedModel(tf.keras.Model): config_class = None pretrained_model_archive_map = {} base_model_prefix = "" - dummy_inputs = tf.constant(DUMMY_INPUTS) # dummy inputs to build the network def __init__(self, config, *inputs, **kwargs): super(TFPreTrainedModel, self).__init__(*inputs, **kwargs) @@ -266,14 +265,15 @@ class TFPreTrainedModel(tf.keras.Model): # Load from a PyTorch checkpoint return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file) - ret = model(model.dummy_inputs, training=False) # build the network with dummy inputs + dummy_inputs = tf.constant(DUMMY_INPUTS) # dummy inputs to build the network + ret = model(dummy_inputs, training=False) # build the network with dummy inputs assert os.path.isfile(resolved_archive_file), "Error retrieving file {}".format(resolved_archive_file) # 'by_name' allow us to do transfer learning by skipping/adding layers # see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1339-L1357 model.load_weights(resolved_archive_file, by_name=True) - ret = model(model.dummy_inputs, training=False) # Make sure restore ops are run + ret = model(dummy_inputs, training=False) # Make sure restore ops are run return model From 124409d0754f5b84ff47daea51dede1d3b37a8dd Mon Sep 17 00:00:00 2001 From: Filip Povolny Date: Tue, 5 Nov 2019 11:48:45 +0100 Subject: [PATCH 004/179] Make dummy inputs a property of TFPreTrainedModel. --- transformers/modeling_tf_utils.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/transformers/modeling_tf_utils.py b/transformers/modeling_tf_utils.py index 110a590f55..33cfdc503d 100644 --- a/transformers/modeling_tf_utils.py +++ b/transformers/modeling_tf_utils.py @@ -52,6 +52,15 @@ class TFPreTrainedModel(tf.keras.Model): pretrained_model_archive_map = {} base_model_prefix = "" + @property + def dummy_inputs(self): + """ Dummy inputs to build the network. + + Returns: + tf.Tensor with dummy inputs + """ + return tf.constant(DUMMY_INPUTS) + def __init__(self, config, *inputs, **kwargs): super(TFPreTrainedModel, self).__init__(*inputs, **kwargs) if not isinstance(config, PretrainedConfig): @@ -265,15 +274,14 @@ class TFPreTrainedModel(tf.keras.Model): # Load from a PyTorch checkpoint return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file) - dummy_inputs = tf.constant(DUMMY_INPUTS) # dummy inputs to build the network - ret = model(dummy_inputs, training=False) # build the network with dummy inputs + ret = model(model.dummy_inputs, training=False) # build the network with dummy inputs assert os.path.isfile(resolved_archive_file), "Error retrieving file {}".format(resolved_archive_file) # 'by_name' allow us to do transfer learning by skipping/adding layers # see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1339-L1357 model.load_weights(resolved_archive_file, by_name=True) - ret = model(dummy_inputs, training=False) # Make sure restore ops are run + ret = model(model.dummy_inputs, training=False) # Make sure restore ops are run return model From 151e4ab4e786b9b4b702205b5077ea2dfe67b4dd Mon Sep 17 00:00:00 2001 From: LysandreJik Date: Tue, 5 Nov 2019 16:26:51 +0000 Subject: [PATCH 005/179] Fix CTRL past --- transformers/modeling_ctrl.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/transformers/modeling_ctrl.py b/transformers/modeling_ctrl.py index 1873040a8e..589a065a11 100644 --- a/transformers/modeling_ctrl.py +++ b/transformers/modeling_ctrl.py @@ -63,7 +63,8 @@ def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=N scaled_attention_logits = matmul_qk / np.sqrt(dk) if mask is not None: - scaled_attention_logits += (mask * -1e4) + nd, ns = scaled_attention_logits.size(-2), scaled_attention_logits.size(-1) + scaled_attention_logits += (mask[ns-nd:ns, :ns] * -1e4) if attention_mask is not None: # Apply the attention mask @@ -357,7 +358,7 @@ class CTRLModel(CTRLPreTrainedModel): inputs_embeds = self.w(input_ids) # inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded seq_len = input_ids.shape[-1] - mask = torch.triu(torch.ones(seq_len, seq_len), 1).to(inputs_embeds.device) + mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(inputs_embeds.device) inputs_embeds *= np.sqrt(self.d_model_size) From 070dcf1c020e28e96e8e4b5acfb29fb818b0b4dd Mon Sep 17 00:00:00 2001 From: Diganta Misra Date: Thu, 7 Nov 2019 03:45:43 +0530 Subject: [PATCH 006/179] Added Mish Activation Function Mish is a new activation function proposed here - https://arxiv.org/abs/1908.08681 It has seen some recent success and has been adopted in SpaCy, Thic, TensorFlow Addons and FastAI-dev. All benchmarks recorded till now (including against ReLU, Swish and GELU) is present in the repository - https://github.com/digantamisra98/Mish Might be a good addition to experiment with especially in the Bert Model. --- transformers/modeling_bert.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/transformers/modeling_bert.py b/transformers/modeling_bert.py index 7c2c6f4602..2baee71f82 100644 --- a/transformers/modeling_bert.py +++ b/transformers/modeling_bert.py @@ -138,7 +138,11 @@ def swish(x): return x * torch.sigmoid(x) -ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new} +def mish(x): + return x * torch.tanh(nn.functional.softplus(x)) + + +ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new, "mish": mish} BertLayerNorm = torch.nn.LayerNorm From 28d0ba35d73d5b8b31fdadd72686a3ac078a6143 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Fri, 8 Nov 2019 11:22:19 +0100 Subject: [PATCH 007/179] only init encoder_attention_mask if stack is decoder We currently initialize `encoder_attention_mask` when it is `None`, whether the stack is that of an encoder or a decoder. Since this may lead to bugs that are difficult to tracks down, I added a condition that assesses whether the current stack is a decoder. --- transformers/modeling_bert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/modeling_bert.py b/transformers/modeling_bert.py index 7c2c6f4602..6bd5ab6a2e 100644 --- a/transformers/modeling_bert.py +++ b/transformers/modeling_bert.py @@ -656,7 +656,7 @@ class BertModel(BertPreTrainedModel): if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) - if encoder_attention_mask is None: + if self.config.is_decoder and encoder_attention_mask is None: encoder_attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) From cd286c2145221f3d1372aef103d0bc3ed03879da Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Fri, 8 Nov 2019 11:31:16 +0100 Subject: [PATCH 008/179] add condition around mask transformation --- transformers/modeling_bert.py | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/transformers/modeling_bert.py b/transformers/modeling_bert.py index 6bd5ab6a2e..893ec51015 100644 --- a/transformers/modeling_bert.py +++ b/transformers/modeling_bert.py @@ -656,8 +656,6 @@ class BertModel(BertPreTrainedModel): if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) - if self.config.is_decoder and encoder_attention_mask is None: - encoder_attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) @@ -688,13 +686,19 @@ class BertModel(BertPreTrainedModel): # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length] - if encoder_attention_mask.dim() == 3: - encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] - if encoder_attention_mask.dim() == 2: - encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] + if self.config.is_decoder: + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(input_shape, device=device) - encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility - encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 + if encoder_attention_mask.dim() == 3: + encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] + if encoder_attention_mask.dim() == 2: + encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] + + encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility + encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 + else: + encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head From ef99852961b9f3bb87a18a58093c9f513c86b683 Mon Sep 17 00:00:00 2001 From: eukaryote Date: Sat, 9 Nov 2019 16:32:40 +0000 Subject: [PATCH 009/179] from_pretrained: convert DialoGPT format DialoGPT checkpoints have "lm_head.decoder.weight" instead of "lm_head.weight". (see: https://www.reddit.com/r/MachineLearning/comments/dt5woy/p_dialogpt_state_of_the_art_conversational_model/f6vmwuy?utm_source=share&utm_medium=web2x) --- transformers/modeling_utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/transformers/modeling_utils.py b/transformers/modeling_utils.py index d51eefab58..61dd2546c6 100644 --- a/transformers/modeling_utils.py +++ b/transformers/modeling_utils.py @@ -417,6 +417,8 @@ class PreTrainedModel(nn.Module): new_key = key.replace('gamma', 'weight') if 'beta' in key: new_key = key.replace('beta', 'bias') + if key == 'lm_head.decoder.weight': + new_key = 'lm_head.weight' if new_key: old_keys.append(key) new_keys.append(new_key) From 90f6e73a35ee85e94b898a6867f19707b264d387 Mon Sep 17 00:00:00 2001 From: eukaryote Date: Sat, 9 Nov 2019 16:46:19 +0000 Subject: [PATCH 010/179] Add DialoGPT support for Pytorch->TF --- transformers/modeling_tf_pytorch_utils.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/transformers/modeling_tf_pytorch_utils.py b/transformers/modeling_tf_pytorch_utils.py index 88ce4d4610..aa74fcc10e 100644 --- a/transformers/modeling_tf_pytorch_utils.py +++ b/transformers/modeling_tf_pytorch_utils.py @@ -118,6 +118,9 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a new_key = key.replace('gamma', 'weight') if 'beta' in key: new_key = key.replace('beta', 'bias') + # DialoGPT format + if key == 'lm_head.decoder.weight': + new_key = 'lm_head.weight' if new_key: old_keys.append(key) new_keys.append(new_key) From 7246d3c2f93c4461f3ec8ada7a26a002d8f196ea Mon Sep 17 00:00:00 2001 From: Michael Watkins Date: Wed, 6 Nov 2019 13:18:16 +0200 Subject: [PATCH 011/179] Consider do_lower_case in PreTrainedTokenizer As pointed out in #1545, when using an uncased model, and adding a new uncased token, the tokenizer does not correctly identify this in the case that the input text contains the token in a cased format. For instance, if we load bert-base-uncased into BertTokenizer, and then use .add_tokens() to add "cool-token", we get the expected result for .tokenize('this is a cool-token'). However, we get a possibly unexpected result for .tokenize('this is a cOOl-Token'), which in fact mirrors the result for the former from before the new token was added. This commit adds - functionality to PreTrainedTokenizer to handle this situation in case a tokenizer (currently Bert, DistilBert, and XLNet) has the do_lower_case=True kwarg by: 1) lowercasing tokens added with .add_tokens() 2) lowercasing text at the beginning of .tokenize() - new common test case for tokenizers https://github.com/huggingface/transformers/issues/1545 --- .../tests/tokenization_tests_commons.py | 31 ++++++++++++++++++- transformers/tokenization_utils.py | 5 +++ 2 files changed, 35 insertions(+), 1 deletion(-) diff --git a/transformers/tests/tokenization_tests_commons.py b/transformers/tests/tokenization_tests_commons.py index a921696b77..287e6fc7b3 100644 --- a/transformers/tests/tokenization_tests_commons.py +++ b/transformers/tests/tokenization_tests_commons.py @@ -110,6 +110,36 @@ class CommonTestCases: self.assertListEqual(subwords, subwords_loaded) + def test_added_tokens_do_lower_case(self): + tokenizer = self.get_tokenizer(do_lower_case=True) + + text = "aaaaa bbbbbb low cccccccccdddddddd l" + text2 = "AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l" + + toks0 = tokenizer.tokenize(text) # toks before adding new_toks + + new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd", 'AAAAA BBBBBB', 'CCCCCCCCCDDDDDDDD'] + added = tokenizer.add_tokens(new_toks) + self.assertEqual(added, 2) + + toks = tokenizer.tokenize(text) + toks2 = tokenizer.tokenize(text2) + + self.assertEqual(len(toks), len(toks2)) + self.assertNotEqual(len(toks), len(toks0)) # toks0 should be longer + self.assertListEqual(toks, toks2) + + tokenizer = self.get_tokenizer(do_lower_case=False) + + added = tokenizer.add_tokens(new_toks) + self.assertEqual(added, 4) + + toks = tokenizer.tokenize(text) + toks2 = tokenizer.tokenize(text2) + + self.assertEqual(len(toks), len(toks2)) # Length should still be the same + self.assertNotEqual(len(toks), len(toks0)) + self.assertNotEqual(toks[0], toks2[0]) # But at least the first tokens should differ def test_add_tokens_tokenizer(self): tokenizer = self.get_tokenizer() @@ -160,7 +190,6 @@ class CommonTestCases: self.assertEqual(tokens[0], tokenizer.eos_token_id) self.assertEqual(tokens[-2], tokenizer.pad_token_id) - def test_required_methods_tokenizer(self): tokenizer = self.get_tokenizer() input_text, output_text = self.get_input_output_texts() diff --git a/transformers/tokenization_utils.py b/transformers/tokenization_utils.py index cd14cc4582..fc31c10d25 100644 --- a/transformers/tokenization_utils.py +++ b/transformers/tokenization_utils.py @@ -512,6 +512,8 @@ class PreTrainedTokenizer(object): to_add_tokens = [] for token in new_tokens: assert isinstance(token, str) or (six.PY2 and isinstance(token, unicode)) + if self.init_kwargs.get('do_lower_case', False): + token = token.lower() if token != self.unk_token and \ self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token) and \ token not in to_add_tokens: @@ -605,6 +607,9 @@ class PreTrainedTokenizer(object): Take care of added tokens. """ + if self.init_kwargs.get('do_lower_case', False): + text = text.lower() + def split_on_token(tok, text): result = [] split_text = text.split(tok) From d08a338c3bbe9964a4d44e5bdb15a45e985256c0 Mon Sep 17 00:00:00 2001 From: Yohei Tamura Date: Sat, 16 Nov 2019 18:47:37 +0900 Subject: [PATCH 012/179] modified: transformers/modeling_utils.py --- transformers/modeling_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/modeling_utils.py b/transformers/modeling_utils.py index d51eefab58..e7fd593bce 100644 --- a/transformers/modeling_utils.py +++ b/transformers/modeling_utils.py @@ -728,7 +728,7 @@ class SequenceSummary(nn.Module): def __init__(self, config): super(SequenceSummary, self).__init__() - self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last' + self.summary_type = config.summary_type if hasattr(config, 'summary_type') else 'last' if self.summary_type == 'attn': # We should use a standard multi-head attention module with absolute positional embedding for that. # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276 From 4193aa9f813c7868117214c1261b01c79fb9420f Mon Sep 17 00:00:00 2001 From: alexzubiaga Date: Tue, 19 Nov 2019 10:32:08 +0100 Subject: [PATCH 013/179] add TFXLNetForTokenClassification implementation and unit test add XLNetForTokenClassification implementation and unit tests --- transformers/__init__.py | 8 +- transformers/modeling_tf_xlnet.py | 53 ++++++++++ transformers/modeling_xlnet.py | 100 +++++++++++++++++++ transformers/tests/modeling_tf_xlnet_test.py | 26 +++++ transformers/tests/modeling_xlnet_test.py | 56 +++++++++-- 5 files changed, 232 insertions(+), 11 deletions(-) diff --git a/transformers/__init__.py b/transformers/__init__.py index cdf0669b39..ecfb8aeff2 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -83,9 +83,10 @@ if is_torch_available(): CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel, - XLNetForSequenceClassification, XLNetForMultipleChoice, - XLNetForQuestionAnsweringSimple, XLNetForQuestionAnswering, - load_tf_weights_in_xlnet, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP) + XLNetForSequenceClassification, XLNetForTokenClassification, + XLNetForMultipleChoice, XLNetForQuestionAnsweringSimple, + XLNetForQuestionAnswering, load_tf_weights_in_xlnet, + XLNET_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_xlm import (XLMPreTrainedModel , XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering, XLMForQuestionAnsweringSimple, @@ -136,6 +137,7 @@ if is_tf_available(): from .modeling_tf_xlnet import (TFXLNetPreTrainedModel, TFXLNetMainLayer, TFXLNetModel, TFXLNetLMHeadModel, TFXLNetForSequenceClassification, + TFXLNetForTokenClassification, TFXLNetForQuestionAnsweringSimple, TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP) diff --git a/transformers/modeling_tf_xlnet.py b/transformers/modeling_tf_xlnet.py index 4733ea8589..b5e824e924 100644 --- a/transformers/modeling_tf_xlnet.py +++ b/transformers/modeling_tf_xlnet.py @@ -939,6 +939,59 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel): return outputs # return logits, (mems), (hidden states), (attentions) +@add_start_docstrings("""XLNet Model with a token classification head on top (a linear layer on top of + the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, + XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING) +class TFXLNetForTokenClassification(TFXLNetPreTrainedModel): + r""" + Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: + **scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.num_labels)`` + Classification scores (before SoftMax). + **mems**: (`optional`, returned when ``config.mem_len > 0``) + list of ``tf.Tensor`` (one for each layer): + that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model + if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context. + See details in the docstring of the `mems` input above. + **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) + list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings) + of shape ``(batch_size, sequence_length, hidden_size)``: + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + **attentions**: (`optional`, returned when ``config.output_attentions=True``) + list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + + Examples:: + + import tensorflow as tf + from transformers import XLNetTokenizer, TFXLNetForTokenClassification + + tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased') + model = TFXLNetForSequenceClassification.from_pretrained('xlnet-large-cased') + input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 + outputs = model(input_ids) + scores = outputs[0] + + """ + def __init__(self, config, *inputs, **kwargs): + super(TFXLNetForTokenClassification, self).__init__(config, *inputs, **kwargs) + self.num_labels = config.num_labels + + self.transformer = TFXLNetMainLayer(config, name='transformer') + self.classifier = tf.keras.layers.Dense(config.num_labels, + kernel_initializer=get_initializer(config.initializer_range), + name='classifier') + + def call(self, inputs, **kwargs): + transformer_outputs = self.transformer(inputs, **kwargs) + output = transformer_outputs[0] + + logits = self.classifier(output) + + outputs = (logits,) + transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it + + return outputs # return logits, (mems), (hidden states), (attentions) + + # @add_start_docstrings("""XLNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of # the hidden-states output to compute `span start logits` and `span end logits`). """, # XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING) diff --git a/transformers/modeling_xlnet.py b/transformers/modeling_xlnet.py index 658048a660..2f4f883905 100644 --- a/transformers/modeling_xlnet.py +++ b/transformers/modeling_xlnet.py @@ -1046,6 +1046,106 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel): return outputs # return (loss), logits, (mems), (hidden states), (attentions) +@add_start_docstrings("""XLNet Model with a token classification head on top (a linear layer on top of + the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, + XLNET_START_DOCSTRING, + XLNET_INPUTS_DOCSTRING) +class XLNetForTokenClassification(XLNetPreTrainedModel): + r""" + Inputs: + **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``: + Indices of input sequence tokens in the vocabulary. + The second dimension of the input (`num_choices`) indicates the number of choices to scores. + **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: + Segment token indices to indicate first and second portions of the inputs. + Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1`` + **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``: + Mask to avoid performing attention on padding token indices. + Mask values selected in ``[0, 1]``: + ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens. + **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``: + Mask to nullify selected heads of the self-attention modules. + Mask values selected in ``[0, 1]``: + ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**. + **inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``: + Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: + Labels for computing the multiple choice classification loss. + Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension + of the input tensors. (see `input_ids` above) + + Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: + **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: + Classification loss. + **scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.num_labels)`` + Classification scores (before SoftMax). + **mems**: (`optional`, returned when ``config.mem_len > 0``) + list of ``torch.FloatTensor`` (one for each layer): + that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model + if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context. + See details in the docstring of the `mems` input above. + **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) + list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) + of shape ``(batch_size, sequence_length, hidden_size)``: + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + **attentions**: (`optional`, returned when ``config.output_attentions=True``) + list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + + Examples:: + + tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased') + model = XLNetForSequenceClassification.from_pretrained('xlnet-large-cased') + input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 + labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0) # Batch size 1 + outputs = model(input_ids, labels=labels) + scores = outputs[0] + + """ + def __init__(self, config): + super(XLNetForTokenClassification, self).__init__(config) + self.num_labels = config.num_labels + + self.transformer = XLNetModel(config) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + self.init_weights() + + def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None, + token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None, labels=None): + + outputs = self.transformer(input_ids, + attention_mask=attention_mask, + mems=mems, + perm_mask=perm_mask, + target_mapping=target_mapping, + token_type_ids=token_type_ids, + input_mask=input_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds) + + sequence_output = outputs[0] + + logits = self.classifier(sequence_output) + + outputs = (logits,) + outputs[1:] # Keep mems, hidden states, attentions if there are in it + if labels is not None: + loss_fct = CrossEntropyLoss() + # Only keep active parts of the loss + if attention_mask is not None: + active_loss = attention_mask.view(-1) == 1 + active_logits = logits.view(-1, self.num_labels)[active_loss] + active_labels = labels.view(-1)[active_loss] + loss = loss_fct(active_logits, active_labels) + else: + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + outputs = (loss,) + outputs + + return outputs # return (loss), logits, (mems), (hidden states), (attentions) + + @add_start_docstrings("""XLNet Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RACE/SWAG tasks. """, XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING) diff --git a/transformers/tests/modeling_tf_xlnet_test.py b/transformers/tests/modeling_tf_xlnet_test.py index 12a8fbe36f..a00a965570 100644 --- a/transformers/tests/modeling_tf_xlnet_test.py +++ b/transformers/tests/modeling_tf_xlnet_test.py @@ -30,6 +30,7 @@ if is_tf_available(): from transformers.modeling_tf_xlnet import (TFXLNetModel, TFXLNetLMHeadModel, TFXLNetForSequenceClassification, + TFXLNetForTokenClassification, TFXLNetForQuestionAnsweringSimple, TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP) else: @@ -42,6 +43,7 @@ class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes=(TFXLNetModel, TFXLNetLMHeadModel, TFXLNetForSequenceClassification, + TFXLNetForTokenClassification, TFXLNetForQuestionAnsweringSimple) if is_tf_available() else () test_pruning = False @@ -258,6 +260,26 @@ class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester): list(list(mem.shape) for mem in result["mems_1"]), [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers) + def create_and_check_xlnet_for_token_classification(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, + target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels): + config.num_labels = input_ids_1.shape[1] + model = TFXLNetForTokenClassification(config) + inputs = {'input_ids': input_ids_1, + 'attention_mask': input_mask, + # 'token_type_ids': token_type_ids + } + logits, mems_1 = model(inputs) + result = { + "mems_1": [mem.numpy() for mem in mems_1], + "logits": logits.numpy(), + } + self.parent.assertListEqual( + list(result["logits"].shape), + [self.batch_size, self.seq_length, config.num_labels]) + self.parent.assertListEqual( + list(list(mem.shape) for mem in result["mems_1"]), + [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers) + def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, @@ -289,6 +311,10 @@ class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs) + def test_xlnet_token_classification(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_xlnet_for_token_classification(*config_and_inputs) + def test_xlnet_qa(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() diff --git a/transformers/tests/modeling_xlnet_test.py b/transformers/tests/modeling_xlnet_test.py index d97ea6a425..8f35d34e14 100644 --- a/transformers/tests/modeling_xlnet_test.py +++ b/transformers/tests/modeling_xlnet_test.py @@ -28,7 +28,8 @@ from transformers import is_torch_available if is_torch_available(): import torch - from transformers import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering) + from transformers import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, + XLNetForTokenClassification, XLNetForQuestionAnswering) from transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_MAP else: pytestmark = pytest.mark.skip("Require Torch") @@ -38,7 +39,7 @@ from .configuration_common_test import ConfigTester class XLNetModelTest(CommonTestCases.CommonModelTester): - all_model_classes=(XLNetModel, XLNetLMHeadModel, + all_model_classes=(XLNetModel, XLNetLMHeadModel, XLNetForTokenClassification, XLNetForSequenceClassification, XLNetForQuestionAnswering) if is_torch_available() else () test_pruning = False @@ -107,10 +108,12 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): sequence_labels = None lm_labels = None is_impossible_labels = None + token_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) is_impossible_labels = ids_tensor([self.batch_size], 2).float() + token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) config = XLNetConfig( vocab_size_or_config_json_file=self.vocab_size, @@ -129,14 +132,14 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): num_labels=self.type_sequence_label_size) return (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, - target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels) + target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels) def set_seed(self): random.seed(self.seed) torch.manual_seed(self.seed) def create_and_check_xlnet_base_model(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, - target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels): + target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): model = XLNetModel(config) model.eval() @@ -164,7 +167,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers) def create_and_check_xlnet_lm_head(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, - target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels): + target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): model = XLNetLMHeadModel(config) model.eval() @@ -204,7 +207,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers) def create_and_check_xlnet_qa(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, - target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels): + target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): model = XLNetForQuestionAnswering(config) model.eval() @@ -261,8 +264,40 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): list(list(mem.size()) for mem in result["mems"]), [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers) + def create_and_check_xlnet_token_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, + target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): + model = XLNetForTokenClassification(config) + model.eval() + + logits, mems_1 = model(input_ids_1) + loss, logits, mems_1 = model(input_ids_1, labels=token_labels) + + result = { + "loss": loss, + "mems_1": mems_1, + "logits": logits, + } + + self.parent.assertListEqual( + list(result["loss"].size()), + []) + self.parent.assertListEqual( + list(result["logits"].size()), + [self.batch_size, self.seq_length, self.type_sequence_label_size]) + self.parent.assertListEqual( + list(list(mem.size()) for mem in result["mems_1"]), + [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers) + + def prepare_config_and_inputs_for_common(self): + config_and_inputs = self.prepare_config_and_inputs() + (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, + target_mapping, segment_ids, lm_labels, + sequence_labels, is_impossible_labels) = config_and_inputs + inputs_dict = {'input_ids': input_ids_1} + return config, inputs_dict + def create_and_check_xlnet_sequence_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, - target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels): + target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): model = XLNetForSequenceClassification(config) model.eval() @@ -289,7 +324,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.prepare_config_and_inputs() (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, - sequence_labels, is_impossible_labels) = config_and_inputs + sequence_labels, is_impossible_labels, token_labels) = config_and_inputs inputs_dict = {'input_ids': input_ids_1} return config, inputs_dict @@ -316,6 +351,11 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs) + def test_xlnet_token_classif(self): + self.model_tester.set_seed() + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs) + def test_xlnet_qa(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() From 337802783f0ba87a58e2e9adddb47ac5cb00646d Mon Sep 17 00:00:00 2001 From: Stefan Schweter Date: Tue, 19 Nov 2019 19:50:32 +0100 Subject: [PATCH 014/179] distilbert: add configuration for new German distilbert model --- transformers/configuration_distilbert.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/transformers/configuration_distilbert.py b/transformers/configuration_distilbert.py index 2a8a149acf..57b9e57fe0 100644 --- a/transformers/configuration_distilbert.py +++ b/transformers/configuration_distilbert.py @@ -27,7 +27,8 @@ logger = logging.getLogger(__name__) DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { 'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json", - 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json" + 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json", + 'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-config.json" } From 22333945fbd1f5eff8cfb664ac6f63363e6b72d4 Mon Sep 17 00:00:00 2001 From: Stefan Schweter Date: Tue, 19 Nov 2019 19:51:01 +0100 Subject: [PATCH 015/179] distilbert: add pytorch model for new German distilbert model --- transformers/modeling_distilbert.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/transformers/modeling_distilbert.py b/transformers/modeling_distilbert.py index d30f493c69..cb2fa1915e 100644 --- a/transformers/modeling_distilbert.py +++ b/transformers/modeling_distilbert.py @@ -42,7 +42,8 @@ logger = logging.getLogger(__name__) DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = { 'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-pytorch_model.bin", - 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin" + 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin", + 'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-pytorch_model.bin" } From f21dfe36baaf316675a4d2f8c918e9e8afc11db2 Mon Sep 17 00:00:00 2001 From: Stefan Schweter Date: Tue, 19 Nov 2019 19:51:31 +0100 Subject: [PATCH 016/179] distilbert: add vocab for new German distilbert model --- transformers/tokenization_distilbert.py | 1 + 1 file changed, 1 insertion(+) diff --git a/transformers/tokenization_distilbert.py b/transformers/tokenization_distilbert.py index dfa02926d8..61f967bf0b 100644 --- a/transformers/tokenization_distilbert.py +++ b/transformers/tokenization_distilbert.py @@ -33,6 +33,7 @@ PRETRAINED_VOCAB_FILES_MAP = { { 'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt", 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt", + 'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-vocab.txt" } } From e631383d4fb55ed2dff53f2b80a29e51cd2f563d Mon Sep 17 00:00:00 2001 From: Stefan Schweter Date: Tue, 19 Nov 2019 19:52:40 +0100 Subject: [PATCH 017/179] docs: add new German distilbert model to pretrained models --- docs/source/pretrained_models.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/source/pretrained_models.rst b/docs/source/pretrained_models.rst index b0a578fd80..3b25a79802 100644 --- a/docs/source/pretrained_models.rst +++ b/docs/source/pretrained_models.rst @@ -151,6 +151,10 @@ Here is the full list of the currently provided pretrained models together with | | ``distilroberta-base`` | | 6-layer, 768-hidden, 12-heads, 82M parameters | | | | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint. | | | | (see `details `__) | +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``distilbert-base-german-cased`` | | 6-layer, 768-hidden, 12-heads, 66M parameters | +| | | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint. | +| | | (see `details `__) | +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ | CTRL | ``ctrl`` | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters | | | | | Salesforce's Large-sized CTRL English model | From e7cf2ccd1567615513013d5fc9f9002733f70e13 Mon Sep 17 00:00:00 2001 From: Stefan Schweter Date: Tue, 19 Nov 2019 19:55:19 +0100 Subject: [PATCH 018/179] distillation: add German distilbert model --- examples/distillation/README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/distillation/README.md b/examples/distillation/README.md index 8efd1ea6f4..c2765c28ff 100644 --- a/examples/distillation/README.md +++ b/examples/distillation/README.md @@ -2,6 +2,8 @@ This folder contains the original code used to train Distil* as well as examples showcasing how to use DistilBERT, DistilRoBERTa and DistilGPT2. +**November 19th, 2019 - Update** We release German **DistilBERT**: 98.8% of `bert-base-german-dbmdz-cased` on NER tasks. + **October 23rd, 2019 - Update** We release **DistilRoBERTa**: 95% of `RoBERTa-base`'s performance on GLUE, twice as fast as RoBERTa while being 35% smaller. **October 3rd, 2019 - Update** We release our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108) explaining our approach on **DistilBERT**. It includes updated results and further experiments. We applied the same method to GPT2 and release the weights of **DistilGPT2**. DistilGPT2 is two times faster and 33% smaller than GPT2. **The paper superseeds our [previous blogpost](https://medium.com/huggingface/distilbert-8cf3380435b5) with a different distillation loss and better performances. Please use the paper as a reference when comparing/reporting results on DistilBERT.** @@ -45,10 +47,11 @@ This part of the library has only be tested with Python3.6+. There are few speci ## How to use DistilBERT -Transformers includes two pre-trained Distil* models, currently only provided for English (we are investigating the possibility to train and release a multilingual version of DistilBERT): +Transformers includes five pre-trained Distil* models, currently only provided for English and German (we are investigating the possibility to train and release a multilingual version of DistilBERT): - `distilbert-base-uncased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-uncased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 66M parameters. - `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knwoledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.9 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score). +- `distilbert-base-german-cased`: DistilBERT German language model pretrained on 1/2 of the data used to pretrain Bert using distillation with the supervision of the `bert-base-german-dbmdz-cased` version of German DBMDZ Bert. For NER tasks the model reaches a F1 score of 83.49 on the CoNLL-2003 test set (for comparison, `bert-base-german-dbmdz-cased` reaches a 84.52 F1 score), and a F1 score of 85.23 on the GermEval 2014 test set (`bert-base-german-dbmdz-cased` reaches a 86.89 F1 score). - `distilgpt2`: DistilGPT2 English language model pretrained with the supervision of `gpt2` (the smallest version of GPT2) on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 124M parameters for GPT2). On average, DistilGPT2 is two times faster than GPT2. - `distilroberta-base`: DistilRoBERTa English language model pretrained with the supervision of `roberta-base` solely on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset (it is ~4 times less training data than the teacher RoBERTa). The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base. - and more to come! 🤗🤗🤗 From 2e2c0375c3e725483d2ec297632dc52ed6a9f5fb Mon Sep 17 00:00:00 2001 From: Stefan Schweter Date: Tue, 19 Nov 2019 20:41:18 +0100 Subject: [PATCH 019/179] distilbert: add German distilbert model to positional embedding sizes map --- transformers/tokenization_distilbert.py | 1 + 1 file changed, 1 insertion(+) diff --git a/transformers/tokenization_distilbert.py b/transformers/tokenization_distilbert.py index 61f967bf0b..c82ac09727 100644 --- a/transformers/tokenization_distilbert.py +++ b/transformers/tokenization_distilbert.py @@ -40,6 +40,7 @@ PRETRAINED_VOCAB_FILES_MAP = { PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { 'distilbert-base-uncased': 512, 'distilbert-base-uncased-distilled-squad': 512, + 'distilbert-base-german-cased': 512, } From da06afafc87b03f7588b6bd319e1b7592b091339 Mon Sep 17 00:00:00 2001 From: Stefan Schweter Date: Tue, 19 Nov 2019 21:57:00 +0100 Subject: [PATCH 020/179] tree-wide: add trailing comma in configuration maps --- transformers/configuration_distilbert.py | 2 +- transformers/modeling_distilbert.py | 2 +- transformers/tokenization_distilbert.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/transformers/configuration_distilbert.py b/transformers/configuration_distilbert.py index 57b9e57fe0..1c35f5c0dd 100644 --- a/transformers/configuration_distilbert.py +++ b/transformers/configuration_distilbert.py @@ -28,7 +28,7 @@ logger = logging.getLogger(__name__) DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { 'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json", 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json", - 'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-config.json" + 'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-config.json", } diff --git a/transformers/modeling_distilbert.py b/transformers/modeling_distilbert.py index cb2fa1915e..4ba75248a1 100644 --- a/transformers/modeling_distilbert.py +++ b/transformers/modeling_distilbert.py @@ -43,7 +43,7 @@ logger = logging.getLogger(__name__) DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = { 'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-pytorch_model.bin", 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin", - 'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-pytorch_model.bin" + 'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-pytorch_model.bin", } diff --git a/transformers/tokenization_distilbert.py b/transformers/tokenization_distilbert.py index c82ac09727..6574e0bc4d 100644 --- a/transformers/tokenization_distilbert.py +++ b/transformers/tokenization_distilbert.py @@ -33,7 +33,7 @@ PRETRAINED_VOCAB_FILES_MAP = { { 'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt", 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt", - 'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-vocab.txt" + 'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-vocab.txt", } } From 3de31f8d287da44a40566fb1d5c44107708b87ea Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 19 Nov 2019 18:14:14 -0500 Subject: [PATCH 021/179] mean does not exist in TF2 --- transformers/modeling_tf_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/modeling_tf_utils.py b/transformers/modeling_tf_utils.py index e08605d154..8be7eaaf67 100644 --- a/transformers/modeling_tf_utils.py +++ b/transformers/modeling_tf_utils.py @@ -454,7 +454,7 @@ class TFSequenceSummary(tf.keras.layers.Layer): elif self.summary_type == 'first': output = hidden_states[:, 0] elif self.summary_type == 'mean': - output = tf.mean(hidden_states, axis=1) + output = tf.reduce_mean(hidden_states, axis=1) elif self.summary_type == 'cls_index': hidden_shape = shape_list(hidden_states) # e.g. [batch, num choices, seq length, hidden dims] if cls_index is None: From 2cf3447e0a3e3fd04b715f1e6f4ee43575e1e7c9 Mon Sep 17 00:00:00 2001 From: Juha Kiili Date: Thu, 21 Nov 2019 12:35:25 +0200 Subject: [PATCH 022/179] Glue: log in Valohai-compatible JSON format too --- examples/run_glue.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/examples/run_glue.py b/examples/run_glue.py index 527e440075..ea5ac5bbb7 100644 --- a/examples/run_glue.py +++ b/examples/run_glue.py @@ -22,6 +22,7 @@ import glob import logging import os import random +import json import numpy as np import torch @@ -171,13 +172,21 @@ def train(args, train_dataset, model, tokenizer): if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Log metrics + logs = {'step': global_step} if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer) for key, value in results.items(): - tb_writer.add_scalar('eval_{}'.format(key), value, global_step) - tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step) - tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step) + eval_key = 'eval_{}'.format(key) + tb_writer.add_scalar(eval_key, value, global_step) + logs[eval_key] = str(value) logging_loss = tr_loss + loss_scalar = (tr_loss - logging_loss) / args.logging_steps + learning_rate_scalar = scheduler.get_lr()[0] + tb_writer.add_scalar('lr', learning_rate_scalar, global_step) + tb_writer.add_scalar('loss', loss_scalar, global_step) + logs['learning_rate'] = learning_rate_scalar + logs['loss'] = loss_scalar + print(json.dumps(logs)) if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint From aac35514075290a46419b9bd969e6f94fef9d43b Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Thu, 21 Nov 2019 12:37:39 +0200 Subject: [PATCH 023/179] Add download_glue_data.py from kamalkraj/ALBERT-TF2.0 Original source: https://github.com/kamalkraj/ALBERT-TF2.0/blob/fa90194e5fe729dbb19f32ac29c8d6d6372c0f93/download_glue_data.py Original license: https://github.com/kamalkraj/ALBERT-TF2.0/blob/fa90194e5fe729dbb19f32ac29c8d6d6372c0f93/LICENSE (Apache-2.0) --- utils/download_glue_data.py | 141 ++++++++++++++++++++++++++++++++++++ 1 file changed, 141 insertions(+) create mode 100644 utils/download_glue_data.py diff --git a/utils/download_glue_data.py b/utils/download_glue_data.py new file mode 100644 index 0000000000..86a4e8951f --- /dev/null +++ b/utils/download_glue_data.py @@ -0,0 +1,141 @@ +''' Script for downloading all GLUE data. + +Note: for legal reasons, we are unable to host MRPC. +You can either use the version hosted by the SentEval team, which is already tokenized, +or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually. +For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example). +You should then rename and place specific files in a folder (see below for an example). + +mkdir MRPC +cabextract MSRParaphraseCorpus.msi -d MRPC +cat MRPC/_2DEC3DBE877E4DB192D17C0256E90F1D | tr -d $'\r' > MRPC/msr_paraphrase_train.txt +cat MRPC/_D7B391F9EAFF4B1B8BCE8F21B20B1B61 | tr -d $'\r' > MRPC/msr_paraphrase_test.txt +rm MRPC/_* +rm MSRParaphraseCorpus.msi + +1/30/19: It looks like SentEval is no longer hosting their extracted and tokenized MRPC data, so you'll need to download the data from the original source for now. +2/11/19: It looks like SentEval actually *is* hosting the extracted data. Hooray! +''' + +import os +import sys +import shutil +import argparse +import tempfile +import urllib.request +import zipfile + +TASKS = ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"] +TASK2PATH = {"CoLA":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4', + "SST":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8', + "MRPC":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc', + "QQP":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5', + "STS":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5', + "MNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce', + "SNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df', + "QNLI": 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601', + "RTE":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb', + "WNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf', + "diagnostic":'https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D'} + +MRPC_TRAIN = 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt' +MRPC_TEST = 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt' + +def download_and_extract(task, data_dir): + print("Downloading and extracting %s..." % task) + data_file = "%s.zip" % task + urllib.request.urlretrieve(TASK2PATH[task], data_file) + with zipfile.ZipFile(data_file) as zip_ref: + zip_ref.extractall(data_dir) + os.remove(data_file) + print("\tCompleted!") + +def format_mrpc(data_dir, path_to_data): + print("Processing MRPC...") + mrpc_dir = os.path.join(data_dir, "MRPC") + if not os.path.isdir(mrpc_dir): + os.mkdir(mrpc_dir) + if path_to_data: + mrpc_train_file = os.path.join(path_to_data, "msr_paraphrase_train.txt") + mrpc_test_file = os.path.join(path_to_data, "msr_paraphrase_test.txt") + else: + print("Local MRPC data not specified, downloading data from %s" % MRPC_TRAIN) + mrpc_train_file = os.path.join(mrpc_dir, "msr_paraphrase_train.txt") + mrpc_test_file = os.path.join(mrpc_dir, "msr_paraphrase_test.txt") + urllib.request.urlretrieve(MRPC_TRAIN, mrpc_train_file) + urllib.request.urlretrieve(MRPC_TEST, mrpc_test_file) + assert os.path.isfile(mrpc_train_file), "Train data not found at %s" % mrpc_train_file + assert os.path.isfile(mrpc_test_file), "Test data not found at %s" % mrpc_test_file + urllib.request.urlretrieve(TASK2PATH["MRPC"], os.path.join(mrpc_dir, "dev_ids.tsv")) + + dev_ids = [] + with open(os.path.join(mrpc_dir, "dev_ids.tsv"), encoding="utf8") as ids_fh: + for row in ids_fh: + dev_ids.append(row.strip().split('\t')) + + with open(mrpc_train_file, encoding="utf8") as data_fh, \ + open(os.path.join(mrpc_dir, "train.tsv"), 'w', encoding="utf8") as train_fh, \ + open(os.path.join(mrpc_dir, "dev.tsv"), 'w', encoding="utf8") as dev_fh: + header = data_fh.readline() + train_fh.write(header) + dev_fh.write(header) + for row in data_fh: + label, id1, id2, s1, s2 = row.strip().split('\t') + if [id1, id2] in dev_ids: + dev_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) + else: + train_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) + + with open(mrpc_test_file, encoding="utf8") as data_fh, \ + open(os.path.join(mrpc_dir, "test.tsv"), 'w', encoding="utf8") as test_fh: + header = data_fh.readline() + test_fh.write("index\t#1 ID\t#2 ID\t#1 String\t#2 String\n") + for idx, row in enumerate(data_fh): + label, id1, id2, s1, s2 = row.strip().split('\t') + test_fh.write("%d\t%s\t%s\t%s\t%s\n" % (idx, id1, id2, s1, s2)) + print("\tCompleted!") + +def download_diagnostic(data_dir): + print("Downloading and extracting diagnostic...") + if not os.path.isdir(os.path.join(data_dir, "diagnostic")): + os.mkdir(os.path.join(data_dir, "diagnostic")) + data_file = os.path.join(data_dir, "diagnostic", "diagnostic.tsv") + urllib.request.urlretrieve(TASK2PATH["diagnostic"], data_file) + print("\tCompleted!") + return + +def get_tasks(task_names): + task_names = task_names.split(',') + if "all" in task_names: + tasks = TASKS + else: + tasks = [] + for task_name in task_names: + assert task_name in TASKS, "Task %s not found!" % task_name + tasks.append(task_name) + return tasks + +def main(arguments): + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', help='directory to save data to', type=str, default='glue_data') + parser.add_argument('--tasks', help='tasks to download data for as a comma separated string', + type=str, default='all') + parser.add_argument('--path_to_mrpc', help='path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt', + type=str, default='') + args = parser.parse_args(arguments) + + if not os.path.isdir(args.data_dir): + os.mkdir(args.data_dir) + tasks = get_tasks(args.tasks) + + for task in tasks: + if task == 'MRPC': + format_mrpc(args.data_dir, args.path_to_mrpc) + elif task == 'diagnostic': + download_diagnostic(args.data_dir) + else: + download_and_extract(task, args.data_dir) + + +if __name__ == '__main__': + sys.exit(main(sys.argv[1:])) From 05d4232f63f121baefec9a87704ea7a15933f6e9 Mon Sep 17 00:00:00 2001 From: Juha Kiili Date: Thu, 21 Nov 2019 12:38:17 +0200 Subject: [PATCH 024/179] Add valohai.yaml --- valohai.yaml | 94 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) create mode 100644 valohai.yaml diff --git a/valohai.yaml b/valohai.yaml new file mode 100644 index 0000000000..2573551b4e --- /dev/null +++ b/valohai.yaml @@ -0,0 +1,94 @@ +--- + +- step: + name: Execute python examples/run_glue.py + image: pytorch/pytorch:nightly-devel-cuda10.0-cudnn7 + command: + - python /valohai/repository/utils/download_glue_data.py --data_dir=/glue_data + - pip install -e . + - pip install -r examples/requirements.txt + - python examples/run_glue.py --do_train --data_dir=/glue_data/{parameter-value:task_name} {parameters} + parameters: + - name: model_type + pass-as: --model_type={v} + type: string + default: bert + - name: model_name_or_path + pass-as: --model_name_or_path={v} + type: string + default: bert-base-uncased + - name: task_name + pass-as: --task_name={v} + type: string + default: MRPC + - name: max_seq_length + pass-as: --max_seq_length={v} + description: The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded. + type: integer + default: 128 + - name: per_gpu_train_batch_size + pass-as: --per_gpu_train_batch_size={v} + description: Batch size per GPU/CPU for training. + type: integer + default: 8 + - name: per_gpu_eval_batch_size + pass-as: --per_gpu_eval_batch_size={v} + description: Batch size per GPU/CPU for evaluation. + type: integer + default: 8 + - name: gradient_accumulation_steps + pass-as: --gradient_accumulation_steps={v} + description: Number of updates steps to accumulate before performing a backward/update pass. + type: integer + default: 1 + - name: learning_rate + pass-as: --learning_rate={v} + description: The initial learning rate for Adam. + type: float + default: 0.00005 + - name: adam_epsilon + pass-as: --adam_epsilon={v} + description: Epsilon for Adam optimizer. + type: float + default: 0.00000001 + - name: max_grad_norm + pass-as: --max_grad_norm={v} + description: Max gradient norm. + type: float + default: 1.0 + - name: num_train_epochs + pass-as: --num_train_epochs={v} + description: Total number of training epochs to perform. + type: integer + default: 3 + - name: max_steps + pass-as: --max_steps={v} + description: If > 0, set total number of training steps to perform. Override num_train_epochs. + type: integer + default: -1 + - name: warmup_steps + pass-as: --warmup_steps={v} + description: Linear warmup over warmup_steps. + type: integer + default: -1 + - name: logging_steps + pass-as: --logging_steps={v} + description: Log every X updates steps. + type: integer + default: 25 + - name: save_steps + pass-as: --save_steps={v} + description: Save checkpoint every X updates steps. + type: integer + default: -1 + - name: output_dir + pass-as: --output_dir={v} + type: string + default: /valohai/outputs + - name: evaluate_during_training + description: Run evaluation during training at each logging step. + type: flag + default: true + - name: do_lower_case + description: Set this flag if you are using an uncased model. + type: flag From 041a901f324eea7e7ee04b0f7a563c7ed5c8a03a Mon Sep 17 00:00:00 2001 From: Nikolay Korolev Date: Fri, 22 Nov 2019 22:56:43 +0300 Subject: [PATCH 025/179] Fix typo in documentation. toto -> to --- transformers/tokenization_gpt2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/tokenization_gpt2.py b/transformers/tokenization_gpt2.py index 4bec515903..a4798b0129 100644 --- a/transformers/tokenization_gpt2.py +++ b/transformers/tokenization_gpt2.py @@ -184,7 +184,7 @@ class GPT2Tokenizer(PreTrainedTokenizer): """ Tokenize a string. Args: - add_prefix_space (boolean, default False): - Begin the sentence with at least one space toto get invariance to word order in GPT-2 (and RoBERTa) tokenizers. + Begin the sentence with at least one space to get invariance to word order in GPT-2 (and RoBERTa) tokenizers. """ if add_prefix_space: text = ' ' + text From 176cd1ce1b337134425b426207fbe155099c18b4 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Sat, 23 Nov 2019 11:18:54 -0500 Subject: [PATCH 026/179] [doc] homogenize instructions slightly --- README.md | 5 ++--- examples/README.md | 4 ++-- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 9cc783fd71..52e74a6f80 100644 --- a/README.md +++ b/README.md @@ -89,13 +89,12 @@ pip install [--editable] . ### Run the examples Examples are included in the repository but are not shipped with the library. -Therefore, in order to run the examples you will first need to clone the -repository and install the bleeding edge version of the library. To do so, create a new virtual environment and follow these steps: +Therefore, in order to run the latest versions of the examples you also need to install from source. To do so, create a new virtual environment and follow these steps: ```bash git clone git@github.com:huggingface/transformers cd transformers -pip install . +pip install [--editable] . ``` ### Tests diff --git a/examples/README.md b/examples/README.md index 622fa07f8f..0c57990ea7 100644 --- a/examples/README.md +++ b/examples/README.md @@ -4,12 +4,12 @@ In this section a few examples are put together. All of these examples work for similar API between the different models. **Important** -To use the examples, execute the following steps in a new virtual environment: +To run the latest versions of the examples, you have to install from source. Execute the following steps in a new virtual environment: ```bash git clone git@github.com:huggingface/transformers cd transformers -pip install . +pip install [--editable] . ``` | Section | Description | From afaa33585109550f9ecaaee4e47f187aaaefedd0 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Sat, 23 Nov 2019 11:34:45 -0500 Subject: [PATCH 027/179] [doc] Fix assets urls --- docs/source/_static/js/custom.js | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/source/_static/js/custom.js b/docs/source/_static/js/custom.js index 2c7836fd20..ec804b3704 100644 --- a/docs/source/_static/js/custom.js +++ b/docs/source/_static/js/custom.js @@ -1,5 +1,5 @@ function addIcon() { - const huggingFaceLogo = "https://huggingface.co/assets/transformers-docs/huggingface_logo.svg"; + const huggingFaceLogo = "https://huggingface.co/landing/assets/transformers-docs/huggingface_logo.svg"; const image = document.createElement("img"); image.setAttribute("src", huggingFaceLogo); @@ -24,10 +24,10 @@ function addCustomFooter() { social.classList.add("footer__Social"); const imageDetails = [ - { link: "https://huggingface.co", imageLink: "https://huggingface.co/assets/transformers-docs/website.svg" }, - { link: "https://twitter.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/twitter.svg" }, - { link: "https://github.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/github.svg" }, - { link: "https://www.linkedin.com/company/huggingface/", imageLink: "https://huggingface.co/assets/transformers-docs/linkedin.svg" } + { link: "https://huggingface.co", imageLink: "https://huggingface.co/landing/assets/transformers-docs/website.svg" }, + { link: "https://twitter.com/huggingface", imageLink: "https://huggingface.co/landing/assets/transformers-docs/twitter.svg" }, + { link: "https://github.com/huggingface", imageLink: "https://huggingface.co/landing/assets/transformers-docs/github.svg" }, + { link: "https://www.linkedin.com/company/huggingface/", imageLink: "https://huggingface.co/landing/assets/transformers-docs/linkedin.svg" } ]; imageDetails.forEach(imageLinks => { From 7485caefb09cb7f4c4b720b40ec69fed345a6b1c Mon Sep 17 00:00:00 2001 From: Lysandre Date: Mon, 25 Nov 2019 09:33:39 -0500 Subject: [PATCH 028/179] fix #1894 --- examples/run_lm_finetuning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py index 52a1b75a65..aded521c1d 100644 --- a/examples/run_lm_finetuning.py +++ b/examples/run_lm_finetuning.py @@ -68,7 +68,7 @@ class TextDataset(Dataset): directory, filename = os.path.split(file_path) cached_features_file = os.path.join(directory, args.model_name_or_path + '_cached_lm_' + str(block_size) + '_' + filename) - if os.path.exists(cached_features_file): + if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) with open(cached_features_file, 'rb') as handle: self.examples = pickle.load(handle) From 99f750d64e78d20fa5213ea11235b6a1b084481e Mon Sep 17 00:00:00 2001 From: Evpok Padding Date: Thu, 21 Nov 2019 10:35:07 +0100 Subject: [PATCH 029/179] add Camembert models to modeling_auto --- transformers/modeling_auto.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/transformers/modeling_auto.py b/transformers/modeling_auto.py index d98110d4bd..a5828148ad 100644 --- a/transformers/modeling_auto.py +++ b/transformers/modeling_auto.py @@ -27,6 +27,7 @@ from .modeling_xlnet import XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassi from .modeling_xlm import XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification from .modeling_distilbert import DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification +from .modeling_camembert import CamembertModel, CamembertForMaskedLM, CamembertForSequenceClassification, CamembertForMultipleChoice from .modeling_utils import PreTrainedModel, SequenceSummary @@ -48,6 +49,7 @@ class AutoModel(object): The base model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertModel (DistilBERT model) + - contains `camembert`: CamemBERTModel (CamemBERT model) - contains `roberta`: RobertaModel (RoBERTa model) - contains `bert`: BertModel (Bert model) - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model) @@ -71,6 +73,7 @@ class AutoModel(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertModel (DistilBERT model) + - contains `camembert`: CamemBERTModel (CamemBERT model) - contains `roberta`: RobertaModel (RoBERTa model) - contains `bert`: BertModel (Bert model) - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model) @@ -138,6 +141,8 @@ class AutoModel(object): """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'camembert' in pretrained_model_name_or_path: + return CamembertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'bert' in pretrained_model_name_or_path: @@ -172,6 +177,7 @@ class AutoModelWithLMHead(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model) + - contains `camembert`: CamemBERTModel (CamemBERT model) - contains `roberta`: RobertaForMaskedLM (RoBERTa model) - contains `bert`: BertForMaskedLM (Bert model) - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model) @@ -198,6 +204,7 @@ class AutoModelWithLMHead(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model) + - contains `camembert`: CamemBERTModel (CamemBERT model) - contains `roberta`: RobertaForMaskedLM (RoBERTa model) - contains `bert`: BertForMaskedLM (Bert model) - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model) @@ -264,6 +271,8 @@ class AutoModelWithLMHead(object): """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + if 'camembert' in pretrained_model_name_or_path: + return CamembertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return RobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'bert' in pretrained_model_name_or_path: @@ -298,6 +307,7 @@ class AutoModelForSequenceClassification(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model) + - contains `camembert`: CamemBERTModel (CamemBERT model) - contains `roberta`: RobertaForSequenceClassification (RoBERTa model) - contains `bert`: BertForSequenceClassification (Bert model) - contains `xlnet`: XLNetForSequenceClassification (XLNet model) @@ -320,6 +330,7 @@ class AutoModelForSequenceClassification(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model) + - contains `camembert`: CamemBERTModel (CamemBERT model) - contains `roberta`: RobertaForSequenceClassification (RoBERTa model) - contains `bert`: BertForSequenceClassification (Bert model) - contains `xlnet`: XLNetForSequenceClassification (XLNet model) @@ -383,6 +394,8 @@ class AutoModelForSequenceClassification(object): """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + if 'camembert' in pretrained_model_name_or_path: + return CamembertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return RobertaForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'bert' in pretrained_model_name_or_path: From c8eb8157b86291413fe8096217f4defc168aa73f Mon Sep 17 00:00:00 2001 From: Evpok Padding Date: Thu, 21 Nov 2019 11:01:20 +0100 Subject: [PATCH 030/179] fix docstrings --- transformers/modeling_auto.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/transformers/modeling_auto.py b/transformers/modeling_auto.py index a5828148ad..ce36f6dc4a 100644 --- a/transformers/modeling_auto.py +++ b/transformers/modeling_auto.py @@ -49,7 +49,7 @@ class AutoModel(object): The base model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertModel (DistilBERT model) - - contains `camembert`: CamemBERTModel (CamemBERT model) + - contains `camembert`: CamembertModel (CamemBERT model) - contains `roberta`: RobertaModel (RoBERTa model) - contains `bert`: BertModel (Bert model) - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model) @@ -73,7 +73,7 @@ class AutoModel(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertModel (DistilBERT model) - - contains `camembert`: CamemBERTModel (CamemBERT model) + - contains `camembert`: CamembertModel (CamemBERT model) - contains `roberta`: RobertaModel (RoBERTa model) - contains `bert`: BertModel (Bert model) - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model) @@ -177,7 +177,7 @@ class AutoModelWithLMHead(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model) - - contains `camembert`: CamemBERTModel (CamemBERT model) + - contains `camembert`: CamembertForMaskedLM (CamemBERT model) - contains `roberta`: RobertaForMaskedLM (RoBERTa model) - contains `bert`: BertForMaskedLM (Bert model) - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model) @@ -204,7 +204,7 @@ class AutoModelWithLMHead(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model) - - contains `camembert`: CamemBERTModel (CamemBERT model) + - contains `camembert`: CamembertForMaskedLM (CamemBERT model) - contains `roberta`: RobertaForMaskedLM (RoBERTa model) - contains `bert`: BertForMaskedLM (Bert model) - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model) @@ -307,7 +307,7 @@ class AutoModelForSequenceClassification(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model) - - contains `camembert`: CamemBERTModel (CamemBERT model) + - contains `camembert`: CamembertForSequenceClassification (CamemBERT model) - contains `roberta`: RobertaForSequenceClassification (RoBERTa model) - contains `bert`: BertForSequenceClassification (Bert model) - contains `xlnet`: XLNetForSequenceClassification (XLNet model) @@ -330,7 +330,7 @@ class AutoModelForSequenceClassification(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model) - - contains `camembert`: CamemBERTModel (CamemBERT model) + - contains `camembert`: CamembertForSequenceClassification (CamemBERT model) - contains `roberta`: RobertaForSequenceClassification (RoBERTa model) - contains `bert`: BertForSequenceClassification (Bert model) - contains `xlnet`: XLNetForSequenceClassification (XLNet model) From fa963ecc59a1dea59c2d0e952b2c4483e1828176 Mon Sep 17 00:00:00 2001 From: Evpok Padding Date: Thu, 21 Nov 2019 11:03:12 +0100 Subject: [PATCH 031/179] =?UTF-8?q?if=E2=86=92elif?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- transformers/modeling_auto.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/transformers/modeling_auto.py b/transformers/modeling_auto.py index ce36f6dc4a..5866420001 100644 --- a/transformers/modeling_auto.py +++ b/transformers/modeling_auto.py @@ -271,7 +271,7 @@ class AutoModelWithLMHead(object): """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) - if 'camembert' in pretrained_model_name_or_path: + elif 'camembert' in pretrained_model_name_or_path: return CamembertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return RobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) @@ -394,7 +394,7 @@ class AutoModelForSequenceClassification(object): """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) - if 'camembert' in pretrained_model_name_or_path: + elif 'camembert' in pretrained_model_name_or_path: return CamembertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return RobertaForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) From 07bf43074f39379d8c7f6afcca105e69685f7531 Mon Sep 17 00:00:00 2001 From: Bilal Khan Date: Thu, 21 Nov 2019 20:52:06 -0600 Subject: [PATCH 032/179] Fix GPT2 docstring --- transformers/tokenization_gpt2.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/transformers/tokenization_gpt2.py b/transformers/tokenization_gpt2.py index a4798b0129..5fda709448 100644 --- a/transformers/tokenization_gpt2.py +++ b/transformers/tokenization_gpt2.py @@ -107,10 +107,10 @@ class GPT2Tokenizer(PreTrainedTokenizer): """ GPT-2 BPE tokenizer. Peculiarities: - Byte-level Byte-Pair-Encoding - - Requires a space to start the input string => the encoding methods should be called with the + - Requires a space to start the input string => the encoding and tokenize methods should be called with the ``add_prefix_space`` flag set to ``True``. - Otherwise, this tokenizer ``encode`` and ``decode`` method will not conserve - the absence of a space at the beginning of a string: `tokenizer.decode(tokenizer.encode("Hello")) = " Hello"` + Otherwise, this tokenizer's ``encode``, ``decode``, and ``tokenize`` methods will not conserve + the spaces at the beginning of a string: `tokenizer.decode(tokenizer.encode(" Hello")) = "Hello"` """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP From aa92a184d2b92faadec975139ad55e2ae749362c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C4=B0brahim=20Ethem=20Demirci?= Date: Sat, 16 Nov 2019 16:49:37 +0300 Subject: [PATCH 033/179] resize model when special tokenizer present --- examples/run_lm_finetuning.py | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py index aded521c1d..c33aa94a32 100644 --- a/examples/run_lm_finetuning.py +++ b/examples/run_lm_finetuning.py @@ -215,6 +215,7 @@ def train(args, train_dataset, model, tokenizer): global_step = 0 tr_loss, logging_loss = 0.0, 0.0 + model.resize_token_embeddings(len(tokenizer)) model.zero_grad() train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) set_seed(args) # Added here for reproducibility (even between python 2 and 3) From 5d3b8daad2cc6287d30f03f8a96d0a1f7bc8d0dc Mon Sep 17 00:00:00 2001 From: manansanghi <52307004+manansanghi@users.noreply.github.com> Date: Fri, 22 Nov 2019 10:31:54 -0800 Subject: [PATCH 034/179] Minor bug fixes on run_ner.py --- examples/run_ner.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/run_ner.py b/examples/run_ner.py index 127d63a6cd..1ab1236d94 100644 --- a/examples/run_ner.py +++ b/examples/run_ner.py @@ -127,7 +127,7 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): "attention_mask": batch[1], "labels": batch[3]} if args.model_type != "distilbert": - inputs["token_type_ids"]: batch[2] if args.model_type in ["bert", "xlnet"] else None # XLM and RoBERTa don"t use segment_ids + inputs["token_type_ids"] = batch[2] if args.model_type in ["bert", "xlnet"] else None # XLM and RoBERTa don"t use segment_ids outputs = model(**inputs) loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc) @@ -217,7 +217,7 @@ def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix="" "attention_mask": batch[1], "labels": batch[3]} if args.model_type != "distilbert": - inputs["token_type_ids"]: batch[2] if args.model_type in ["bert", "xlnet"] else None # XLM and RoBERTa don"t use segment_ids + inputs["token_type_ids"] = batch[2] if args.model_type in ["bert", "xlnet"] else None # XLM and RoBERTa don"t use segment_ids outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] From 8e5d84fcc1a645d3c13b8a2f64fa995637440dad Mon Sep 17 00:00:00 2001 From: v_sboliu Date: Tue, 26 Nov 2019 12:04:31 +0800 Subject: [PATCH 035/179] Fixed typo --- transformers/modeling_bert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/modeling_bert.py b/transformers/modeling_bert.py index 7c2c6f4602..81d92d8f1b 100644 --- a/transformers/modeling_bert.py +++ b/transformers/modeling_bert.py @@ -278,7 +278,7 @@ class BertAttention(nn.Module): if len(heads) == 0: return mask = torch.ones(self.self.num_attention_heads, self.self.attention_head_size) - heads = set(heads) - self.pruned_heads # Convert to set and emove already pruned heads + heads = set(heads) - self.pruned_heads # Convert to set and remove already pruned heads for head in heads: # Compute how many pruned heads are before the head and move the index accordingly head = head - sum(1 if h < head else 0 for h in self.pruned_heads) From c0c2088333e2e8ce2b24d0c7f4bf071dcccbd7ea Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 29 Oct 2019 19:57:38 +0000 Subject: [PATCH 036/179] ALBERT model --- transformers/configuration_albert.py | 72 ++++ ...lbert_original_tf_checkpoint_to_pytorch.py | 44 +++ transformers/modeling_albert.py | 331 ++++++++++++++++++ 3 files changed, 447 insertions(+) create mode 100644 transformers/configuration_albert.py create mode 100644 transformers/convert_albert_original_tf_checkpoint_to_pytorch.py create mode 100644 transformers/modeling_albert.py diff --git a/transformers/configuration_albert.py b/transformers/configuration_albert.py new file mode 100644 index 0000000000..c86c9565cb --- /dev/null +++ b/transformers/configuration_albert.py @@ -0,0 +1,72 @@ +from .configuration_utils import PretrainedConfig + +class AlbertConfig(PretrainedConfig): + """Configuration for `AlbertModel`. + + The default settings match the configuration of model `albert_xxlarge`. + """ + + def __init__(self, + vocab_size_or_config_json_file, + embedding_size=128, + hidden_size=4096, + num_hidden_layers=12, + num_hidden_groups=1, + num_attention_heads=64, + intermediate_size=16384, + inner_group_num=1, + down_scale_factor=1, + hidden_act="gelu", + hidden_dropout_prob=0, + attention_probs_dropout_prob=0, + max_position_embeddings=512, + type_vocab_size=2, + initializer_range=0.02, + layer_norm_eps=1e-12, **kwargs): + """Constructs AlbertConfig. + + Args: + vocab_size: Vocabulary size of `inputs_ids` in `AlbertModel`. + embedding_size: size of voc embeddings. + hidden_size: Size of the encoder layers and the pooler layer. + num_hidden_layers: Number of hidden layers in the Transformer encoder. + num_hidden_groups: Number of group for the hidden layers, parameters in + the same group are shared. + num_attention_heads: Number of attention heads for each attention layer in + the Transformer encoder. + intermediate_size: The size of the "intermediate" (i.e., feed-forward) + layer in the Transformer encoder. + inner_group_num: int, number of inner repetition of attention and ffn. + down_scale_factor: float, the scale to apply + hidden_act: The non-linear activation function (function or string) in the + encoder and pooler. + hidden_dropout_prob: The dropout probability for all fully connected + layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob: The dropout ratio for the attention + probabilities. + max_position_embeddings: The maximum sequence length that this model might + ever be used with. Typically set this to something large just in case + (e.g., 512 or 1024 or 2048). + type_vocab_size: The vocabulary size of the `token_type_ids` passed into + `AlbertModel`. + initializer_range: The stdev of the truncated_normal_initializer for + initializing all weight matrices. + """ + super(AlbertConfig, self).__init__(**kwargs) + + self.vocab_size = vocab_size_or_config_json_file + self.embedding_size = embedding_size + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_hidden_groups = num_hidden_groups + self.num_attention_heads = num_attention_heads + self.inner_group_num = inner_group_num + self.down_scale_factor = down_scale_factor + self.hidden_act = hidden_act + self.intermediate_size = intermediate_size + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_size = type_vocab_size + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps \ No newline at end of file diff --git a/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py b/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py new file mode 100644 index 0000000000..04877d41b9 --- /dev/null +++ b/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py @@ -0,0 +1,44 @@ + + + +from transformers import AlbertConfig, BertForPreTraining, load_tf_weights_in_bert + + + +def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path): + # Initialise PyTorch model + config = BertConfig.from_json_file(bert_config_file) + print("Building PyTorch model from configuration: {}".format(str(config))) + model = BertForPreTraining(config) + + # Load weights from tf checkpoint + load_tf_weights_in_bert(model, config, tf_checkpoint_path) + + # Save pytorch-model + print("Save PyTorch model to {}".format(pytorch_dump_path)) + torch.save(model.state_dict(), pytorch_dump_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + ## Required parameters + parser.add_argument("--tf_checkpoint_path", + default = None, + type = str, + required = True, + help = "Path to the TensorFlow checkpoint path.") + parser.add_argument("--albert_config_file", + default = None, + type = str, + required = True, + help = "The config json file corresponding to the pre-trained BERT model. \n" + "This specifies the model architecture.") + parser.add_argument("--pytorch_dump_path", + default = None, + type = str, + required = True, + help = "Path to the output PyTorch model.") + args = parser.parse_args() + convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, + args.bert_config_file, + args.pytorch_dump_path) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py new file mode 100644 index 0000000000..b006cbe8fd --- /dev/null +++ b/transformers/modeling_albert.py @@ -0,0 +1,331 @@ + +import os +import math +import logging +import torch +import torch.nn as nn +from transformers.configuration_albert import AlbertConfig +logger = logging.getLogger(__name__) + +def load_tf_weights_in_albert(model, config, tf_checkpoint_path): + """ Load tf checkpoints in a pytorch model.""" + try: + import re + import numpy as np + import tensorflow as tf + except ImportError: + logger.error("Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " + "https://www.tensorflow.org/install/ for installation instructions.") + raise + tf_path = os.path.abspath(tf_checkpoint_path) + logger.info("Converting TensorFlow checkpoint from {}".format(tf_path)) + # Load weights from TF model + init_vars = tf.train.list_variables(tf_path) + names = [] + arrays = [] + for name, shape in init_vars: + logger.info("Loading TF weight {} with shape {}".format(name, shape)) + array = tf.train.load_variable(tf_path, name) + names.append(name) + arrays.append(array) + + print(model) + + for name, array in zip(names, arrays): + og = name + name = name.replace("transformer/group_0/inner_group_0", "transformer") + name = name.replace("LayerNorm", "layer_norm") + name = name.replace("ffn_1", "ffn") + name = name.replace("ffn/intermediate/output", "ffn_output") + name = name.replace("attention_1", "attention") + name = name.replace("cls/predictions/transform", "predictions") + name = name.replace("transformer/layer_norm_1", "transformer/attention/output/LayerNorm") + name = name.split('/') + + print(name) + pointer = model + for m_name in name: + if re.fullmatch(r'[A-Za-z]+_\d+', m_name): + l = re.split(r'_(\d+)', m_name) + else: + l = [m_name] + + if l[0] == 'kernel' or l[0] == 'gamma': + pointer = getattr(pointer, 'weight') + elif l[0] == 'output_bias' or l[0] == 'beta': + pointer = getattr(pointer, 'bias') + elif l[0] == 'output_weights': + pointer = getattr(pointer, 'weight') + elif l[0] == 'squad': + pointer = getattr(pointer, 'classifier') + else: + try: + pointer = getattr(pointer, l[0]) + except AttributeError: + logger.info("Skipping {}".format("/".join(name))) + continue + if len(l) >= 2: + num = int(l[1]) + pointer = pointer[num] + + if m_name[-11:] == '_embeddings': + pointer = getattr(pointer, 'weight') + elif m_name == 'kernel': + array = np.transpose(array) + print("transposed") + try: + assert pointer.shape == array.shape + except AssertionError as e: + e.args += (pointer.shape, array.shape) + raise + print("Initialize PyTorch weight {} from {}".format(name, og)) + pointer.data = torch.from_numpy(array) + + return model + + +class AlbertEmbeddings(nn.Module): + """ + Construct the embeddings from word, position and token_type embeddings. + """ + def __init__(self, config): + super(AlbertEmbeddings, self).__init__() + + self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=0) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size) + self.layer_norm = torch.nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, input_ids, token_type_ids=None, position_ids=None): + seq_length = input_ids.size(1) + if position_ids is None: + position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) + position_ids = position_ids.unsqueeze(0).expand_as(input_ids) + if token_type_ids is None: + token_type_ids = torch.zeros_like(input_ids) + + word_embeddings = self.word_embeddings(input_ids) + position_embeddings = self.position_embeddings(position_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = word_embeddings + position_embeddings + token_type_embeddings + embeddings = self.layer_norm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + + def get_word_embeddings_table(self): + return self.word_embeddings + + +class AlbertModel(nn.Module): + def __init__(self, config): + super(AlbertModel, self).__init__() + + self.config = config + self.embeddings = AlbertEmbeddings(config) + self.encoder = AlbertEncoder(config) + self.pooler = nn.Linear(config.hidden_size, config.hidden_size) + self.pooler_activation = nn.Tanh() + + def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): + if attention_mask is None: + attention_mask = torch.ones_like(input_ids) + if token_type_ids is None: + token_type_ids = torch.zeros_like(input_ids) + + extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) + extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + if head_mask is not None: + if head_mask.dim() == 1: + head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) + head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1) + elif head_mask.dim() == 2: + head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer + head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility + else: + head_mask = [None] * self.config.num_hidden_layers + + embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids) + encoder_outputs = self.encoder(embedding_output, + extended_attention_mask, + head_mask=head_mask) + sequence_output = encoder_outputs[0] + print(sequence_output.shape, sequence_output[:, 0].shape, self.pooler(sequence_output[:, 0]).shape) + pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0])) + + outputs = (sequence_output, pooled_output,) + encoder_outputs[1:] # add hidden_states and attentions if they are here + return outputs + + +class AlbertForMaskedLM(nn.Module): + def __init__(self, config): + super(AlbertForMaskedLM, self).__init__() + + self.config = config + self.bert = AlbertModel(config) + self.layer_norm = nn.LayerNorm(config.embedding_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.dense = nn.Linear(config.hidden_size, config.embedding_size) + self.word_embeddings = nn.Linear(config.embedding_size, config.vocab_size) + + def tie_weights(self): + """ Make sure we are sharing the input and output embeddings. + Export to TorchScript can't handle parameter sharing so we are cloning them instead. + """ + self._tie_or_clone_weights(self.classifier.word_embeddings, + self.transformer.embeddings.word_embeddings) + + def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): + hidden_states = self.bert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None)[0] + hidden_states = self.dense(hidden_states) + hidden_states = gelu_new(hidden_states) + hidden_states = self.layer_norm(hidden_states) + + logits = self.word_embeddings(hidden_states) + + return logits + + +class AlbertAttention(nn.Module): + def __init__(self, config): + super(AlbertAttention, self).__init__() + + if config.hidden_size % config.num_attention_heads != 0: + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads)) + self.output_attentions = config.output_attentions + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, input_ids, attention_mask=None, head_mask=None): + mixed_query_layer = self.query(input_ids) + mixed_key_layer = self.key(input_ids) + mixed_value_layer = self.value(input_ids) + + query_layer = self.transpose_for_scores(mixed_query_layer) + key_layer = self.transpose_for_scores(mixed_key_layer) + value_layer = self.transpose_for_scores(mixed_value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + reshaped_context_layer = context_layer.view(*new_context_layer_shape) + w = self.dense.weight.T.view(16, 64, 1024) + b = self.dense.bias + + projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b + projected_context_layer = self.dropout(projected_context_layer) + layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer) + return layernormed_context_layer, projected_context_layer, reshaped_context_layer, context_layer, attention_scores, attention_probs, attention_mask + + +class AlbertTransformer(nn.Module): + def __init__(self, config): + super(AlbertTransformer, self).__init__() + + self.config =config + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.attention = AlbertAttention(config) + self.ffn = nn.Linear(config.hidden_size, config.intermediate_size) + self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states, attention_mask=None, head_mask=None): + for i in range(self.config.num_hidden_layers): + attention_output = self.attention(hidden_states, attention_mask)[0] + ffn_output = self.ffn(attention_output) + ffn_output = gelu_new(ffn_output) + ffn_output = self.ffn_output(ffn_output) + hidden_states = self.layer_norm(ffn_output + attention_output) + + return hidden_states + + +def gelu_new(x): + """ Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT). + Also see https://arxiv.org/abs/1606.08415 + """ + return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) + + +class AlbertEncoder(nn.Module): + def __init__(self, config): + super(AlbertEncoder, self).__init__() + + self.output_attentions = config.output_attentions + self.output_hidden_states = config.output_hidden_states + self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size) + self.transformer = AlbertTransformer(config) + + def forward(self, hidden_states, attention_mask=None, head_mask=None): + hidden_states = self.embedding_hidden_mapping_in(hidden_states) + hidden_states = self.transformer(hidden_states, attention_mask, head_mask) + + outputs = (hidden_states,) + if self.output_hidden_states: + outputs = outputs + (all_hidden_states,) + if self.output_attentions: + outputs = outputs + (all_attentions,) + return outputs # last-layer hidden state, (all hidden states), (all attentions) + +# config = AlbertConfig.from_json_file("config.json") +# # model = AlbertForMaskedLM(config) +# model = AlbertModel(config) + +# model = load_tf_weights_in_albert(model, config, "albert/albert") + +# print(model) + +# input_ids = torch.tensor([[31, 51, 99], [15, 5, 0]]) +# input_mask = torch.tensor([[1, 1, 1], [1, 1, 0]]) +# segment_ids = torch.tensor([[0, 0, 1], [0, 0, 0]]) + +# # sequence_output, pooled_outputs = model() + +# logits = model(input_ids, attention_mask=input_mask, token_type_ids=segment_ids)[1] + + +# embeddings_output = +# print("pooled output", logits) +# # print("Pooled output", pooled_outputs) + +config = AlbertConfig.from_json_file("/home/hf/google-research/albert/config.json") +model = AlbertModel(config) +model = load_tf_weights_in_albert(model, config, "/home/hf/transformers/albert/albert") \ No newline at end of file From 91ccbae788de11f0f5ffb862421e345b27a20a76 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 29 Oct 2019 21:21:57 +0000 Subject: [PATCH 037/179] Accepts multiple sizes --- transformers/modeling_albert.py | 143 ++++++++++++++------------------ 1 file changed, 60 insertions(+), 83 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index b006cbe8fd..f3cebdc3d9 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -5,6 +5,7 @@ import logging import torch import torch.nn as nn from transformers.configuration_albert import AlbertConfig +from transformers.modeling_bert import BertEmbeddings, BertModel, BertSelfAttention, prune_linear_layer, gelu_new logger = logging.getLogger(__name__) def load_tf_weights_in_albert(model, config, tf_checkpoint_path): @@ -32,14 +33,14 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path): print(model) for name, array in zip(names, arrays): + print(name) og = name name = name.replace("transformer/group_0/inner_group_0", "transformer") - name = name.replace("LayerNorm", "layer_norm") name = name.replace("ffn_1", "ffn") name = name.replace("ffn/intermediate/output", "ffn_output") name = name.replace("attention_1", "attention") name = name.replace("cls/predictions/transform", "predictions") - name = name.replace("transformer/layer_norm_1", "transformer/attention/output/LayerNorm") + name = name.replace("transformer/LayerNorm_1", "transformer/attention/LayerNorm") name = name.split('/') print(name) @@ -84,44 +85,22 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path): return model -class AlbertEmbeddings(nn.Module): +class AlbertEmbeddings(BertEmbeddings): """ Construct the embeddings from word, position and token_type embeddings. """ def __init__(self, config): - super(AlbertEmbeddings, self).__init__() + super(AlbertEmbeddings, self).__init__(config) self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=0) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size) - self.layer_norm = torch.nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, input_ids, token_type_ids=None, position_ids=None): - seq_length = input_ids.size(1) - if position_ids is None: - position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) - position_ids = position_ids.unsqueeze(0).expand_as(input_ids) - if token_type_ids is None: - token_type_ids = torch.zeros_like(input_ids) - - word_embeddings = self.word_embeddings(input_ids) - position_embeddings = self.position_embeddings(position_ids) - token_type_embeddings = self.token_type_embeddings(token_type_ids) - - embeddings = word_embeddings + position_embeddings + token_type_embeddings - embeddings = self.layer_norm(embeddings) - embeddings = self.dropout(embeddings) - return embeddings + self.LayerNorm = torch.nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) - def get_word_embeddings_table(self): - return self.word_embeddings - - -class AlbertModel(nn.Module): +class AlbertModel(BertModel): def __init__(self, config): - super(AlbertModel, self).__init__() + super(AlbertModel, self).__init__(config) self.config = config self.embeddings = AlbertEmbeddings(config) @@ -129,6 +108,7 @@ class AlbertModel(nn.Module): self.pooler = nn.Linear(config.hidden_size, config.hidden_size) self.pooler_activation = nn.Tanh() + def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): if attention_mask is None: attention_mask = torch.ones_like(input_ids) @@ -166,7 +146,7 @@ class AlbertForMaskedLM(nn.Module): self.config = config self.bert = AlbertModel(config) - self.layer_norm = nn.LayerNorm(config.embedding_size) + self.LayerNorm = nn.LayerNorm(config.embedding_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.dense = nn.Linear(config.hidden_size, config.embedding_size) self.word_embeddings = nn.Linear(config.embedding_size, config.vocab_size) @@ -182,39 +162,47 @@ class AlbertForMaskedLM(nn.Module): hidden_states = self.bert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None)[0] hidden_states = self.dense(hidden_states) hidden_states = gelu_new(hidden_states) - hidden_states = self.layer_norm(hidden_states) + hidden_states = self.LayerNorm(hidden_states) logits = self.word_embeddings(hidden_states) return logits -class AlbertAttention(nn.Module): +class AlbertAttention(BertSelfAttention): def __init__(self, config): - super(AlbertAttention, self).__init__() - - if config.hidden_size % config.num_attention_heads != 0: - raise ValueError( - "The hidden size (%d) is not a multiple of the number of attention " - "heads (%d)" % (config.hidden_size, config.num_attention_heads)) - self.output_attentions = config.output_attentions + super(AlbertAttention, self).__init__(config) self.num_attention_heads = config.num_attention_heads - self.attention_head_size = int(config.hidden_size / config.num_attention_heads) - self.all_head_size = self.num_attention_heads * self.attention_head_size - - self.query = nn.Linear(config.hidden_size, self.all_head_size) - self.key = nn.Linear(config.hidden_size, self.all_head_size) - self.value = nn.Linear(config.hidden_size, self.all_head_size) - + self.hidden_size = config.hidden_size + self.attention_head_size = config.hidden_size // config.num_attention_heads self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.pruned_heads = set() - def transpose_for_scores(self, x): - new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) - x = x.view(*new_x_shape) - return x.permute(0, 2, 1, 3) + def prune_heads(self, heads): + if len(heads) == 0: + return + mask = torch.ones(self.num_attention_heads, self.attention_head_size) + heads = set(heads) - self.pruned_heads # Convert to set and emove already pruned heads + for head in heads: + # Compute how many pruned heads are before the head and move the index accordingly + head = head - sum(1 if h < head else 0 for h in self.pruned_heads) + mask[head] = 0 + mask = mask.view(-1).contiguous().eq(1) + index = torch.arange(len(mask))[mask].long() + + # Prune linear layers + self.query = prune_linear_layer(self.query, index) + self.key = prune_linear_layer(self.key, index) + self.value = prune_linear_layer(self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.num_attention_heads = self.num_attention_heads - len(heads) + self.all_head_size = self.attention_head_size * self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) def forward(self, input_ids, attention_mask=None, head_mask=None): mixed_query_layer = self.query(input_ids) @@ -248,7 +236,8 @@ class AlbertAttention(nn.Module): context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) reshaped_context_layer = context_layer.view(*new_context_layer_shape) - w = self.dense.weight.T.view(16, 64, 1024) + print(self.dense.weight.T.shape) + w = self.dense.weight.T.view(self.num_attention_heads, self.attention_head_size, self.hidden_size) b = self.dense.bias projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b @@ -262,7 +251,7 @@ class AlbertTransformer(nn.Module): super(AlbertTransformer, self).__init__() self.config =config - self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.attention = AlbertAttention(config) self.ffn = nn.Linear(config.hidden_size, config.intermediate_size) self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size) @@ -273,18 +262,11 @@ class AlbertTransformer(nn.Module): ffn_output = self.ffn(attention_output) ffn_output = gelu_new(ffn_output) ffn_output = self.ffn_output(ffn_output) - hidden_states = self.layer_norm(ffn_output + attention_output) + hidden_states = self.LayerNorm(ffn_output + attention_output) return hidden_states -def gelu_new(x): - """ Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT). - Also see https://arxiv.org/abs/1606.08415 - """ - return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) - - class AlbertEncoder(nn.Module): def __init__(self, config): super(AlbertEncoder, self).__init__() @@ -305,27 +287,22 @@ class AlbertEncoder(nn.Module): outputs = outputs + (all_attentions,) return outputs # last-layer hidden state, (all hidden states), (all attentions) -# config = AlbertConfig.from_json_file("config.json") -# # model = AlbertForMaskedLM(config) -# model = AlbertModel(config) - -# model = load_tf_weights_in_albert(model, config, "albert/albert") - -# print(model) - -# input_ids = torch.tensor([[31, 51, 99], [15, 5, 0]]) -# input_mask = torch.tensor([[1, 1, 1], [1, 1, 0]]) -# segment_ids = torch.tensor([[0, 0, 1], [0, 0, 0]]) - -# # sequence_output, pooled_outputs = model() - -# logits = model(input_ids, attention_mask=input_mask, token_type_ids=segment_ids)[1] - - -# embeddings_output = -# print("pooled output", logits) -# # print("Pooled output", pooled_outputs) - -config = AlbertConfig.from_json_file("/home/hf/google-research/albert/config.json") +model_size = "base" +config = AlbertConfig.from_json_file("/home/hf/google-research/albert/config_{}.json".format(model_size)) model = AlbertModel(config) -model = load_tf_weights_in_albert(model, config, "/home/hf/transformers/albert/albert") \ No newline at end of file +model = load_tf_weights_in_albert(model, config, "/home/hf/transformers/albert-{}/albert-{}".format(model_size, model_size)) +model.eval() +print(sum(p.numel() for p in model.parameters() if p.requires_grad)) + + +input_ids = [[31, 51, 99, 88, 54, 34, 23, 23, 12], [15, 5, 0, 88, 54, 34, 23, 23, 12]] +input_mask = [[1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 0, 0, 0]] +segment_ids = [[0, 0, 1, 0, 0, 1, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0]] + +pt_input_ids = torch.tensor(input_ids) +pt_input_mask = torch.tensor(input_mask) +pt_segment_ids = torch.tensor(segment_ids) + +pt_dict = {"input_ids": pt_input_ids, "attention_mask": pt_input_mask, "token_type_ids": pt_segment_ids} +pt_output = model(**pt_dict) +print(pt_output) \ No newline at end of file From 139affaa8de89c6ef16a4712d24a22a8fea91eda Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 29 Oct 2019 22:45:24 +0000 Subject: [PATCH 038/179] Albert layer/layer groups --- transformers/modeling_albert.py | 80 ++++++++++++++++++++------------- 1 file changed, 50 insertions(+), 30 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index f3cebdc3d9..440ccf2bce 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -30,17 +30,19 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path): names.append(name) arrays.append(array) - print(model) + for name, array in zip(names, arrays): + print(name) for name, array in zip(names, arrays): print(name) og = name - name = name.replace("transformer/group_0/inner_group_0", "transformer") name = name.replace("ffn_1", "ffn") name = name.replace("ffn/intermediate/output", "ffn_output") name = name.replace("attention_1", "attention") name = name.replace("cls/predictions/transform", "predictions") - name = name.replace("transformer/LayerNorm_1", "transformer/attention/LayerNorm") + name = name.replace("LayerNorm_1", "attention/LayerNorm") + name = name.replace("inner_group_", "albert_layers/") + name = name.replace("group_", "albert_layer_groups/") name = name.split('/') print(name) @@ -104,7 +106,7 @@ class AlbertModel(BertModel): self.config = config self.embeddings = AlbertEmbeddings(config) - self.encoder = AlbertEncoder(config) + self.encoder = AlbertTransformer(config) self.pooler = nn.Linear(config.hidden_size, config.hidden_size) self.pooler_activation = nn.Tanh() @@ -133,6 +135,7 @@ class AlbertModel(BertModel): extended_attention_mask, head_mask=head_mask) sequence_output = encoder_outputs[0] + print(sequence_output.shape, sequence_output[:, 0].shape, self.pooler(sequence_output[:, 0]).shape) pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0])) @@ -246,18 +249,18 @@ class AlbertAttention(BertSelfAttention): return layernormed_context_layer, projected_context_layer, reshaped_context_layer, context_layer, attention_scores, attention_probs, attention_mask -class AlbertTransformer(nn.Module): +class AlbertLayer(nn.Module): def __init__(self, config): - super(AlbertTransformer, self).__init__() + super(AlbertLayer, self).__init__() - self.config =config + self.config = config self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.attention = AlbertAttention(config) self.ffn = nn.Linear(config.hidden_size, config.intermediate_size) self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states, attention_mask=None, head_mask=None): - for i in range(self.config.num_hidden_layers): + for _ in range(self.config.inner_group_num): attention_output = self.attention(hidden_states, attention_mask)[0] ffn_output = self.ffn(attention_output) ffn_output = gelu_new(ffn_output) @@ -267,42 +270,59 @@ class AlbertTransformer(nn.Module): return hidden_states -class AlbertEncoder(nn.Module): +class AlbertLayerGroup(nn.Module): def __init__(self, config): - super(AlbertEncoder, self).__init__() + super(AlbertLayerGroup, self).__init__() + + self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)]) + def forward(self, hidden_states, attention_mask=None, head_mask=None): + for albert_layer in self.albert_layers: + hidden_states = albert_layer(hidden_states, attention_mask, head_mask) + + return hidden_states + + +class AlbertTransformer(nn.Module): + def __init__(self, config): + super(AlbertTransformer, self).__init__() + + self.config = config self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size) - self.transformer = AlbertTransformer(config) + self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)]) def forward(self, hidden_states, attention_mask=None, head_mask=None): hidden_states = self.embedding_hidden_mapping_in(hidden_states) - hidden_states = self.transformer(hidden_states, attention_mask, head_mask) - outputs = (hidden_states,) - if self.output_hidden_states: - outputs = outputs + (all_hidden_states,) - if self.output_attentions: - outputs = outputs + (all_attentions,) - return outputs # last-layer hidden state, (all hidden states), (all attentions) + for layer_idx in range(self.config.num_hidden_layers): + group_idx = int(layer_idx / self.config.num_hidden_layers * self.config.num_hidden_groups) + hidden_states = self.albert_layer_groups[group_idx](hidden_states, attention_mask, head_mask) + + return (hidden_states,) -model_size = "base" -config = AlbertConfig.from_json_file("/home/hf/google-research/albert/config_{}.json".format(model_size)) + +model_size = 'base' +hidden_groups = 1 +inner_groups = 1 +config = AlbertConfig.from_json_file("/home/hf/google-research/albert/config_{}-{}-hg-{}-ig.json".format(model_size, hidden_groups, inner_groups)) model = AlbertModel(config) -model = load_tf_weights_in_albert(model, config, "/home/hf/transformers/albert-{}/albert-{}".format(model_size, model_size)) + +print(model) +model = load_tf_weights_in_albert(model, config, "/home/hf/transformers/albert-{}-{}-hg-{}-ig/albert-{}-{}-hg-{}-ig".format(model_size, hidden_groups, inner_groups, model_size, hidden_groups, inner_groups)) model.eval() print(sum(p.numel() for p in model.parameters() if p.requires_grad)) -input_ids = [[31, 51, 99, 88, 54, 34, 23, 23, 12], [15, 5, 0, 88, 54, 34, 23, 23, 12]] -input_mask = [[1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 0, 0, 0]] -segment_ids = [[0, 0, 1, 0, 0, 1, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0]] +# input_ids = [[31, 51, 99, 88, 54, 34, 23, 23, 12], [15, 5, 0, 88, 54, 34, 23, 23, 12]] +# input_mask = [[1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 0, 0, 0]] +# segment_ids = [[0, 0, 1, 0, 0, 1, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0]] -pt_input_ids = torch.tensor(input_ids) -pt_input_mask = torch.tensor(input_mask) -pt_segment_ids = torch.tensor(segment_ids) +# pt_input_ids = torch.tensor(input_ids) +# pt_input_mask = torch.tensor(input_mask) +# pt_segment_ids = torch.tensor(segment_ids) -pt_dict = {"input_ids": pt_input_ids, "attention_mask": pt_input_mask, "token_type_ids": pt_segment_ids} -pt_output = model(**pt_dict) -print(pt_output) \ No newline at end of file +# pt_dict = {"input_ids": pt_input_ids, "attention_mask": pt_input_mask, "token_type_ids": pt_segment_ids} +# pt_output = model(**pt_dict) +# print(pt_output) \ No newline at end of file From 12290c0d5ce8475e884190b6ba480a8b3e671b3e Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 29 Oct 2019 23:19:02 +0000 Subject: [PATCH 039/179] Handles multi layer and multi groups --- transformers/modeling_albert.py | 30 ++++++++++++++---------------- 1 file changed, 14 insertions(+), 16 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 440ccf2bce..90e9b162e6 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -136,7 +136,6 @@ class AlbertModel(BertModel): head_mask=head_mask) sequence_output = encoder_outputs[0] - print(sequence_output.shape, sequence_output[:, 0].shape, self.pooler(sequence_output[:, 0]).shape) pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0])) outputs = (sequence_output, pooled_output,) + encoder_outputs[1:] # add hidden_states and attentions if they are here @@ -260,12 +259,11 @@ class AlbertLayer(nn.Module): self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states, attention_mask=None, head_mask=None): - for _ in range(self.config.inner_group_num): - attention_output = self.attention(hidden_states, attention_mask)[0] - ffn_output = self.ffn(attention_output) - ffn_output = gelu_new(ffn_output) - ffn_output = self.ffn_output(ffn_output) - hidden_states = self.LayerNorm(ffn_output + attention_output) + attention_output = self.attention(hidden_states, attention_mask)[0] + ffn_output = self.ffn(attention_output) + ffn_output = gelu_new(ffn_output) + ffn_output = self.ffn_output(ffn_output) + hidden_states = self.LayerNorm(ffn_output + attention_output) return hidden_states @@ -303,16 +301,16 @@ class AlbertTransformer(nn.Module): return (hidden_states,) -model_size = 'base' -hidden_groups = 1 -inner_groups = 1 -config = AlbertConfig.from_json_file("/home/hf/google-research/albert/config_{}-{}-hg-{}-ig.json".format(model_size, hidden_groups, inner_groups)) -model = AlbertModel(config) +# model_size = 'base' +# hidden_groups = 1 +# inner_groups = 2 +# config = AlbertConfig.from_json_file("/home/hf/google-research/albert/config_{}-{}-hg-{}-ig.json".format(model_size, hidden_groups, inner_groups)) +# model = AlbertModel(config) -print(model) -model = load_tf_weights_in_albert(model, config, "/home/hf/transformers/albert-{}-{}-hg-{}-ig/albert-{}-{}-hg-{}-ig".format(model_size, hidden_groups, inner_groups, model_size, hidden_groups, inner_groups)) -model.eval() -print(sum(p.numel() for p in model.parameters() if p.requires_grad)) +# # print(model) +# model = load_tf_weights_in_albert(model, config, "/home/hf/transformers/albert-{}-{}-hg-{}-ig/albert-{}-{}-hg-{}-ig".format(model_size, hidden_groups, inner_groups, model_size, hidden_groups, inner_groups)) +# # model.eval() +# # print(sum(p.numel() for p in model.parameters() if p.requires_grad)) # input_ids = [[31, 51, 99, 88, 54, 34, 23, 23, 12], [15, 5, 0, 88, 54, 34, 23, 23, 12]] From 1b92564330aa2f40b065a0b9a2a94a28a595bbc6 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 29 Oct 2019 23:23:18 +0000 Subject: [PATCH 040/179] Reorganize and cleanup --- transformers/modeling_albert.py | 287 +++++++++++++++----------------- 1 file changed, 132 insertions(+), 155 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 90e9b162e6..c6662cb6d3 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -100,6 +100,138 @@ class AlbertEmbeddings(BertEmbeddings): self.LayerNorm = torch.nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) +class AlbertAttention(BertSelfAttention): + def __init__(self, config): + super(AlbertAttention, self).__init__(config) + + self.num_attention_heads = config.num_attention_heads + self.hidden_size = config.hidden_size + self.attention_head_size = config.hidden_size // config.num_attention_heads + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + mask = torch.ones(self.num_attention_heads, self.attention_head_size) + heads = set(heads) - self.pruned_heads # Convert to set and emove already pruned heads + for head in heads: + # Compute how many pruned heads are before the head and move the index accordingly + head = head - sum(1 if h < head else 0 for h in self.pruned_heads) + mask[head] = 0 + mask = mask.view(-1).contiguous().eq(1) + index = torch.arange(len(mask))[mask].long() + + # Prune linear layers + self.query = prune_linear_layer(self.query, index) + self.key = prune_linear_layer(self.key, index) + self.value = prune_linear_layer(self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.num_attention_heads = self.num_attention_heads - len(heads) + self.all_head_size = self.attention_head_size * self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward(self, input_ids, attention_mask=None, head_mask=None): + mixed_query_layer = self.query(input_ids) + mixed_key_layer = self.key(input_ids) + mixed_value_layer = self.value(input_ids) + + query_layer = self.transpose_for_scores(mixed_query_layer) + key_layer = self.transpose_for_scores(mixed_key_layer) + value_layer = self.transpose_for_scores(mixed_value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + reshaped_context_layer = context_layer.view(*new_context_layer_shape) + + + # Should find a better way to do this + w = self.dense.weight.T.view(self.num_attention_heads, self.attention_head_size, self.hidden_size) + b = self.dense.bias + + projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b + projected_context_layer = self.dropout(projected_context_layer) + layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer) + return layernormed_context_layer, projected_context_layer, reshaped_context_layer, context_layer, attention_scores, attention_probs, attention_mask + + +class AlbertLayer(nn.Module): + def __init__(self, config): + super(AlbertLayer, self).__init__() + + self.config = config + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.attention = AlbertAttention(config) + self.ffn = nn.Linear(config.hidden_size, config.intermediate_size) + self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states, attention_mask=None, head_mask=None): + attention_output = self.attention(hidden_states, attention_mask)[0] + ffn_output = self.ffn(attention_output) + ffn_output = gelu_new(ffn_output) + ffn_output = self.ffn_output(ffn_output) + hidden_states = self.LayerNorm(ffn_output + attention_output) + + return hidden_states + + +class AlbertLayerGroup(nn.Module): + def __init__(self, config): + super(AlbertLayerGroup, self).__init__() + + self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)]) + + def forward(self, hidden_states, attention_mask=None, head_mask=None): + for albert_layer in self.albert_layers: + hidden_states = albert_layer(hidden_states, attention_mask, head_mask) + + return hidden_states + + +class AlbertTransformer(nn.Module): + def __init__(self, config): + super(AlbertTransformer, self).__init__() + + self.config = config + self.output_attentions = config.output_attentions + self.output_hidden_states = config.output_hidden_states + self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size) + self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)]) + + def forward(self, hidden_states, attention_mask=None, head_mask=None): + hidden_states = self.embedding_hidden_mapping_in(hidden_states) + + for layer_idx in range(self.config.num_hidden_layers): + group_idx = int(layer_idx / self.config.num_hidden_layers * self.config.num_hidden_groups) + hidden_states = self.albert_layer_groups[group_idx](hidden_states, attention_mask, head_mask) + + return (hidden_states,) + + class AlbertModel(BertModel): def __init__(self, config): super(AlbertModel, self).__init__(config) @@ -169,158 +301,3 @@ class AlbertForMaskedLM(nn.Module): logits = self.word_embeddings(hidden_states) return logits - - -class AlbertAttention(BertSelfAttention): - def __init__(self, config): - super(AlbertAttention, self).__init__(config) - - self.num_attention_heads = config.num_attention_heads - self.hidden_size = config.hidden_size - self.attention_head_size = config.hidden_size // config.num_attention_heads - self.dropout = nn.Dropout(config.attention_probs_dropout_prob) - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.pruned_heads = set() - - def prune_heads(self, heads): - if len(heads) == 0: - return - mask = torch.ones(self.num_attention_heads, self.attention_head_size) - heads = set(heads) - self.pruned_heads # Convert to set and emove already pruned heads - for head in heads: - # Compute how many pruned heads are before the head and move the index accordingly - head = head - sum(1 if h < head else 0 for h in self.pruned_heads) - mask[head] = 0 - mask = mask.view(-1).contiguous().eq(1) - index = torch.arange(len(mask))[mask].long() - - # Prune linear layers - self.query = prune_linear_layer(self.query, index) - self.key = prune_linear_layer(self.key, index) - self.value = prune_linear_layer(self.value, index) - self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) - - # Update hyper params and store pruned heads - self.num_attention_heads = self.num_attention_heads - len(heads) - self.all_head_size = self.attention_head_size * self.num_attention_heads - self.pruned_heads = self.pruned_heads.union(heads) - - def forward(self, input_ids, attention_mask=None, head_mask=None): - mixed_query_layer = self.query(input_ids) - mixed_key_layer = self.key(input_ids) - mixed_value_layer = self.value(input_ids) - - query_layer = self.transpose_for_scores(mixed_query_layer) - key_layer = self.transpose_for_scores(mixed_key_layer) - value_layer = self.transpose_for_scores(mixed_value_layer) - - # Take the dot product between "query" and "key" to get the raw attention scores. - attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) - attention_scores = attention_scores / math.sqrt(self.attention_head_size) - if attention_mask is not None: - # Apply the attention mask is (precomputed for all layers in BertModel forward() function) - attention_scores = attention_scores + attention_mask - - # Normalize the attention scores to probabilities. - attention_probs = nn.Softmax(dim=-1)(attention_scores) - - # This is actually dropping out entire tokens to attend to, which might - # seem a bit unusual, but is taken from the original Transformer paper. - attention_probs = self.dropout(attention_probs) - - # Mask heads if we want to - if head_mask is not None: - attention_probs = attention_probs * head_mask - - context_layer = torch.matmul(attention_probs, value_layer) - - context_layer = context_layer.permute(0, 2, 1, 3).contiguous() - new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) - reshaped_context_layer = context_layer.view(*new_context_layer_shape) - print(self.dense.weight.T.shape) - w = self.dense.weight.T.view(self.num_attention_heads, self.attention_head_size, self.hidden_size) - b = self.dense.bias - - projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b - projected_context_layer = self.dropout(projected_context_layer) - layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer) - return layernormed_context_layer, projected_context_layer, reshaped_context_layer, context_layer, attention_scores, attention_probs, attention_mask - - -class AlbertLayer(nn.Module): - def __init__(self, config): - super(AlbertLayer, self).__init__() - - self.config = config - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.attention = AlbertAttention(config) - self.ffn = nn.Linear(config.hidden_size, config.intermediate_size) - self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size) - - def forward(self, hidden_states, attention_mask=None, head_mask=None): - attention_output = self.attention(hidden_states, attention_mask)[0] - ffn_output = self.ffn(attention_output) - ffn_output = gelu_new(ffn_output) - ffn_output = self.ffn_output(ffn_output) - hidden_states = self.LayerNorm(ffn_output + attention_output) - - return hidden_states - - -class AlbertLayerGroup(nn.Module): - def __init__(self, config): - super(AlbertLayerGroup, self).__init__() - - self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)]) - - def forward(self, hidden_states, attention_mask=None, head_mask=None): - for albert_layer in self.albert_layers: - hidden_states = albert_layer(hidden_states, attention_mask, head_mask) - - return hidden_states - - -class AlbertTransformer(nn.Module): - def __init__(self, config): - super(AlbertTransformer, self).__init__() - - self.config = config - self.output_attentions = config.output_attentions - self.output_hidden_states = config.output_hidden_states - self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size) - self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)]) - - def forward(self, hidden_states, attention_mask=None, head_mask=None): - hidden_states = self.embedding_hidden_mapping_in(hidden_states) - - for layer_idx in range(self.config.num_hidden_layers): - group_idx = int(layer_idx / self.config.num_hidden_layers * self.config.num_hidden_groups) - hidden_states = self.albert_layer_groups[group_idx](hidden_states, attention_mask, head_mask) - - return (hidden_states,) - - -# model_size = 'base' -# hidden_groups = 1 -# inner_groups = 2 -# config = AlbertConfig.from_json_file("/home/hf/google-research/albert/config_{}-{}-hg-{}-ig.json".format(model_size, hidden_groups, inner_groups)) -# model = AlbertModel(config) - -# # print(model) -# model = load_tf_weights_in_albert(model, config, "/home/hf/transformers/albert-{}-{}-hg-{}-ig/albert-{}-{}-hg-{}-ig".format(model_size, hidden_groups, inner_groups, model_size, hidden_groups, inner_groups)) -# # model.eval() -# # print(sum(p.numel() for p in model.parameters() if p.requires_grad)) - - -# input_ids = [[31, 51, 99, 88, 54, 34, 23, 23, 12], [15, 5, 0, 88, 54, 34, 23, 23, 12]] -# input_mask = [[1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 0, 0, 0]] -# segment_ids = [[0, 0, 1, 0, 0, 1, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0]] - -# pt_input_ids = torch.tensor(input_ids) -# pt_input_mask = torch.tensor(input_mask) -# pt_segment_ids = torch.tensor(segment_ids) - -# pt_dict = {"input_ids": pt_input_ids, "attention_mask": pt_input_mask, "token_type_ids": pt_segment_ids} -# pt_output = model(**pt_dict) -# print(pt_output) \ No newline at end of file From 67b422662c7002319e54679a73d654ba313702ef Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 29 Oct 2019 23:33:53 +0000 Subject: [PATCH 041/179] Documentation + improved AlbertForMaskedLM --- transformers/modeling_albert.py | 85 +++++++++++++++++++++++++++++++-- 1 file changed, 80 insertions(+), 5 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index c6662cb6d3..9b3c51fe25 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -4,8 +4,11 @@ import math import logging import torch import torch.nn as nn +from torch.nn import CrossEntropyLoss from transformers.configuration_albert import AlbertConfig from transformers.modeling_bert import BertEmbeddings, BertModel, BertSelfAttention, prune_linear_layer, gelu_new +from .file_utils import add_start_docstrings + logger = logging.getLogger(__name__) def load_tf_weights_in_albert(model, config, tf_checkpoint_path): @@ -232,6 +235,70 @@ class AlbertTransformer(nn.Module): return (hidden_states,) +ALBERT_START_DOCSTRING = r""" The ALBERT model was proposed in + `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`_ + by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents + two parameter-reduction techniques to lower memory consumption and increase the trainig speed of BERT. + + This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and + refer to the PyTorch documentation for all matter related to general usage and behavior. + + .. _`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`: + https://arxiv.org/abs/1909.11942 + + .. _`torch.nn.Module`: + https://pytorch.org/docs/stable/nn.html#module + + Parameters: + config (:class:`~transformers.AlbertConfig`): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the configuration. + Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights. +""" + +ALBERT_INPUTS_DOCSTRING = r""" + Inputs: + **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: + Indices of input sequence tokens in the vocabulary. + To match pre-training, BERT input sequence should be formatted with [CLS] and [SEP] tokens as follows: + + (a) For sequence pairs: + + ``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]`` + + ``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1`` + + (b) For single sequences: + + ``tokens: [CLS] the dog is hairy . [SEP]`` + + ``token_type_ids: 0 0 0 0 0 0 0`` + + Albert is a model with absolute position embeddings so it's usually advised to pad the inputs on + the right rather than the left. + + Indices can be obtained using :class:`transformers.AlbertTokenizer`. + See :func:`transformers.PreTrainedTokenizer.encode` and + :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details. + **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``: + Mask to avoid performing attention on padding token indices. + Mask values selected in ``[0, 1]``: + ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens. + **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: + Segment token indices to indicate first and second portions of the inputs. + Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1`` + corresponds to a `sentence B` token + (see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details). + **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: + Indices of positions of each input sequence tokens in the position embeddings. + Selected in the range ``[0, config.max_position_embeddings - 1]``. + **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``: + Mask to nullify selected heads of the self-attention modules. + Mask values selected in ``[0, 1]``: + ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**. +""" + +@add_start_docstrings("The bare ALBERT Model transformer outputting raw hidden-states without any specific head on top.", + BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING) class AlbertModel(BertModel): def __init__(self, config): super(AlbertModel, self).__init__(config) @@ -274,6 +341,7 @@ class AlbertModel(BertModel): return outputs +@add_start_docstrings("Bert Model with a `language modeling` head on top.", ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) class AlbertForMaskedLM(nn.Module): def __init__(self, config): super(AlbertForMaskedLM, self).__init__() @@ -292,12 +360,19 @@ class AlbertForMaskedLM(nn.Module): self._tie_or_clone_weights(self.classifier.word_embeddings, self.transformer.embeddings.word_embeddings) - def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): - hidden_states = self.bert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None)[0] - hidden_states = self.dense(hidden_states) + def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, + masked_lm_labels=None): + outputs = self.bert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None) + sequence_outputs = outputs[0] + hidden_states = self.dense(sequence_outputs) hidden_states = gelu_new(hidden_states) hidden_states = self.LayerNorm(hidden_states) + prediction_scores = self.word_embeddings(hidden_states) - logits = self.word_embeddings(hidden_states) + outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here + if masked_lm_labels is not None: + loss_fct = CrossEntropyLoss(ignore_index=-1) + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1)) + outputs = (masked_lm_loss,) + outputs - return logits + return outputs From fedac786d401a9da31847b45df842bbee185afe6 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 30 Oct 2019 14:39:16 +0000 Subject: [PATCH 042/179] Tokenization + small fixes --- transformers/modeling_albert.py | 2 +- transformers/tokenization_albert.py | 210 ++++++++++++++++++++++++++++ 2 files changed, 211 insertions(+), 1 deletion(-) create mode 100644 transformers/tokenization_albert.py diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 9b3c51fe25..a1c0d5f610 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -298,7 +298,7 @@ ALBERT_INPUTS_DOCSTRING = r""" """ @add_start_docstrings("The bare ALBERT Model transformer outputting raw hidden-states without any specific head on top.", - BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING) + ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) class AlbertModel(BertModel): def __init__(self, config): super(AlbertModel, self).__init__(config) diff --git a/transformers/tokenization_albert.py b/transformers/tokenization_albert.py new file mode 100644 index 0000000000..f2e37222f6 --- /dev/null +++ b/transformers/tokenization_albert.py @@ -0,0 +1,210 @@ + +from .tokenization_utils import PreTrainedTokenizer +import logging +import unicodedata +import six +import os +from shutil import copyfile + +logger = logging.getLogger(__name__) + +SPIECE_UNDERLINE = u'▁' + +class AlbertTokenizer(PreTrainedTokenizer): + """ + SentencePiece based tokenizer. Peculiarities: + + - requires `SentencePiece `_ + """ + # vocab_files_names = VOCAB_FILES_NAMES + # pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP + # max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES + + def __init__(self, vocab_file, + do_lower_case=False, remove_space=True, keep_accents=False, + bos_token="[CLS]", eos_token="[SEP]", unk_token="", sep_token="[SEP]", + pad_token="", cls_token="[CLS]", mask_token="[MASK]>", **kwargs): + super(AlbertTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, + unk_token=unk_token, sep_token=sep_token, + pad_token=pad_token, cls_token=cls_token, + mask_token=mask_token, **kwargs) + + self.max_len_single_sentence = self.max_len - 2 # take into account special tokens + self.max_len_sentences_pair = self.max_len - 3 # take into account special tokens + + try: + import sentencepiece as spm + except ImportError: + logger.warning("You need to install SentencePiece to use AlbertTokenizer: https://github.com/google/sentencepiece" + "pip install sentencepiece") + + self.do_lower_case = do_lower_case + self.remove_space = remove_space + self.keep_accents = keep_accents + self.vocab_file = vocab_file + + self.sp_model = spm.SentencePieceProcessor() + self.sp_model.Load(vocab_file) + + @property + def vocab_size(self): + return len(self.sp_model) + + def __getstate__(self): + state = self.__dict__.copy() + state["sp_model"] = None + return state + + def __setstate__(self, d): + self.__dict__ = d + try: + import sentencepiece as spm + except ImportError: + logger.warning("You need to install SentencePiece to use AlbertTokenizer: https://github.com/google/sentencepiece" + "pip install sentencepiece") + self.sp_model = spm.SentencePieceProcessor() + self.sp_model.Load(self.vocab_file) + + def preprocess_text(self, inputs): + if self.remove_space: + outputs = ' '.join(inputs.strip().split()) + else: + outputs = inputs + outputs = outputs.replace("``", '"').replace("''", '"') + + if six.PY2 and isinstance(outputs, str): + outputs = outputs.decode('utf-8') + + if not self.keep_accents: + outputs = unicodedata.normalize('NFKD', outputs) + outputs = ''.join([c for c in outputs if not unicodedata.combining(c)]) + if self.do_lower_case: + outputs = outputs.lower() + + return outputs + + def _tokenize(self, text, return_unicode=True, sample=False): + """ Tokenize a string. + return_unicode is used only for py2 + """ + text = self.preprocess_text(text) + # note(zhiliny): in some systems, sentencepiece only accepts str for py2 + if six.PY2 and isinstance(text, unicode): + text = text.encode('utf-8') + + if not sample: + pieces = self.sp_model.EncodeAsPieces(text) + else: + pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1) + new_pieces = [] + for piece in pieces: + if len(piece) > 1 and piece[-1] == ',' and piece[-2].isdigit(): + cur_pieces = self.sp_model.EncodeAsPieces( + piece[:-1].replace(SPIECE_UNDERLINE, '')) + if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: + if len(cur_pieces[0]) == 1: + cur_pieces = cur_pieces[1:] + else: + cur_pieces[0] = cur_pieces[0][1:] + cur_pieces.append(piece[-1]) + new_pieces.extend(cur_pieces) + else: + new_pieces.append(piece) + + # note(zhiliny): convert back to unicode for py2 + if six.PY2 and return_unicode: + ret_pieces = [] + for piece in new_pieces: + if isinstance(piece, str): + piece = piece.decode('utf-8') + ret_pieces.append(piece) + new_pieces = ret_pieces + + return new_pieces + + def _convert_token_to_id(self, token): + """ Converts a token (str/unicode) in an id using the vocab. """ + return self.sp_model.PieceToId(token) + + def _convert_id_to_token(self, index, return_unicode=True): + """Converts an index (integer) in a token (string/unicode) using the vocab.""" + token = self.sp_model.IdToPiece(index) + if six.PY2 and return_unicode and isinstance(token, str): + token = token.decode('utf-8') + return token + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (strings for sub-words) in a single string.""" + out_string = ''.join(tokens).replace(SPIECE_UNDERLINE, ' ').strip() + return out_string + + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks + by concatenating and adding special tokens. + A RoBERTa sequence has the following format: + single sequence: X + pair of sequences: A B + """ + sep = [self.sep_token_id] + cls = [self.cls_token_id] + if token_ids_1 is None: + return token_ids_0 + sep + cls + return token_ids_0 + sep + token_ids_1 + sep + cls + + def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): + """ + Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods. + + Args: + token_ids_0: list of ids (must not contain special tokens) + token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids + for sequence pairs + already_has_special_tokens: (default False) Set to True if the token list is already formated with + special tokens for the model + + Returns: + A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token. + """ + + if already_has_special_tokens: + if token_ids_1 is not None: + raise ValueError("You should not supply a second sequence if the provided sequence of " + "ids is already formated with special tokens for the model.") + return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) + + if token_ids_1 is not None: + return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1] + return ([0] * len(token_ids_0)) + [1, 1] + + def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): + """ + Creates a mask from the two sequences passed to be used in a sequence-pair classification task. + A BERT sequence pair mask has the following format: + 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2 + | first sequence | second sequence | CLS segment ID + + if token_ids_1 is None, only returns the first portion of the mask (0's). + """ + sep = [self.sep_token_id] + cls = [self.cls_token_id] + cls_segment_id = [2] + + if token_ids_1 is None: + return len(token_ids_0 + sep + cls) * [0] + return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id + + def save_vocabulary(self, save_directory): + """ Save the sentencepiece vocabulary (copy original file) and special tokens file + to a directory. + """ + if not os.path.isdir(save_directory): + logger.error("Vocabulary path ({}) should be a directory".format(save_directory)) + return + out_vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file']) + + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): + copyfile(self.vocab_file, out_vocab_file) + + return (out_vocab_file,) From e3ea5d1d8db36091ab840328cf6691c4f46f2e89 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 30 Oct 2019 15:03:30 +0000 Subject: [PATCH 043/179] Docstrings --- transformers/modeling_albert.py | 40 +++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index a1c0d5f610..ad8b979cef 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -300,6 +300,25 @@ ALBERT_INPUTS_DOCSTRING = r""" @add_start_docstrings("The bare ALBERT Model transformer outputting raw hidden-states without any specific head on top.", ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) class AlbertModel(BertModel): + r""" + Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: + **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)`` + Sequence of hidden-states at the output of the last layer of the model. + **pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)`` + Last layer hidden-state of the first token of the sequence (classification token) + further processed by a Linear layer and a Tanh activation function. The Linear + layer weights are trained from the next sentence prediction (classification) + objective during Bert pretraining. This output is usually *not* a good summary + of the semantic content of the input, you're often better with averaging or pooling + the sequence of hidden-states for the whole input sequence. + **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) + list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) + of shape ``(batch_size, sequence_length, hidden_size)``: + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + **attentions**: (`optional`, returned when ``config.output_attentions=True``) + list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + """ def __init__(self, config): super(AlbertModel, self).__init__(config) @@ -343,6 +362,27 @@ class AlbertModel(BertModel): @add_start_docstrings("Bert Model with a `language modeling` head on top.", ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) class AlbertForMaskedLM(nn.Module): + r""" + **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: + Labels for computing the masked language modeling loss. + Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) + Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels + in ``[0, ..., config.vocab_size]`` + + Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: + **loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: + Masked language modeling loss. + **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) + list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) + of shape ``(batch_size, sequence_length, hidden_size)``: + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + **attentions**: (`optional`, returned when ``config.output_attentions=True``) + list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + """ + def __init__(self, config): super(AlbertForMaskedLM, self).__init__() From ee20201d339b62948acd05783efc48c4e2b466bb Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 30 Oct 2019 16:19:49 +0000 Subject: [PATCH 044/179] Tokenization tests + fixes + init --- transformers/__init__.py | 5 ++ transformers/tests/fixtures/30k-clean.model | Bin 0 -> 760289 bytes .../tests/tokenization_albert_test.py | 78 ++++++++++++++++++ transformers/tokenization_albert.py | 30 +++---- transformers/tokenization_xlnet.py | 8 +- 5 files changed, 102 insertions(+), 19 deletions(-) create mode 100644 transformers/tests/fixtures/30k-clean.model create mode 100644 transformers/tests/tokenization_albert_test.py diff --git a/transformers/__init__.py b/transformers/__init__.py index 5c7b0a6197..152d520e7b 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -42,6 +42,7 @@ from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE from .tokenization_xlm import XLMTokenizer from .tokenization_roberta import RobertaTokenizer from .tokenization_distilbert import DistilBertTokenizer +from .tokenization_albert import AlbertTokenizer from .tokenization_camembert import CamembertTokenizer # Configurations @@ -57,6 +58,8 @@ from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP from .configuration_xlm import XLMConfig, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP +from .configuration_albert import AlbertConfig, ALBERT +from .configuration_albert import AlbertConfig from .configuration_camembert import CamembertConfig, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP # Modeling @@ -104,6 +107,8 @@ if is_torch_available(): CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model + from .modeling_albert import (AlbertModel, AlbertForMaskedLM) + # Optimization from .optimization import (AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup) diff --git 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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) + encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) + + assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [tokenizer.sep_token_id] + + +if __name__ == '__main__': + unittest.main() diff --git a/transformers/tokenization_albert.py b/transformers/tokenization_albert.py index f2e37222f6..0785e55ad2 100644 --- a/transformers/tokenization_albert.py +++ b/transformers/tokenization_albert.py @@ -8,6 +8,7 @@ from shutil import copyfile logger = logging.getLogger(__name__) +VOCAB_FILES_NAMES = {'vocab_file': '30k-clean.model'} SPIECE_UNDERLINE = u'▁' class AlbertTokenizer(PreTrainedTokenizer): @@ -16,12 +17,12 @@ class AlbertTokenizer(PreTrainedTokenizer): - requires `SentencePiece `_ """ - # vocab_files_names = VOCAB_FILES_NAMES + vocab_files_names = VOCAB_FILES_NAMES # pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP # max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__(self, vocab_file, - do_lower_case=False, remove_space=True, keep_accents=False, + do_lower_case=True, remove_space=True, keep_accents=False, bos_token="[CLS]", eos_token="[SEP]", unk_token="", sep_token="[SEP]", pad_token="", cls_token="[CLS]", mask_token="[MASK]>", **kwargs): super(AlbertTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, @@ -142,15 +143,15 @@ class AlbertTokenizer(PreTrainedTokenizer): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. - A RoBERTa sequence has the following format: - single sequence: X - pair of sequences: A B + An ALBERT sequence has the following format: + single sequence: [CLS] X [SEP] + pair of sequences: [CLS] A [SEP] B [SEP] """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: - return token_ids_0 + sep + cls - return token_ids_0 + sep + token_ids_1 + sep + cls + return cls + token_ids_0 + sep + return cls + token_ids_0 + sep + token_ids_1 + sep def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ @@ -175,25 +176,24 @@ class AlbertTokenizer(PreTrainedTokenizer): return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) if token_ids_1 is not None: - return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1] - return ([0] * len(token_ids_0)) + [1, 1] + return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + return [1] + ([0] * len(token_ids_0)) + [1] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. - A BERT sequence pair mask has the following format: - 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2 - | first sequence | second sequence | CLS segment ID + An ALBERT sequence pair mask has the following format: + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence if token_ids_1 is None, only returns the first portion of the mask (0's). """ sep = [self.sep_token_id] cls = [self.cls_token_id] - cls_segment_id = [2] if token_ids_1 is None: - return len(token_ids_0 + sep + cls) * [0] - return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id + return len(cls + token_ids_0 + sep) * [0] + return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] def save_vocabulary(self, save_directory): """ Save the sentencepiece vocabulary (copy original file) and special tokens file diff --git a/transformers/tokenization_xlnet.py b/transformers/tokenization_xlnet.py index a4f1a6e3ba..c01fbbbeeb 100644 --- a/transformers/tokenization_xlnet.py +++ b/transformers/tokenization_xlnet.py @@ -185,9 +185,9 @@ class XLNetTokenizer(PreTrainedTokenizer): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. - A RoBERTa sequence has the following format: - single sequence: X - pair of sequences: A B + An XLNet sequence has the following format: + single sequence: X + pair of sequences: A B """ sep = [self.sep_token_id] cls = [self.cls_token_id] @@ -224,7 +224,7 @@ class XLNetTokenizer(PreTrainedTokenizer): def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. - A BERT sequence pair mask has the following format: + An XLNet sequence pair mask has the following format: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2 | first sequence | second sequence | CLS segment ID From 1e5b31c3881cb1313216ce0f3cffae89b0845d4f Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 30 Oct 2019 20:25:32 +0000 Subject: [PATCH 045/179] Several fixes and improvements --- transformers/modeling_albert.py | 36 +++++++++--------- .../{30k-clean.model => spiece.model} | Bin transformers/tokenization_albert.py | 2 +- 3 files changed, 19 insertions(+), 19 deletions(-) rename transformers/tests/fixtures/{30k-clean.model => spiece.model} (100%) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index ad8b979cef..371a2e535c 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -7,6 +7,7 @@ import torch.nn as nn from torch.nn import CrossEntropyLoss from transformers.configuration_albert import AlbertConfig from transformers.modeling_bert import BertEmbeddings, BertModel, BertSelfAttention, prune_linear_layer, gelu_new +from transformers.modeling_utils import PreTrainedModel from .file_utils import add_start_docstrings logger = logging.getLogger(__name__) @@ -37,18 +38,17 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path): print(name) for name, array in zip(names, arrays): - print(name) - og = name + original_name = name name = name.replace("ffn_1", "ffn") name = name.replace("ffn/intermediate/output", "ffn_output") name = name.replace("attention_1", "attention") - name = name.replace("cls/predictions/transform", "predictions") - name = name.replace("LayerNorm_1", "attention/LayerNorm") + name = name.replace("cls/predictions", "predictions") + name = name.replace("transform/", "") + name = name.replace("LayerNorm_1", "full_layer_layer_norm") + name = name.replace("LayerNorm", "attention/LayerNorm") name = name.replace("inner_group_", "albert_layers/") name = name.replace("group_", "albert_layer_groups/") name = name.split('/') - - print(name) pointer = model for m_name in name: if re.fullmatch(r'[A-Za-z]+_\d+', m_name): @@ -78,13 +78,12 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path): pointer = getattr(pointer, 'weight') elif m_name == 'kernel': array = np.transpose(array) - print("transposed") try: assert pointer.shape == array.shape except AssertionError as e: e.args += (pointer.shape, array.shape) raise - print("Initialize PyTorch weight {} from {}".format(name, og)) + print("Initialize PyTorch weight {} from {}".format(name, original_name)) pointer.data = torch.from_numpy(array) return model @@ -177,9 +176,9 @@ class AlbertAttention(BertSelfAttention): b = self.dense.bias projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b - projected_context_layer = self.dropout(projected_context_layer) - layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer) - return layernormed_context_layer, projected_context_layer, reshaped_context_layer, context_layer, attention_scores, attention_probs, attention_mask + projected_context_layer_dropout = self.dropout(projected_context_layer) + layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer_dropout) + return layernormed_context_layer class AlbertLayer(nn.Module): @@ -187,17 +186,17 @@ class AlbertLayer(nn.Module): super(AlbertLayer, self).__init__() self.config = config - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.attention = AlbertAttention(config) self.ffn = nn.Linear(config.hidden_size, config.intermediate_size) self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states, attention_mask=None, head_mask=None): - attention_output = self.attention(hidden_states, attention_mask)[0] + attention_output = self.attention(hidden_states, attention_mask) ffn_output = self.ffn(attention_output) ffn_output = gelu_new(ffn_output) ffn_output = self.ffn_output(ffn_output) - hidden_states = self.LayerNorm(ffn_output + attention_output) + hidden_states = self.full_layer_layer_norm(ffn_output + attention_output) return hidden_states @@ -352,16 +351,17 @@ class AlbertModel(BertModel): encoder_outputs = self.encoder(embedding_output, extended_attention_mask, head_mask=head_mask) + sequence_output = encoder_outputs[0] - + pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0])) - outputs = (sequence_output, pooled_output,) + encoder_outputs[1:] # add hidden_states and attentions if they are here + outputs = (sequence_output, pooled_output) + encoder_outputs[1:] # add hidden_states and attentions if they are here return outputs @add_start_docstrings("Bert Model with a `language modeling` head on top.", ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) -class AlbertForMaskedLM(nn.Module): +class AlbertForMaskedLM(PreTrainedModel): r""" **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Labels for computing the masked language modeling loss. @@ -384,7 +384,7 @@ class AlbertForMaskedLM(nn.Module): """ def __init__(self, config): - super(AlbertForMaskedLM, self).__init__() + super(AlbertForMaskedLM, self).__init__(config) self.config = config self.bert = AlbertModel(config) diff --git a/transformers/tests/fixtures/30k-clean.model b/transformers/tests/fixtures/spiece.model similarity index 100% rename from transformers/tests/fixtures/30k-clean.model rename to transformers/tests/fixtures/spiece.model diff --git a/transformers/tokenization_albert.py b/transformers/tokenization_albert.py index 0785e55ad2..7b16bb573f 100644 --- a/transformers/tokenization_albert.py +++ b/transformers/tokenization_albert.py @@ -8,7 +8,7 @@ from shutil import copyfile logger = logging.getLogger(__name__) -VOCAB_FILES_NAMES = {'vocab_file': '30k-clean.model'} +VOCAB_FILES_NAMES = {'vocab_file': 'spiece.model'} SPIECE_UNDERLINE = u'▁' class AlbertTokenizer(PreTrainedTokenizer): From 5680a1106302b1ebeb960de0700d6379c0aeef5c Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 30 Oct 2019 20:42:49 +0000 Subject: [PATCH 046/179] Activation function managed from the config file --- transformers/configuration_albert.py | 2 +- transformers/modeling_albert.py | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/transformers/configuration_albert.py b/transformers/configuration_albert.py index c86c9565cb..15437dbbea 100644 --- a/transformers/configuration_albert.py +++ b/transformers/configuration_albert.py @@ -16,7 +16,7 @@ class AlbertConfig(PretrainedConfig): intermediate_size=16384, inner_group_num=1, down_scale_factor=1, - hidden_act="gelu", + hidden_act="gelu_new", hidden_dropout_prob=0, attention_probs_dropout_prob=0, max_position_embeddings=512, diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 371a2e535c..7e9f7f1c46 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn from torch.nn import CrossEntropyLoss from transformers.configuration_albert import AlbertConfig -from transformers.modeling_bert import BertEmbeddings, BertModel, BertSelfAttention, prune_linear_layer, gelu_new +from transformers.modeling_bert import BertEmbeddings, BertModel, BertSelfAttention, prune_linear_layer, ACT2FN from transformers.modeling_utils import PreTrainedModel from .file_utils import add_start_docstrings @@ -190,11 +190,12 @@ class AlbertLayer(nn.Module): self.attention = AlbertAttention(config) self.ffn = nn.Linear(config.hidden_size, config.intermediate_size) self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size) + self.activation = ACT2FN[config.hidden_act] def forward(self, hidden_states, attention_mask=None, head_mask=None): attention_output = self.attention(hidden_states, attention_mask) ffn_output = self.ffn(attention_output) - ffn_output = gelu_new(ffn_output) + ffn_output = self.activation(ffn_output) ffn_output = self.ffn_output(ffn_output) hidden_states = self.full_layer_layer_norm(ffn_output + attention_output) @@ -392,6 +393,7 @@ class AlbertForMaskedLM(PreTrainedModel): self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.dense = nn.Linear(config.hidden_size, config.embedding_size) self.word_embeddings = nn.Linear(config.embedding_size, config.vocab_size) + self.activation = ACT2FN[config.hidden_act] def tie_weights(self): """ Make sure we are sharing the input and output embeddings. @@ -405,7 +407,7 @@ class AlbertForMaskedLM(PreTrainedModel): outputs = self.bert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None) sequence_outputs = outputs[0] hidden_states = self.dense(sequence_outputs) - hidden_states = gelu_new(hidden_states) + hidden_states = self.activation(hidden_states) hidden_states = self.LayerNorm(hidden_states) prediction_scores = self.word_embeddings(hidden_states) From ce9eade29c75fa676ac528d1fe21d9f4ac3c5622 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 30 Oct 2019 20:50:44 +0000 Subject: [PATCH 047/179] Initializer range using BertPreTrainedModel --- transformers/modeling_albert.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 7e9f7f1c46..b45208b696 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -6,8 +6,7 @@ import torch import torch.nn as nn from torch.nn import CrossEntropyLoss from transformers.configuration_albert import AlbertConfig -from transformers.modeling_bert import BertEmbeddings, BertModel, BertSelfAttention, prune_linear_layer, ACT2FN -from transformers.modeling_utils import PreTrainedModel +from transformers.modeling_bert import BertEmbeddings, BertPreTrainedModel, BertModel, BertSelfAttention, prune_linear_layer, ACT2FN from .file_utils import add_start_docstrings logger = logging.getLogger(__name__) @@ -362,7 +361,7 @@ class AlbertModel(BertModel): @add_start_docstrings("Bert Model with a `language modeling` head on top.", ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) -class AlbertForMaskedLM(PreTrainedModel): +class AlbertForMaskedLM(BertPreTrainedModel): r""" **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Labels for computing the masked language modeling loss. From 25a31953e8820eb5c88d8ad35ee547efccfe577c Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 30 Oct 2019 21:18:06 +0000 Subject: [PATCH 048/179] Output Attentions + output hidden states --- transformers/modeling_albert.py | 58 ++++++++++++++++++++++++++++----- 1 file changed, 49 insertions(+), 9 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index b45208b696..52cab2ea69 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -105,6 +105,7 @@ class AlbertAttention(BertSelfAttention): def __init__(self, config): super(AlbertAttention, self).__init__(config) + self.output_attentions = config.output_attentions self.num_attention_heads = config.num_attention_heads self.hidden_size = config.hidden_size self.attention_head_size = config.hidden_size // config.num_attention_heads @@ -177,7 +178,7 @@ class AlbertAttention(BertSelfAttention): projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b projected_context_layer_dropout = self.dropout(projected_context_layer) layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer_dropout) - return layernormed_context_layer + return (layernormed_context_layer, attention_probs) if self.output_attentions else (layernormed_context_layer,) class AlbertLayer(nn.Module): @@ -193,25 +194,45 @@ class AlbertLayer(nn.Module): def forward(self, hidden_states, attention_mask=None, head_mask=None): attention_output = self.attention(hidden_states, attention_mask) - ffn_output = self.ffn(attention_output) + ffn_output = self.ffn(attention_output[0]) ffn_output = self.activation(ffn_output) ffn_output = self.ffn_output(ffn_output) - hidden_states = self.full_layer_layer_norm(ffn_output + attention_output) + hidden_states = self.full_layer_layer_norm(ffn_output + attention_output[0]) - return hidden_states + return (hidden_states,) + attention_output[1:] # add attentions if we output them class AlbertLayerGroup(nn.Module): def __init__(self, config): super(AlbertLayerGroup, self).__init__() + self.output_attentions = config.output_attentions + self.output_hidden_states = config.output_hidden_states self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)]) def forward(self, hidden_states, attention_mask=None, head_mask=None): - for albert_layer in self.albert_layers: - hidden_states = albert_layer(hidden_states, attention_mask, head_mask) + layer_hidden_states = () + layer_attentions = () - return hidden_states + for albert_layer in self.albert_layers: + if self.output_hidden_states: + layer_hidden_states = layer_hidden_states + (hidden_states,) + + layer_output = albert_layer(hidden_states, attention_mask, head_mask) + hidden_states = layer_output[0] + + if self.output_attentions: + layer_attentions = layer_attentions + (layer_output[1],) + + if self.output_hidden_states: + layer_hidden_states = layer_hidden_states + (hidden_states,) + + outputs = (hidden_states,) + if self.output_hidden_states: + outputs = outputs + (layer_hidden_states,) + if self.output_attentions: + outputs = outputs + (layer_attentions,) + return outputs # last-layer hidden state, (layer hidden states), (layer attentions) class AlbertTransformer(nn.Module): @@ -227,11 +248,30 @@ class AlbertTransformer(nn.Module): def forward(self, hidden_states, attention_mask=None, head_mask=None): hidden_states = self.embedding_hidden_mapping_in(hidden_states) + all_attentions = () + + if self.output_hidden_states: + all_hidden_states = (hidden_states,) + for layer_idx in range(self.config.num_hidden_layers): group_idx = int(layer_idx / self.config.num_hidden_layers * self.config.num_hidden_groups) - hidden_states = self.albert_layer_groups[group_idx](hidden_states, attention_mask, head_mask) + layer_group_output = self.albert_layer_groups[group_idx](hidden_states, attention_mask, head_mask) - return (hidden_states,) + hidden_states = layer_group_output[0] + + if self.output_attentions: + all_attentions = all_attentions + layer_group_output[1] + + if self.output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + + outputs = (hidden_states,) + if self.output_hidden_states: + outputs = outputs + (all_hidden_states,) + if self.output_attentions: + outputs = outputs + (all_attentions,) + return outputs # last-layer hidden state, (all hidden states), (all attentions) ALBERT_START_DOCSTRING = r""" The ALBERT model was proposed in From 870320a24e28f187d0dfd10b82c4d60d5269374d Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 30 Oct 2019 22:30:21 +0000 Subject: [PATCH 049/179] Early tests --- transformers/modeling_albert.py | 59 +++---- transformers/tests/modeling_albert_test.py | 191 +++++++++++++++++++++ 2 files changed, 219 insertions(+), 31 deletions(-) create mode 100644 transformers/tests/modeling_albert_test.py diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 52cab2ea69..f906352311 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -11,6 +11,15 @@ from .file_utils import add_start_docstrings logger = logging.getLogger(__name__) + +ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = { + 'albert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-pytorch_model.bin", + 'albert-large': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-pytorch_model.bin", + 'albert-xlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-pytorch_model.bin", + 'albert-xxlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-pytorch_model.bin", +} + + def load_tf_weights_in_albert(model, config, tf_checkpoint_path): """ Load tf checkpoints in a pytorch model.""" try: @@ -39,6 +48,7 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path): for name, array in zip(names, arrays): original_name = name name = name.replace("ffn_1", "ffn") + name = name.replace("/bert/", "/albert/") name = name.replace("ffn/intermediate/output", "ffn_output") name = name.replace("attention_1", "attention") name = name.replace("cls/predictions", "predictions") @@ -114,29 +124,6 @@ class AlbertAttention(BertSelfAttention): self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pruned_heads = set() - def prune_heads(self, heads): - if len(heads) == 0: - return - mask = torch.ones(self.num_attention_heads, self.attention_head_size) - heads = set(heads) - self.pruned_heads # Convert to set and emove already pruned heads - for head in heads: - # Compute how many pruned heads are before the head and move the index accordingly - head = head - sum(1 if h < head else 0 for h in self.pruned_heads) - mask[head] = 0 - mask = mask.view(-1).contiguous().eq(1) - index = torch.arange(len(mask))[mask].long() - - # Prune linear layers - self.query = prune_linear_layer(self.query, index) - self.key = prune_linear_layer(self.key, index) - self.value = prune_linear_layer(self.value, index) - self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) - - # Update hyper params and store pruned heads - self.num_attention_heads = self.num_attention_heads - len(heads) - self.all_head_size = self.attention_head_size * self.num_attention_heads - self.pruned_heads = self.pruned_heads.union(heads) - def forward(self, input_ids, attention_mask=None, head_mask=None): mixed_query_layer = self.query(input_ids) mixed_key_layer = self.key(input_ids) @@ -225,7 +212,7 @@ class AlbertLayerGroup(nn.Module): layer_attentions = layer_attentions + (layer_output[1],) if self.output_hidden_states: - layer_hidden_states = layer_hidden_states + (hidden_states,) + layer_hidden_states = layer_hidden_states + (hidden_states,) outputs = (hidden_states,) if self.output_hidden_states: @@ -367,6 +354,8 @@ class AlbertModel(BertModel): self.pooler = nn.Linear(config.hidden_size, config.hidden_size) self.pooler_activation = nn.Tanh() + self.init_weights() + def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): if attention_mask is None: @@ -422,33 +411,41 @@ class AlbertForMaskedLM(BertPreTrainedModel): list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ - + + config_class = AlbertConfig + pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP + load_tf_weights = load_tf_weights_in_albert + base_model_prefix = "albert" + def __init__(self, config): super(AlbertForMaskedLM, self).__init__(config) self.config = config - self.bert = AlbertModel(config) + self.albert = AlbertModel(config) self.LayerNorm = nn.LayerNorm(config.embedding_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.dense = nn.Linear(config.hidden_size, config.embedding_size) - self.word_embeddings = nn.Linear(config.embedding_size, config.vocab_size) + self.decoder = nn.Linear(config.embedding_size, config.vocab_size) self.activation = ACT2FN[config.hidden_act] + self.init_weights() + self.tie_weights() + def tie_weights(self): """ Make sure we are sharing the input and output embeddings. Export to TorchScript can't handle parameter sharing so we are cloning them instead. """ - self._tie_or_clone_weights(self.classifier.word_embeddings, - self.transformer.embeddings.word_embeddings) + self._tie_or_clone_weights(self.decoder, + self.albert.embeddings.word_embeddings) def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, masked_lm_labels=None): - outputs = self.bert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None) + outputs = self.albert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None) sequence_outputs = outputs[0] hidden_states = self.dense(sequence_outputs) hidden_states = self.activation(hidden_states) hidden_states = self.LayerNorm(hidden_states) - prediction_scores = self.word_embeddings(hidden_states) + prediction_scores = self.decoder(hidden_states) outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here if masked_lm_labels is not None: diff --git a/transformers/tests/modeling_albert_test.py b/transformers/tests/modeling_albert_test.py new file mode 100644 index 0000000000..46a2eeb729 --- /dev/null +++ b/transformers/tests/modeling_albert_test.py @@ -0,0 +1,191 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import unittest +import shutil +import pytest + +from transformers import is_torch_available + +from .modeling_common_test import (CommonTestCases, ids_tensor) +from .configuration_common_test import ConfigTester + +if is_torch_available(): + from transformers import (AlbertConfig, AlbertModel, AlbertForMaskedLM) + from transformers.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP +else: + pytestmark = pytest.mark.skip("Require Torch") + + +class AlbertModelTest(CommonTestCases.CommonModelTester): + + all_model_classes = (AlbertModel, AlbertForMaskedLM) if is_torch_available() else () + test_pruning = False + test_head_masking = False + + class AlbertModelTester(object): + + def __init__(self, + parent, + batch_size=13, + seq_length=7, + is_training=True, + use_input_mask=True, + use_token_type_ids=True, + use_labels=True, + vocab_size=99, + hidden_size=32, + num_hidden_layers=5, + num_attention_heads=4, + intermediate_size=37, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512, + type_vocab_size=16, + type_sequence_label_size=2, + initializer_range=0.02, + num_labels=3, + num_choices=4, + scope=None, + ): + self.parent = parent + self.batch_size = batch_size + self.seq_length = seq_length + self.is_training = is_training + self.use_input_mask = use_input_mask + self.use_token_type_ids = use_token_type_ids + self.use_labels = use_labels + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_size = type_vocab_size + self.type_sequence_label_size = type_sequence_label_size + self.initializer_range = initializer_range + self.num_labels = num_labels + self.num_choices = num_choices + self.scope = scope + + def prepare_config_and_inputs(self): + input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) + + input_mask = None + if self.use_input_mask: + input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) + + token_type_ids = None + if self.use_token_type_ids: + token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) + + sequence_labels = None + token_labels = None + choice_labels = None + if self.use_labels: + sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) + token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) + choice_labels = ids_tensor([self.batch_size], self.num_choices) + + config = AlbertConfig( + vocab_size_or_config_json_file=self.vocab_size, + hidden_size=self.hidden_size, + num_hidden_layers=self.num_hidden_layers, + num_attention_heads=self.num_attention_heads, + intermediate_size=self.intermediate_size, + hidden_act=self.hidden_act, + hidden_dropout_prob=self.hidden_dropout_prob, + attention_probs_dropout_prob=self.attention_probs_dropout_prob, + max_position_embeddings=self.max_position_embeddings, + type_vocab_size=self.type_vocab_size, + initializer_range=self.initializer_range) + + return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels + + def check_loss_output(self, result): + self.parent.assertListEqual( + list(result["loss"].size()), + []) + + def create_and_check_albert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): + model = AlbertModel(config=config) + model.eval() + sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) + sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids) + sequence_output, pooled_output = model(input_ids) + + result = { + "sequence_output": sequence_output, + "pooled_output": pooled_output, + } + self.parent.assertListEqual( + list(result["sequence_output"].size()), + [self.batch_size, self.seq_length, self.hidden_size]) + self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size]) + + + def create_and_check_albert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): + model = AlbertForMaskedLM(config=config) + model.eval() + loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels) + result = { + "loss": loss, + "prediction_scores": prediction_scores, + } + self.parent.assertListEqual( + list(result["prediction_scores"].size()), + [self.batch_size, self.seq_length, self.vocab_size]) + self.check_loss_output(result) + + + def prepare_config_and_inputs_for_common(self): + config_and_inputs = self.prepare_config_and_inputs() + (config, input_ids, token_type_ids, input_mask, + sequence_labels, token_labels, choice_labels) = config_and_inputs + inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask} + return config, inputs_dict + + def setUp(self): + self.model_tester = AlbertModelTest.AlbertModelTester(self) + self.config_tester = ConfigTester(self, config_class=AlbertConfig, hidden_size=37) + + def test_config(self): + self.config_tester.run_common_tests() + + def test_albert_model(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_albert_model(*config_and_inputs) + + def test_for_masked_lm(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_albert_for_masked_lm(*config_and_inputs) + + @pytest.mark.slow + def test_model_from_pretrained(self): + cache_dir = "/tmp/transformers_test/" + for model_name in list(ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: + model = AlbertModel.from_pretrained(model_name, cache_dir=cache_dir) + shutil.rmtree(cache_dir) + self.assertIsNotNone(model) + +if __name__ == "__main__": + unittest.main() From c14a22272f3fc17bb2eaeca62986c31a7d26bc85 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Thu, 31 Oct 2019 14:04:10 +0000 Subject: [PATCH 050/179] ALBERT passes all tests --- transformers/configuration_albert.py | 4 +--- transformers/modeling_albert.py | 9 +++------ transformers/tests/tokenization_albert_test.py | 2 +- 3 files changed, 5 insertions(+), 10 deletions(-) diff --git a/transformers/configuration_albert.py b/transformers/configuration_albert.py index 15437dbbea..04f9fa8d60 100644 --- a/transformers/configuration_albert.py +++ b/transformers/configuration_albert.py @@ -7,7 +7,7 @@ class AlbertConfig(PretrainedConfig): """ def __init__(self, - vocab_size_or_config_json_file, + vocab_size_or_config_json_file=30000, embedding_size=128, hidden_size=4096, num_hidden_layers=12, @@ -15,7 +15,6 @@ class AlbertConfig(PretrainedConfig): num_attention_heads=64, intermediate_size=16384, inner_group_num=1, - down_scale_factor=1, hidden_act="gelu_new", hidden_dropout_prob=0, attention_probs_dropout_prob=0, @@ -61,7 +60,6 @@ class AlbertConfig(PretrainedConfig): self.num_hidden_groups = num_hidden_groups self.num_attention_heads = num_attention_heads self.inner_group_num = inner_group_num - self.down_scale_factor = down_scale_factor self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index f906352311..9bb38dead9 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -202,17 +202,14 @@ class AlbertLayerGroup(nn.Module): layer_attentions = () for albert_layer in self.albert_layers: - if self.output_hidden_states: - layer_hidden_states = layer_hidden_states + (hidden_states,) - layer_output = albert_layer(hidden_states, attention_mask, head_mask) hidden_states = layer_output[0] if self.output_attentions: layer_attentions = layer_attentions + (layer_output[1],) - if self.output_hidden_states: - layer_hidden_states = layer_hidden_states + (hidden_states,) + if self.output_hidden_states: + layer_hidden_states = layer_hidden_states + (hidden_states,) outputs = (hidden_states,) if self.output_hidden_states: @@ -247,7 +244,7 @@ class AlbertTransformer(nn.Module): hidden_states = layer_group_output[0] if self.output_attentions: - all_attentions = all_attentions + layer_group_output[1] + all_attentions = all_attentions + layer_group_output[-1] if self.output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) diff --git a/transformers/tests/tokenization_albert_test.py b/transformers/tests/tokenization_albert_test.py index dd63f6756b..59eb3bceb0 100644 --- a/transformers/tests/tokenization_albert_test.py +++ b/transformers/tests/tokenization_albert_test.py @@ -22,7 +22,7 @@ from transformers.tokenization_albert import (AlbertTokenizer, SPIECE_UNDERLINE) from .tokenization_tests_commons import CommonTestCases SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), - 'fixtures/30k-clean.model') + 'fixtures/spiece.model') class AlbertTokenizationTest(CommonTestCases.CommonTokenizerTester): From b21402fc86257feca05ab050d061e15441a49929 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Thu, 31 Oct 2019 14:19:31 +0000 Subject: [PATCH 051/179] Python 2 tests + licence --- transformers/modeling_albert.py | 16 ++++++++++++++++ transformers/tokenization_albert.py | 19 ++++++++++++++++++- 2 files changed, 34 insertions(+), 1 deletion(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 9bb38dead9..b6d1291725 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -1,4 +1,20 @@ +# coding=utf-8 +# Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch ALBERT model. """ + import os import math import logging diff --git a/transformers/tokenization_albert.py b/transformers/tokenization_albert.py index 7b16bb573f..7cba99b9e4 100644 --- a/transformers/tokenization_albert.py +++ b/transformers/tokenization_albert.py @@ -1,4 +1,21 @@ - +# coding=utf-8 +# Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Tokenization classes for ALBERT model.""" +from __future__ import (absolute_import, division, print_function, + unicode_literals) + from .tokenization_utils import PreTrainedTokenizer import logging import unicodedata From c4403006b8301a24bfeec99c39ce8d5d47df570f Mon Sep 17 00:00:00 2001 From: Lysandre Date: Thu, 31 Oct 2019 15:30:11 +0000 Subject: [PATCH 052/179] External MLM head --- transformers/configuration_albert.py | 17 ++++++++++++++ transformers/modeling_albert.py | 35 +++++++++++++++++++--------- 2 files changed, 41 insertions(+), 11 deletions(-) diff --git a/transformers/configuration_albert.py b/transformers/configuration_albert.py index 04f9fa8d60..b72bbb971e 100644 --- a/transformers/configuration_albert.py +++ b/transformers/configuration_albert.py @@ -1,3 +1,20 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" ALBERT model configuration """ + from .configuration_utils import PretrainedConfig class AlbertConfig(PretrainedConfig): diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index b6d1291725..487455e561 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -401,6 +401,26 @@ class AlbertModel(BertModel): outputs = (sequence_output, pooled_output) + encoder_outputs[1:] # add hidden_states and attentions if they are here return outputs +class AlbertMLMHead(nn.Module): + def __init__(self, config): + super(AlbertMLMHead, self).__init__() + + self.LayerNorm = nn.LayerNorm(config.embedding_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.dense = nn.Linear(config.hidden_size, config.embedding_size) + self.decoder = nn.Linear(config.embedding_size, config.vocab_size) + self.activation = ACT2FN[config.hidden_act] + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.activation(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + hidden_states = self.decoder(hidden_states) + + prediction_scores = hidden_states + self.bias + + return prediction_scores + @add_start_docstrings("Bert Model with a `language modeling` head on top.", ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) class AlbertForMaskedLM(BertPreTrainedModel): @@ -433,13 +453,8 @@ class AlbertForMaskedLM(BertPreTrainedModel): def __init__(self, config): super(AlbertForMaskedLM, self).__init__(config) - self.config = config self.albert = AlbertModel(config) - self.LayerNorm = nn.LayerNorm(config.embedding_size) - self.bias = nn.Parameter(torch.zeros(config.vocab_size)) - self.dense = nn.Linear(config.hidden_size, config.embedding_size) - self.decoder = nn.Linear(config.embedding_size, config.vocab_size) - self.activation = ACT2FN[config.hidden_act] + self.predictions = AlbertMLMHead(config) self.init_weights() self.tie_weights() @@ -448,17 +463,15 @@ class AlbertForMaskedLM(BertPreTrainedModel): """ Make sure we are sharing the input and output embeddings. Export to TorchScript can't handle parameter sharing so we are cloning them instead. """ - self._tie_or_clone_weights(self.decoder, + self._tie_or_clone_weights(self.predictions.decoder, self.albert.embeddings.word_embeddings) def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, masked_lm_labels=None): outputs = self.albert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None) sequence_outputs = outputs[0] - hidden_states = self.dense(sequence_outputs) - hidden_states = self.activation(hidden_states) - hidden_states = self.LayerNorm(hidden_states) - prediction_scores = self.decoder(hidden_states) + + prediction_scores = self.predictions(sequence_outputs) outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here if masked_lm_labels is not None: From 4f3a54bfc8fa8749f6d5b29f110148738a646fcd Mon Sep 17 00:00:00 2001 From: Lysandre Date: Thu, 31 Oct 2019 16:37:34 +0000 Subject: [PATCH 053/179] ALBERT can load pre-trained models. Doesn't inherit from BERT anymore. --- transformers/__init__.py | 2 +- transformers/configuration_albert.py | 9 ++++++ transformers/modeling_albert.py | 44 +++++++++++++++++++++++----- transformers/tokenization_albert.py | 25 +++++++++++++--- 4 files changed, 68 insertions(+), 12 deletions(-) diff --git a/transformers/__init__.py b/transformers/__init__.py index 152d520e7b..bdfb1a0922 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -107,7 +107,7 @@ if is_torch_available(): CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model - from .modeling_albert import (AlbertModel, AlbertForMaskedLM) + from .modeling_albert import (AlbertModel, AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) # Optimization from .optimization import (AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, diff --git a/transformers/configuration_albert.py b/transformers/configuration_albert.py index b72bbb971e..c35426768f 100644 --- a/transformers/configuration_albert.py +++ b/transformers/configuration_albert.py @@ -17,12 +17,21 @@ from .configuration_utils import PretrainedConfig +ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { + 'albert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-config.json", + 'albert-large': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-config.json", + 'albert-xlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-config.json", + 'albert-xxlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-config.json", +} + class AlbertConfig(PretrainedConfig): """Configuration for `AlbertModel`. The default settings match the configuration of model `albert_xxlarge`. """ + pretrained_config_archive_map = ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP + def __init__(self, vocab_size_or_config_json_file=30000, embedding_size=128, diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 487455e561..4da10ed1cb 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -21,6 +21,7 @@ import logging import torch import torch.nn as nn from torch.nn import CrossEntropyLoss +from transformers.modeling_utils import PreTrainedModel from transformers.configuration_albert import AlbertConfig from transformers.modeling_bert import BertEmbeddings, BertPreTrainedModel, BertModel, BertSelfAttention, prune_linear_layer, ACT2FN from .file_utils import add_start_docstrings @@ -274,6 +275,29 @@ class AlbertTransformer(nn.Module): return outputs # last-layer hidden state, (all hidden states), (all attentions) + +class AlbertPreTrainedModel(PreTrainedModel): + """ An abstract class to handle weights initialization and + a simple interface for dowloading and loading pretrained models. + """ + config_class = AlbertConfig + pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP + base_model_prefix = "albert" + + def _init_weights(self, module): + """ Initialize the weights. + """ + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if isinstance(module, (nn.Linear)) and module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + ALBERT_START_DOCSTRING = r""" The ALBERT model was proposed in `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`_ by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents @@ -338,7 +362,7 @@ ALBERT_INPUTS_DOCSTRING = r""" @add_start_docstrings("The bare ALBERT Model transformer outputting raw hidden-states without any specific head on top.", ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) -class AlbertModel(BertModel): +class AlbertModel(AlbertPreTrainedModel): r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)`` @@ -358,6 +382,12 @@ class AlbertModel(BertModel): list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ + + config_class = AlbertConfig + pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP + load_tf_weights = load_tf_weights_in_albert + base_model_prefix = "albert" + def __init__(self, config): super(AlbertModel, self).__init__(config) @@ -369,6 +399,11 @@ class AlbertModel(BertModel): self.init_weights() + def _resize_token_embeddings(self, new_num_tokens): + old_embeddings = self.embeddings.word_embeddings + new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens) + self.embeddings.word_embeddings = new_embeddings + return self.embeddings.word_embeddings def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): if attention_mask is None: @@ -423,7 +458,7 @@ class AlbertMLMHead(nn.Module): @add_start_docstrings("Bert Model with a `language modeling` head on top.", ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) -class AlbertForMaskedLM(BertPreTrainedModel): +class AlbertForMaskedLM(AlbertPreTrainedModel): r""" **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Labels for computing the masked language modeling loss. @@ -445,11 +480,6 @@ class AlbertForMaskedLM(BertPreTrainedModel): Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ - config_class = AlbertConfig - pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP - load_tf_weights = load_tf_weights_in_albert - base_model_prefix = "albert" - def __init__(self, config): super(AlbertForMaskedLM, self).__init__(config) diff --git a/transformers/tokenization_albert.py b/transformers/tokenization_albert.py index 7cba99b9e4..acf67c1154 100644 --- a/transformers/tokenization_albert.py +++ b/transformers/tokenization_albert.py @@ -15,7 +15,7 @@ """ Tokenization classes for ALBERT model.""" from __future__ import (absolute_import, division, print_function, unicode_literals) - + from .tokenization_utils import PreTrainedTokenizer import logging import unicodedata @@ -24,8 +24,25 @@ import os from shutil import copyfile logger = logging.getLogger(__name__) - VOCAB_FILES_NAMES = {'vocab_file': 'spiece.model'} + +PRETRAINED_VOCAB_FILES_MAP = { + 'vocab_file': + { + 'albert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-spiece.model", + 'albert-large': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-spiece.model", + 'albert-xlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-spiece.model", + 'albert-xxlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-spiece.model", + } +} + +PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { + 'albert-base': 512, + 'albert-large': 512, + 'albert-xlarge': 512, + 'albert-xxlarge': 512, +} + SPIECE_UNDERLINE = u'▁' class AlbertTokenizer(PreTrainedTokenizer): @@ -35,8 +52,8 @@ class AlbertTokenizer(PreTrainedTokenizer): - requires `SentencePiece `_ """ vocab_files_names = VOCAB_FILES_NAMES - # pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP - # max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES + pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP + max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__(self, vocab_file, do_lower_case=True, remove_space=True, keep_accents=False, From c9875455929a63f12b81e2dcc8fe30f72137c06b Mon Sep 17 00:00:00 2001 From: Lysandre Date: Thu, 31 Oct 2019 18:48:02 +0000 Subject: [PATCH 054/179] Converting script --- transformers/__init__.py | 2 +- ...lbert_original_tf_checkpoint_to_pytorch.py | 36 +++++++++++++++---- 2 files changed, 30 insertions(+), 8 deletions(-) diff --git a/transformers/__init__.py b/transformers/__init__.py index bdfb1a0922..db98d5fd44 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -107,7 +107,7 @@ if is_torch_available(): CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model - from .modeling_albert import (AlbertModel, AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) + from .modeling_albert import (AlbertModel, AlbertForMaskedLM, load_tf_weights_in_albert, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) # Optimization from .optimization import (AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, diff --git a/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py b/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py index 04877d41b9..5bbaab8c21 100644 --- a/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py +++ b/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py @@ -1,18 +1,39 @@ +# coding=utf-8 +# Copyright 2018 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert ALBERT checkpoint.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import argparse +import torch -from transformers import AlbertConfig, BertForPreTraining, load_tf_weights_in_bert - +from transformers import AlbertConfig, AlbertForMaskedLM, load_tf_weights_in_albert +import logging +logging.basicConfig(level=logging.INFO) def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path): # Initialise PyTorch model - config = BertConfig.from_json_file(bert_config_file) + config = AlbertConfig.from_json_file(bert_config_file) print("Building PyTorch model from configuration: {}".format(str(config))) - model = BertForPreTraining(config) + model = AlbertForMaskedLM(config) # Load weights from tf checkpoint - load_tf_weights_in_bert(model, config, tf_checkpoint_path) + load_tf_weights_in_albert(model, config, tf_checkpoint_path) # Save pytorch-model print("Save PyTorch model to {}".format(pytorch_dump_path)) @@ -31,7 +52,7 @@ if __name__ == "__main__": default = None, type = str, required = True, - help = "The config json file corresponding to the pre-trained BERT model. \n" + help = "The config json file corresponding to the pre-trained ALBERT model. \n" "This specifies the model architecture.") parser.add_argument("--pytorch_dump_path", default = None, @@ -40,5 +61,6 @@ if __name__ == "__main__": help = "Path to the output PyTorch model.") args = parser.parse_args() convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, - args.bert_config_file, + args.albert_config_file, args.pytorch_dump_path) + \ No newline at end of file From 0d07a23c04c9837234cda402b32246d7581e5bc4 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Fri, 1 Nov 2019 15:07:01 +0000 Subject: [PATCH 055/179] LAMB implementation --- transformers/optimization.py | 93 ++++++++++++++++++++++++++++++++++++ 1 file changed, 93 insertions(+) diff --git a/transformers/optimization.py b/transformers/optimization.py index 99e6cc75e4..90f3dbca9b 100644 --- a/transformers/optimization.py +++ b/transformers/optimization.py @@ -167,3 +167,96 @@ class AdamW(Optimizer): p.data.add_(-group['lr'] * group['weight_decay'], p.data) return loss + + + +class Lamb(Optimizer): + """ Implements the LAMB algorithm (Layer-wise Adaptive Moments optimizer for Batch training). + + Adapted from the huggingface/transformers ADAM optimizer + Inspired from the Google Research implementation available in ALBERT: https://github.com/google-research/google-research/blob/master/albert/lamb_optimizer.py + Inspired from cybertronai's PyTorch LAMB implementation: https://github.com/cybertronai/pytorch-lamb/blob/master/pytorch_lamb/lamb.py + + + Parameters: + lr (float): learning rate. Default 1e-3. + betas (tuple of 2 floats): Adams beta parameters (b1, b2). Default: (0.9, 0.999) + eps (float): Adams epsilon. Default: 1e-6 + weight_decay (float): Weight decay. Default: 0.0 + """ + + def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6, weight_decay=0.0, correct_bias=True): + if lr < 0.0: + raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[1])) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(eps)) + defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, + correct_bias=correct_bias) + super(Lamb, self).__init__(params, defaults) + + def step(self, closure=None): + """Performs a single optimization step. + + Arguments: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group['params']: + if p.grad is None: + continue + grad = p.grad.data + if grad.is_sparse: + raise RuntimeError('LAMB does not support sparse gradients.') + + state = self.state[p] + + # State initialization + if len(state) == 0: + state['step'] = 0 + # Exponential moving average of gradient values + state['exp_avg'] = torch.zeros_like(p.data) + # Exponential moving average of squared gradient values + state['exp_avg_sq'] = torch.zeros_like(p.data) + + exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] + beta1, beta2 = group['betas'] + + state['step'] += 1 + + # Decay the first and second moment running average coefficient + # In-place operations to update the averages at the same time + exp_avg.mul_(beta1).add_(1.0 - beta1, grad) + exp_avg_sq.mul_(beta2).addcmul_(1.0 - beta2, grad, grad) + denom = exp_avg_sq.sqrt().add_(group['eps']) + + + # Inspired from cybertronai's PyTorch LAMB implementation: https://github.com/cybertronai/pytorch-lamb/blob/master/pytorch_lamb/lamb.py + step_size = group['lr'] + weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10) + + adam_step = exp_avg / exp_avg_sq.sqrt().add(group['eps']) + if group['weight_decay'] != 0: + adam_step.add_(group['weight_decay'], p.data) + + adam_norm = adam_step.pow(2).sum().sqrt() + if weight_norm == 0 or adam_norm == 0: + trust_ratio = 1 + else: + trust_ratio = weight_norm / adam_norm + + + state['weight_norm'] = weight_norm + state['adam_norm'] = adam_norm + state['trust_ratio'] = trust_ratio + + p.data.add_(-step_size * trust_ratio, adam_step) + return loss From 6637a77f807615ef2427c0390a015f4eb4814fb4 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Fri, 1 Nov 2019 15:17:31 +0000 Subject: [PATCH 056/179] AlbertForSequenceClassification --- transformers/__init__.py | 3 +- transformers/modeling_albert.py | 77 ++++++++++++++++++++++++++++++++- 2 files changed, 77 insertions(+), 3 deletions(-) diff --git a/transformers/__init__.py b/transformers/__init__.py index db98d5fd44..51995942ce 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -107,7 +107,8 @@ if is_torch_available(): CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model - from .modeling_albert import (AlbertModel, AlbertForMaskedLM, load_tf_weights_in_albert, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) + from .modeling_albert import (AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, + load_tf_weights_in_albert, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) # Optimization from .optimization import (AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 4da10ed1cb..bba6767079 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -20,10 +20,10 @@ import math import logging import torch import torch.nn as nn -from torch.nn import CrossEntropyLoss +from torch.nn import CrossEntropyLoss, MSELoss from transformers.modeling_utils import PreTrainedModel from transformers.configuration_albert import AlbertConfig -from transformers.modeling_bert import BertEmbeddings, BertPreTrainedModel, BertModel, BertSelfAttention, prune_linear_layer, ACT2FN +from transformers.modeling_bert import BertEmbeddings, BertSelfAttention, prune_linear_layer, ACT2FN from .file_utils import add_start_docstrings logger = logging.getLogger(__name__) @@ -510,3 +510,76 @@ class AlbertForMaskedLM(AlbertPreTrainedModel): outputs = (masked_lm_loss,) + outputs return outputs + + +@add_start_docstrings("""Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of + the pooled output) e.g. for GLUE tasks. """, + ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) +class AlbertForSequenceClassification(AlbertPreTrainedModel): + r""" + **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: + Labels for computing the sequence classification/regression loss. + Indices should be in ``[0, ..., config.num_labels - 1]``. + If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss), + If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy). + + Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: + **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: + Classification (or regression if config.num_labels==1) loss. + **logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)`` + Classification (or regression if config.num_labels==1) scores (before SoftMax). + **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) + list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) + of shape ``(batch_size, sequence_length, hidden_size)``: + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + **attentions**: (`optional`, returned when ``config.output_attentions=True``) + list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + + Examples:: + + tokenizer = AlbertTokenizer.from_pretrained('albert-base') + model = AlbertForSequenceClassification.from_pretrained('albert-base') + input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 + labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 + outputs = model(input_ids, labels=labels) + loss, logits = outputs[:2] + + """ + def __init__(self, config): + super(AlbertForSequenceClassification, self).__init__(config) + self.num_labels = config.num_labels + + self.albert = AlbertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, self.config.num_labels) + + self.init_weights() + + def forward(self, input_ids, attention_mask=None, token_type_ids=None, + position_ids=None, head_mask=None, labels=None): + + outputs = self.albert(input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask) + + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + + outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here + + if labels is not None: + if self.num_labels == 1: + # We are doing regression + loss_fct = MSELoss() + loss = loss_fct(logits.view(-1), labels.view(-1)) + else: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + outputs = (loss,) + outputs + + return outputs # (loss), logits, (hidden_states), (attentions) \ No newline at end of file From c110c41fdb3b363528336ceda3b9fb46f026ad9c Mon Sep 17 00:00:00 2001 From: Lysandre Date: Fri, 1 Nov 2019 21:59:40 +0000 Subject: [PATCH 057/179] Run GLUE and remove LAMB --- examples/run_glue.py | 14 ++++-- transformers/optimization.py | 93 ------------------------------------ 2 files changed, 10 insertions(+), 97 deletions(-) diff --git a/examples/run_glue.py b/examples/run_glue.py index 527e440075..550a0b8175 100644 --- a/examples/run_glue.py +++ b/examples/run_glue.py @@ -47,7 +47,11 @@ from transformers import (WEIGHTS_NAME, BertConfig, XLNetTokenizer, DistilBertConfig, DistilBertForSequenceClassification, - DistilBertTokenizer) + DistilBertTokenizer, + AlbertConfig, + AlbertForSequenceClassification, + AlbertTokenizer, + ) from transformers import AdamW, get_linear_schedule_with_warmup @@ -66,7 +70,8 @@ MODEL_CLASSES = { 'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer), 'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer), 'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer), - 'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer) + 'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer), + 'albert': (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer) } @@ -99,6 +104,7 @@ def train(args, train_dataset, model, tokenizer): {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay}, {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] + optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total) if args.fp16: @@ -317,7 +323,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False): all_labels = torch.tensor([f.label for f in features], dtype=torch.long) elif output_mode == "regression": all_labels = torch.tensor([f.label for f in features], dtype=torch.float) - + dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels) return dataset @@ -361,7 +367,7 @@ def main(): parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.") parser.add_argument('--gradient_accumulation_steps', type=int, default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.") + help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--weight_decay", default=0.0, type=float, diff --git a/transformers/optimization.py b/transformers/optimization.py index 90f3dbca9b..99e6cc75e4 100644 --- a/transformers/optimization.py +++ b/transformers/optimization.py @@ -167,96 +167,3 @@ class AdamW(Optimizer): p.data.add_(-group['lr'] * group['weight_decay'], p.data) return loss - - - -class Lamb(Optimizer): - """ Implements the LAMB algorithm (Layer-wise Adaptive Moments optimizer for Batch training). - - Adapted from the huggingface/transformers ADAM optimizer - Inspired from the Google Research implementation available in ALBERT: https://github.com/google-research/google-research/blob/master/albert/lamb_optimizer.py - Inspired from cybertronai's PyTorch LAMB implementation: https://github.com/cybertronai/pytorch-lamb/blob/master/pytorch_lamb/lamb.py - - - Parameters: - lr (float): learning rate. Default 1e-3. - betas (tuple of 2 floats): Adams beta parameters (b1, b2). Default: (0.9, 0.999) - eps (float): Adams epsilon. Default: 1e-6 - weight_decay (float): Weight decay. Default: 0.0 - """ - - def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6, weight_decay=0.0, correct_bias=True): - if lr < 0.0: - raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr)) - if not 0.0 <= betas[0] < 1.0: - raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[0])) - if not 0.0 <= betas[1] < 1.0: - raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[1])) - if not 0.0 <= eps: - raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(eps)) - defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, - correct_bias=correct_bias) - super(Lamb, self).__init__(params, defaults) - - def step(self, closure=None): - """Performs a single optimization step. - - Arguments: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group['params']: - if p.grad is None: - continue - grad = p.grad.data - if grad.is_sparse: - raise RuntimeError('LAMB does not support sparse gradients.') - - state = self.state[p] - - # State initialization - if len(state) == 0: - state['step'] = 0 - # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p.data) - # Exponential moving average of squared gradient values - state['exp_avg_sq'] = torch.zeros_like(p.data) - - exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] - beta1, beta2 = group['betas'] - - state['step'] += 1 - - # Decay the first and second moment running average coefficient - # In-place operations to update the averages at the same time - exp_avg.mul_(beta1).add_(1.0 - beta1, grad) - exp_avg_sq.mul_(beta2).addcmul_(1.0 - beta2, grad, grad) - denom = exp_avg_sq.sqrt().add_(group['eps']) - - - # Inspired from cybertronai's PyTorch LAMB implementation: https://github.com/cybertronai/pytorch-lamb/blob/master/pytorch_lamb/lamb.py - step_size = group['lr'] - weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10) - - adam_step = exp_avg / exp_avg_sq.sqrt().add(group['eps']) - if group['weight_decay'] != 0: - adam_step.add_(group['weight_decay'], p.data) - - adam_norm = adam_step.pow(2).sum().sqrt() - if weight_norm == 0 or adam_norm == 0: - trust_ratio = 1 - else: - trust_ratio = weight_norm / adam_norm - - - state['weight_norm'] = weight_norm - state['adam_norm'] = adam_norm - state['trust_ratio'] = trust_ratio - - p.data.add_(-step_size * trust_ratio, adam_step) - return loss From 70d99980ded1565c9e8efa2cd04c21572b664f2f Mon Sep 17 00:00:00 2001 From: Lysandre Date: Mon, 4 Nov 2019 11:34:30 -0500 Subject: [PATCH 058/179] ALBERT-V2 --- transformers/configuration_albert.py | 12 ++++++---- ...lbert_original_tf_checkpoint_to_pytorch.py | 5 ++-- transformers/modeling_albert.py | 16 ++++++++----- transformers/tokenization_albert.py | 24 ++++++++++++------- 4 files changed, 37 insertions(+), 20 deletions(-) diff --git a/transformers/configuration_albert.py b/transformers/configuration_albert.py index c35426768f..de665c9b1c 100644 --- a/transformers/configuration_albert.py +++ b/transformers/configuration_albert.py @@ -18,10 +18,14 @@ from .configuration_utils import PretrainedConfig ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { - 'albert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-config.json", - 'albert-large': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-config.json", - 'albert-xlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-config.json", - 'albert-xxlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-config.json", + 'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-config.json", + 'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-config.json", + 'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-config.json", + 'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-config.json", + 'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-config.json", + 'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-config.json", + 'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-config.json", + 'albert-xxlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-config.json", } class AlbertConfig(PretrainedConfig): diff --git a/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py b/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py index 5bbaab8c21..b6476b4fb6 100644 --- a/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py +++ b/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py @@ -26,9 +26,10 @@ from transformers import AlbertConfig, AlbertForMaskedLM, load_tf_weights_in_alb import logging logging.basicConfig(level=logging.INFO) -def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path): + +def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path): # Initialise PyTorch model - config = AlbertConfig.from_json_file(bert_config_file) + config = AlbertConfig.from_json_file(albert_config_file) print("Building PyTorch model from configuration: {}".format(str(config))) model = AlbertForMaskedLM(config) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index bba6767079..51cb0a6d23 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -30,10 +30,14 @@ logger = logging.getLogger(__name__) ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = { - 'albert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-pytorch_model.bin", - 'albert-large': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-pytorch_model.bin", - 'albert-xlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-pytorch_model.bin", - 'albert-xxlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-pytorch_model.bin", + 'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-pytorch_model.bin", + 'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-pytorch_model.bin", + 'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-pytorch_model.bin", + 'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-pytorch_model.bin", + 'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-pytorch_model.bin", + 'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-pytorch_model.bin", + 'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-pytorch_model.bin", + 'albert-xxlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-pytorch_model.bin", } @@ -538,8 +542,8 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel): Examples:: - tokenizer = AlbertTokenizer.from_pretrained('albert-base') - model = AlbertForSequenceClassification.from_pretrained('albert-base') + tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') + model = AlbertForSequenceClassification.from_pretrained('albert-base-v2') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=labels) diff --git a/transformers/tokenization_albert.py b/transformers/tokenization_albert.py index acf67c1154..2f9af0b0bc 100644 --- a/transformers/tokenization_albert.py +++ b/transformers/tokenization_albert.py @@ -29,18 +29,26 @@ VOCAB_FILES_NAMES = {'vocab_file': 'spiece.model'} PRETRAINED_VOCAB_FILES_MAP = { 'vocab_file': { - 'albert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-spiece.model", - 'albert-large': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-spiece.model", - 'albert-xlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-spiece.model", - 'albert-xxlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-spiece.model", + 'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-spiece.model", + 'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-spiece.model", + 'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-spiece.model", + 'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-spiece.model", + 'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-spiece.model", + 'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-spiece.model", + 'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-spiece.model", + 'albert-xxlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-spiece.model", } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { - 'albert-base': 512, - 'albert-large': 512, - 'albert-xlarge': 512, - 'albert-xxlarge': 512, + 'albert-base-v1': 512, + 'albert-large-v1': 512, + 'albert-xlarge-v1': 512, + 'albert-xxlarge-v1': 512, + 'albert-base-v2': 512, + 'albert-large-v2': 512, + 'albert-xlarge-v2': 512, + 'albert-xxlarge-v2': 512, } SPIECE_UNDERLINE = u'▁' From 4374eaea786399a948b4b34d39fc614ded5e1de6 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 6 Nov 2019 20:47:42 +0000 Subject: [PATCH 059/179] ALBERT for SQuAD --- examples/run_squad.py | 12 +++-- transformers/__init__.py | 1 + transformers/modeling_albert.py | 93 ++++++++++++++++++++++++++++++++- 3 files changed, 100 insertions(+), 6 deletions(-) diff --git a/examples/run_squad.py b/examples/run_squad.py index 69088d73c3..59683c0668 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -43,7 +43,8 @@ from transformers import (WEIGHTS_NAME, BertConfig, XLMTokenizer, XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer, - DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer) + DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer, + AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer) from transformers import AdamW, get_linear_schedule_with_warmup @@ -65,7 +66,8 @@ MODEL_CLASSES = { 'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer), 'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer), 'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer), - 'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer) + 'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer), + 'albert': (AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer) } def set_seed(args): @@ -128,7 +130,7 @@ def train(args, train_dataset, model, tokenizer): logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) - global_step = 0 + global_step = 1 tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) @@ -537,7 +539,7 @@ def main(): torch.save(args, os.path.join(args.output_dir, 'training_args.bin')) # Load a trained model and vocabulary that you have fine-tuned - model = model_class.from_pretrained(args.output_dir) + model = model_class.from_pretrained(args.output_dir, force_download=True) tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) model.to(args.device) @@ -555,7 +557,7 @@ def main(): for checkpoint in checkpoints: # Reload the model global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" - model = model_class.from_pretrained(checkpoint) + model = model_class.from_pretrained(checkpoint, force_download=True) model.to(args.device) # Evaluate diff --git a/transformers/__init__.py b/transformers/__init__.py index 51995942ce..81e659329d 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -108,6 +108,7 @@ if is_torch_available(): from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model from .modeling_albert import (AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, + AlbertForQuestionAnswering, load_tf_weights_in_albert, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) # Optimization diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 51cb0a6d23..2540218e69 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -586,4 +586,95 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel): loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) outputs = (loss,) + outputs - return outputs # (loss), logits, (hidden_states), (attentions) \ No newline at end of file + return outputs # (loss), logits, (hidden_states), (attentions) + + + +@add_start_docstrings("""Albert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of + the hidden-states output to compute `span start logits` and `span end logits`). """, + ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) +class AlbertForQuestionAnswering(AlbertPreTrainedModel): + r""" + **start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). + Position outside of the sequence are not taken into account for computing the loss. + **end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). + Position outside of the sequence are not taken into account for computing the loss. + + Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: + **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: + Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. + **start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)`` + Span-start scores (before SoftMax). + **end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)`` + Span-end scores (before SoftMax). + **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) + list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) + of shape ``(batch_size, sequence_length, hidden_size)``: + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + **attentions**: (`optional`, returned when ``config.output_attentions=True``) + list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + + Examples:: + + tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') + model = AlbertForQuestionAnswering.from_pretrained('albert-base-v2') + question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" + input_text = "[CLS] " + question + " [SEP] " + text + " [SEP]" + input_ids = tokenizer.encode(input_text) + token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))] + start_scores, end_scores = model(torch.tensor([input_ids]), token_type_ids=torch.tensor([token_type_ids])) + all_tokens = tokenizer.convert_ids_to_tokens(input_ids) + print(' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])) + # a nice puppet + + + """ + def __init__(self, config): + super(AlbertForQuestionAnswering, self).__init__(config) + self.num_labels = config.num_labels + + self.albert = AlbertModel(config) + self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) + + self.init_weights() + + def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, + start_positions=None, end_positions=None): + + outputs = self.albert(input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1) + end_logits = end_logits.squeeze(-1) + + outputs = (start_logits, end_logits,) + outputs[2:] + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions.clamp_(0, ignored_index) + end_positions.clamp_(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + outputs = (total_loss,) + outputs + + return outputs # (loss), start_logits, end_logits, (hidden_states), (attentions) From abb23a78bab17ec09dde4635c32f4aa21c15fa83 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Thu, 7 Nov 2019 17:09:16 +0000 Subject: [PATCH 060/179] Head pruning for ALBERT --- transformers/modeling_albert.py | 42 ++++++++++++++++++++++ transformers/tests/modeling_albert_test.py | 12 ++++--- 2 files changed, 49 insertions(+), 5 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 2540218e69..89ece4b61e 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -145,6 +145,29 @@ class AlbertAttention(BertSelfAttention): self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pruned_heads = set() + def prune_heads(self, heads): + if len(heads) == 0: + return + mask = torch.ones(self.num_attention_heads, self.attention_head_size) + heads = set(heads) - self.pruned_heads # Convert to set and emove already pruned heads + for head in heads: + # Compute how many pruned heads are before the head and move the index accordingly + head = head - sum(1 if h < head else 0 for h in self.pruned_heads) + mask[head] = 0 + mask = mask.view(-1).contiguous().eq(1) + index = torch.arange(len(mask))[mask].long() + + # Prune linear layers + self.query = prune_linear_layer(self.query, index) + self.key = prune_linear_layer(self.key, index) + self.value = prune_linear_layer(self.value, index) + self.dense = prune_linear_layer(self.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.num_attention_heads = self.num_attention_heads - len(heads) + self.all_head_size = self.attention_head_size * self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + def forward(self, input_ids, attention_mask=None, head_mask=None): mixed_query_layer = self.query(input_ids) mixed_key_layer = self.key(input_ids) @@ -409,6 +432,25 @@ class AlbertModel(AlbertPreTrainedModel): self.embeddings.word_embeddings = new_embeddings return self.embeddings.word_embeddings + def _prune_heads(self, heads_to_prune): + """ Prunes heads of the model. + heads_to_prune: dict of {layer_num: list of heads to prune in this layer} + ALBERT has a different architecture in that its layers are shared across groups, which then has inner groups. + If an ALBERT model has 12 hidden layers and 2 hidden groups, with two inner groups, there + is a total of 4 different layers. + + These layers are flattened: the indices [0,1] correspond to the two inner groups of the first hidden layer, + while [2,3] correspond to the two inner groups of the second hidden layer. + + Any layer with in index other than [0,1,2,3] will result in an error. + See base class PreTrainedModel for more information about head pruning + """ + for layer, heads in heads_to_prune.items(): + group_idx = int(layer / self.config.inner_group_num) + inner_group_idx = int(layer - group_idx * self.config.inner_group_num) + self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads) + + def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): if attention_mask is None: attention_mask = torch.ones_like(input_ids) diff --git a/transformers/tests/modeling_albert_test.py b/transformers/tests/modeling_albert_test.py index 46a2eeb729..979e0488eb 100644 --- a/transformers/tests/modeling_albert_test.py +++ b/transformers/tests/modeling_albert_test.py @@ -35,7 +35,6 @@ else: class AlbertModelTest(CommonTestCases.CommonModelTester): all_model_classes = (AlbertModel, AlbertForMaskedLM) if is_torch_available() else () - test_pruning = False test_head_masking = False class AlbertModelTester(object): @@ -49,9 +48,10 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): use_token_type_ids=True, use_labels=True, vocab_size=99, - hidden_size=32, - num_hidden_layers=5, - num_attention_heads=4, + hidden_size=36, + num_hidden_layers=6, + num_hidden_groups=6, + num_attention_heads=6, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, @@ -86,6 +86,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): self.num_labels = num_labels self.num_choices = num_choices self.scope = scope + self.num_hidden_groups = num_hidden_groups def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) @@ -117,7 +118,8 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, - initializer_range=self.initializer_range) + initializer_range=self.initializer_range, + num_hidden_groups=self.num_hidden_groups) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels From 16263f9685eaf459408f33c9790a967012b93fa5 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Thu, 7 Nov 2019 17:29:29 +0000 Subject: [PATCH 061/179] Headmasking --- transformers/modeling_albert.py | 11 ++++++----- transformers/tests/modeling_albert_test.py | 1 - 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 89ece4b61e..6682930d89 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -224,7 +224,7 @@ class AlbertLayer(nn.Module): self.activation = ACT2FN[config.hidden_act] def forward(self, hidden_states, attention_mask=None, head_mask=None): - attention_output = self.attention(hidden_states, attention_mask) + attention_output = self.attention(hidden_states, attention_mask, head_mask) ffn_output = self.ffn(attention_output[0]) ffn_output = self.activation(ffn_output) ffn_output = self.ffn_output(ffn_output) @@ -245,8 +245,8 @@ class AlbertLayerGroup(nn.Module): layer_hidden_states = () layer_attentions = () - for albert_layer in self.albert_layers: - layer_output = albert_layer(hidden_states, attention_mask, head_mask) + for layer_index, albert_layer in enumerate(self.albert_layers): + layer_output = albert_layer(hidden_states, attention_mask, head_mask[layer_index]) hidden_states = layer_output[0] if self.output_attentions: @@ -283,7 +283,8 @@ class AlbertTransformer(nn.Module): for layer_idx in range(self.config.num_hidden_layers): group_idx = int(layer_idx / self.config.num_hidden_layers * self.config.num_hidden_groups) - layer_group_output = self.albert_layer_groups[group_idx](hidden_states, attention_mask, head_mask) + layers_per_group = int(self.config.num_hidden_layers / self.config.num_hidden_groups) + layer_group_output = self.albert_layer_groups[group_idx](hidden_states, attention_mask, head_mask[group_idx*layers_per_group:(group_idx+1)*layers_per_group]) hidden_states = layer_group_output[0] @@ -544,7 +545,7 @@ class AlbertForMaskedLM(AlbertPreTrainedModel): def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, masked_lm_labels=None): - outputs = self.albert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None) + outputs = self.albert(input_ids, attention_mask, token_type_ids, position_ids, head_mask) sequence_outputs = outputs[0] prediction_scores = self.predictions(sequence_outputs) diff --git a/transformers/tests/modeling_albert_test.py b/transformers/tests/modeling_albert_test.py index 979e0488eb..466f473332 100644 --- a/transformers/tests/modeling_albert_test.py +++ b/transformers/tests/modeling_albert_test.py @@ -35,7 +35,6 @@ else: class AlbertModelTest(CommonTestCases.CommonModelTester): all_model_classes = (AlbertModel, AlbertForMaskedLM) if is_torch_available() else () - test_head_masking = False class AlbertModelTester(object): From 9d5c49546fedb3d52f76f97f4043a07e08ded918 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Thu, 7 Nov 2019 17:32:52 +0000 Subject: [PATCH 062/179] Tests for AlbertForQuestionAnswering AlbertForSequenceClassification --- transformers/tests/modeling_albert_test.py | 45 +++++++++++++++++++++- 1 file changed, 44 insertions(+), 1 deletion(-) diff --git a/transformers/tests/modeling_albert_test.py b/transformers/tests/modeling_albert_test.py index 466f473332..da87709df1 100644 --- a/transformers/tests/modeling_albert_test.py +++ b/transformers/tests/modeling_albert_test.py @@ -26,7 +26,9 @@ from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester if is_torch_available(): - from transformers import (AlbertConfig, AlbertModel, AlbertForMaskedLM) + from transformers import (AlbertConfig, AlbertModel, AlbertForMaskedLM, + AlbertForSequenceClassification, AlbertForQuestionAnswering, + ) from transformers.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP else: pytestmark = pytest.mark.skip("Require Torch") @@ -157,6 +159,39 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): [self.batch_size, self.seq_length, self.vocab_size]) self.check_loss_output(result) + def create_and_check_albert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): + model = AlbertForQuestionAnswering(config=config) + model.eval() + loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, + start_positions=sequence_labels, end_positions=sequence_labels) + result = { + "loss": loss, + "start_logits": start_logits, + "end_logits": end_logits, + } + self.parent.assertListEqual( + list(result["start_logits"].size()), + [self.batch_size, self.seq_length]) + self.parent.assertListEqual( + list(result["end_logits"].size()), + [self.batch_size, self.seq_length]) + self.check_loss_output(result) + + + def create_and_check_albert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): + config.num_labels = self.num_labels + model = AlbertForSequenceClassification(config) + model.eval() + loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) + result = { + "loss": loss, + "logits": logits, + } + self.parent.assertListEqual( + list(result["logits"].size()), + [self.batch_size, self.num_labels]) + self.check_loss_output(result) + def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() @@ -180,6 +215,14 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_albert_for_masked_lm(*config_and_inputs) + def test_for_question_answering(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_albert_for_question_answering(*config_and_inputs) + + def test_for_sequence_classification(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_albert_for_sequence_classification(*config_and_inputs) + @pytest.mark.slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" From d9daad98c744e115bfb425f316a5e7d4f405a9a5 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Thu, 7 Nov 2019 19:55:43 +0000 Subject: [PATCH 063/179] Re-ordering of group_idx/layer_idx + Python 2 tests --- transformers/modeling_albert.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 6682930d89..640af537d0 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -281,11 +281,17 @@ class AlbertTransformer(nn.Module): if self.output_hidden_states: all_hidden_states = (hidden_states,) - for layer_idx in range(self.config.num_hidden_layers): - group_idx = int(layer_idx / self.config.num_hidden_layers * self.config.num_hidden_groups) + for i in range(self.config.num_hidden_layers): + # Number of layers in a hidden group layers_per_group = int(self.config.num_hidden_layers / self.config.num_hidden_groups) + + # Index of the hidden group + group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups)) + + # Index of the layer inside the group + layer_idx = int(i - group_idx * layers_per_group) + layer_group_output = self.albert_layer_groups[group_idx](hidden_states, attention_mask, head_mask[group_idx*layers_per_group:(group_idx+1)*layers_per_group]) - hidden_states = layer_group_output[0] if self.output_attentions: From f6f382532bf40a5869dc14e3eefa451646c19ded Mon Sep 17 00:00:00 2001 From: Lysandre Date: Thu, 7 Nov 2019 23:40:45 +0000 Subject: [PATCH 064/179] ALBERT in TF2 --- transformers/__init__.py | 6 +- .../convert_pytorch_checkpoint_to_tf2.py | 13 +- transformers/modeling_tf_albert.py | 723 ++++++++++++++++++ 3 files changed, 736 insertions(+), 6 deletions(-) create mode 100644 transformers/modeling_tf_albert.py diff --git a/transformers/__init__.py b/transformers/__init__.py index 81e659329d..a409ef772e 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -58,8 +58,7 @@ from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP from .configuration_xlm import XLMConfig, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP -from .configuration_albert import AlbertConfig, ALBERT -from .configuration_albert import AlbertConfig +from .configuration_albert import AlbertConfig, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP from .configuration_camembert import CamembertConfig, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP # Modeling @@ -169,6 +168,9 @@ if is_tf_available(): TFCTRLLMHeadModel, TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP) + from .modeling_tf_albert import (TFAlbertPreTrainedModel, TFAlbertModel, TFAlbertForMaskedLM, + TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) + # TF 2.0 <=> PyTorch conversion utilities from .modeling_tf_pytorch_utils import (convert_tf_weight_name_to_pt_weight_name, load_pytorch_checkpoint_in_tf2_model, diff --git a/transformers/convert_pytorch_checkpoint_to_tf2.py b/transformers/convert_pytorch_checkpoint_to_tf2.py index e673b77dcc..d1776e9c14 100644 --- a/transformers/convert_pytorch_checkpoint_to_tf2.py +++ b/transformers/convert_pytorch_checkpoint_to_tf2.py @@ -33,7 +33,8 @@ from transformers import (load_pytorch_checkpoint_in_tf2_model, OpenAIGPTConfig, TFOpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, - CTRLConfig, TFCTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP) + CTRLConfig, TFCTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, + AlbertConfig, TFAlbertForMaskedLM, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP) if is_torch_available(): import torch @@ -46,7 +47,8 @@ if is_torch_available(): OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP, RobertaForMaskedLM, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP, DistilBertForMaskedLM, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP, - CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP) + CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, + AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) else: (BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, @@ -56,7 +58,8 @@ else: OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP, RobertaForMaskedLM, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP, DistilBertForMaskedLM, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP, - CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP) = ( + CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, + AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) = ( None, None, None, None, None, None, None, None, @@ -65,6 +68,7 @@ else: None, None, None, None, None, None, None, None, + None, None, None, None) @@ -85,7 +89,8 @@ MODEL_CLASSES = { 'roberta-large-mnli': (RobertaConfig, TFRobertaForSequenceClassification, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP), 'distilbert': (DistilBertConfig, TFDistilBertForMaskedLM, DistilBertForMaskedLM, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP), 'distilbert-base-uncased-distilled-squad': (DistilBertConfig, TFDistilBertForQuestionAnswering, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP), - 'ctrl': (CTRLConfig, TFCTRLLMHeadModel, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP) + 'ctrl': (CTRLConfig, TFCTRLLMHeadModel, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP), + 'albert': (AlbertConfig, TFAlbertForMaskedLM, AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP) } def convert_pt_checkpoint_to_tf(model_type, pytorch_checkpoint_path, config_file, tf_dump_path, compare_with_pt_model=False, use_cached_models=True): diff --git a/transformers/modeling_tf_albert.py b/transformers/modeling_tf_albert.py new file mode 100644 index 0000000000..8861b7add8 --- /dev/null +++ b/transformers/modeling_tf_albert.py @@ -0,0 +1,723 @@ +# coding=utf-8 +# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" TF 2.0 ALBERT model. """ +from __future__ import absolute_import, division, print_function, unicode_literals + +import json +import logging +import math +import os +import sys +from io import open + +import numpy as np +import tensorflow as tf + +from .configuration_albert import AlbertConfig +from .modeling_tf_utils import TFPreTrainedModel, get_initializer +from .modeling_tf_bert import ACT2FN, TFBertSelfAttention +from .file_utils import add_start_docstrings + +import logging + +logger = logging.getLogger(__name__) + +TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = { + # TODO FILL THAT UP +} + + +class TFAlbertEmbeddings(tf.keras.layers.Layer): + """Construct the embeddings from word, position and token_type embeddings. + """ + + def __init__(self, config, **kwargs): + super(TFAlbertEmbeddings, self).__init__(**kwargs) + + self.config = config + self.position_embeddings = tf.keras.layers.Embedding(config.max_position_embeddings, + config.embedding_size, + embeddings_initializer=get_initializer( + self.config.initializer_range), + name='position_embeddings') + self.token_type_embeddings = tf.keras.layers.Embedding(config.type_vocab_size, + config.embedding_size, + embeddings_initializer=get_initializer( + self.config.initializer_range), + name='token_type_embeddings') + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = tf.keras.layers.LayerNormalization( + epsilon=config.layer_norm_eps, name='LayerNorm') + self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) + + def build(self, input_shape): + """Build shared word embedding layer """ + with tf.name_scope("word_embeddings"): + # Create and initialize weights. The random normal initializer was chosen + # arbitrarily, and works well. + self.word_embeddings = self.add_weight( + "weight", + shape=[self.config.vocab_size, self.config.embedding_size], + initializer=get_initializer(self.config.initializer_range)) + super(TFAlbertEmbeddings, self).build(input_shape) + + def call(self, inputs, mode="embedding", training=False): + """Get token embeddings of inputs. + Args: + inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids) + mode: string, a valid value is one of "embedding" and "linear". + Returns: + outputs: (1) If mode == "embedding", output embedding tensor, float32 with + shape [batch_size, length, embedding_size]; (2) mode == "linear", output + linear tensor, float32 with shape [batch_size, length, vocab_size]. + Raises: + ValueError: if mode is not valid. + + Shared weights logic adapted from + https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24 + """ + if mode == "embedding": + return self._embedding(inputs, training=training) + elif mode == "linear": + return self._linear(inputs) + else: + raise ValueError("mode {} is not valid.".format(mode)) + + def _embedding(self, inputs, training=False): + """Applies embedding based on inputs tensor.""" + input_ids, position_ids, token_type_ids = inputs + + seq_length = tf.shape(input_ids)[1] + if position_ids is None: + position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :] + if token_type_ids is None: + token_type_ids = tf.fill(tf.shape(input_ids), 0) + + words_embeddings = tf.gather(self.word_embeddings, input_ids) + position_embeddings = self.position_embeddings(position_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = words_embeddings + position_embeddings + token_type_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings, training=training) + return embeddings + + def _linear(self, inputs): + """Computes logits by running inputs through a linear layer. + Args: + inputs: A float32 tensor with shape [batch_size, length, embedding_size] + Returns: + float32 tensor with shape [batch_size, length, vocab_size]. + """ + batch_size = tf.shape(inputs)[0] + length = tf.shape(inputs)[1] + + print(inputs.shape) + + x = tf.reshape(inputs, [-1, self.config.embedding_size]) + + print(x.shape, self.word_embeddings) + + logits = tf.matmul(x, self.word_embeddings, transpose_b=True) + + print([batch_size, length, self.config.vocab_size]) + return tf.reshape(logits, [batch_size, length, self.config.vocab_size]) + + +class TFAlbertSelfAttention(tf.keras.layers.Layer): + def __init__(self, config, **kwargs): + super(TFAlbertSelfAttention, self).__init__(**kwargs) + if config.hidden_size % config.num_attention_heads != 0: + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads)) + self.output_attentions = config.output_attentions + + self.num_attention_heads = config.num_attention_heads + assert config.hidden_size % config.num_attention_heads == 0 + self.attention_head_size = int( + config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = tf.keras.layers.Dense(self.all_head_size, + kernel_initializer=get_initializer( + config.initializer_range), + name='query') + self.key = tf.keras.layers.Dense(self.all_head_size, + kernel_initializer=get_initializer( + config.initializer_range), + name='key') + self.value = tf.keras.layers.Dense(self.all_head_size, + kernel_initializer=get_initializer( + config.initializer_range), + name='value') + + self.dropout = tf.keras.layers.Dropout( + config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x, batch_size): + x = tf.reshape( + x, (batch_size, -1, self.num_attention_heads, self.attention_head_size)) + return tf.transpose(x, perm=[0, 2, 1, 3]) + + def call(self, inputs, training=False): + hidden_states, attention_mask, head_mask = inputs + + batch_size = tf.shape(hidden_states)[0] + mixed_query_layer = self.query(hidden_states) + mixed_key_layer = self.key(hidden_states) + mixed_value_layer = self.value(hidden_states) + + query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) + key_layer = self.transpose_for_scores(mixed_key_layer, batch_size) + value_layer = self.transpose_for_scores(mixed_value_layer, batch_size) + + # Take the dot product between "query" and "key" to get the raw attention scores. + # (batch size, num_heads, seq_len_q, seq_len_k) + attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) + # scale attention_scores + dk = tf.cast(tf.shape(key_layer)[-1], tf.float32) + attention_scores = attention_scores / tf.math.sqrt(dk) + + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in TFAlbertModel call() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = tf.nn.softmax(attention_scores, axis=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs, training=training) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = tf.matmul(attention_probs, value_layer) + + context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3]) + context_layer = tf.reshape(context_layer, + (batch_size, -1, self.all_head_size)) # (batch_size, seq_len_q, all_head_size) + + outputs = (context_layer, attention_probs) if self.output_attentions else ( + context_layer,) + return outputs + + +class TFAlbertSelfOutput(tf.keras.layers.Layer): + def __init__(self, config, **kwargs): + super(TFAlbertSelfOutput, self).__init__(**kwargs) + self.dense = tf.keras.layers.Dense(config.hidden_size, + kernel_initializer=get_initializer( + config.initializer_range), + name='dense') + self.LayerNorm = tf.keras.layers.LayerNormalization( + epsilon=config.layer_norm_eps, name='LayerNorm') + self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) + + def call(self, inputs, training=False): + hidden_states, input_tensor = inputs + + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states, training=training) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class TFAlbertAttention(TFBertSelfAttention): + def __init__(self, config, **kwargs): + super(TFAlbertAttention, self).__init__(config, **kwargs) + + self.hidden_size = config.hidden_size + self.dense = tf.keras.layers.Dense(config.hidden_size, + kernel_initializer=get_initializer( + config.initializer_range), + name='dense') + self.LayerNorm = tf.keras.layers.LayerNormalization( + epsilon=config.layer_norm_eps, name='LayerNorm') + self.pruned_heads = set() + + def prune_heads(self, heads): + raise NotImplementedError + + def call(self, inputs, training=False): + input_tensor, attention_mask, head_mask = inputs + + batch_size = tf.shape(input_tensor)[0] + mixed_query_layer = self.query(input_tensor) + mixed_key_layer = self.key(input_tensor) + mixed_value_layer = self.value(input_tensor) + + query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) + key_layer = self.transpose_for_scores(mixed_key_layer, batch_size) + value_layer = self.transpose_for_scores(mixed_value_layer, batch_size) + + # Take the dot product between "query" and "key" to get the raw attention scores. + # (batch size, num_heads, seq_len_q, seq_len_k) + attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) + # scale attention_scores + dk = tf.cast(tf.shape(key_layer)[-1], tf.float32) + attention_scores = attention_scores / tf.math.sqrt(dk) + + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in TFBertModel call() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = tf.nn.softmax(attention_scores, axis=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs, training=training) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = tf.matmul(attention_probs, value_layer) + + context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3]) + context_layer = tf.reshape(context_layer, + (batch_size, -1, self.all_head_size)) # (batch_size, seq_len_q, all_head_size) + + self_outputs = (context_layer, attention_probs) if self.output_attentions else ( + context_layer,) + + hidden_states = self_outputs[0] + + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states, training=training) + attention_output = self.LayerNorm(hidden_states + input_tensor) + + # add attentions if we output them + outputs = (attention_output,) + self_outputs[1:] + return outputs + + +class TFAlbertLayer(tf.keras.layers.Layer): + def __init__(self, config, **kwargs): + super(TFAlbertLayer, self).__init__(**kwargs) + self.attention = TFAlbertAttention(config, name='attention') + + self.ffn = tf.keras.layers.Dense(config.intermediate_size, kernel_initializer=get_initializer( + config.initializer_range), name='ffn') + + if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)): + self.activation = ACT2FN[config.hidden_act] + else: + self.activation = config.hidden_act + + self.ffn_output = tf.keras.layers.Dense(config.hidden_size, kernel_initializer=get_initializer( + config.initializer_range), name='ffn_output') + self.full_layer_layer_norm = tf.keras.layers.LayerNormalization( + epsilon=config.layer_norm_eps, name='full_layer_layer_norm') + self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) + + def call(self, inputs, training=False): + hidden_states, attention_mask, head_mask = inputs + + attention_outputs = self.attention( + [hidden_states, attention_mask, head_mask], training=training) + ffn_output = self.ffn(attention_outputs[0]) + ffn_output = self.activation(ffn_output) + ffn_output = self.ffn_output(ffn_output) + + hidden_states = self.dropout(hidden_states, training=training) + hidden_states = self.full_layer_layer_norm( + ffn_output + attention_outputs[0]) + + # add attentions if we output them + outputs = (hidden_states,) + attention_outputs[1:] + return outputs + + +class TFAlbertLayerGroup(tf.keras.layers.Layer): + def __init__(self, config, **kwargs): + super(TFAlbertLayerGroup, self).__init__(**kwargs) + + self.output_attentions = config.output_attentions + self.output_hidden_states = config.output_hidden_states + self.albert_layers = [TFAlbertLayer(config, name="albert_layers_._{}".format( + i)) for i in range(config.inner_group_num)] + + def call(self, inputs, training=False): + hidden_states, attention_mask, head_mask = inputs + + layer_hidden_states = () + layer_attentions = () + + for layer_index, albert_layer in enumerate(self.albert_layers): + layer_output = albert_layer( + [hidden_states, attention_mask, head_mask[layer_index]], training=training) + hidden_states = layer_output[0] + + if self.output_attentions: + layer_attentions = layer_attentions + (layer_output[1],) + + if self.output_hidden_states: + layer_hidden_states = layer_hidden_states + (hidden_states,) + + outputs = (hidden_states,) + if self.output_hidden_states: + outputs = outputs + (layer_hidden_states,) + if self.output_attentions: + outputs = outputs + (layer_attentions,) + # last-layer hidden state, (layer hidden states), (layer attentions) + return outputs + + +class TFAlbertTransformer(tf.keras.layers.Layer): + def __init__(self, config, **kwargs): + super(TFAlbertTransformer, self).__init__(**kwargs) + + self.config = config + self.output_attentions = config.output_attentions + self.output_hidden_states = config.output_hidden_states + self.embedding_hidden_mapping_in = tf.keras.layers.Dense(config.hidden_size, kernel_initializer=get_initializer( + config.initializer_range), name='embedding_hidden_mapping_in') + self.albert_layer_groups = [TFAlbertLayerGroup( + config, name="albert_layer_groups_._{}".format(i)) for i in range(config.num_hidden_groups)] + + def call(self, inputs, training=False): + hidden_states, attention_mask, head_mask = inputs + + hidden_states = self.embedding_hidden_mapping_in(hidden_states) + all_attentions = () + + if self.output_hidden_states: + all_hidden_states = (hidden_states,) + + for i in range(self.config.num_hidden_layers): + # Number of layers in a hidden group + layers_per_group = int( + self.config.num_hidden_layers / self.config.num_hidden_groups) + + # Index of the hidden group + group_idx = int( + i / (self.config.num_hidden_layers / self.config.num_hidden_groups)) + + layer_group_output = self.albert_layer_groups[group_idx]( + [hidden_states, attention_mask, head_mask[group_idx*layers_per_group:(group_idx+1)*layers_per_group]], training=training) + hidden_states = layer_group_output[0] + + if self.output_attentions: + all_attentions = all_attentions + layer_group_output[-1] + + if self.output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + outputs = (hidden_states,) + if self.output_hidden_states: + outputs = outputs + (all_hidden_states,) + if self.output_attentions: + outputs = outputs + (all_attentions,) + + # last-layer hidden state, (all hidden states), (all attentions) + return outputs + + +class TFAlbertPreTrainedModel(TFPreTrainedModel): + """ An abstract class to handle weights initialization and + a simple interface for dowloading and loading pretrained models. + """ + config_class = AlbertConfig + pretrained_model_archive_map = TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP + base_model_prefix = "albert" + + +class TFAlbertMLMHead(tf.keras.layers.Layer): + def __init__(self, config, input_embeddings, **kwargs): + super(TFAlbertMLMHead, self).__init__(**kwargs) + self.vocab_size = config.vocab_size + + self.dense = tf.keras.layers.Dense(config.embedding_size, + kernel_initializer=get_initializer( + config.initializer_range), + name='dense') + if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)): + self.activation = ACT2FN[config.hidden_act] + else: + self.activation = config.hidden_act + + self.LayerNorm = tf.keras.layers.LayerNormalization( + epsilon=config.layer_norm_eps, name='LayerNorm') + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.input_embeddings = input_embeddings + + def build(self, input_shape): + self.bias = self.add_weight(shape=(self.vocab_size,), + initializer='zeros', + trainable=True, + name='bias') + super(TFAlbertMLMHead, self).build(input_shape) + + def call(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.activation(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + hidden_states = self.input_embeddings(hidden_states, mode="linear") + hidden_states = hidden_states + self.bias + return hidden_states + + +ALBERT_START_DOCSTRING = r""" The ALBERT model was proposed in + `ALBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ + by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer + pre-trained using a combination of masked language modeling objective and next sentence prediction + on a large corpus comprising the Toronto Book Corpus and Wikipedia. + + This model is a tf.keras.Model `tf.keras.Model`_ sub-class. Use it as a regular TF 2.0 Keras Model and + refer to the TF 2.0 documentation for all matter related to general usage and behavior. + + .. _`ALBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`: + https://arxiv.org/abs/1810.04805 + + .. _`tf.keras.Model`: + https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model + + Note on the model inputs: + TF 2.0 models accepts two formats as inputs: + + - having all inputs as keyword arguments (like PyTorch models), or + - having all inputs as a list, tuple or dict in the first positional arguments. + + This second option is usefull when using `tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`. + + If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument : + + - a single Tensor with input_ids only and nothing else: `model(inputs_ids) + - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: + `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` + - a dictionary with one or several input Tensors associaed to the input names given in the docstring: + `model({'input_ids': input_ids, 'token_type_ids': token_type_ids})` + + Parameters: + config (:class:`~transformers.AlbertConfig`): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the configuration. + Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights. +""" + +ALBERT_INPUTS_DOCSTRING = r""" + Inputs: + **input_ids**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``: + Indices of input sequence tokens in the vocabulary. + To match pre-training, ALBERT input sequence should be formatted with [CLS] and [SEP] tokens as follows: + + (a) For sequence pairs: + + ``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]`` + + ``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1`` + + (b) For single sequences: + + ``tokens: [CLS] the dog is hairy . [SEP]`` + + ``token_type_ids: 0 0 0 0 0 0 0`` + + Albert is a model with absolute position embeddings so it's usually advised to pad the inputs on + the right rather than the left. + + Indices can be obtained using :class:`transformers.AlbertTokenizer`. + See :func:`transformers.PreTrainedTokenizer.encode` and + :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details. + **attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``: + Mask to avoid performing attention on padding token indices. + Mask values selected in ``[0, 1]``: + ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens. + **token_type_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``: + Segment token indices to indicate first and second portions of the inputs. + Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1`` + corresponds to a `sentence B` token + (see `ALBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details). + **position_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``: + Indices of positions of each input sequence tokens in the position embeddings. + Selected in the range ``[0, config.max_position_embeddings - 1]``. + **head_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``: + Mask to nullify selected heads of the self-attention modules. + Mask values selected in ``[0, 1]``: + ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**. +""" + +@add_start_docstrings("The bare Albert Model transformer outputing raw hidden-states without any specific head on top.", + ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) +class TFAlbertModel(TFAlbertPreTrainedModel): + r""" + Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: + **last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)`` + Sequence of hidden-states at the output of the last layer of the model. + **pooler_output**: ``tf.Tensor`` of shape ``(batch_size, hidden_size)`` + Last layer hidden-state of the first token of the sequence (classification token) + further processed by a Linear layer and a Tanh activation function. The Linear + layer weights are trained from the next sentence prediction (classification) + objective during Albert pretraining. This output is usually *not* a good summary + of the semantic content of the input, you're often better with averaging or pooling + the sequence of hidden-states for the whole input sequence. + **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) + list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings) + of shape ``(batch_size, sequence_length, hidden_size)``: + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + **attentions**: (`optional`, returned when ``config.output_attentions=True``) + list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + + Examples:: + + import tensorflow as tf + from transformers import AlbertTokenizer, TFAlbertModel + + tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased') + model = TFAlbertModel.from_pretrained('bert-base-uncased') + input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 + outputs = model(input_ids) + last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple + + """ + + def __init__(self, config, **kwargs): + super(TFAlbertModel, self).__init__(config, **kwargs) + self.num_hidden_layers = config.num_hidden_layers + + self.embeddings = TFAlbertEmbeddings(config, name="embeddings") + self.encoder = TFAlbertTransformer(config, name="encoder") + self.pooler = tf.keras.layers.Dense(config.hidden_size, kernel_initializer=get_initializer( + config.initializer_range), activation='tanh', name='pooler') + + def _resize_token_embeddings(self, new_num_tokens): + raise NotImplementedError + + def _prune_heads(self, heads_to_prune): + """ Prunes heads of the model. + heads_to_prune: dict of {layer_num: list of heads to prune in this layer} + See base class PreTrainedModel + """ + raise NotImplementedError + + def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False): + if isinstance(inputs, (tuple, list)): + input_ids = inputs[0] + attention_mask = inputs[1] if len(inputs) > 1 else attention_mask + token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids + position_ids = inputs[3] if len(inputs) > 3 else position_ids + head_mask = inputs[4] if len(inputs) > 4 else head_mask + assert len(inputs) <= 5, "Too many inputs." + elif isinstance(inputs, dict): + input_ids = inputs.get('input_ids') + attention_mask = inputs.get('attention_mask', attention_mask) + token_type_ids = inputs.get('token_type_ids', token_type_ids) + position_ids = inputs.get('position_ids', position_ids) + head_mask = inputs.get('head_mask', head_mask) + assert len(inputs) <= 5, "Too many inputs." + else: + input_ids = inputs + + if attention_mask is None: + attention_mask = tf.fill(tf.shape(input_ids), 1) + if token_type_ids is None: + token_type_ids = tf.fill(tf.shape(input_ids), 0) + + # We create a 3D attention mask from a 2D tensor mask. + # Sizes are [batch_size, 1, 1, to_seq_length] + # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] + # this attention mask is more simple than the triangular masking of causal attention + # used in OpenAI GPT, we just need to prepare the broadcast dimension here. + extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :] + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + + extended_attention_mask = tf.cast(extended_attention_mask, tf.float32) + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + if not head_mask is None: + raise NotImplementedError + else: + head_mask = [None] * self.num_hidden_layers + # head_mask = tf.constant([0] * self.num_hidden_layers) + + embedding_output = self.embeddings( + [input_ids, position_ids, token_type_ids], training=training) + encoder_outputs = self.encoder( + [embedding_output, extended_attention_mask, head_mask], training=training) + + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) + + # add hidden_states and attentions if they are here + outputs = (sequence_output, pooled_output,) + encoder_outputs[1:] + # sequence_output, pooled_output, (hidden_states), (attentions) + return outputs + + +@add_start_docstrings("""Albert Model with a `language modeling` head on top. """, + ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) +class TFAlbertForMaskedLM(TFAlbertPreTrainedModel): + r""" + Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: + **prediction_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) + list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings) + of shape ``(batch_size, sequence_length, hidden_size)``: + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + **attentions**: (`optional`, returned when ``config.output_attentions=True``) + list of ``Numpy array`` or ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + + Examples:: + + import tensorflow as tf + from transformers import AlbertTokenizer, TFAlbertForMaskedLM + + tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased') + model = TFAlbertForMaskedLM.from_pretrained('bert-base-uncased') + input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 + outputs = model(input_ids) + prediction_scores = outputs[0] + + """ + + def __init__(self, config, *inputs, **kwargs): + super(TFAlbertForMaskedLM, self).__init__(config, *inputs, **kwargs) + + self.albert = TFAlbertModel(config, name='albert') + self.predictions = TFAlbertMLMHead( + config, self.albert.embeddings, name='predictions') + + def call(self, inputs, **kwargs): + outputs = self.albert(inputs, **kwargs) + + sequence_output = outputs[0] + prediction_scores = self.predictions( + sequence_output, training=kwargs.get('training', False)) + + # Add hidden states and attention if they are here + outputs = (prediction_scores,) + outputs[2:] + + return outputs # prediction_scores, (hidden_states), (attentions) From 7bddbf5961f35d03db5151a4747085f7a57d7ff7 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Thu, 7 Nov 2019 23:50:05 +0000 Subject: [PATCH 065/179] TFAlbertForSequenceClassification --- transformers/modeling_tf_albert.py | 66 ++++++++++++++++++++++++++---- 1 file changed, 58 insertions(+), 8 deletions(-) diff --git a/transformers/modeling_tf_albert.py b/transformers/modeling_tf_albert.py index 8861b7add8..410d83ff78 100644 --- a/transformers/modeling_tf_albert.py +++ b/transformers/modeling_tf_albert.py @@ -479,16 +479,15 @@ class TFAlbertMLMHead(tf.keras.layers.Layer): ALBERT_START_DOCSTRING = r""" The ALBERT model was proposed in - `ALBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ - by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer - pre-trained using a combination of masked language modeling objective and next sentence prediction - on a large corpus comprising the Toronto Book Corpus and Wikipedia. + `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`_ + by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents + two parameter-reduction techniques to lower memory consumption and increase the trainig speed of BERT. This model is a tf.keras.Model `tf.keras.Model`_ sub-class. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. - .. _`ALBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`: - https://arxiv.org/abs/1810.04805 + .. _`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`: + https://arxiv.org/abs/1909.11942 .. _`tf.keras.Model`: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model @@ -695,8 +694,8 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel): import tensorflow as tf from transformers import AlbertTokenizer, TFAlbertForMaskedLM - tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased') - model = TFAlbertForMaskedLM.from_pretrained('bert-base-uncased') + tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') + model = TFAlbertForMaskedLM.from_pretrained('albert-base-v2') input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 outputs = model(input_ids) prediction_scores = outputs[0] @@ -721,3 +720,54 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel): outputs = (prediction_scores,) + outputs[2:] return outputs # prediction_scores, (hidden_states), (attentions) + + +@add_start_docstrings("""Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of + the pooled output) e.g. for GLUE tasks. """, + ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING) +class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel): + r""" + Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: + **logits**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, config.num_labels)`` + Classification (or regression if config.num_labels==1) scores (before SoftMax). + **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) + list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings) + of shape ``(batch_size, sequence_length, hidden_size)``: + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + **attentions**: (`optional`, returned when ``config.output_attentions=True``) + list of ``Numpy array`` or ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + + Examples:: + + import tensorflow as tf + from transformers import AlbertTokenizer, TFAlbertForSequenceClassification + + tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') + model = TFAlbertForSequenceClassification.from_pretrained('albert-base-v2') + input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 + outputs = model(input_ids) + logits = outputs[0] + + """ + def __init__(self, config, *inputs, **kwargs): + super(TFAlbertForSequenceClassification, self).__init__(config, *inputs, **kwargs) + self.num_labels = config.num_labels + + self.albert = TFAlbertModel(config, name='albert') + self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) + self.classifier = tf.keras.layers.Dense(config.num_labels, + kernel_initializer=get_initializer(config.initializer_range), + name='classifier') + + def call(self, inputs, **kwargs): + outputs = self.albert(inputs, **kwargs) + + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output, training=kwargs.get('training', False)) + logits = self.classifier(pooled_output) + + outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here + + return outputs # logits, (hidden_states), (attentions) \ No newline at end of file From b18509c2085dce16e655dd86b7ea0129335da4e1 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Fri, 8 Nov 2019 00:12:21 +0000 Subject: [PATCH 066/179] Tests for ALBERT in TF2 + fixes --- transformers/__init__.py | 1 + transformers/modeling_tf_albert.py | 18 +- transformers/tests/modeling_tf_albert_test.py | 229 ++++++++++++++++++ 3 files changed, 237 insertions(+), 11 deletions(-) create mode 100644 transformers/tests/modeling_tf_albert_test.py diff --git a/transformers/__init__.py b/transformers/__init__.py index a409ef772e..baf430c17b 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -169,6 +169,7 @@ if is_tf_available(): TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_tf_albert import (TFAlbertPreTrainedModel, TFAlbertModel, TFAlbertForMaskedLM, + TFAlbertForSequenceClassification, TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) # TF 2.0 <=> PyTorch conversion utilities diff --git a/transformers/modeling_tf_albert.py b/transformers/modeling_tf_albert.py index 410d83ff78..a3f183b192 100644 --- a/transformers/modeling_tf_albert.py +++ b/transformers/modeling_tf_albert.py @@ -126,16 +126,8 @@ class TFAlbertEmbeddings(tf.keras.layers.Layer): """ batch_size = tf.shape(inputs)[0] length = tf.shape(inputs)[1] - - print(inputs.shape) - x = tf.reshape(inputs, [-1, self.config.embedding_size]) - - print(x.shape, self.word_embeddings) - logits = tf.matmul(x, self.word_embeddings, transpose_b=True) - - print([batch_size, length, self.config.vocab_size]) return tf.reshape(logits, [batch_size, length, self.config.vocab_size]) @@ -460,20 +452,24 @@ class TFAlbertMLMHead(tf.keras.layers.Layer): # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. - self.input_embeddings = input_embeddings + self.decoder = input_embeddings def build(self, input_shape): self.bias = self.add_weight(shape=(self.vocab_size,), initializer='zeros', trainable=True, name='bias') + self.decoder_bias = self.add_weight(shape=(self.vocab_size,), + initializer='zeros', + trainable=True, + name='decoder/bias') super(TFAlbertMLMHead, self).build(input_shape) def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) hidden_states = self.LayerNorm(hidden_states) - hidden_states = self.input_embeddings(hidden_states, mode="linear") + hidden_states = self.decoder(hidden_states, mode="linear") + self.decoder_bias hidden_states = hidden_states + self.bias return hidden_states @@ -666,7 +662,7 @@ class TFAlbertModel(TFAlbertPreTrainedModel): [embedding_output, extended_attention_mask, head_mask], training=training) sequence_output = encoder_outputs[0] - pooled_output = self.pooler(sequence_output) + pooled_output = self.pooler(sequence_output[:, 0]) # add hidden_states and attentions if they are here outputs = (sequence_output, pooled_output,) + encoder_outputs[1:] diff --git a/transformers/tests/modeling_tf_albert_test.py b/transformers/tests/modeling_tf_albert_test.py new file mode 100644 index 0000000000..85fc62f34f --- /dev/null +++ b/transformers/tests/modeling_tf_albert_test.py @@ -0,0 +1,229 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import unittest +import shutil +import pytest +import sys + +from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) +from .configuration_common_test import ConfigTester + +from transformers import AlbertConfig, is_tf_available + +if is_tf_available(): + import tensorflow as tf + from transformers.modeling_tf_albert import (TFAlbertModel, TFAlbertForMaskedLM, + TFAlbertForSequenceClassification, + TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) +else: + pytestmark = pytest.mark.skip("Require TensorFlow") + + +class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester): + + all_model_classes = ( + TFAlbertModel, + TFAlbertForMaskedLM, + TFAlbertForSequenceClassification + ) if is_tf_available() else () + + class TFAlbertModelTester(object): + + def __init__(self, + parent, + batch_size=13, + seq_length=7, + is_training=True, + use_input_mask=True, + use_token_type_ids=True, + use_labels=True, + vocab_size=99, + hidden_size=32, + num_hidden_layers=5, + num_attention_heads=4, + intermediate_size=37, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512, + type_vocab_size=16, + type_sequence_label_size=2, + initializer_range=0.02, + num_labels=3, + num_choices=4, + scope=None, + ): + self.parent = parent + self.batch_size = batch_size + self.seq_length = seq_length + self.is_training = is_training + self.use_input_mask = use_input_mask + self.use_token_type_ids = use_token_type_ids + self.use_labels = use_labels + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_size = type_vocab_size + self.type_sequence_label_size = type_sequence_label_size + self.initializer_range = initializer_range + self.num_labels = num_labels + self.num_choices = num_choices + self.scope = scope + + def prepare_config_and_inputs(self): + input_ids = ids_tensor( + [self.batch_size, self.seq_length], self.vocab_size) + + input_mask = None + if self.use_input_mask: + input_mask = ids_tensor( + [self.batch_size, self.seq_length], vocab_size=2) + + token_type_ids = None + if self.use_token_type_ids: + token_type_ids = ids_tensor( + [self.batch_size, self.seq_length], self.type_vocab_size) + + sequence_labels = None + token_labels = None + choice_labels = None + if self.use_labels: + sequence_labels = ids_tensor( + [self.batch_size], self.type_sequence_label_size) + token_labels = ids_tensor( + [self.batch_size, self.seq_length], self.num_labels) + choice_labels = ids_tensor([self.batch_size], self.num_choices) + + config = AlbertConfig( + vocab_size_or_config_json_file=self.vocab_size, + hidden_size=self.hidden_size, + num_hidden_layers=self.num_hidden_layers, + num_attention_heads=self.num_attention_heads, + intermediate_size=self.intermediate_size, + hidden_act=self.hidden_act, + hidden_dropout_prob=self.hidden_dropout_prob, + attention_probs_dropout_prob=self.attention_probs_dropout_prob, + max_position_embeddings=self.max_position_embeddings, + type_vocab_size=self.type_vocab_size, + initializer_range=self.initializer_range) + + return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels + + def create_and_check_albert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): + model = TFAlbertModel(config=config) + # inputs = {'input_ids': input_ids, + # 'attention_mask': input_mask, + # 'token_type_ids': token_type_ids} + # sequence_output, pooled_output = model(**inputs) + inputs = {'input_ids': input_ids, + 'attention_mask': input_mask, + 'token_type_ids': token_type_ids} + sequence_output, pooled_output = model(inputs) + + inputs = [input_ids, input_mask] + sequence_output, pooled_output = model(inputs) + + sequence_output, pooled_output = model(input_ids) + + result = { + "sequence_output": sequence_output.numpy(), + "pooled_output": pooled_output.numpy(), + } + self.parent.assertListEqual( + list(result["sequence_output"].shape), + [self.batch_size, self.seq_length, self.hidden_size]) + self.parent.assertListEqual(list(result["pooled_output"].shape), [ + self.batch_size, self.hidden_size]) + + def create_and_check_albert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): + model = TFAlbertForMaskedLM(config=config) + inputs = {'input_ids': input_ids, + 'attention_mask': input_mask, + 'token_type_ids': token_type_ids} + prediction_scores, = model(inputs) + result = { + "prediction_scores": prediction_scores.numpy(), + } + self.parent.assertListEqual( + list(result["prediction_scores"].shape), + [self.batch_size, self.seq_length, self.vocab_size]) + + def create_and_check_albert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): + config.num_labels = self.num_labels + model = TFAlbertForSequenceClassification(config=config) + inputs = {'input_ids': input_ids, + 'attention_mask': input_mask, + 'token_type_ids': token_type_ids} + logits, = model(inputs) + result = { + "logits": logits.numpy(), + } + self.parent.assertListEqual( + list(result["logits"].shape), + [self.batch_size, self.num_labels]) + + def prepare_config_and_inputs_for_common(self): + config_and_inputs = self.prepare_config_and_inputs() + (config, input_ids, token_type_ids, input_mask, + sequence_labels, token_labels, choice_labels) = config_and_inputs + inputs_dict = {'input_ids': input_ids, + 'token_type_ids': token_type_ids, 'attention_mask': input_mask} + return config, inputs_dict + + def setUp(self): + self.model_tester = TFAlbertModelTest.TFAlbertModelTester(self) + self.config_tester = ConfigTester( + self, config_class=AlbertConfig, hidden_size=37) + + def test_config(self): + self.config_tester.run_common_tests() + + def test_albert_model(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_albert_model(*config_and_inputs) + + def test_for_masked_lm(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_albert_for_masked_lm( + *config_and_inputs) + + def test_for_sequence_classification(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_albert_for_sequence_classification( + *config_and_inputs) + + @pytest.mark.slow + def test_model_from_pretrained(self): + cache_dir = "/tmp/transformers_test/" + # for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: + for model_name in ['albert-base-uncased']: + model = TFAlbertModel.from_pretrained( + model_name, cache_dir=cache_dir) + shutil.rmtree(cache_dir) + self.assertIsNotNone(model) + + +if __name__ == "__main__": + unittest.main() From c9cb7f8a0fbe784665b00bfdca6bfc54ad10d5f5 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Mon, 11 Nov 2019 15:12:54 -0500 Subject: [PATCH 067/179] Torch 1.1.0 compatibility + FP16 O1 + TF checkpoints Co-authored-by: wassname --- transformers/modeling_albert.py | 4 ++-- transformers/modeling_tf_albert.py | 9 ++++++++- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 640af537d0..ff20ca78dc 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -203,8 +203,8 @@ class AlbertAttention(BertSelfAttention): # Should find a better way to do this - w = self.dense.weight.T.view(self.num_attention_heads, self.attention_head_size, self.hidden_size) - b = self.dense.bias + w = self.dense.weight.t().view(self.num_attention_heads, self.attention_head_size, self.hidden_size).to(context_layer.dtype) + b = self.dense.bias.to(context_layer.dtype) projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b projected_context_layer_dropout = self.dropout(projected_context_layer) diff --git a/transformers/modeling_tf_albert.py b/transformers/modeling_tf_albert.py index a3f183b192..ee8712eb28 100644 --- a/transformers/modeling_tf_albert.py +++ b/transformers/modeling_tf_albert.py @@ -36,7 +36,14 @@ import logging logger = logging.getLogger(__name__) TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = { - # TODO FILL THAT UP + 'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-tf_model.h5", + 'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-tf_model.h5", + 'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-tf_model.h5", + 'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-tf_model.h5", + 'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-tf_model.h5", + 'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-tf_model.h5", + 'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-tf_model.h5", + 'albert-xxlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-tf_model.h5", } From f873b55e433e1c9ef364e597ad1d4662db4bc038 Mon Sep 17 00:00:00 2001 From: LysandreJik Date: Tue, 26 Nov 2019 10:28:41 -0500 Subject: [PATCH 068/179] Warning for ALBERT-v2 models --- transformers/modeling_utils.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/transformers/modeling_utils.py b/transformers/modeling_utils.py index d51eefab58..5d51caa951 100644 --- a/transformers/modeling_utils.py +++ b/transformers/modeling_utils.py @@ -315,6 +315,10 @@ class PreTrainedModel(nn.Module): model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ + if "albert" in pretrained_model_name_or_path and "v2" in pretrained_model_name_or_path: + logger.warning("There is currently an upstream reproducibility issue with ALBERT v2 models. Please see " + + "https://github.com/google-research/google-research/issues/119 for more information.") + config = kwargs.pop('config', None) state_dict = kwargs.pop('state_dict', None) cache_dir = kwargs.pop('cache_dir', None) From c536c2a4809a51356fd50ff62a80955fea79f790 Mon Sep 17 00:00:00 2001 From: LysandreJik Date: Tue, 26 Nov 2019 11:22:52 -0500 Subject: [PATCH 069/179] ALBERT Input Embeds --- transformers/modeling_albert.py | 77 ++++++++++++++++++++++-------- transformers/modeling_tf_albert.py | 45 ++++++++++++----- 2 files changed, 90 insertions(+), 32 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index ff20ca78dc..7882356d24 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -433,6 +433,12 @@ class AlbertModel(AlbertPreTrainedModel): self.init_weights() + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + def _resize_token_embeddings(self, new_num_tokens): old_embeddings = self.embeddings.word_embeddings new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens) @@ -457,12 +463,24 @@ class AlbertModel(AlbertPreTrainedModel): inner_group_idx = int(layer - group_idx * self.config.inner_group_num) self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads) + def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, + inputs_embeds=None): + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device - def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): if attention_mask is None: - attention_mask = torch.ones_like(input_ids) + attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: - token_type_ids = torch.zeros_like(input_ids) + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility @@ -477,7 +495,8 @@ class AlbertModel(AlbertPreTrainedModel): else: head_mask = [None] * self.config.num_hidden_layers - embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids) + embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds) encoder_outputs = self.encoder(embedding_output, extended_attention_mask, head_mask=head_mask) @@ -549,9 +568,19 @@ class AlbertForMaskedLM(AlbertPreTrainedModel): self._tie_or_clone_weights(self.predictions.decoder, self.albert.embeddings.word_embeddings) - def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, - masked_lm_labels=None): - outputs = self.albert(input_ids, attention_mask, token_type_ids, position_ids, head_mask) + def get_output_embeddings(self): + return self.predictions.decoder + + def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, + masked_lm_labels=None, inputs_embeds=None): + outputs = self.albert( + input_ids=input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds + ) sequence_outputs = outputs[0] prediction_scores = self.predictions(sequence_outputs) @@ -609,14 +638,17 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel): self.init_weights() - def forward(self, input_ids, attention_mask=None, token_type_ids=None, - position_ids=None, head_mask=None, labels=None): + def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, + position_ids=None, head_mask=None, inputs_embeds=None, labels=None): - outputs = self.albert(input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask) + outputs = self.albert( + input_ids=input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds + ) pooled_output = outputs[1] @@ -692,14 +724,17 @@ class AlbertForQuestionAnswering(AlbertPreTrainedModel): self.init_weights() - def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, - start_positions=None, end_positions=None): + def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, + inputs_embeds=None, start_positions=None, end_positions=None): - outputs = self.albert(input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask) + outputs = self.albert( + input_ids=input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds + ) sequence_output = outputs[0] diff --git a/transformers/modeling_tf_albert.py b/transformers/modeling_tf_albert.py index ee8712eb28..ee122205b9 100644 --- a/transformers/modeling_tf_albert.py +++ b/transformers/modeling_tf_albert.py @@ -107,19 +107,25 @@ class TFAlbertEmbeddings(tf.keras.layers.Layer): def _embedding(self, inputs, training=False): """Applies embedding based on inputs tensor.""" - input_ids, position_ids, token_type_ids = inputs + input_ids, position_ids, token_type_ids, inputs_embeds = inputs - seq_length = tf.shape(input_ids)[1] + if input_ids is not None: + input_shape = tf.shape(input_ids) + else: + input_shape = tf.shape(inputs_embeds)[:-1] + + seq_length = input_shape[1] if position_ids is None: position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :] if token_type_ids is None: - token_type_ids = tf.fill(tf.shape(input_ids), 0) + token_type_ids = tf.fill(input_shape, 0) - words_embeddings = tf.gather(self.word_embeddings, input_ids) + if inputs_embeds is None: + inputs_embeds = tf.gather(self.word_embeddings, input_ids) position_embeddings = self.position_embeddings(position_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) - embeddings = words_embeddings + position_embeddings + token_type_embeddings + embeddings = inputs_embeds + position_embeddings + token_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings, training=training) return embeddings @@ -603,6 +609,9 @@ class TFAlbertModel(TFAlbertPreTrainedModel): self.pooler = tf.keras.layers.Dense(config.hidden_size, kernel_initializer=get_initializer( config.initializer_range), activation='tanh', name='pooler') + def get_input_embeddings(self): + return self.embeddings + def _resize_token_embeddings(self, new_num_tokens): raise NotImplementedError @@ -613,28 +622,39 @@ class TFAlbertModel(TFAlbertPreTrainedModel): """ raise NotImplementedError - def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False): + def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, training=False): if isinstance(inputs, (tuple, list)): input_ids = inputs[0] attention_mask = inputs[1] if len(inputs) > 1 else attention_mask token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids position_ids = inputs[3] if len(inputs) > 3 else position_ids head_mask = inputs[4] if len(inputs) > 4 else head_mask - assert len(inputs) <= 5, "Too many inputs." + inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds + assert len(inputs) <= 6, "Too many inputs." elif isinstance(inputs, dict): input_ids = inputs.get('input_ids') attention_mask = inputs.get('attention_mask', attention_mask) token_type_ids = inputs.get('token_type_ids', token_type_ids) position_ids = inputs.get('position_ids', position_ids) head_mask = inputs.get('head_mask', head_mask) - assert len(inputs) <= 5, "Too many inputs." + inputs_embeds = inputs.get('inputs_embeds', inputs_embeds) + assert len(inputs) <= 6, "Too many inputs." else: input_ids = inputs + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.shape + elif inputs_embeds is not None: + input_shape = inputs_embeds.shape[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + if attention_mask is None: - attention_mask = tf.fill(tf.shape(input_ids), 1) + attention_mask = tf.fill(input_shape, 1) if token_type_ids is None: - token_type_ids = tf.fill(tf.shape(input_ids), 0) + token_type_ids = tf.fill(input_shape, 0) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] @@ -664,7 +684,7 @@ class TFAlbertModel(TFAlbertPreTrainedModel): # head_mask = tf.constant([0] * self.num_hidden_layers) embedding_output = self.embeddings( - [input_ids, position_ids, token_type_ids], training=training) + [input_ids, position_ids, token_type_ids, inputs_embeds], training=training) encoder_outputs = self.encoder( [embedding_output, extended_attention_mask, head_mask], training=training) @@ -712,6 +732,9 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel): self.predictions = TFAlbertMLMHead( config, self.albert.embeddings, name='predictions') + def get_output_embeddings(self): + return self.albert.embeddings + def call(self, inputs, **kwargs): outputs = self.albert(inputs, **kwargs) From bdfe21ab242c2175972f53ca6b4af29ada02a2bb Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Tue, 26 Nov 2019 12:54:36 -0500 Subject: [PATCH 070/179] Change param order for consistency --- transformers/modeling_albert.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 7882356d24..5b7b2d3900 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -571,8 +571,8 @@ class AlbertForMaskedLM(AlbertPreTrainedModel): def get_output_embeddings(self): return self.predictions.decoder - def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, - masked_lm_labels=None, inputs_embeds=None): + def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, + masked_lm_labels=None): outputs = self.albert( input_ids=input_ids, attention_mask=attention_mask, From f2f329408db66285fd59e6628ca394381bb7f94e Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 26 Nov 2019 12:59:28 -0500 Subject: [PATCH 071/179] Fix input embeddings --- transformers/tests/modeling_albert_test.py | 2 ++ transformers/tests/modeling_tf_albert_test.py | 2 ++ transformers/tests/modeling_tf_common_test.py | 7 ++++--- 3 files changed, 8 insertions(+), 3 deletions(-) diff --git a/transformers/tests/modeling_albert_test.py b/transformers/tests/modeling_albert_test.py index da87709df1..976feff9db 100644 --- a/transformers/tests/modeling_albert_test.py +++ b/transformers/tests/modeling_albert_test.py @@ -49,6 +49,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): use_token_type_ids=True, use_labels=True, vocab_size=99, + embedding_size=16, hidden_size=36, num_hidden_layers=6, num_hidden_groups=6, @@ -73,6 +74,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size + self.embedding_size = embedding_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads diff --git a/transformers/tests/modeling_tf_albert_test.py b/transformers/tests/modeling_tf_albert_test.py index 85fc62f34f..fbd519b8f6 100644 --- a/transformers/tests/modeling_tf_albert_test.py +++ b/transformers/tests/modeling_tf_albert_test.py @@ -54,6 +54,7 @@ class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester): use_token_type_ids=True, use_labels=True, vocab_size=99, + embedding_size=16, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, @@ -77,6 +78,7 @@ class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester): self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size + self.embedding_size = embedding_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads diff --git a/transformers/tests/modeling_tf_common_test.py b/transformers/tests/modeling_tf_common_test.py index 2bb7cc9c5f..31a30766cf 100644 --- a/transformers/tests/modeling_tf_common_test.py +++ b/transformers/tests/modeling_tf_common_test.py @@ -426,9 +426,10 @@ class TFCommonTestCases: try: x = wte([input_ids], mode="embedding") except: - x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32) - # ^^ In our TF models, the input_embeddings can take slightly different forms, - # so we try two of them and fall back to just synthetically creating a dummy tensor of ones. + if hasattr(self.model_tester, "embedding_size"): + x = tf.ones(input_ids.shape + [model.config.embedding_size], dtype=tf.dtypes.float32) + else: + x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32) inputs_dict["inputs_embeds"] = x outputs = model(inputs_dict) From ae98d4599179b299563b679dd33f8a86da12980d Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 26 Nov 2019 14:12:44 -0500 Subject: [PATCH 072/179] Release: v2.2.0 --- .circleci/deploy.sh | 1 + README.md | 2 +- deploy_multi_version_doc.sh | 22 ++++++++++++++++++++++ docs/source/conf.py | 2 +- setup.py | 2 +- transformers/__init__.py | 2 +- 6 files changed, 27 insertions(+), 4 deletions(-) create mode 100644 deploy_multi_version_doc.sh diff --git a/.circleci/deploy.sh b/.circleci/deploy.sh index 98151963d8..c4b802c9e9 100755 --- a/.circleci/deploy.sh +++ b/.circleci/deploy.sh @@ -23,3 +23,4 @@ deploy_doc "fe02e45" v1.1.0 deploy_doc "89fd345" v1.2.0 deploy_doc "fc9faa8" v2.0.0 deploy_doc "3ddce1d" v2.1.1 +deploy_doc "f2f3294" v2.2.0 diff --git a/README.md b/README.md index 52e74a6f80..bb6a8eaa1a 100644 --- a/README.md +++ b/README.md @@ -58,7 +58,7 @@ Choose the right framework for every part of a model's lifetime | [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation | | [Migrating from pytorch-transformers to transformers](#Migrating-from-pytorch-transformers-to-transformers) | Migrating your code from pytorch-transformers to transformers | | [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-transformers) | Migrating your code from pytorch-pretrained-bert to transformers | -| [Documentation](https://huggingface.co/transformers/) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) | Full API documentation and more | +| [Documentation][(v2.2.0)](https://huggingface.co/transformers/v2.2.0) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) [(master](https://huggingface.co/transformers) | Full API documentation and more | ## Installation diff --git a/deploy_multi_version_doc.sh b/deploy_multi_version_doc.sh new file mode 100644 index 0000000000..bd567213eb --- /dev/null +++ b/deploy_multi_version_doc.sh @@ -0,0 +1,22 @@ +cd docs + +function deploy_doc(){ + echo "Creating doc at commit $1 and pushing to folder $2" + git checkout $1 + if [ ! -z "$2" ] + then + echo "Pushing version" $2 + make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2 + else + echo "Pushing master" + make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir + fi +} + +deploy_doc "master" +deploy_doc "b33a385" v1.0.0 +deploy_doc "fe02e45" v1.1.0 +deploy_doc "89fd345" v1.2.0 +deploy_doc "fc9faa8" v2.0.0 +deploy_doc "3ddce1d" v2.1.1 +deploy_doc "f2f3294" v2.2.0 \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 00c020ab39..f762a89cd2 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -26,7 +26,7 @@ author = u'huggingface' # The short X.Y version version = u'' # The full version, including alpha/beta/rc tags -release = u'2.1.1' +release = u'2.2.0' # -- General configuration --------------------------------------------------- diff --git a/setup.py b/setup.py index f49aee68d4..d8dcf7b898 100644 --- a/setup.py +++ b/setup.py @@ -38,7 +38,7 @@ from setuptools import find_packages, setup setup( name="transformers", - version="2.1.1", + version="2.2.0", author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors", author_email="thomas@huggingface.co", description="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch", diff --git a/transformers/__init__.py b/transformers/__init__.py index baf430c17b..dff2479d4b 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -1,4 +1,4 @@ -__version__ = "2.1.1" +__version__ = "2.2.0" # Work around to update TensorFlow's absl.logging threshold which alters the # default Python logging output behavior when present. From b6321452738925983ba1306fb5396982d64f408a Mon Sep 17 00:00:00 2001 From: Lysandre Debut Date: Tue, 26 Nov 2019 14:27:15 -0500 Subject: [PATCH 073/179] Update master documentation link in README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bb6a8eaa1a..67fb80aecf 100644 --- a/README.md +++ b/README.md @@ -58,7 +58,7 @@ Choose the right framework for every part of a model's lifetime | [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation | | [Migrating from pytorch-transformers to transformers](#Migrating-from-pytorch-transformers-to-transformers) | Migrating your code from pytorch-transformers to transformers | | [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-transformers) | Migrating your code from pytorch-pretrained-bert to transformers | -| [Documentation][(v2.2.0)](https://huggingface.co/transformers/v2.2.0) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) [(master](https://huggingface.co/transformers) | Full API documentation and more | +| [Documentation][(v2.2.0)](https://huggingface.co/transformers/v2.2.0) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) [(master)](https://huggingface.co/transformers) | Full API documentation and more | ## Installation From cf62bdc962c53d9fb7a5820217f1bf844bb6da3b Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Tue, 26 Nov 2019 14:37:32 -0500 Subject: [PATCH 074/179] Improve test protocol for inputs_embeds in TF cc @lysandrejik --- transformers/tests/modeling_tf_common_test.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/transformers/tests/modeling_tf_common_test.py b/transformers/tests/modeling_tf_common_test.py index 31a30766cf..232991915c 100644 --- a/transformers/tests/modeling_tf_common_test.py +++ b/transformers/tests/modeling_tf_common_test.py @@ -426,10 +426,15 @@ class TFCommonTestCases: try: x = wte([input_ids], mode="embedding") except: - if hasattr(self.model_tester, "embedding_size"): - x = tf.ones(input_ids.shape + [model.config.embedding_size], dtype=tf.dtypes.float32) - else: - x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32) + x = wte([input_ids, None, None, None], mode="embedding") + # ^^ In our TF models, the input_embeddings can take slightly different forms, + # so we try a few of them. + # We used to fall back to just synthetically creating a dummy tensor of ones: + # + # if hasattr(self.model_tester, "embedding_size"): + # x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32) + # else: + # x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32) inputs_dict["inputs_embeds"] = x outputs = model(inputs_dict) From 8742baa53136b906e392fdae57f1c191d1b2370e Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Tue, 26 Nov 2019 14:39:47 -0500 Subject: [PATCH 075/179] Improve test protocol for inputs_embeds in TF --- transformers/tests/modeling_tf_common_test.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/transformers/tests/modeling_tf_common_test.py b/transformers/tests/modeling_tf_common_test.py index 232991915c..ea8cd1aecd 100644 --- a/transformers/tests/modeling_tf_common_test.py +++ b/transformers/tests/modeling_tf_common_test.py @@ -426,15 +426,17 @@ class TFCommonTestCases: try: x = wte([input_ids], mode="embedding") except: - x = wte([input_ids, None, None, None], mode="embedding") + try: + x = wte([input_ids, None, None, None], mode="embedding") + except: + if hasattr(self.model_tester, "embedding_size"): + x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32) + else: + x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32) # ^^ In our TF models, the input_embeddings can take slightly different forms, # so we try a few of them. # We used to fall back to just synthetically creating a dummy tensor of ones: # - # if hasattr(self.model_tester, "embedding_size"): - # x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32) - # else: - # x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32) inputs_dict["inputs_embeds"] = x outputs = model(inputs_dict) From 668aac45d206bc0c8ca95e7a8b565d1ff701da7f Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 26 Nov 2019 14:52:42 -0500 Subject: [PATCH 076/179] Pretrained models --- docs/source/pretrained_models.rst | 33 +++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/docs/source/pretrained_models.rst b/docs/source/pretrained_models.rst index b0a578fd80..d017a3be6c 100644 --- a/docs/source/pretrained_models.rst +++ b/docs/source/pretrained_models.rst @@ -159,5 +159,38 @@ Here is the full list of the currently provided pretrained models together with | | | | CamemBERT using the BERT-base architecture | | | | (see `details `__) | +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| ALBERT | ``albert-base-v1`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters | +| | | | ALBERT base model | +| | | (see `details `__) | ++--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``albert-large-v1`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters | +| | | | ALBERT large model | +| | | (see `details `__) | ++--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``albert-xlarge-v1`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters | +| | | | ALBERT xlarge model | +| | | (see `details `__) | ++--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``albert-xxlarge-v1`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters | +| | | | ALBERT xxlarge model | +| | | (see `details `__) | ++--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``albert-base-v2`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters | +| | | | ALBERT base model with no dropout, additional training data and longer training | +| | | (see `details `__) | ++--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``albert-large-v2`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters | +| | | | ALBERT large model with no dropout, additional training data and longer training | +| | | (see `details `__) | ++--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``albert-xlarge-v2`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters | +| | | | ALBERT xlarge model with no dropout, additional training data and longer training | +| | | (see `details `__) | ++--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``albert-xxlarge-v2`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters | +| | | | ALBERT xxlarge model with no dropout, additional training data and longer training | +| | | (see `details `__) | ++--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ + .. `__ From 7c6000e412288ddce7d0dbc4f4d9a12f49d28690 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 26 Nov 2019 14:55:29 -0500 Subject: [PATCH 077/179] Updated v2.2.0 doc --- .circleci/deploy.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.circleci/deploy.sh b/.circleci/deploy.sh index c4b802c9e9..73960cbe99 100755 --- a/.circleci/deploy.sh +++ b/.circleci/deploy.sh @@ -23,4 +23,4 @@ deploy_doc "fe02e45" v1.1.0 deploy_doc "89fd345" v1.2.0 deploy_doc "fc9faa8" v2.0.0 deploy_doc "3ddce1d" v2.1.1 -deploy_doc "f2f3294" v2.2.0 +deploy_doc "668aac4" v2.2.0 From ee4647bd5c1343304f7fdf20f06db1fc2524f892 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 26 Nov 2019 15:10:51 -0500 Subject: [PATCH 078/179] CamemBERT & ALBERT doc --- docs/source/index.rst | 5 ++ docs/source/model_doc/albert.rst | 71 +++++++++++++++++++++++++++++ docs/source/model_doc/camembert.rst | 50 ++++++++++++++++++++ 3 files changed, 126 insertions(+) create mode 100644 docs/source/model_doc/albert.rst create mode 100644 docs/source/model_doc/camembert.rst diff --git a/docs/source/index.rst b/docs/source/index.rst index 4cd1f48ba8..55ead33b4d 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -47,6 +47,9 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train 6. `XLM `_ (from Facebook) released together with the paper `Cross-lingual Language Model Pretraining `_ by Guillaume Lample and Alexis Conneau. 7. `RoBERTa `_ (from Facebook), released together with the paper a `Robustly Optimized BERT Pretraining Approach `_ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. 8. `DistilBERT `_ (from HuggingFace) released together with the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter `_ by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2 `_. +9. `CTRL `_ (from Salesforce), released together with the paper `CTRL: A Conditional Transformer Language Model for Controllable Generation `_ by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher. +10. `CamemBERT `_ (from FAIR, Inria, Sorbonne Université) released together with the paper `CamemBERT: a Tasty French Language Model `_ by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Djame Seddah, and Benoît Sagot. +11. `ALBERT `_ (from Google Research), released together with the paper a `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations `_ by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. .. toctree:: :maxdepth: 2 @@ -89,3 +92,5 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train model_doc/roberta model_doc/distilbert model_doc/ctrl + model_doc/camembert + model_doc/albert diff --git a/docs/source/model_doc/albert.rst b/docs/source/model_doc/albert.rst new file mode 100644 index 0000000000..cf52f35eb6 --- /dev/null +++ b/docs/source/model_doc/albert.rst @@ -0,0 +1,71 @@ +ALBERT +---------------------------------------------------- + +``AlbrtConfig`` +~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.AlbertConfig + :members: + + +``AlbertTokenizer`` +~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.AlbertTokenizer + :members: + + +``AlbertModel`` +~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.AlbertModel + :members: + + +``AlbertForMaskedLM`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.AlbertForMaskedLM + :members: + + +``AlbertForSequenceClassification`` +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.AlbertForSequenceClassification + :members: + + +``AlbertForQuestionAnswering`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.AlbertForQuestionAnswering + :members: + + +``TFAlbertModel`` +~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.TFAlbertModel + :members: + + +``TFBertForPreTraining`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.TFBertForPreTraining + :members: + + +``TFAlbertForMaskedLM`` +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.TFAlbertForMaskedLM + :members: + + +``TFAlbertForSequenceClassification`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.TFAlbertForSequenceClassification + :members: diff --git a/docs/source/model_doc/camembert.rst b/docs/source/model_doc/camembert.rst new file mode 100644 index 0000000000..82ca9de945 --- /dev/null +++ b/docs/source/model_doc/camembert.rst @@ -0,0 +1,50 @@ +CamemBERT +---------------------------------------------------- + +``CamembertConfig`` +~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.CamembertConfig + :members: + + +``CamembertTokenizer`` +~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.CamembertTokenizer + :members: + + +``CamembertModel`` +~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.CamembertModel + :members: + + +``CamembertForMaskedLM`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.CamembertForMaskedLM + :members: + + +``CamembertForSequenceClassification`` +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.CamembertForSequenceClassification + :members: + + +``CamembertForMultipleChoice`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.CamembertForMultipleChoice + :members: + + +``CamembertForTokenClassification`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.CamembertForTokenClassification + :members: From 44b82c777f0a4e173fa9c68de630a63fbac1b946 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 26 Nov 2019 15:15:11 -0500 Subject: [PATCH 079/179] Updated v2.2.0 doc --- .circleci/deploy.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.circleci/deploy.sh b/.circleci/deploy.sh index 73960cbe99..8081153b9f 100755 --- a/.circleci/deploy.sh +++ b/.circleci/deploy.sh @@ -23,4 +23,4 @@ deploy_doc "fe02e45" v1.1.0 deploy_doc "89fd345" v1.2.0 deploy_doc "fc9faa8" v2.0.0 deploy_doc "3ddce1d" v2.1.1 -deploy_doc "668aac4" v2.2.0 +deploy_doc "ee4647b" v2.2.0 From cf26a0c85e0520d3e3770c9ad8dce94318ea3440 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 26 Nov 2019 15:40:03 -0500 Subject: [PATCH 080/179] Fix pretrained models table --- README.md | 1 + docs/source/pretrained_models.rst | 14 +++++++------- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 67fb80aecf..dd2c865036 100644 --- a/README.md +++ b/README.md @@ -134,6 +134,7 @@ At some point in the future, you'll be able to seamlessly move from pre-training 8. **[DistilBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation). 9. **[CTRL](https://github.com/salesforce/ctrl/)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher. 10. **[CamemBERT](https://camembert-model.fr)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot. +11. **[ALBERT](https://github.com/google-research/google-research/tree/master/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. 11. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR. These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Peason R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html). diff --git a/docs/source/pretrained_models.rst b/docs/source/pretrained_models.rst index d017a3be6c..a6bf508d18 100644 --- a/docs/source/pretrained_models.rst +++ b/docs/source/pretrained_models.rst @@ -162,31 +162,31 @@ Here is the full list of the currently provided pretrained models together with | ALBERT | ``albert-base-v1`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters | | | | | ALBERT base model | | | | (see `details `__) | -+--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ | | ``albert-large-v1`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters | | | | | ALBERT large model | | | | (see `details `__) | -+--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ | | ``albert-xlarge-v1`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters | | | | | ALBERT xlarge model | | | | (see `details `__) | -+--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ | | ``albert-xxlarge-v1`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters | | | | | ALBERT xxlarge model | | | | (see `details `__) | -+--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ | | ``albert-base-v2`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters | | | | | ALBERT base model with no dropout, additional training data and longer training | | | | (see `details `__) | -+--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ | | ``albert-large-v2`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters | | | | | ALBERT large model with no dropout, additional training data and longer training | | | | (see `details `__) | -+--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ | | ``albert-xlarge-v2`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters | | | | | ALBERT xlarge model with no dropout, additional training data and longer training | | | | (see `details `__) | -+--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ | | ``albert-xxlarge-v2`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters | | | | | ALBERT xxlarge model with no dropout, additional training data and longer training | | | | (see `details `__) | From ce02550d50f2074d54d58b37cbc9845d9a159818 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 26 Nov 2019 15:47:02 -0500 Subject: [PATCH 081/179] Fix pretrained models table --- docs/source/pretrained_models.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/pretrained_models.rst b/docs/source/pretrained_models.rst index a6bf508d18..a312346df7 100644 --- a/docs/source/pretrained_models.rst +++ b/docs/source/pretrained_models.rst @@ -190,7 +190,7 @@ Here is the full list of the currently provided pretrained models together with | | ``albert-xxlarge-v2`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters | | | | | ALBERT xxlarge model with no dropout, additional training data and longer training | | | | (see `details `__) | -+--------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ ++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ .. `__ From cc7968227e08858df4a5c618c739e1a3ca050196 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Tue, 26 Nov 2019 15:52:25 -0500 Subject: [PATCH 082/179] Updated v2.2.0 doc --- .circleci/deploy.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.circleci/deploy.sh b/.circleci/deploy.sh index 8081153b9f..61b9f90909 100755 --- a/.circleci/deploy.sh +++ b/.circleci/deploy.sh @@ -23,4 +23,4 @@ deploy_doc "fe02e45" v1.1.0 deploy_doc "89fd345" v1.2.0 deploy_doc "fc9faa8" v2.0.0 deploy_doc "3ddce1d" v2.1.1 -deploy_doc "ee4647b" v2.2.0 +deploy_doc "ce02550" v2.2.0 From 361620954acf16b27727d763a591257b03f90b5d Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 27 Nov 2019 10:11:37 -0500 Subject: [PATCH 083/179] Remove TFBertForPreTraining from ALBERT doc --- docs/source/model_doc/albert.rst | 7 ------- 1 file changed, 7 deletions(-) diff --git a/docs/source/model_doc/albert.rst b/docs/source/model_doc/albert.rst index cf52f35eb6..92970c9328 100644 --- a/docs/source/model_doc/albert.rst +++ b/docs/source/model_doc/albert.rst @@ -50,13 +50,6 @@ ALBERT :members: -``TFBertForPreTraining`` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autoclass:: transformers.TFBertForPreTraining - :members: - - ``TFAlbertForMaskedLM`` ~~~~~~~~~~~~~~~~~~~~~~~~~~ From 45d767297a78697e4ff75ec3d83bdf35e43e4fff Mon Sep 17 00:00:00 2001 From: Lysandre Date: Wed, 27 Nov 2019 10:12:20 -0500 Subject: [PATCH 084/179] Updated v2.2.0 doc --- .circleci/deploy.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.circleci/deploy.sh b/.circleci/deploy.sh index 61b9f90909..a32581baef 100755 --- a/.circleci/deploy.sh +++ b/.circleci/deploy.sh @@ -23,4 +23,4 @@ deploy_doc "fe02e45" v1.1.0 deploy_doc "89fd345" v1.2.0 deploy_doc "fc9faa8" v2.0.0 deploy_doc "3ddce1d" v2.1.1 -deploy_doc "ce02550" v2.2.0 +deploy_doc "3616209" v2.2.0 From 88b317739fe56888528c857fc8e90967148a0051 Mon Sep 17 00:00:00 2001 From: root Date: Wed, 27 Nov 2019 09:58:38 +0000 Subject: [PATCH 085/179] Fix issue: #1962, input's shape seem to cause error in 2.2.0 version tf_albert_model --- transformers/modeling_tf_albert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/modeling_tf_albert.py b/transformers/modeling_tf_albert.py index ee122205b9..b2bf66f750 100644 --- a/transformers/modeling_tf_albert.py +++ b/transformers/modeling_tf_albert.py @@ -645,7 +645,7 @@ class TFAlbertModel(TFAlbertPreTrainedModel): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: - input_shape = input_ids.shape + input_shape = tf.shape(input_ids) elif inputs_embeds is not None: input_shape = inputs_embeds.shape[:-1] else: From de2696f68e20019fef3a5e1b54de10351abb4145 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Louf?= Date: Tue, 26 Nov 2019 16:30:27 +0100 Subject: [PATCH 086/179] suggest to track repo w/ https rather than ssh --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 136ef8df81..8228dd59d8 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -106,7 +106,7 @@ Follow these steps to start contributing: ```bash $ git clone git@github.com:/transformers.git $ cd transformers - $ git remote add upstream git@github.com:huggingface/transformers.git + $ git remote add upstream https://github.com/huggingface/transformers.git ``` 3. Create a new branch to hold your development changes: From b5d884d25c96069962030b9e4c9dfb4b1f2f6fa0 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Wed, 27 Nov 2019 11:05:18 -0500 Subject: [PATCH 087/179] Uniformize #1952 --- README.md | 2 +- examples/README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index dd2c865036..dd20b80590 100644 --- a/README.md +++ b/README.md @@ -92,7 +92,7 @@ Examples are included in the repository but are not shipped with the library. Therefore, in order to run the latest versions of the examples you also need to install from source. To do so, create a new virtual environment and follow these steps: ```bash -git clone git@github.com:huggingface/transformers +git clone https://github.com/huggingface/transformers cd transformers pip install [--editable] . ``` diff --git a/examples/README.md b/examples/README.md index 0c57990ea7..e109a12171 100644 --- a/examples/README.md +++ b/examples/README.md @@ -7,7 +7,7 @@ similar API between the different models. To run the latest versions of the examples, you have to install from source. Execute the following steps in a new virtual environment: ```bash -git clone git@github.com:huggingface/transformers +git clone https://github.com/huggingface/transformers cd transformers pip install [--editable] . ``` From 71f71ddb3e04aa18aaa1e3633a2881493e67b438 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 29 Oct 2019 11:50:42 -0400 Subject: [PATCH 088/179] run_xnli + utils_xnli --- examples/run_xnli.py | 534 +++++++++++++++++++++++++++++++++++++++++ examples/utils_xnli.py | 93 +++++++ 2 files changed, 627 insertions(+) create mode 100644 examples/run_xnli.py create mode 100644 examples/utils_xnli.py diff --git a/examples/run_xnli.py b/examples/run_xnli.py new file mode 100644 index 0000000000..ee37296832 --- /dev/null +++ b/examples/run_xnli.py @@ -0,0 +1,534 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Finetuning multi-lingual models on XNLI (Bert, XLM). + Adapted from `examples/run_glue.py`""" + +from __future__ import absolute_import, division, print_function + +import argparse +import glob +import logging +import os +import random + +import numpy as np +import torch +from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, + TensorDataset) +from torch.utils.data.distributed import DistributedSampler + +try: + from torch.utils.tensorboard import SummaryWriter +except: + from tensorboardX import SummaryWriter + +from tqdm import tqdm, trange + +from transformers import (WEIGHTS_NAME, + BertConfig, BertForSequenceClassification, BertTokenizer, + XLMConfig, XLMForSequenceClassification, XLMTokenizer, + DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer) + +from transformers import AdamW, WarmupLinearSchedule + +from utils_xnli import xnli_compute_metrics as compute_metrics +from utils_xnli import xnli_output_modes as output_modes +from utils_xnli import xnli_processors as processors + +from transformers import glue_convert_examples_to_features as convert_examples_to_features + +logger = logging.getLogger(__name__) + +ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLMConfig)), ()) + +MODEL_CLASSES = { + 'bert': (BertConfig, BertForSequenceClassification, BertTokenizer), + 'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer), + # 'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer) +} + + +def set_seed(args): + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if args.n_gpu > 0: + torch.cuda.manual_seed_all(args.seed) + + +def train(args, train_dataset, model, tokenizer): + """ Train the model """ + if args.local_rank in [-1, 0]: + tb_writer = SummaryWriter() + + args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) + train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) + train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) + + if args.max_steps > 0: + t_total = args.max_steps + args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 + else: + t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs + + # Prepare optimizer and schedule (linear warmup and decay) + no_decay = ['bias', 'LayerNorm.weight'] + optimizer_grouped_parameters = [ + {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay}, + {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} + ] + optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) + scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total) + if args.fp16: + try: + from apex import amp + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") + model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) + + # multi-gpu training (should be after apex fp16 initialization) + if args.n_gpu > 1: + model = torch.nn.DataParallel(model) + + # Distributed training (should be after apex fp16 initialization) + if args.local_rank != -1: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], + output_device=args.local_rank, + find_unused_parameters=True) + + # Train! + logger.info("***** Running training *****") + logger.info(" Num examples = %d", len(train_dataset)) + logger.info(" Num Epochs = %d", args.num_train_epochs) + logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) + logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", + args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1)) + logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) + logger.info(" Total optimization steps = %d", t_total) + + global_step = 0 + tr_loss, logging_loss = 0.0, 0.0 + model.zero_grad() + train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) + set_seed(args) # Added here for reproductibility (even between python 2 and 3) + for _ in train_iterator: + epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) + for step, batch in enumerate(epoch_iterator): + model.train() + batch = tuple(t.to(args.device) for t in batch) + inputs = {'input_ids': batch[0], + 'attention_mask': batch[1], + 'labels': batch[3]} + if args.model_type != 'distilbert': + inputs['token_type_ids'] = batch[2] if args.model_type in ['bert'] else None # XLM and DistilBERT don't use segment_ids + outputs = model(**inputs) + loss = outputs[0] # model outputs are always tuple in transformers (see doc) + + if args.n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu parallel training + if args.gradient_accumulation_steps > 1: + loss = loss / args.gradient_accumulation_steps + + if args.fp16: + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + else: + loss.backward() + + tr_loss += loss.item() + if (step + 1) % args.gradient_accumulation_steps == 0 and not args.tpu: + if args.fp16: + torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) + else: + torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) + + optimizer.step() + scheduler.step() # Update learning rate schedule + model.zero_grad() + global_step += 1 + + if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: + # Log metrics + if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well + results = evaluate(args, model, tokenizer) + for key, value in results.items(): + tb_writer.add_scalar('eval_{}'.format(key), value, global_step) + tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step) + tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step) + logging_loss = tr_loss + + if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: + # Save model checkpoint + output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step)) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training + model_to_save.save_pretrained(output_dir) + torch.save(args, os.path.join(output_dir, 'training_args.bin')) + logger.info("Saving model checkpoint to %s", output_dir) + + if args.tpu: + args.xla_model.optimizer_step(optimizer, barrier=True) + model.zero_grad() + global_step += 1 + + if args.max_steps > 0 and global_step > args.max_steps: + epoch_iterator.close() + break + if args.max_steps > 0 and global_step > args.max_steps: + train_iterator.close() + break + + if args.local_rank in [-1, 0]: + tb_writer.close() + + return global_step, tr_loss / global_step + + +def evaluate(args, model, tokenizer, prefix=""): + eval_task_names = (args.task_name,) + eval_outputs_dirs = (args.output_dir,) + + results = {} + for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs): + eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True) + + if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: + os.makedirs(eval_output_dir) + + args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) + # Note that DistributedSampler samples randomly + eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) + eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) + + # Eval! + logger.info("***** Running evaluation {} *****".format(prefix)) + logger.info(" Num examples = %d", len(eval_dataset)) + logger.info(" Batch size = %d", args.eval_batch_size) + eval_loss = 0.0 + nb_eval_steps = 0 + preds = None + out_label_ids = None + for batch in tqdm(eval_dataloader, desc="Evaluating"): + model.eval() + batch = tuple(t.to(args.device) for t in batch) + + with torch.no_grad(): + inputs = {'input_ids': batch[0], + 'attention_mask': batch[1], + 'labels': batch[3]} + if args.model_type != 'distilbert': + inputs['token_type_ids'] = batch[2] if args.model_type in ['bert'] else None # XLM and DistilBERT don't use segment_ids + outputs = model(**inputs) + tmp_eval_loss, logits = outputs[:2] + + eval_loss += tmp_eval_loss.mean().item() + nb_eval_steps += 1 + if preds is None: + preds = logits.detach().cpu().numpy() + out_label_ids = inputs['labels'].detach().cpu().numpy() + else: + preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) + out_label_ids = np.append(out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0) + + eval_loss = eval_loss / nb_eval_steps + if args.output_mode == "classification": + preds = np.argmax(preds, axis=1) + elif args.output_mode == "regression": + preds = np.squeeze(preds) + result = compute_metrics(eval_task, preds, out_label_ids) + results.update(result) + + output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt") + with open(output_eval_file, "w") as writer: + logger.info("***** Eval results {} *****".format(prefix)) + for key in sorted(result.keys()): + logger.info(" %s = %s", key, str(result[key])) + writer.write("%s = %s\n" % (key, str(result[key]))) + + return results + + +def load_and_cache_examples(args, task, tokenizer, evaluate=False): + if args.local_rank not in [-1, 0] and not evaluate: + torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache + + processor = processors[task](language=args.language, train_language=args.train_language) + output_mode = output_modes[task] + # Load data features from cache or dataset file + cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}_{}_{}_{}'.format( + 'dev' if evaluate else 'train', + list(filter(None, args.model_name_or_path.split('/'))).pop(), + str(args.max_seq_length), + str(task), + str(args.train_language if (not evaluate and args.train_language is not None) else args.language))) + if os.path.exists(cached_features_file) and not args.overwrite_cache: + logger.info("Loading features from cached file %s", cached_features_file) + features = torch.load(cached_features_file) + else: + logger.info("Creating features from dataset file at %s", args.data_dir) + label_list = processor.get_labels() + examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir) + features = convert_examples_to_features(examples, + tokenizer, + label_list=label_list, + max_length=args.max_seq_length, + output_mode=output_mode, + pad_on_left=False, + pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0], + pad_token_segment_id=0, + ) + if args.local_rank in [-1, 0]: + logger.info("Saving features into cached file %s", cached_features_file) + torch.save(features, cached_features_file) + + if args.local_rank == 0 and not evaluate: + torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache + + # Convert to Tensors and build dataset + all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) + all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long) + all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long) + if output_mode == "classification": + all_labels = torch.tensor([f.label for f in features], dtype=torch.long) + else: + raise ValueError(f'No other `output_mode` for XNLI.') + + dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels) + return dataset + + +def main(): + parser = argparse.ArgumentParser() + + ## Required parameters + parser.add_argument("--data_dir", default=None, type=str, required=True, + help="The input data dir. Should contain the .tsv files (or other data files) for the task.") + parser.add_argument("--model_type", default=None, type=str, required=True, + help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys())) + parser.add_argument("--model_name_or_path", default=None, type=str, required=True, + help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS)) + parser.add_argument("--language", default=None, type=str, required=True, + help="Evaluation language. Also train language if `train_language` is set to None.") + parser.add_argument("--train_language", default=None, type=str, + help="Train language if is different of the evaluation language.") + parser.add_argument("--output_dir", default=None, type=str, required=True, + help="The output directory where the model predictions and checkpoints will be written.") + + ## Other parameters + parser.add_argument("--config_name", default="", type=str, + help="Pretrained config name or path if not the same as model_name") + parser.add_argument("--tokenizer_name", default="", type=str, + help="Pretrained tokenizer name or path if not the same as model_name") + parser.add_argument("--cache_dir", default="", type=str, + help="Where do you want to store the pre-trained models downloaded from s3") + parser.add_argument("--max_seq_length", default=128, type=int, + help="The maximum total input sequence length after tokenization. Sequences longer " + "than this will be truncated, sequences shorter will be padded.") + parser.add_argument("--do_train", action='store_true', + help="Whether to run training.") + parser.add_argument("--do_eval", action='store_true', + help="Whether to run eval on the dev set.") + parser.add_argument("--evaluate_during_training", action='store_true', + help="Rul evaluation during training at each logging step.") + parser.add_argument("--do_lower_case", action='store_true', + help="Set this flag if you are using an uncased model.") + + parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, + help="Batch size per GPU/CPU for training.") + parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int, + help="Batch size per GPU/CPU for evaluation.") + parser.add_argument('--gradient_accumulation_steps', type=int, default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.") + parser.add_argument("--learning_rate", default=5e-5, type=float, + help="The initial learning rate for Adam.") + parser.add_argument("--weight_decay", default=0.0, type=float, + help="Weight deay if we apply some.") + parser.add_argument("--adam_epsilon", default=1e-8, type=float, + help="Epsilon for Adam optimizer.") + parser.add_argument("--max_grad_norm", default=1.0, type=float, + help="Max gradient norm.") + parser.add_argument("--num_train_epochs", default=3.0, type=float, + help="Total number of training epochs to perform.") + parser.add_argument("--max_steps", default=-1, type=int, + help="If > 0: set total number of training steps to perform. Override num_train_epochs.") + parser.add_argument("--warmup_steps", default=0, type=int, + help="Linear warmup over warmup_steps.") + + parser.add_argument('--logging_steps', type=int, default=50, + help="Log every X updates steps.") + parser.add_argument('--save_steps', type=int, default=50, + help="Save checkpoint every X updates steps.") + parser.add_argument("--eval_all_checkpoints", action='store_true', + help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number") + parser.add_argument("--no_cuda", action='store_true', + help="Avoid using CUDA when available") + parser.add_argument('--overwrite_output_dir', action='store_true', + help="Overwrite the content of the output directory") + parser.add_argument('--overwrite_cache', action='store_true', + help="Overwrite the cached training and evaluation sets") + parser.add_argument('--seed', type=int, default=42, + help="random seed for initialization") + + parser.add_argument('--tpu', action='store_true', + help="Whether to run on the TPU defined in the environment variables") + parser.add_argument('--tpu_ip_address', type=str, default='', + help="TPU IP address if none are set in the environment variables") + parser.add_argument('--tpu_name', type=str, default='', + help="TPU name if none are set in the environment variables") + parser.add_argument('--xrt_tpu_config', type=str, default='', + help="XRT TPU config if none are set in the environment variables") + + parser.add_argument('--fp16', action='store_true', + help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit") + parser.add_argument('--fp16_opt_level', type=str, default='O1', + help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." + "See details at https://nvidia.github.io/apex/amp.html") + parser.add_argument("--local_rank", type=int, default=-1, + help="For distributed training: local_rank") + parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.") + parser.add_argument('--server_port', type=str, default='', help="For distant debugging.") + args = parser.parse_args() + + if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir: + raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir)) + + # Setup distant debugging if needed + if args.server_ip and args.server_port: + # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script + import ptvsd + print("Waiting for debugger attach") + ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) + ptvsd.wait_for_attach() + + # Setup CUDA, GPU & distributed training + if args.local_rank == -1 or args.no_cuda: + device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") + args.n_gpu = torch.cuda.device_count() + else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs + torch.cuda.set_device(args.local_rank) + device = torch.device("cuda", args.local_rank) + torch.distributed.init_process_group(backend='nccl') + args.n_gpu = 1 + args.device = device + + if args.tpu: + if args.tpu_ip_address: + os.environ["TPU_IP_ADDRESS"] = args.tpu_ip_address + if args.tpu_name: + os.environ["TPU_NAME"] = args.tpu_name + if args.xrt_tpu_config: + os.environ["XRT_TPU_CONFIG"] = args.xrt_tpu_config + + assert "TPU_IP_ADDRESS" in os.environ + assert "TPU_NAME" in os.environ + assert "XRT_TPU_CONFIG" in os.environ + + import torch_xla + import torch_xla.core.xla_model as xm + args.device = xm.xla_device() + args.xla_model = xm + + # Setup logging + logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', + datefmt = '%m/%d/%Y %H:%M:%S', + level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN) + logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", + args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16) + + # Set seed + set_seed(args) + + # Prepare XNLI task + args.task_name = 'xnli' + if args.task_name not in processors: + raise ValueError("Task not found: %s" % (args.task_name)) + processor = processors[args.task_name](language=args.language, train_language=args.train_language) + args.output_mode = output_modes[args.task_name] + label_list = processor.get_labels() + num_labels = len(label_list) + + # Load pretrained model and tokenizer + if args.local_rank not in [-1, 0]: + torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab + + args.model_type = args.model_type.lower() + config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] + config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name) + tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case) + model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config) + + if args.local_rank == 0: + torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab + + model.to(args.device) + + logger.info("Training/evaluation parameters %s", args) + + + # Training + if args.do_train: + train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False) + global_step, tr_loss = train(args, train_dataset, model, tokenizer) + logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) + + + # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() + if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0) and not args.tpu: + # Create output directory if needed + if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: + os.makedirs(args.output_dir) + + logger.info("Saving model checkpoint to %s", args.output_dir) + # Save a trained model, configuration and tokenizer using `save_pretrained()`. + # They can then be reloaded using `from_pretrained()` + model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training + model_to_save.save_pretrained(args.output_dir) + tokenizer.save_pretrained(args.output_dir) + + # Good practice: save your training arguments together with the trained model + torch.save(args, os.path.join(args.output_dir, 'training_args.bin')) + + # Load a trained model and vocabulary that you have fine-tuned + model = model_class.from_pretrained(args.output_dir) + tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) + model.to(args.device) + + + # Evaluation + results = {} + if args.do_eval and args.local_rank in [-1, 0]: + tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) + checkpoints = [args.output_dir] + if args.eval_all_checkpoints: + checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))) + logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging + logger.info("Evaluate the following checkpoints: %s", checkpoints) + for checkpoint in checkpoints: + global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" + prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else "" + + model = model_class.from_pretrained(checkpoint) + model.to(args.device) + result = evaluate(args, model, tokenizer, prefix=prefix) + result = dict((k + '_{}'.format(global_step), v) for k, v in result.items()) + results.update(result) + + return results + + +if __name__ == "__main__": + main() diff --git a/examples/utils_xnli.py b/examples/utils_xnli.py new file mode 100644 index 0000000000..f0238f4664 --- /dev/null +++ b/examples/utils_xnli.py @@ -0,0 +1,93 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" XNLI utils (dataset loading and evaluation) """ + +from __future__ import absolute_import, division, print_function + +import logging +import os + +from transformers.data.processors import DataProcessor, InputExample +from transformers.data.metrics import simple_accuracy + +logger = logging.getLogger(__name__) + +class XnliProcessor(DataProcessor): + """Processor for the XNLI dataset. + Adapted from https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/run_classifier.py#L207""" + + def __init__(self, language, train_language = None): + self.language = language + self.train_language = train_language + + def get_train_examples(self, data_dir): + """See base class.""" + lg = self.language if self.train_language is None else self.train_language + lines = self._read_tsv(os.path.join(data_dir, f"XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv")) + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % ('train', i) + text_a = line[0] + text_b = line[1] + label = "contradiction" if line[2] == "contradictory" else line[2] + assert isinstance(text_a, str) and isinstance(text_b, str) and isinstance(label, str) + examples.append( + InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_dev_examples(self, data_dir): + """See base class.""" + lines = self._read_tsv(os.path.join(data_dir, "XNLI-1.0/xnli.dev.tsv")) + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + language = line[0] + if language != self.language: + continue + guid = "%s-%s" % ('dev', i) + text_a = line[6] + text_b = line[7] + label = line[1] + assert isinstance(text_a, str) and isinstance(text_b, str) and isinstance(label, str) + examples.append( + InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_labels(self): + """See base class.""" + return ["contradiction", "entailment", "neutral"] + +def xnli_compute_metrics(task_name, preds, labels): + assert len(preds) == len(labels) + if task_name == "xnli": + return {"acc": simple_accuracy(preds, labels)} + else: + raise ValueError(f'{task_name} is not a supported task.') + +xnli_processors = { + "xnli": XnliProcessor, +} + +xnli_output_modes = { + "xnli": "classification", +} + +xnli_tasks_num_labels = { + "xnli": 3, +} From d52e98ff9af4509bb803641f0c0d81d67ce73cc3 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 29 Oct 2019 11:51:15 -0400 Subject: [PATCH 089/179] add xnli examples/README.md --- examples/README.md | 40 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) diff --git a/examples/README.md b/examples/README.md index e109a12171..6f8d6bd26e 100644 --- a/examples/README.md +++ b/examples/README.md @@ -21,6 +21,7 @@ pip install [--editable] . | [SQuAD](#squad) | Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training. | | [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. | [Named Entity Recognition](#named-entity-recognition) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. | +| [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. | | [Abstractive summarization](#abstractive-summarization) | Fine-tuning the library models for abstractive summarization tasks on the CNN/Daily Mail dataset. | ## TensorFlow 2.0 Bert models on GLUE @@ -600,3 +601,42 @@ python run_summarization_finetuning.py \ --do_train \ --data_path=$DATA_PATH \ ``` + +## XNLI + +Based on the script [`run_xnli.py`](TODO). + +[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-ressource language such as English and low-ressource languages such as Swahili). + +#### Fine-tuning on XNLI + +This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in TODO min +on a single tesla V100 16GB. The data for XNLI can be downloaded with the following links and should be both saved (and un-zipped) in a +`$XNLI_DIR` directory. + +* [XNLI 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip) +* [XNLI-MT 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-MT-1.0.zip) + +```bash +export XNLI_DIR=/path/to/XNLI + +python run_xnli.py \ + --model_type bert \ + --model_name_or_path bert-base-multilingual-cased \ + --language en \ + --train_language en \ + --do_train \ + --do_eval \ + --data_dir $SQUAD_DIR \ + --per_gpu_train_batch_size 32 \ + --learning_rate 5e-5 \ + --num_train_epochs 2.0 \ + --max_seq_length 128 \ + --output_dir /tmp/debug_xnli/ +``` + +Training with the previously defined hyper-parameters yields the following results: + +```bash +TODO +``` From c4336ecbbdbb8bddcbfb31e0fcc4d382b430a9a5 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 29 Oct 2019 12:04:20 -0400 Subject: [PATCH 090/179] xnli - output_mode consistency --- examples/run_xnli.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/run_xnli.py b/examples/run_xnli.py index ee37296832..a9b2e46c13 100644 --- a/examples/run_xnli.py +++ b/examples/run_xnli.py @@ -247,8 +247,8 @@ def evaluate(args, model, tokenizer, prefix=""): eval_loss = eval_loss / nb_eval_steps if args.output_mode == "classification": preds = np.argmax(preds, axis=1) - elif args.output_mode == "regression": - preds = np.squeeze(preds) + else: + raise ValueError(f'No other `output_mode` for XNLI.') result = compute_metrics(eval_task, preds, out_label_ids) results.update(result) From 84a0b522cf1b3f69bfbc92eb2309ba5ff652e521 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 29 Oct 2019 18:53:45 -0400 Subject: [PATCH 091/179] mbert reproducibility results --- examples/README.md | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/examples/README.md b/examples/README.md index 6f8d6bd26e..3e5fd03c45 100644 --- a/examples/README.md +++ b/examples/README.md @@ -604,13 +604,13 @@ python run_summarization_finetuning.py \ ## XNLI -Based on the script [`run_xnli.py`](TODO). +Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/run_xnli.py). [XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-ressource language such as English and low-ressource languages such as Swahili). #### Fine-tuning on XNLI -This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in TODO min +This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in 106 mins on a single tesla V100 16GB. The data for XNLI can be downloaded with the following links and should be both saved (and un-zipped) in a `$XNLI_DIR` directory. @@ -623,20 +623,21 @@ export XNLI_DIR=/path/to/XNLI python run_xnli.py \ --model_type bert \ --model_name_or_path bert-base-multilingual-cased \ - --language en \ + --language es \ --train_language en \ --do_train \ --do_eval \ - --data_dir $SQUAD_DIR \ + --data_dir $XNLI_DIR \ --per_gpu_train_batch_size 32 \ --learning_rate 5e-5 \ --num_train_epochs 2.0 \ --max_seq_length 128 \ - --output_dir /tmp/debug_xnli/ + --output_dir /tmp/debug_xnli/ \ + --save_steps -1 ``` -Training with the previously defined hyper-parameters yields the following results: +Training with the previously defined hyper-parameters yields the following results on the dev set: ```bash -TODO +acc = 0.738152610441767 ``` From cb7b77a8a2ee52812c4358817a6a586f19687cda Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Wed, 30 Oct 2019 18:13:52 -0400 Subject: [PATCH 092/179] fix some typos --- transformers/tokenization_xlm.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/transformers/tokenization_xlm.py b/transformers/tokenization_xlm.py index 01f8721d98..ba994dc356 100644 --- a/transformers/tokenization_xlm.py +++ b/transformers/tokenization_xlm.py @@ -12,7 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Tokenization classes for OpenAI GPT.""" +"""Tokenization classes for XLM.""" from __future__ import (absolute_import, division, print_function, unicode_literals) @@ -758,9 +758,9 @@ class XLMTokenizer(PreTrainedTokenizer): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. - A RoBERTa sequence has the following format: + A XLM sequence has the following format: single sequence: X - pair of sequences: A B + pair of sequences: A B """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] From 289cf4d2b7cf090942e2e7dc4e6134a81095adf6 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 5 Nov 2019 10:10:02 -0500 Subject: [PATCH 093/179] change default for XNLI: dev --> test --- examples/run_xnli.py | 6 +++--- examples/utils_xnli.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/run_xnli.py b/examples/run_xnli.py index a9b2e46c13..7fbbf7d152 100644 --- a/examples/run_xnli.py +++ b/examples/run_xnli.py @@ -270,7 +270,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False): output_mode = output_modes[task] # Load data features from cache or dataset file cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}_{}_{}_{}'.format( - 'dev' if evaluate else 'train', + 'test' if evaluate else 'train', list(filter(None, args.model_name_or_path.split('/'))).pop(), str(args.max_seq_length), str(task), @@ -281,7 +281,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False): else: logger.info("Creating features from dataset file at %s", args.data_dir) label_list = processor.get_labels() - examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir) + examples = processor.get_test_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir) features = convert_examples_to_features(examples, tokenizer, label_list=label_list, @@ -341,7 +341,7 @@ def main(): parser.add_argument("--do_train", action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", action='store_true', - help="Whether to run eval on the dev set.") + help="Whether to run eval on the test set.") parser.add_argument("--evaluate_during_training", action='store_true', help="Rul evaluation during training at each logging step.") parser.add_argument("--do_lower_case", action='store_true', diff --git a/examples/utils_xnli.py b/examples/utils_xnli.py index f0238f4664..482e79a81f 100644 --- a/examples/utils_xnli.py +++ b/examples/utils_xnli.py @@ -50,9 +50,9 @@ class XnliProcessor(DataProcessor): InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples - def get_dev_examples(self, data_dir): + def get_test_examples(self, data_dir): """See base class.""" - lines = self._read_tsv(os.path.join(data_dir, "XNLI-1.0/xnli.dev.tsv")) + lines = self._read_tsv(os.path.join(data_dir, "XNLI-1.0/xnli.test.tsv")) examples = [] for (i, line) in enumerate(lines): if i == 0: @@ -60,7 +60,7 @@ class XnliProcessor(DataProcessor): language = line[0] if language != self.language: continue - guid = "%s-%s" % ('dev', i) + guid = "%s-%s" % ('test', i) text_a = line[6] text_b = line[7] label = line[1] From d5910b312fbe02a4b08097646e46399dac179084 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 5 Nov 2019 10:21:25 -0500 Subject: [PATCH 094/179] move xnli processor (and utils) to transformers/data/processors --- examples/run_xnli.py | 6 +++--- transformers/__init__.py | 3 ++- transformers/data/__init__.py | 1 + transformers/data/processors/__init__.py | 2 +- .../utils_xnli.py => transformers/data/processors/xnli.py | 2 +- 5 files changed, 8 insertions(+), 6 deletions(-) rename examples/utils_xnli.py => transformers/data/processors/xnli.py (97%) diff --git a/examples/run_xnli.py b/examples/run_xnli.py index 7fbbf7d152..0c1743e6c4 100644 --- a/examples/run_xnli.py +++ b/examples/run_xnli.py @@ -44,9 +44,9 @@ from transformers import (WEIGHTS_NAME, from transformers import AdamW, WarmupLinearSchedule -from utils_xnli import xnli_compute_metrics as compute_metrics -from utils_xnli import xnli_output_modes as output_modes -from utils_xnli import xnli_processors as processors +from transformers import xnli_compute_metrics as compute_metrics +from transformers import xnli_output_modes as output_modes +from transformers import xnli_processors as processors from transformers import glue_convert_examples_to_features as convert_examples_to_features diff --git a/transformers/__init__.py b/transformers/__init__.py index dff2479d4b..a133425a9c 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -25,7 +25,8 @@ from .file_utils import (TRANSFORMERS_CACHE, PYTORCH_TRANSFORMERS_CACHE, PYTORCH from .data import (is_sklearn_available, InputExample, InputFeatures, DataProcessor, glue_output_modes, glue_convert_examples_to_features, - glue_processors, glue_tasks_num_labels) + glue_processors, glue_tasks_num_labels, + xnli_output_modes, xnli_processors, xnli_tasks_num_labels) if is_sklearn_available(): from .data import glue_compute_metrics diff --git a/transformers/data/__init__.py b/transformers/data/__init__.py index e910d6da2e..46615608a4 100644 --- a/transformers/data/__init__.py +++ b/transformers/data/__init__.py @@ -1,5 +1,6 @@ from .processors import InputExample, InputFeatures, DataProcessor from .processors import glue_output_modes, glue_processors, glue_tasks_num_labels, glue_convert_examples_to_features +from .processors import xnli_output_modes, xnli_processors, xnli_tasks_num_labels from .metrics import is_sklearn_available if is_sklearn_available(): diff --git a/transformers/data/processors/__init__.py b/transformers/data/processors/__init__.py index af38c54beb..1c41553ba4 100644 --- a/transformers/data/processors/__init__.py +++ b/transformers/data/processors/__init__.py @@ -1,3 +1,3 @@ from .utils import InputExample, InputFeatures, DataProcessor from .glue import glue_output_modes, glue_processors, glue_tasks_num_labels, glue_convert_examples_to_features - +from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels diff --git a/examples/utils_xnli.py b/transformers/data/processors/xnli.py similarity index 97% rename from examples/utils_xnli.py rename to transformers/data/processors/xnli.py index 482e79a81f..96fac761a9 100644 --- a/examples/utils_xnli.py +++ b/transformers/data/processors/xnli.py @@ -20,7 +20,7 @@ from __future__ import absolute_import, division, print_function import logging import os -from transformers.data.processors import DataProcessor, InputExample +from .utils import DataProcessor, InputExample from transformers.data.metrics import simple_accuracy logger = logging.getLogger(__name__) From d75d49a51de28df48b39280b2f06df78d3fa04bf Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 5 Nov 2019 10:32:20 -0500 Subject: [PATCH 095/179] add XnliProcessor to doc --- docs/source/main_classes/processors.rst | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/docs/source/main_classes/processors.rst b/docs/source/main_classes/processors.rst index a85c126956..a093e621ad 100644 --- a/docs/source/main_classes/processors.rst +++ b/docs/source/main_classes/processors.rst @@ -55,4 +55,27 @@ Example usage ^^^^^^^^^^^^^^^^^^^^^^^^^ An example using these processors is given in the -`run_glue.py `__ script. \ No newline at end of file +`run_glue.py `__ script. + + +XNLI +~~~~~~~~~~~~~~~~~~~~~ + +`The Cross-Lingual NLI Corpus (XNLI) `__ is a benchmark that evaluates +the quality of cross-lingual text representations. +XNLI is crowd-sourced dataset based on `MultiNLI `: pairs of text are labeled with textual entailment +annotations for 15 different languages (including both high-ressource language such as English and low-ressource languages such as Swahili). + +It was released together with the paper +`XNLI: Evaluating Cross-lingual Sentence Representations `__ + +This library hosts the processor to load the XNLI data: + - :class:`~transformers.data.processors.utils.XnliProcessor` + +Please note that since the gold labels are available on the test set, evaluation is performed on the test set. + +Example usage +^^^^^^^^^^^^^^^^^^^^^^^^^ + +An example using these processors is given in the +`run_xnli.py `__ script. \ No newline at end of file From abd397e95483cc7486bed47fd271ab417e1a288e Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 5 Nov 2019 10:57:24 -0500 Subject: [PATCH 096/179] uniformize w/ the cache_dir update --- examples/run_xnli.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/examples/run_xnli.py b/examples/run_xnli.py index 0c1743e6c4..e8457e542b 100644 --- a/examples/run_xnli.py +++ b/examples/run_xnli.py @@ -467,9 +467,17 @@ def main(): args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] - config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name) - tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case) - model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config) + config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path, + num_labels=num_labels, + finetuning_task=args.task_name, + cache_dir=args.cache_dir if args.cache_dir else None) + tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, + do_lower_case=args.do_lower_case, + cache_dir=args.cache_dir if args.cache_dir else None) + model = model_class.from_pretrained(args.model_name_or_path, + from_tf=bool('.ckpt' in args.model_name_or_path), + config=config, + cache_dir=args.cache_dir if args.cache_dir else None) if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab From 3e7656f7ac369de08a2ebac3cd1d6870e60605bc Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 5 Nov 2019 11:58:53 -0500 Subject: [PATCH 097/179] update readme --- examples/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/README.md b/examples/README.md index 3e5fd03c45..960b218f11 100644 --- a/examples/README.md +++ b/examples/README.md @@ -623,7 +623,7 @@ export XNLI_DIR=/path/to/XNLI python run_xnli.py \ --model_type bert \ --model_name_or_path bert-base-multilingual-cased \ - --language es \ + --language de \ --train_language en \ --do_train \ --do_eval \ @@ -636,8 +636,8 @@ python run_xnli.py \ --save_steps -1 ``` -Training with the previously defined hyper-parameters yields the following results on the dev set: +Training with the previously defined hyper-parameters yields the following results on the **test** set: ```bash -acc = 0.738152610441767 +acc = 0.7093812375249501 ``` From 73fe2e7385af4c6062f366825af570d44cd22fd8 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 5 Nov 2019 12:51:43 -0500 Subject: [PATCH 098/179] remove fstrings --- examples/run_xnli.py | 4 ++-- transformers/data/processors/xnli.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/run_xnli.py b/examples/run_xnli.py index e8457e542b..952128f4ad 100644 --- a/examples/run_xnli.py +++ b/examples/run_xnli.py @@ -248,7 +248,7 @@ def evaluate(args, model, tokenizer, prefix=""): if args.output_mode == "classification": preds = np.argmax(preds, axis=1) else: - raise ValueError(f'No other `output_mode` for XNLI.') + raise ValueError('No other `output_mode` for XNLI.') result = compute_metrics(eval_task, preds, out_label_ids) results.update(result) @@ -305,7 +305,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False): if output_mode == "classification": all_labels = torch.tensor([f.label for f in features], dtype=torch.long) else: - raise ValueError(f'No other `output_mode` for XNLI.') + raise ValueError('No other `output_mode` for XNLI.') dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels) return dataset diff --git a/transformers/data/processors/xnli.py b/transformers/data/processors/xnli.py index 96fac761a9..a4807dd901 100644 --- a/transformers/data/processors/xnli.py +++ b/transformers/data/processors/xnli.py @@ -36,7 +36,7 @@ class XnliProcessor(DataProcessor): def get_train_examples(self, data_dir): """See base class.""" lg = self.language if self.train_language is None else self.train_language - lines = self._read_tsv(os.path.join(data_dir, f"XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv")) + lines = self._read_tsv(os.path.join(data_dir, "XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv".format(lg))) examples = [] for (i, line) in enumerate(lines): if i == 0: @@ -78,7 +78,7 @@ def xnli_compute_metrics(task_name, preds, labels): if task_name == "xnli": return {"acc": simple_accuracy(preds, labels)} else: - raise ValueError(f'{task_name} is not a supported task.') + raise ValueError('{} is not a supported task.'.format(task_name)) xnli_processors = { "xnli": XnliProcessor, From bcd8dc6b48a335b899f00b59f016f740c5230d41 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 5 Nov 2019 12:53:08 -0500 Subject: [PATCH 099/179] move xnli_compute_metrics to data/metrics --- transformers/__init__.py | 2 +- transformers/data/__init__.py | 2 +- transformers/data/metrics/__init__.py | 8 ++++++++ transformers/data/processors/xnli.py | 7 ------- 4 files changed, 10 insertions(+), 9 deletions(-) diff --git a/transformers/__init__.py b/transformers/__init__.py index a133425a9c..b29ad38e73 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -29,7 +29,7 @@ from .data import (is_sklearn_available, xnli_output_modes, xnli_processors, xnli_tasks_num_labels) if is_sklearn_available(): - from .data import glue_compute_metrics + from .data import glue_compute_metrics, xnli_compute_metrics # Tokenizers from .tokenization_utils import (PreTrainedTokenizer) diff --git a/transformers/data/__init__.py b/transformers/data/__init__.py index 46615608a4..b811a35807 100644 --- a/transformers/data/__init__.py +++ b/transformers/data/__init__.py @@ -4,4 +4,4 @@ from .processors import xnli_output_modes, xnli_processors, xnli_tasks_num_label from .metrics import is_sklearn_available if is_sklearn_available(): - from .metrics import glue_compute_metrics + from .metrics import glue_compute_metrics, xnli_compute_metrics diff --git a/transformers/data/metrics/__init__.py b/transformers/data/metrics/__init__.py index c9ebaac38d..5a46eb05d3 100644 --- a/transformers/data/metrics/__init__.py +++ b/transformers/data/metrics/__init__.py @@ -81,3 +81,11 @@ if _has_sklearn: return {"acc": simple_accuracy(preds, labels)} else: raise KeyError(task_name) + + + def xnli_compute_metrics(task_name, preds, labels): + assert len(preds) == len(labels) + if task_name == "xnli": + return {"acc": simple_accuracy(preds, labels)} + else: + raise KeyError(task_name) diff --git a/transformers/data/processors/xnli.py b/transformers/data/processors/xnli.py index a4807dd901..ce582f31a6 100644 --- a/transformers/data/processors/xnli.py +++ b/transformers/data/processors/xnli.py @@ -73,13 +73,6 @@ class XnliProcessor(DataProcessor): """See base class.""" return ["contradiction", "entailment", "neutral"] -def xnli_compute_metrics(task_name, preds, labels): - assert len(preds) == len(labels) - if task_name == "xnli": - return {"acc": simple_accuracy(preds, labels)} - else: - raise ValueError('{} is not a supported task.'.format(task_name)) - xnli_processors = { "xnli": XnliProcessor, } From d47402263964e30ee17cbc06811622bf2df50d6d Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 5 Nov 2019 12:56:03 -0500 Subject: [PATCH 100/179] cleaning simple_accuracy since not used anymore --- transformers/data/processors/xnli.py | 1 - 1 file changed, 1 deletion(-) diff --git a/transformers/data/processors/xnli.py b/transformers/data/processors/xnli.py index ce582f31a6..efbe3762f6 100644 --- a/transformers/data/processors/xnli.py +++ b/transformers/data/processors/xnli.py @@ -21,7 +21,6 @@ import logging import os from .utils import DataProcessor, InputExample -from transformers.data.metrics import simple_accuracy logger = logging.getLogger(__name__) From 07ab8d7af6041f0d3562badcb3d6f173062329a1 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 5 Nov 2019 17:33:14 -0500 Subject: [PATCH 101/179] fix bug --- transformers/data/processors/xnli.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/data/processors/xnli.py b/transformers/data/processors/xnli.py index efbe3762f6..958bdf62f9 100644 --- a/transformers/data/processors/xnli.py +++ b/transformers/data/processors/xnli.py @@ -35,7 +35,7 @@ class XnliProcessor(DataProcessor): def get_train_examples(self, data_dir): """See base class.""" lg = self.language if self.train_language is None else self.train_language - lines = self._read_tsv(os.path.join(data_dir, "XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv".format(lg))) + lines = self._read_tsv(os.path.join(data_dir, "XNLI-MT-1.0/multinli/multinli.train.{}.tsv".format(lg))) examples = [] for (i, line) in enumerate(lines): if i == 0: From d5478b939d64db58972e46b7218c765c918b76ac Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Mon, 25 Nov 2019 19:40:48 +0000 Subject: [PATCH 102/179] add distilbert + update run_xnli wrt run_glue --- examples/run_xnli.py | 51 +++++++++++--------------------------------- 1 file changed, 12 insertions(+), 39 deletions(-) diff --git a/examples/run_xnli.py b/examples/run_xnli.py index 952128f4ad..a3bc0d4604 100644 --- a/examples/run_xnli.py +++ b/examples/run_xnli.py @@ -13,7 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -""" Finetuning multi-lingual models on XNLI (Bert, XLM). +""" Finetuning multi-lingual models on XNLI (Bert, DistilBERT, XLM). Adapted from `examples/run_glue.py`""" from __future__ import absolute_import, division, print_function @@ -42,7 +42,7 @@ from transformers import (WEIGHTS_NAME, XLMConfig, XLMForSequenceClassification, XLMTokenizer, DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer) -from transformers import AdamW, WarmupLinearSchedule +from transformers import AdamW, get_linear_schedule_with_warmup from transformers import xnli_compute_metrics as compute_metrics from transformers import xnli_output_modes as output_modes @@ -52,12 +52,12 @@ from transformers import glue_convert_examples_to_features as convert_examples_t logger = logging.getLogger(__name__) -ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLMConfig)), ()) +ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, DistilBertConfig, XLMConfig)), ()) MODEL_CLASSES = { 'bert': (BertConfig, BertForSequenceClassification, BertTokenizer), 'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer), - # 'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer) + 'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer) } @@ -91,7 +91,7 @@ def train(args, train_dataset, model, tokenizer): {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) - scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total) + scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total) if args.fp16: try: from apex import amp @@ -149,7 +149,7 @@ def train(args, train_dataset, model, tokenizer): loss.backward() tr_loss += loss.item() - if (step + 1) % args.gradient_accumulation_steps == 0 and not args.tpu: + if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: @@ -180,11 +180,6 @@ def train(args, train_dataset, model, tokenizer): torch.save(args, os.path.join(output_dir, 'training_args.bin')) logger.info("Saving model checkpoint to %s", output_dir) - if args.tpu: - args.xla_model.optimizer_step(optimizer, barrier=True) - model.zero_grad() - global_step += 1 - if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break @@ -214,6 +209,10 @@ def evaluate(args, model, tokenizer, prefix=""): eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) + # multi-gpu eval + if args.n_gpu > 1: + model = torch.nn.DataParallel(model) + # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(eval_dataset)) @@ -383,15 +382,6 @@ def main(): parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") - parser.add_argument('--tpu', action='store_true', - help="Whether to run on the TPU defined in the environment variables") - parser.add_argument('--tpu_ip_address', type=str, default='', - help="TPU IP address if none are set in the environment variables") - parser.add_argument('--tpu_name', type=str, default='', - help="TPU name if none are set in the environment variables") - parser.add_argument('--xrt_tpu_config', type=str, default='', - help="XRT TPU config if none are set in the environment variables") - parser.add_argument('--fp16', action='store_true', help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit") parser.add_argument('--fp16_opt_level', type=str, default='O1', @@ -425,23 +415,6 @@ def main(): args.n_gpu = 1 args.device = device - if args.tpu: - if args.tpu_ip_address: - os.environ["TPU_IP_ADDRESS"] = args.tpu_ip_address - if args.tpu_name: - os.environ["TPU_NAME"] = args.tpu_name - if args.xrt_tpu_config: - os.environ["XRT_TPU_CONFIG"] = args.xrt_tpu_config - - assert "TPU_IP_ADDRESS" in os.environ - assert "TPU_NAME" in os.environ - assert "XRT_TPU_CONFIG" in os.environ - - import torch_xla - import torch_xla.core.xla_model as xm - args.device = xm.xla_device() - args.xla_model = xm - # Setup logging logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', @@ -495,7 +468,7 @@ def main(): # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() - if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0) and not args.tpu: + if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # Create output directory if needed if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) @@ -512,7 +485,7 @@ def main(): # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained(args.output_dir) - tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) + tokenizer = tokenizer_class.from_pretrained(args.output_dir) model.to(args.device) From 10bd1ddb39235b2f58594e48867595e7d38cd619 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Mon, 25 Nov 2019 19:41:00 +0000 Subject: [PATCH 103/179] soft launch distilbert multilingual --- transformers/configuration_distilbert.py | 3 ++- transformers/modeling_distilbert.py | 3 ++- transformers/tokenization_distilbert.py | 2 ++ 3 files changed, 6 insertions(+), 2 deletions(-) diff --git a/transformers/configuration_distilbert.py b/transformers/configuration_distilbert.py index 2a8a149acf..f929a9bc39 100644 --- a/transformers/configuration_distilbert.py +++ b/transformers/configuration_distilbert.py @@ -27,7 +27,8 @@ logger = logging.getLogger(__name__) DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { 'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json", - 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json" + 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json", + 'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-config.json", } diff --git a/transformers/modeling_distilbert.py b/transformers/modeling_distilbert.py index d30f493c69..62c623ff6c 100644 --- a/transformers/modeling_distilbert.py +++ b/transformers/modeling_distilbert.py @@ -42,7 +42,8 @@ logger = logging.getLogger(__name__) DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = { 'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-pytorch_model.bin", - 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin" + 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin", + 'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-pytorch_model.bin", } diff --git a/transformers/tokenization_distilbert.py b/transformers/tokenization_distilbert.py index dfa02926d8..832f0c3d0b 100644 --- a/transformers/tokenization_distilbert.py +++ b/transformers/tokenization_distilbert.py @@ -33,12 +33,14 @@ PRETRAINED_VOCAB_FILES_MAP = { { 'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt", 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt", + 'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt", } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { 'distilbert-base-uncased': 512, 'distilbert-base-uncased-distilled-squad': 512, + 'distilbert-base-multilingual-cased': 512, } From 3c28a2daac43386dbc63b0bd014a966f22888850 Mon Sep 17 00:00:00 2001 From: Yao Lu <95luyao@gmail.com> Date: Wed, 27 Nov 2019 11:45:22 -0500 Subject: [PATCH 104/179] add add_special_tokens=True for input examples --- transformers/modeling_bert.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/transformers/modeling_bert.py b/transformers/modeling_bert.py index 81d92d8f1b..34bb8f89ba 100644 --- a/transformers/modeling_bert.py +++ b/transformers/modeling_bert.py @@ -597,7 +597,7 @@ class BertModel(BertPreTrainedModel): tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') - input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 + input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 outputs = model(input_ids) last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple @@ -760,7 +760,7 @@ class BertForPreTraining(BertPreTrainedModel): tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertForPreTraining.from_pretrained('bert-base-uncased') - input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 + input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 outputs = model(input_ids) prediction_scores, seq_relationship_scores = outputs[:2] @@ -836,7 +836,7 @@ class BertForMaskedLM(BertPreTrainedModel): tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertForMaskedLM.from_pretrained('bert-base-uncased') - input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 + input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 outputs = model(input_ids, masked_lm_labels=input_ids) loss, prediction_scores = outputs[:2] @@ -919,7 +919,7 @@ class BertForNextSentencePrediction(BertPreTrainedModel): tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased') - input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 + input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 outputs = model(input_ids) seq_relationship_scores = outputs[0] @@ -984,7 +984,7 @@ class BertForSequenceClassification(BertPreTrainedModel): tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertForSequenceClassification.from_pretrained('bert-base-uncased') - input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 + input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=labels) loss, logits = outputs[:2] @@ -1060,7 +1060,7 @@ class BertForMultipleChoice(BertPreTrainedModel): tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertForMultipleChoice.from_pretrained('bert-base-uncased') choices = ["Hello, my dog is cute", "Hello, my cat is amazing"] - input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices + input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices labels = torch.tensor(1).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=labels) loss, classification_scores = outputs[:2] @@ -1134,7 +1134,7 @@ class BertForTokenClassification(BertPreTrainedModel): tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertForTokenClassification.from_pretrained('bert-base-uncased') - input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 + input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=labels) loss, scores = outputs[:2] From 8da47b078d92bee2de3e5fb50a37483d8cb02f13 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Wed, 27 Nov 2019 23:11:37 +0100 Subject: [PATCH 105/179] fix merge tests --- transformers/modeling_ctrl.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/modeling_ctrl.py b/transformers/modeling_ctrl.py index 849487655d..3a252941ac 100644 --- a/transformers/modeling_ctrl.py +++ b/transformers/modeling_ctrl.py @@ -373,7 +373,7 @@ class CTRLModel(CTRLPreTrainedModel): if inputs_embeds is None: inputs_embeds = self.w(input_ids) # inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded - seq_len = input_shape.shape[-1] + seq_len = input_shape[-1] mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(inputs_embeds.device) inputs_embeds *= np.sqrt(self.d_model_size) From 41aa0e80039d3148c55f4fe967247d4f7bbbfec5 Mon Sep 17 00:00:00 2001 From: Juha Kiili Date: Fri, 29 Nov 2019 15:33:25 +0200 Subject: [PATCH 106/179] Refactor logs and fix loss bug --- examples/run_glue.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/run_glue.py b/examples/run_glue.py index ea5ac5bbb7..8749593a1f 100644 --- a/examples/run_glue.py +++ b/examples/run_glue.py @@ -171,22 +171,22 @@ def train(args, train_dataset, model, tokenizer): global_step += 1 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: - # Log metrics - logs = {'step': global_step} + logs = {} if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer) for key, value in results.items(): eval_key = 'eval_{}'.format(key) - tb_writer.add_scalar(eval_key, value, global_step) - logs[eval_key] = str(value) - logging_loss = tr_loss + logs[eval_key] = value + loss_scalar = (tr_loss - logging_loss) / args.logging_steps learning_rate_scalar = scheduler.get_lr()[0] - tb_writer.add_scalar('lr', learning_rate_scalar, global_step) - tb_writer.add_scalar('loss', loss_scalar, global_step) logs['learning_rate'] = learning_rate_scalar logs['loss'] = loss_scalar - print(json.dumps(logs)) + logging_loss = tr_loss + + for key, value in logs.items(): + tb_writer.add_scalar(key, value, global_step) + print(json.dumps({**logs, **{'step': global_step}})) if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint From 2421e54f8c354fc110a7f8819a9161163813f7ad Mon Sep 17 00:00:00 2001 From: Juha Kiili Date: Fri, 29 Nov 2019 15:39:28 +0200 Subject: [PATCH 107/179] Add link to original source and license to download_glue.data.py --- utils/download_glue_data.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/utils/download_glue_data.py b/utils/download_glue_data.py index 86a4e8951f..f676a71c76 100644 --- a/utils/download_glue_data.py +++ b/utils/download_glue_data.py @@ -1,5 +1,8 @@ ''' Script for downloading all GLUE data. +Original source: https://github.com/kamalkraj/ALBERT-TF2.0/blob/fa90194e5fe729dbb19f32ac29c8d6d6372c0f93/download_glue_data.py +Original license: https://github.com/kamalkraj/ALBERT-TF2.0/blob/fa90194e5fe729dbb19f32ac29c8d6d6372c0f93/LICENSE (Apache-2.0) + Note: for legal reasons, we are unable to host MRPC. You can either use the version hosted by the SentEval team, which is already tokenized, or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually. From adb5c79ff2ffcd2e4a43a12f082cca55f7630a96 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 28 Nov 2019 15:51:43 +0100 Subject: [PATCH 108/179] update all tf.shape and tensor.shape to shape_list --- .../adding_a_new_model/modeling_tf_xxx.py | 6 ++--- transformers/__init__.py | 2 +- transformers/modeling_tf_albert.py | 27 ++++++++----------- transformers/modeling_tf_bert.py | 26 +++++++++--------- transformers/modeling_tf_ctrl.py | 2 +- transformers/modeling_tf_distilbert.py | 8 +++--- transformers/modeling_tf_gpt2.py | 2 +- transformers/modeling_tf_openai.py | 2 +- transformers/modeling_tf_roberta.py | 6 ++--- transformers/modeling_tf_transfo_xl.py | 2 +- .../modeling_tf_transfo_xl_utilities.py | 4 +-- transformers/modeling_tf_utils.py | 2 +- transformers/modeling_tf_xlnet.py | 13 +++++---- 13 files changed, 48 insertions(+), 54 deletions(-) diff --git a/templates/adding_a_new_model/modeling_tf_xxx.py b/templates/adding_a_new_model/modeling_tf_xxx.py index f1d898b47a..59f798bdbf 100644 --- a/templates/adding_a_new_model/modeling_tf_xxx.py +++ b/templates/adding_a_new_model/modeling_tf_xxx.py @@ -32,7 +32,7 @@ import numpy as np import tensorflow as tf from .configuration_xxx import XxxConfig -from .modeling_tf_utils import TFPreTrainedModel, get_initializer +from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list from .file_utils import add_start_docstrings logger = logging.getLogger(__name__) @@ -121,9 +121,9 @@ class TFXxxMainLayer(tf.keras.layers.Layer): input_ids = inputs if attention_mask is None: - attention_mask = tf.fill(tf.shape(input_ids), 1) + attention_mask = tf.fill(shape_list(input_ids), 1) if token_type_ids is None: - token_type_ids = tf.fill(tf.shape(input_ids), 0) + token_type_ids = tf.fill(shape_list(input_ids), 0) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] diff --git a/transformers/__init__.py b/transformers/__init__.py index b29ad38e73..de25c24b9e 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -118,7 +118,7 @@ if is_torch_available(): # TensorFlow if is_tf_available(): - from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary + from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary, shape_list from .modeling_tf_auto import (TFAutoModel, TFAutoModelForSequenceClassification, TFAutoModelForQuestionAnswering, TFAutoModelWithLMHead) diff --git a/transformers/modeling_tf_albert.py b/transformers/modeling_tf_albert.py index b2bf66f750..164dc74320 100644 --- a/transformers/modeling_tf_albert.py +++ b/transformers/modeling_tf_albert.py @@ -16,18 +16,13 @@ """ TF 2.0 ALBERT model. """ from __future__ import absolute_import, division, print_function, unicode_literals -import json import logging -import math -import os import sys -from io import open -import numpy as np import tensorflow as tf from .configuration_albert import AlbertConfig -from .modeling_tf_utils import TFPreTrainedModel, get_initializer +from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list from .modeling_tf_bert import ACT2FN, TFBertSelfAttention from .file_utils import add_start_docstrings @@ -110,9 +105,9 @@ class TFAlbertEmbeddings(tf.keras.layers.Layer): input_ids, position_ids, token_type_ids, inputs_embeds = inputs if input_ids is not None: - input_shape = tf.shape(input_ids) + input_shape = shape_list(input_ids) else: - input_shape = tf.shape(inputs_embeds)[:-1] + input_shape = shape_list(inputs_embeds)[:-1] seq_length = input_shape[1] if position_ids is None: @@ -137,8 +132,8 @@ class TFAlbertEmbeddings(tf.keras.layers.Layer): Returns: float32 tensor with shape [batch_size, length, vocab_size]. """ - batch_size = tf.shape(inputs)[0] - length = tf.shape(inputs)[1] + batch_size = shape_list(inputs)[0] + length = shape_list(inputs)[1] x = tf.reshape(inputs, [-1, self.config.embedding_size]) logits = tf.matmul(x, self.word_embeddings, transpose_b=True) return tf.reshape(logits, [batch_size, length, self.config.vocab_size]) @@ -183,7 +178,7 @@ class TFAlbertSelfAttention(tf.keras.layers.Layer): def call(self, inputs, training=False): hidden_states, attention_mask, head_mask = inputs - batch_size = tf.shape(hidden_states)[0] + batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(hidden_states) mixed_value_layer = self.value(hidden_states) @@ -196,7 +191,7 @@ class TFAlbertSelfAttention(tf.keras.layers.Layer): # (batch size, num_heads, seq_len_q, seq_len_k) attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) # scale attention_scores - dk = tf.cast(tf.shape(key_layer)[-1], tf.float32) + dk = tf.cast(shape_list(key_layer)[-1], tf.float32) attention_scores = attention_scores / tf.math.sqrt(dk) if attention_mask is not None: @@ -264,7 +259,7 @@ class TFAlbertAttention(TFBertSelfAttention): def call(self, inputs, training=False): input_tensor, attention_mask, head_mask = inputs - batch_size = tf.shape(input_tensor)[0] + batch_size = shape_list(input_tensor)[0] mixed_query_layer = self.query(input_tensor) mixed_key_layer = self.key(input_tensor) mixed_value_layer = self.value(input_tensor) @@ -277,7 +272,7 @@ class TFAlbertAttention(TFBertSelfAttention): # (batch size, num_heads, seq_len_q, seq_len_k) attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) # scale attention_scores - dk = tf.cast(tf.shape(key_layer)[-1], tf.float32) + dk = tf.cast(shape_list(key_layer)[-1], tf.float32) attention_scores = attention_scores / tf.math.sqrt(dk) if attention_mask is not None: @@ -645,9 +640,9 @@ class TFAlbertModel(TFAlbertPreTrainedModel): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: - input_shape = tf.shape(input_ids) + input_shape = shape_list(input_ids) elif inputs_embeds is not None: - input_shape = inputs_embeds.shape[:-1] + input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") diff --git a/transformers/modeling_tf_bert.py b/transformers/modeling_tf_bert.py index ad0815e2ca..5aa7bb3da2 100644 --- a/transformers/modeling_tf_bert.py +++ b/transformers/modeling_tf_bert.py @@ -28,7 +28,7 @@ import numpy as np import tensorflow as tf from .configuration_bert import BertConfig -from .modeling_tf_utils import TFPreTrainedModel, get_initializer +from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list from .file_utils import add_start_docstrings logger = logging.getLogger(__name__) @@ -145,9 +145,9 @@ class TFBertEmbeddings(tf.keras.layers.Layer): input_ids, position_ids, token_type_ids, inputs_embeds = inputs if input_ids is not None: - input_shape = tf.shape(input_ids) + input_shape = shape_list(input_ids) else: - input_shape = tf.shape(inputs_embeds)[:-1] + input_shape = shape_list(inputs_embeds)[:-1] seq_length = input_shape[1] if position_ids is None: @@ -172,8 +172,8 @@ class TFBertEmbeddings(tf.keras.layers.Layer): Returns: float32 tensor with shape [batch_size, length, vocab_size]. """ - batch_size = tf.shape(inputs)[0] - length = tf.shape(inputs)[1] + batch_size = shape_list(inputs)[0] + length = shape_list(inputs)[1] x = tf.reshape(inputs, [-1, self.hidden_size]) logits = tf.matmul(x, self.word_embeddings, transpose_b=True) @@ -214,7 +214,7 @@ class TFBertSelfAttention(tf.keras.layers.Layer): def call(self, inputs, training=False): hidden_states, attention_mask, head_mask = inputs - batch_size = tf.shape(hidden_states)[0] + batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(hidden_states) mixed_value_layer = self.value(hidden_states) @@ -225,7 +225,7 @@ class TFBertSelfAttention(tf.keras.layers.Layer): # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) # (batch size, num_heads, seq_len_q, seq_len_k) - dk = tf.cast(tf.shape(key_layer)[-1], tf.float32) # scale attention_scores + dk = tf.cast(shape_list(key_layer)[-1], tf.float32) # scale attention_scores attention_scores = attention_scores / tf.math.sqrt(dk) if attention_mask is not None: @@ -502,9 +502,9 @@ class TFBertMainLayer(tf.keras.layers.Layer): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: - input_shape = input_ids.shape + input_shape = shape_list(input_ids) elif inputs_embeds is not None: - input_shape = inputs_embeds.shape[:-1] + input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") @@ -939,11 +939,11 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel): input_ids = inputs if input_ids is not None: - num_choices = tf.shape(input_ids)[1] - seq_length = tf.shape(input_ids)[2] + num_choices = shape_list(input_ids)[1] + seq_length = shape_list(input_ids)[2] else: - num_choices = tf.shape(inputs_embeds)[1] - seq_length = tf.shape(inputs_embeds)[2] + num_choices = shape_list(inputs_embeds)[1] + seq_length = shape_list(inputs_embeds)[2] flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None diff --git a/transformers/modeling_tf_ctrl.py b/transformers/modeling_tf_ctrl.py index ae66dbc82c..6d0d6a57ad 100644 --- a/transformers/modeling_tf_ctrl.py +++ b/transformers/modeling_tf_ctrl.py @@ -95,7 +95,7 @@ class TFMultiHeadAttention(tf.keras.layers.Layer): def call(self, inputs, training=False): v, k, q, mask, layer_past, attention_mask, head_mask = inputs - batch_size = q.shape[0] + batch_size = shape_list(q)[0] q = self.Wq(q) k = self.Wk(k) diff --git a/transformers/modeling_tf_distilbert.py b/transformers/modeling_tf_distilbert.py index 6d393bb95d..b3d4889475 100644 --- a/transformers/modeling_tf_distilbert.py +++ b/transformers/modeling_tf_distilbert.py @@ -137,9 +137,9 @@ class TFEmbeddings(tf.keras.layers.Layer): input_ids, position_ids = inputs if input_ids is not None: - seq_length = tf.shape(input_ids)[1] + seq_length = shape_list(input_ids)[1] else: - seq_length = tf.shape(inputs_embeds)[1] + seq_length = shape_list(inputs_embeds)[1] if position_ids is None: position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :] @@ -160,8 +160,8 @@ class TFEmbeddings(tf.keras.layers.Layer): Returns: float32 tensor with shape [batch_size, length, vocab_size]. """ - batch_size = tf.shape(inputs)[0] - length = tf.shape(inputs)[1] + batch_size = shape_list(inputs)[0] + length = shape_list(inputs)[1] x = tf.reshape(inputs, [-1, self.dim]) logits = tf.matmul(x, self.word_embeddings, transpose_b=True) diff --git a/transformers/modeling_tf_gpt2.py b/transformers/modeling_tf_gpt2.py index 5e416a5e3a..aebe790114 100644 --- a/transformers/modeling_tf_gpt2.py +++ b/transformers/modeling_tf_gpt2.py @@ -92,7 +92,7 @@ class TFAttention(tf.keras.layers.Layer): # q, k, v have shape [batch, heads, sequence, features] w = tf.matmul(q, k, transpose_b=True) if self.scale: - dk = tf.cast(tf.shape(k)[-1], tf.float32) # scale attention_scores + dk = tf.cast(shape_list(k)[-1], tf.float32) # scale attention_scores w = w / tf.math.sqrt(dk) # w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst. diff --git a/transformers/modeling_tf_openai.py b/transformers/modeling_tf_openai.py index c553d92317..dac3b17590 100644 --- a/transformers/modeling_tf_openai.py +++ b/transformers/modeling_tf_openai.py @@ -98,7 +98,7 @@ class TFAttention(tf.keras.layers.Layer): # q, k, v have shape [batch, heads, sequence, features] w = tf.matmul(q, k, transpose_b=True) if self.scale: - dk = tf.cast(tf.shape(k)[-1], tf.float32) # scale attention_scores + dk = tf.cast(shape_list(k)[-1], tf.float32) # scale attention_scores w = w / tf.math.sqrt(dk) # w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst. diff --git a/transformers/modeling_tf_roberta.py b/transformers/modeling_tf_roberta.py index 450c0c72f2..954279f873 100644 --- a/transformers/modeling_tf_roberta.py +++ b/transformers/modeling_tf_roberta.py @@ -24,7 +24,7 @@ import numpy as np import tensorflow as tf from .configuration_roberta import RobertaConfig -from .modeling_tf_utils import TFPreTrainedModel, get_initializer +from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list from .file_utils import add_start_docstrings from .modeling_tf_bert import TFBertEmbeddings, TFBertMainLayer, gelu, gelu_new @@ -51,9 +51,9 @@ class TFRobertaEmbeddings(TFBertEmbeddings): input_ids, position_ids, token_type_ids, inputs_embeds = inputs if input_ids is not None: - seq_length = tf.shape(input_ids)[1] + seq_length = shape_list(input_ids)[1] else: - seq_length = tf.shape(inputs_embeds)[1] + seq_length = shape_list(inputs_embeds)[1] if position_ids is None: position_ids = tf.range(self.padding_idx+1, seq_length+self.padding_idx+1, dtype=tf.int32)[tf.newaxis, :] diff --git a/transformers/modeling_tf_transfo_xl.py b/transformers/modeling_tf_transfo_xl.py index 8a8d11cfbc..fd325e218e 100644 --- a/transformers/modeling_tf_transfo_xl.py +++ b/transformers/modeling_tf_transfo_xl.py @@ -337,7 +337,7 @@ class TFAdaptiveEmbedding(tf.keras.layers.Layer): emb_i = tf.einsum('id,de->ie', emb_i, self.emb_projs[i]) mask_idx = tf.cast(tf.where(mask_i), dtype=tf.int64) - emb_flat += tf.scatter_nd(mask_idx, emb_i, tf.cast(tf.shape(emb_flat), dtype=tf.int64)) + emb_flat += tf.scatter_nd(mask_idx, emb_i, tf.cast(shape_list(emb_flat), dtype=tf.int64)) embed_shape = shape_list(inp) + [self.d_proj] embed = tf.reshape(emb_flat, embed_shape) diff --git a/transformers/modeling_tf_transfo_xl_utilities.py b/transformers/modeling_tf_transfo_xl_utilities.py index d7666a650e..e6a6dfe686 100644 --- a/transformers/modeling_tf_transfo_xl_utilities.py +++ b/transformers/modeling_tf_transfo_xl_utilities.py @@ -105,7 +105,7 @@ class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer): @staticmethod def _gather_logprob(logprob, target): - lp_size = tf.shape(logprob) + lp_size = shape_list(logprob) r = tf.range(lp_size[0]) idx = tf.stack([r, target], 1) return tf.gather_nd(logprob, idx) @@ -159,7 +159,7 @@ class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer): cur_logprob = self._gather_logprob(cur_tail_logprob, cur_target) cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1] if target is not None: - loss += tf.scatter_nd(mask_idx, -cur_logprob, tf.cast(tf.shape(loss), dtype=tf.int64)) + loss += tf.scatter_nd(mask_idx, -cur_logprob, tf.cast(shape_list(loss), dtype=tf.int64)) out = tf.concat(out, axis=-1) if target is not None: diff --git a/transformers/modeling_tf_utils.py b/transformers/modeling_tf_utils.py index 569b2faa4b..e4ba55e25e 100644 --- a/transformers/modeling_tf_utils.py +++ b/transformers/modeling_tf_utils.py @@ -494,7 +494,7 @@ class TFSequenceSummary(tf.keras.layers.Layer): def shape_list(x): """Deal with dynamic shape in tensorflow cleanly.""" static = x.shape.as_list() - dynamic = tf.shape(x) + dynamic = shape_list(x) return [dynamic[i] if s is None else s for i, s in enumerate(static)] def get_initializer(initializer_range=0.02): diff --git a/transformers/modeling_tf_xlnet.py b/transformers/modeling_tf_xlnet.py index 4733ea8589..215d906f57 100644 --- a/transformers/modeling_tf_xlnet.py +++ b/transformers/modeling_tf_xlnet.py @@ -112,8 +112,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer): def prune_heads(self, heads): raise NotImplementedError - @staticmethod - def rel_shift(x, klen=-1): + def rel_shift(self, x, klen=-1): """perform relative shift to form the relative attention score.""" x_size = shape_list(x) @@ -135,7 +134,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer): # position based attention score bd = tf.einsum('ibnd,jbnd->ijbn', q_head + self.r_r_bias, k_head_r) - bd = self.rel_shift(bd, klen=ac.shape[1]) + bd = self.rel_shift(bd, klen=shape_list(ac)[1]) # segment based attention score if seg_mat is None: @@ -192,7 +191,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer): if g is not None: ###### Two-stream attention with relative positional encoding. # content based attention score - if mems is not None and mems.shape.ndims > 1: + if mems is not None and len(shape_list(mems)) > 1: cat = tf.concat([mems, h], axis=0) else: cat = h @@ -252,7 +251,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer): else: ###### Multi-head attention with relative positional encoding - if mems is not None and mems.shape.ndims > 1: + if mems is not None and len(shape_list(mems)) > 1: cat = tf.concat([mems, h], axis=0) else: cat = h @@ -565,7 +564,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer): if data_mask is not None: # all mems can be attended to - mems_mask = tf.zeros([tf.shape(data_mask)[0], mlen, bsz], + mems_mask = tf.zeros([shape_list(data_mask)[0], mlen, bsz], dtype=dtype_float) data_mask = tf.concat([mems_mask, data_mask], axis=1) if attn_mask is None: @@ -590,7 +589,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer): word_emb_k = self.word_embedding(input_ids) output_h = self.dropout(word_emb_k, training=training) if target_mapping is not None: - word_emb_q = tf.tile(self.mask_emb, [tf.shape(target_mapping)[0], bsz, 1]) + word_emb_q = tf.tile(self.mask_emb, [shape_list(target_mapping)[0], bsz, 1]) # else: # We removed the inp_q input which was same as target mapping # inp_q_ext = inp_q[:, :, None] # word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k From 4a666885b501ed6bfd344ec2c4c16d80da8aab79 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 28 Nov 2019 15:56:53 +0100 Subject: [PATCH 109/179] reducing my level of enthousiasm --- transformers/modeling_tf_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/modeling_tf_utils.py b/transformers/modeling_tf_utils.py index e4ba55e25e..569b2faa4b 100644 --- a/transformers/modeling_tf_utils.py +++ b/transformers/modeling_tf_utils.py @@ -494,7 +494,7 @@ class TFSequenceSummary(tf.keras.layers.Layer): def shape_list(x): """Deal with dynamic shape in tensorflow cleanly.""" static = x.shape.as_list() - dynamic = shape_list(x) + dynamic = tf.shape(x) return [dynamic[i] if s is None else s for i, s in enumerate(static)] def get_initializer(initializer_range=0.02): From ecf15ebf3b7f5d2b0144f1a428e2d9f39494c8ba Mon Sep 17 00:00:00 2001 From: Elad Segal Date: Fri, 29 Nov 2019 12:48:41 +0200 Subject: [PATCH 110/179] Add ALBERT to AutoClasses --- transformers/configuration_auto.py | 15 +++++++++----- transformers/modeling_auto.py | 33 +++++++++++++++++++++++------- transformers/tokenization_auto.py | 15 +++++++++----- 3 files changed, 46 insertions(+), 17 deletions(-) diff --git a/transformers/configuration_auto.py b/transformers/configuration_auto.py index 37b8c0e3df..43f251bd0c 100644 --- a/transformers/configuration_auto.py +++ b/transformers/configuration_auto.py @@ -28,6 +28,7 @@ from .configuration_roberta import RobertaConfig from .configuration_distilbert import DistilBertConfig from .configuration_ctrl import CTRLConfig from .configuration_camembert import CamembertConfig +from .configuration_albert import AlbertConfig logger = logging.getLogger(__name__) @@ -44,14 +45,15 @@ class AutoConfig(object): The base model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertConfig (DistilBERT model) + - contains `albert`: AlbertConfig (ALBERT model) + - contains `camembert`: CamembertConfig (CamemBERT model) + - contains `roberta`: RobertaConfig (RoBERTa model) - contains `bert`: BertConfig (Bert model) - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model) - contains `gpt2`: GPT2Config (OpenAI GPT-2 model) - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model) - contains `xlnet`: XLNetConfig (XLNet model) - contains `xlm`: XLMConfig (XLM model) - - contains `roberta`: RobertaConfig (RoBERTa model) - - contains `camembert`: CamembertConfig (CamemBERT model) - contains `ctrl` : CTRLConfig (CTRL model) This class cannot be instantiated using `__init__()` (throw an error). """ @@ -67,14 +69,15 @@ class AutoConfig(object): The configuration class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertConfig (DistilBERT model) + - contains `albert`: AlbertConfig (ALBERT model) + - contains `camembert`: CamembertConfig (CamemBERT model) + - contains `roberta`: RobertaConfig (RoBERTa model) - contains `bert`: BertConfig (Bert model) - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model) - contains `gpt2`: GPT2Config (OpenAI GPT-2 model) - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model) - contains `xlnet`: XLNetConfig (XLNet model) - contains `xlm`: XLMConfig (XLM model) - - contains `roberta`: RobertaConfig (RoBERTa model) - - contains `camembert`: CamembertConfig (CamemBERT model) - contains `ctrl` : CTRLConfig (CTRL model) Params: pretrained_model_name_or_path: either: @@ -122,6 +125,8 @@ class AutoConfig(object): """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) + elif 'albert' in pretrained_model_name_or_path: + return AlbertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) elif 'camembert' in pretrained_model_name_or_path: return CamembertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) elif 'roberta' in pretrained_model_name_or_path: @@ -142,4 +147,4 @@ class AutoConfig(object): return CTRLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) raise ValueError("Unrecognized model identifier in {}. Should contains one of " "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', " - "'xlm', 'roberta', 'camembert', 'ctrl'".format(pretrained_model_name_or_path)) + "'xlm', 'roberta', 'distilbert', 'camembert', 'ctrl', 'albert'".format(pretrained_model_name_or_path)) diff --git a/transformers/modeling_auto.py b/transformers/modeling_auto.py index fa33dbc0c8..b63e43d73b 100644 --- a/transformers/modeling_auto.py +++ b/transformers/modeling_auto.py @@ -28,6 +28,8 @@ from .modeling_xlm import XLMModel, XLMWithLMHeadModel, XLMForSequenceClassifica from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification from .modeling_distilbert import DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification from .modeling_camembert import CamembertModel, CamembertForMaskedLM, CamembertForSequenceClassification, CamembertForMultipleChoice +from .modeling_camembert import CamembertModel, CamembertForMaskedLM, CamembertForSequenceClassification, CamembertForMultipleChoice +from .modeling_albert import AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, AlbertForQuestionAnswering from .modeling_utils import PreTrainedModel, SequenceSummary @@ -49,15 +51,16 @@ class AutoModel(object): The base model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertModel (DistilBERT model) + - contains `albert`: AlbertModel (ALBERT model) - contains `camembert`: CamembertModel (CamemBERT model) - contains `roberta`: RobertaModel (RoBERTa model) - contains `bert`: BertModel (Bert model) - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model) - contains `gpt2`: GPT2Model (OpenAI GPT-2 model) - - contains `ctrl`: CTRLModel (Salesforce CTRL model) - contains `transfo-xl`: TransfoXLModel (Transformer-XL model) - contains `xlnet`: XLNetModel (XLNet model) - contains `xlm`: XLMModel (XLM model) + - contains `ctrl`: CTRLModel (Salesforce CTRL model) This class cannot be instantiated using `__init__()` (throws an error). """ @@ -73,15 +76,16 @@ class AutoModel(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertModel (DistilBERT model) + - contains `albert`: AlbertModel (ALBERT model) - contains `camembert`: CamembertModel (CamemBERT model) - contains `roberta`: RobertaModel (RoBERTa model) - contains `bert`: BertModel (Bert model) - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model) - contains `gpt2`: GPT2Model (OpenAI GPT-2 model) - - contains `ctrl`: CTRLModel (Salesforce CTRL model) - contains `transfo-xl`: TransfoXLModel (Transformer-XL model) - contains `xlnet`: XLNetModel (XLNet model) - contains `xlm`: XLMModel (XLM model) + - contains `ctrl`: CTRLModel (Salesforce CTRL model) The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated) To train the model, you should first set it back in training mode with `model.train()` @@ -144,6 +148,8 @@ class AutoModel(object): """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'albert' in pretrained_model_name_or_path: + return AlbertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'camembert' in pretrained_model_name_or_path: return CamembertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: @@ -164,7 +170,7 @@ class AutoModel(object): return CTRLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) raise ValueError("Unrecognized model identifier in {}. Should contains one of " "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', " - "'xlm', 'roberta, 'ctrl'".format(pretrained_model_name_or_path)) + "'xlm', 'roberta, 'ctrl', 'distilbert', 'camembert', 'albert'".format(pretrained_model_name_or_path)) class AutoModelWithLMHead(object): @@ -180,15 +186,16 @@ class AutoModelWithLMHead(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model) + - contains `albert`: AlbertForMaskedLM (ALBERT model) - contains `camembert`: CamembertForMaskedLM (CamemBERT model) - contains `roberta`: RobertaForMaskedLM (RoBERTa model) - contains `bert`: BertForMaskedLM (Bert model) - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model) - contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model) - - contains `ctrl`: CTRLLMModel (Salesforce CTRL model) - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model) - contains `xlnet`: XLNetLMHeadModel (XLNet model) - contains `xlm`: XLMWithLMHeadModel (XLM model) + - contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model) This class cannot be instantiated using `__init__()` (throws an error). """ @@ -207,6 +214,7 @@ class AutoModelWithLMHead(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model) + - contains `albert`: AlbertForMaskedLM (ALBERT model) - contains `camembert`: CamembertForMaskedLM (CamemBERT model) - contains `roberta`: RobertaForMaskedLM (RoBERTa model) - contains `bert`: BertForMaskedLM (Bert model) @@ -215,6 +223,7 @@ class AutoModelWithLMHead(object): - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model) - contains `xlnet`: XLNetLMHeadModel (XLNet model) - contains `xlm`: XLMWithLMHeadModel (XLM model) + - contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model) The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated) To train the model, you should first set it back in training mode with `model.train()` @@ -276,6 +285,8 @@ class AutoModelWithLMHead(object): """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'albert' in pretrained_model_name_or_path: + return AlbertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'camembert' in pretrained_model_name_or_path: return CamembertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: @@ -296,7 +307,7 @@ class AutoModelWithLMHead(object): return CTRLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) raise ValueError("Unrecognized model identifier in {}. Should contains one of " "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', " - "'xlm', 'roberta','ctrl'".format(pretrained_model_name_or_path)) + "'xlm', 'roberta','ctrl', 'distilbert', 'camembert', 'albert'".format(pretrained_model_name_or_path)) class AutoModelForSequenceClassification(object): @@ -312,6 +323,7 @@ class AutoModelForSequenceClassification(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model) + - contains `albert`: AlbertForSequenceClassification (ALBERT model) - contains `camembert`: CamembertForSequenceClassification (CamemBERT model) - contains `roberta`: RobertaForSequenceClassification (RoBERTa model) - contains `bert`: BertForSequenceClassification (Bert model) @@ -335,6 +347,7 @@ class AutoModelForSequenceClassification(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model) + - contains `albert`: AlbertForSequenceClassification (ALBERT model) - contains `camembert`: CamembertForSequenceClassification (CamemBERT model) - contains `roberta`: RobertaForSequenceClassification (RoBERTa model) - contains `bert`: BertForSequenceClassification (Bert model) @@ -402,6 +415,8 @@ class AutoModelForSequenceClassification(object): """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'albert' in pretrained_model_name_or_path: + return AlbertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'camembert' in pretrained_model_name_or_path: return CamembertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: @@ -414,7 +429,7 @@ class AutoModelForSequenceClassification(object): return XLMForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) raise ValueError("Unrecognized model identifier in {}. Should contains one of " - "'bert', 'xlnet', 'xlm', 'roberta'".format(pretrained_model_name_or_path)) + "'bert', 'xlnet', 'xlm', 'roberta', 'distilbert', 'camembert', 'albert'".format(pretrained_model_name_or_path)) class AutoModelForQuestionAnswering(object): @@ -430,6 +445,7 @@ class AutoModelForQuestionAnswering(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model) + - contains `albert`: AlbertForQuestionAnswering (ALBERT model) - contains `bert`: BertForQuestionAnswering (Bert model) - contains `xlnet`: XLNetForQuestionAnswering (XLNet model) - contains `xlm`: XLMForQuestionAnswering (XLM model) @@ -451,6 +467,7 @@ class AutoModelForQuestionAnswering(object): The model class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model) + - contains `albert`: AlbertForQuestionAnswering (ALBERT model) - contains `bert`: BertForQuestionAnswering (Bert model) - contains `xlnet`: XLNetForQuestionAnswering (XLNet model) - contains `xlm`: XLMForQuestionAnswering (XLM model) @@ -513,6 +530,8 @@ class AutoModelForQuestionAnswering(object): """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'albert' in pretrained_model_name_or_path: + return AlbertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'bert' in pretrained_model_name_or_path: return BertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlnet' in pretrained_model_name_or_path: @@ -521,4 +540,4 @@ class AutoModelForQuestionAnswering(object): return XLMForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) raise ValueError("Unrecognized model identifier in {}. Should contains one of " - "'bert', 'xlnet', 'xlm'".format(pretrained_model_name_or_path)) + "'bert', 'xlnet', 'xlm', 'distilbert', 'albert'".format(pretrained_model_name_or_path)) diff --git a/transformers/tokenization_auto.py b/transformers/tokenization_auto.py index 2e15e38073..b7c5046961 100644 --- a/transformers/tokenization_auto.py +++ b/transformers/tokenization_auto.py @@ -28,6 +28,7 @@ from .tokenization_xlm import XLMTokenizer from .tokenization_roberta import RobertaTokenizer from .tokenization_distilbert import DistilBertTokenizer from .tokenization_camembert import CamembertTokenizer +from .tokenization_albert import AlbertTokenizer logger = logging.getLogger(__name__) @@ -42,16 +43,17 @@ class AutoTokenizer(object): The tokenizer class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - - contains `camembert`: CamembertTokenizer (CamemBERT model) - contains `distilbert`: DistilBertTokenizer (DistilBert model) + - contains `albert`: AlbertTokenizer (ALBERT model) + - contains `camembert`: CamembertTokenizer (CamemBERT model) - contains `roberta`: RobertaTokenizer (RoBERTa model) - contains `bert`: BertTokenizer (Bert model) - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model) - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model) - - contains `ctrl`: CTRLTokenizer (Salesforce CTRL model) - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model) - contains `xlnet`: XLNetTokenizer (XLNet model) - contains `xlm`: XLMTokenizer (XLM model) + - contains `ctrl`: CTRLTokenizer (Salesforce CTRL model) This class cannot be instantiated using `__init__()` (throw an error). """ @@ -66,16 +68,17 @@ class AutoTokenizer(object): The tokenizer class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - - contains `camembert`: CamembertTokenizer (CamemBERT model) - contains `distilbert`: DistilBertTokenizer (DistilBert model) + - contains `albert`: AlbertTokenizer (ALBERT model) + - contains `camembert`: CamembertTokenizer (CamemBERT model) - contains `roberta`: RobertaTokenizer (RoBERTa model) - contains `bert`: BertTokenizer (Bert model) - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model) - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model) - - contains `ctrl`: CTRLTokenizer (Salesforce CTRL model) - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model) - contains `xlnet`: XLNetTokenizer (XLNet model) - contains `xlm`: XLMTokenizer (XLM model) + - contains `ctrl`: CTRLTokenizer (Salesforce CTRL model) Params: pretrained_model_name_or_path: either: @@ -109,6 +112,8 @@ class AutoTokenizer(object): """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) + elif 'albert' in pretrained_model_name_or_path: + return AlbertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) elif 'camembert' in pretrained_model_name_or_path: return CamembertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) elif 'roberta' in pretrained_model_name_or_path: @@ -129,4 +134,4 @@ class AutoTokenizer(object): return CTRLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) raise ValueError("Unrecognized model identifier in {}. Should contains one of " "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', " - "'xlm', 'roberta', 'camembert', 'ctrl'".format(pretrained_model_name_or_path)) + "'xlm', 'roberta', 'distilbert,' 'camembert', 'ctrl', 'albert'".format(pretrained_model_name_or_path)) From b0ee7c7df3d49a819c4d6cef977214bd91f5c075 Mon Sep 17 00:00:00 2001 From: maxvidal <44881831+maxvidal@users.noreply.github.com> Date: Fri, 29 Nov 2019 12:32:37 +0100 Subject: [PATCH 111/179] Added Camembert to available models --- examples/run_lm_finetuning.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py index c33aa94a32..4acea00c55 100644 --- a/examples/run_lm_finetuning.py +++ b/examples/run_lm_finetuning.py @@ -47,7 +47,8 @@ from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup, GPT2Config, GPT2LMHeadModel, GPT2Tokenizer, OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, RobertaConfig, RobertaForMaskedLM, RobertaTokenizer, - DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer) + DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer, + CamembertConfig, CamembertForMaskedLM, CamembertTokenizer) logger = logging.getLogger(__name__) @@ -58,7 +59,8 @@ MODEL_CLASSES = { 'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), 'bert': (BertConfig, BertForMaskedLM, BertTokenizer), 'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer), - 'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer) + 'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer), + 'camembert': (CamembertConfig, CamembertForMaskedLM, CamembertTokenizer) } @@ -432,7 +434,7 @@ def main(): parser.add_argument('--server_port', type=str, default='', help="For distant debugging.") args = parser.parse_args() - if args.model_type in ["bert", "roberta", "distilbert"] and not args.mlm: + if args.model_type in ["bert", "roberta", "distilbert", "camembert"] and not args.mlm: raise ValueError("BERT and RoBERTa do not have LM heads but masked LM heads. They must be run using the --mlm " "flag (masked language modeling).") if args.eval_data_file is None and args.do_eval: From b90791e95026dfa95a8cea72605257e4b9355956 Mon Sep 17 00:00:00 2001 From: Rostislav Nedelchev Date: Sat, 30 Nov 2019 15:57:51 +0100 Subject: [PATCH 112/179] fixed XLNet attenttion output for both attention streams --- transformers/modeling_xlnet.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/transformers/modeling_xlnet.py b/transformers/modeling_xlnet.py index 658048a660..476d9ab13d 100644 --- a/transformers/modeling_xlnet.py +++ b/transformers/modeling_xlnet.py @@ -581,7 +581,7 @@ class XLNetModel(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: @@ -878,7 +878,7 @@ class XLNetModel(XLNetPreTrainedModel): hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states) outputs = outputs + (hidden_states,) if self.output_attentions: - attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions) + attentions = tuple(tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attentions) outputs = outputs + (attentions,) return outputs # outputs, (new_mems), (hidden_states), (attentions) @@ -911,7 +911,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: @@ -993,7 +993,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: @@ -1093,7 +1093,7 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: @@ -1178,7 +1178,7 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: @@ -1292,7 +1292,7 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: From 76c0bc06d549e0ecf746fdd3d72eb235a9c4aec2 Mon Sep 17 00:00:00 2001 From: Rostislav Nedelchev Date: Sat, 30 Nov 2019 21:01:04 +0100 Subject: [PATCH 113/179] [XLNet] Changed post-processing of attention w.r.t to target_mapping Whenever target_mapping is provided to the input, XLNet outputs two different attention streams. Based on that the attention output would be on of the two: - a list of tensors (usual case for most transformers) - a list of 2-tuples of tensors, one tesor for each of attention streams Docs and unit-tests have been updated --- transformers/modeling_xlnet.py | 24 ++++++++++++++++------- transformers/tests/modeling_xlnet_test.py | 18 +++++++++++++++++ 2 files changed, 35 insertions(+), 7 deletions(-) diff --git a/transformers/modeling_xlnet.py b/transformers/modeling_xlnet.py index 476d9ab13d..56d755c11b 100644 --- a/transformers/modeling_xlnet.py +++ b/transformers/modeling_xlnet.py @@ -581,8 +581,9 @@ class XLNetModel(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``. Examples:: @@ -878,7 +879,11 @@ class XLNetModel(XLNetPreTrainedModel): hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states) outputs = outputs + (hidden_states,) if self.output_attentions: - attentions = tuple(tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attentions) + if target_mapping is not None: + # when target_mapping is provided, there are 2-tuple of attentions + attentions = tuple(tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attentions) + else: + attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions) outputs = outputs + (attentions,) return outputs # outputs, (new_mems), (hidden_states), (attentions) @@ -911,8 +916,9 @@ class XLNetLMHeadModel(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``. Examples:: @@ -993,8 +999,9 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``. Examples:: @@ -1093,8 +1100,9 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``. Examples:: @@ -1178,8 +1186,9 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``. Examples:: @@ -1292,8 +1301,9 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel): of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) - list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``. Examples:: diff --git a/transformers/tests/modeling_xlnet_test.py b/transformers/tests/modeling_xlnet_test.py index d97ea6a425..a5ee9b1e0e 100644 --- a/transformers/tests/modeling_xlnet_test.py +++ b/transformers/tests/modeling_xlnet_test.py @@ -163,6 +163,18 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): list(list(mem.size()) for mem in result["mems_1"]), [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers) + def create_and_check_xlnet_base_model_with_att_output(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, + target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels): + model = XLNetModel(config) + model.eval() + + _, _, attentions = model(input_ids_1, target_mapping=target_mapping) + + self.parent.assertEqual(len(attentions), config.n_layer) + self.parent.assertIsInstance(attentions[0], tuple) + self.parent.assertEqual(len(attentions[0]), 2) + self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape) + def create_and_check_xlnet_lm_head(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels): model = XLNetLMHeadModel(config) @@ -306,6 +318,12 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs) + def test_xlnet_base_model_with_att_output(self): + self.model_tester.set_seed() + config_and_inputs = self.model_tester.prepare_config_and_inputs() + config_and_inputs[0].output_attentions = True + self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs) + def test_xlnet_lm_head(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() From c356290c8ddd9037bd08c854b118673983ef6def Mon Sep 17 00:00:00 2001 From: Aditya Soni Date: Sun, 1 Dec 2019 14:08:14 +0530 Subject: [PATCH 114/179] typo fix as per Pytorch v1.1+ --- docs/source/migration.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/migration.md b/docs/source/migration.md index d04b66d5e4..f50d1dff0a 100644 --- a/docs/source/migration.md +++ b/docs/source/migration.md @@ -104,6 +104,6 @@ for batch in train_data: loss = model(batch) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) # Gradient clipping is not in AdamW anymore (so you can use amp without issue) - scheduler.step() optimizer.step() + scheduler.step() ``` From 5ab93083e4d9b610df1dd082f17a455a0b7193ac Mon Sep 17 00:00:00 2001 From: Aymeric Augustin Date: Sun, 1 Dec 2019 18:25:15 +0100 Subject: [PATCH 115/179] Mark tests in TFAutoModelTest as slow. Each test forces downloading the same 536MB file, which is slow even with a decent internet connection. --- transformers/tests/modeling_tf_auto_test.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/transformers/tests/modeling_tf_auto_test.py b/transformers/tests/modeling_tf_auto_test.py index 2cda3abc1c..fa90906e86 100644 --- a/transformers/tests/modeling_tf_auto_test.py +++ b/transformers/tests/modeling_tf_auto_test.py @@ -38,6 +38,7 @@ else: class TFAutoModelTest(unittest.TestCase): + @pytest.mark.slow def test_model_from_pretrained(self): import h5py self.assertTrue(h5py.version.hdf5_version.startswith("1.10")) @@ -53,6 +54,7 @@ class TFAutoModelTest(unittest.TestCase): self.assertIsNotNone(model) self.assertIsInstance(model, TFBertModel) + @pytest.mark.slow def test_lmhead_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: @@ -65,6 +67,7 @@ class TFAutoModelTest(unittest.TestCase): self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) + @pytest.mark.slow def test_sequence_classification_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: @@ -77,6 +80,7 @@ class TFAutoModelTest(unittest.TestCase): self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForSequenceClassification) + @pytest.mark.slow def test_question_answering_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: From b3d834ae11381ca493da97f717d77d185ca7d780 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Mon, 2 Dec 2019 15:01:52 -0500 Subject: [PATCH 116/179] Reorganize ALBERT conversion script --- transformers/modeling_albert.py | 30 ++++++++++++++++++++++++++---- 1 file changed, 26 insertions(+), 4 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 5b7b2d3900..49d120ffae 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -68,14 +68,36 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path): for name, array in zip(names, arrays): original_name = name + + # If saved from the TF HUB module + name = name.replace("module/", "") + + # Renaming and simplifying name = name.replace("ffn_1", "ffn") - name = name.replace("/bert/", "/albert/") - name = name.replace("ffn/intermediate/output", "ffn_output") + name = name.replace("bert/", "albert/") name = name.replace("attention_1", "attention") - name = name.replace("cls/predictions", "predictions") name = name.replace("transform/", "") name = name.replace("LayerNorm_1", "full_layer_layer_norm") - name = name.replace("LayerNorm", "attention/LayerNorm") + name = name.replace("LayerNorm", "attention/LayerNorm") + name = name.replace("transformer/", "") + + # The feed forward layer had an 'intermediate' step which has been abstracted away + name = name.replace("intermediate/dense/", "") + name = name.replace("ffn/intermediate/output/dense/", "ffn_output/") + + # ALBERT attention was split between self and output which have been abstracted away + name = name.replace("/output/", "/") + name = name.replace("/self/", "/") + + # The pooler is a linear layer + name = name.replace("pooler/dense", "pooler") + + # The classifier was simplified to predictions from cls/predictions + name = name.replace("cls/predictions", "predictions") + name = name.replace("predictions/attention", "predictions") + + # Naming was changed to be more explicit + name = name.replace("embeddings/attention", "embeddings") name = name.replace("inner_group_", "albert_layers/") name = name.replace("group_", "albert_layer_groups/") name = name.split('/') From e85855f2c408f65a4aaf5d15baab6ca90fd26050 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Mon, 2 Dec 2019 18:00:19 -0500 Subject: [PATCH 117/179] Fix ALBERT exports with pretraining + sp classifier; Fix naming for ALBERT TF models --- transformers/modeling_albert.py | 17 ++++++++++++++++- transformers/modeling_tf_albert.py | 8 ++++---- 2 files changed, 20 insertions(+), 5 deletions(-) diff --git a/transformers/modeling_albert.py b/transformers/modeling_albert.py index 49d120ffae..0f67bf8f36 100644 --- a/transformers/modeling_albert.py +++ b/transformers/modeling_albert.py @@ -99,8 +99,23 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path): # Naming was changed to be more explicit name = name.replace("embeddings/attention", "embeddings") name = name.replace("inner_group_", "albert_layers/") - name = name.replace("group_", "albert_layer_groups/") + name = name.replace("group_", "albert_layer_groups/") + + # Classifier + if len(name.split("/")) == 1 and ("output_bias" in name or "output_weights" in name): + name = "classifier/" + name + + # No ALBERT model currently handles the next sentence prediction task + if "seq_relationship" in name: + continue + name = name.split('/') + + # Ignore the gradients applied by the LAMB/ADAM optimizers. + if "adam_m" in name or "adam_v" in name or "global_step" in name: + logger.info("Skipping {}".format("/".join(name))) + continue + pointer = model for m_name in name: if re.fullmatch(r'[A-Za-z]+_\d+', m_name): diff --git a/transformers/modeling_tf_albert.py b/transformers/modeling_tf_albert.py index 164dc74320..d1650d41a8 100644 --- a/transformers/modeling_tf_albert.py +++ b/transformers/modeling_tf_albert.py @@ -31,10 +31,10 @@ import logging logger = logging.getLogger(__name__) TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = { - 'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-tf_model.h5", - 'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-tf_model.h5", - 'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-tf_model.h5", - 'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-tf_model.h5", + 'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v1-tf_model.h5", + 'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v1-tf_model.h5", + 'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v1-tf_model.h5", + 'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v1-tf_model.h5", 'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-tf_model.h5", 'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-tf_model.h5", 'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-tf_model.h5", From fbaf05bd92249b6dd961f5f8d60eb0892c541ac8 Mon Sep 17 00:00:00 2001 From: LysandreJik Date: Mon, 2 Dec 2019 18:23:00 -0500 Subject: [PATCH 118/179] Remove annoying tokenization message --- transformers/tokenization_utils.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/transformers/tokenization_utils.py b/transformers/tokenization_utils.py index 60df822677..5d683629f0 100644 --- a/transformers/tokenization_utils.py +++ b/transformers/tokenization_utils.py @@ -981,7 +981,6 @@ class PreTrainedTokenizer(object): return (ids, pair_ids, overflowing_tokens) def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): - logger.warning("This tokenizer does not make use of special tokens.") if token_ids_1 is None: return len(token_ids_0) * [0] return [0] * len(token_ids_0) + [1] * len(token_ids_1) @@ -994,7 +993,6 @@ class PreTrainedTokenizer(object): single sequence: X pair of sequences: A B """ - logger.warning("This tokenizer does not make use of special tokens. Input is returned with no modification.") if token_ids_1 is None: return token_ids_0 return token_ids_0 + token_ids_1 From 66fc8d25a5de43c08baeea8b22b9bcf57346c325 Mon Sep 17 00:00:00 2001 From: Juha Kiili Date: Tue, 3 Dec 2019 10:49:50 +0200 Subject: [PATCH 119/179] Change ref to original GLUE downloader script --- utils/download_glue_data.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/utils/download_glue_data.py b/utils/download_glue_data.py index f676a71c76..de8cfa9e73 100644 --- a/utils/download_glue_data.py +++ b/utils/download_glue_data.py @@ -1,7 +1,5 @@ ''' Script for downloading all GLUE data. - -Original source: https://github.com/kamalkraj/ALBERT-TF2.0/blob/fa90194e5fe729dbb19f32ac29c8d6d6372c0f93/download_glue_data.py -Original license: https://github.com/kamalkraj/ALBERT-TF2.0/blob/fa90194e5fe729dbb19f32ac29c8d6d6372c0f93/LICENSE (Apache-2.0) +Original source: https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e Note: for legal reasons, we are unable to host MRPC. You can either use the version hosted by the SentEval team, which is already tokenized, From 572c24cfa25c167e48dd08a7d2429afeb69acbe0 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Tue, 5 Nov 2019 15:34:37 +0000 Subject: [PATCH 120/179] PPLM (squashed) Co-authored-by: piero Co-authored-by: Rosanne Liu --- examples/run_pplm.py | 782 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 782 insertions(+) create mode 100644 examples/run_pplm.py diff --git a/examples/run_pplm.py b/examples/run_pplm.py new file mode 100644 index 0000000000..30853c68c3 --- /dev/null +++ b/examples/run_pplm.py @@ -0,0 +1,782 @@ +# coding=utf-8 +# Copyright 2018 The Uber AI Team Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# TODO: add code for training a custom discriminator + +""" +Example command with bag of words: +python examples/run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95 + +Example command with discriminator: +python examples/run_pplm.py -D sentiment --label_class 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 30 --num_samples 10 --stepsize 0.01 --kl_scale 0.01 --gm_scale 0.95 +""" + +import argparse +from operator import add +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch.autograd import Variable +from tqdm import trange + +from transformers import GPT2Tokenizer +from transformers.file_utils import cached_path +from transformers.modeling_gpt2 import GPT2LMHeadModel + +PPLM_BOW = 1 +PPLM_DISCRIM = 2 +PPLM_BOW_DISCRIM = 3 +SMALL_CONST = 1e-15 +TOKENIZER = GPT2Tokenizer.from_pretrained("gpt2-medium") + +BAG_OF_WORDS_ARCHIVE_MAP = { + 'kitchen': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/kitchen.txt", + 'legal': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/legal.txt", + 'military': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/military.txt", + 'monsters': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/monsters.txt", + 'politics': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/politics.txt", + 'positive_words': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/positive_words.txt", + 'religion': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/religion.txt", + 'science': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/science.txt", + 'space': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/space.txt", + 'technology': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/technology.txt", +} + +DISCRIMINATOR_MODELS_PARAMS = { + "clickbait": { + "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/clickbait_classifierhead.pt", + "class_size": 2, + "embed_size": 1024, + "class_vocab": {"non_clickbait": 0, "clickbait": 1}, + "default_class": 1, + }, + "sentiment": { + "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/sentiment_classifierhead.pt", + "class_size": 5, + "embed_size": 1024, + "class_vocab": {"very_positive": 2, "very_negative": 3}, + "default_class": 3, + }, + "toxicity": { + "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/toxicity_classifierhead.pt", + "class_size": 2, + "embed_size": 1024, + "class_vocab": {"non_toxic": 0, "toxic": 1}, + "default_class": 0, + }, +} + + +class ClassificationHead(torch.nn.Module): + """ Classification Head for the transformer """ + + def __init__(self, class_size=5, embed_size=2048): + super(ClassificationHead, self).__init__() + self.class_size = class_size + self.embed_size = embed_size + # self.mlp1 = torch.nn.Linear(embed_size, embed_size) + # self.mlp2 = (torch.nn.Linear(embed_size, class_size)) + self.mlp = torch.nn.Linear(embed_size, class_size) + + def forward(self, hidden_state): + # hidden_state = F.relu(self.mlp1(hidden_state)) + # hidden_state = self.mlp2(hidden_state) + logits = self.mlp(hidden_state) + return logits + + +def to_var(x, requires_grad=False, volatile=False): + if torch.cuda.is_available(): + x = x.cuda() + return Variable(x, requires_grad=requires_grad, volatile=volatile) + + +def top_k_filter(logits, k, probs=False): + """ + Masks everything but the k top entries as -infinity (1e10). + Used to mask logits such that e^-infinity -> 0 won't contribute to the + sum of the denominator. + """ + if k <= 0: + return logits + + else: + values = torch.topk(logits, k)[0] + batch_mins = values[:, -1].view(-1, 1).expand_as(logits) + + if probs: + return torch.where( + logits < batch_mins, + torch.ones_like(logits) * 0.0, + logits + ) + + return torch.where( + logits < batch_mins, + torch.ones_like(logits) * -1e10, + logits + ) + + +def perturb_past( + past, + model, + last, + unpert_past=None, + unpert_logits=None, + accumulated_hidden=None, + grad_norms=None, + stepsize=0.01, + classifier=None, + label_class=None, + one_hot_bows_vectors=None, + loss_type=0, + num_iterations=3, + kl_scale=0.01, + window_length=0, + horizon_length=1, + decay=False, + gamma=1.5, +): + # initializie perturbation accumulator + grad_accumulator = [ + (np.zeros(p.shape).astype("float32")) + for p in past + ] + + if accumulated_hidden is None: + accumulated_hidden = 0 + + if decay: + decay_mask = torch.arange( + 0.0, + 1.0 + SMALL_CONST, + 1.0 / (window_length) + )[1:] + else: + decay_mask = 1.0 + + # TODO fix this comment (SUMANTH) + # generate a mask if perturbated gradient is based on a past window + _, _, _, curr_length, _ = past[0].shape + if curr_length > window_length and window_length > 0: + ones_key_val_shape = ( + tuple(past[0].shape[:-2]) + + tuple([window_length]) + + tuple(past[0].shape[-1:]) + ) + + zeros_key_val_shape = ( + tuple(past[0].shape[:-2]) + + tuple([curr_length - window_length]) + + tuple(past[0].shape[-1:]) + ) + + ones_mask = torch.ones(ones_key_val_shape) + ones_mask = decay_mask * ones_mask.permute(0, 1, 2, 4, 3) + ones_mask = ones_mask.permute(0, 1, 2, 4, 3) + + window_mask = torch.cat( + (ones_mask, torch.zeros(zeros_key_val_shape)), + dim=-2 + ).cuda() + + else: + window_mask = torch.ones_like(past[0]).cuda() + + # accumulate perturbations for num_iterations + loss_per_iter = [] + for i in range(num_iterations): + print("Iteration ", i + 1) + + curr_perturbation = [ + to_var(torch.from_numpy(p_), requires_grad=True) + for p_ in grad_accumulator + ] + + # Compute hidden using perturbed past + curr_pert_past = list(map(add, past, curr_perturbation)) + all_logits, _, all_hidden = model(last, past=curr_pert_past) + hidden = all_hidden[-1] + accumulated_hidden += torch.sum(hidden, dim=1).detach() + logits = all_logits[:, -1, :] + probs = F.softmax(logits, dim=-1) + + # compute loss + bow_loss = 0.0 + discrim_loss = 0.0 + kl_loss = 0.0 + + if loss_type == PPLM_BOW or loss_type == PPLM_BOW_DISCRIM: + for one_hot_bow in one_hot_bows_vectors: + bow_logits = torch.mm(probs, torch.t(one_hot_bow)) + bow_loss += -torch.log(torch.sum(bow_logits)) + print(" pplm_bow_loss:", bow_loss.data.cpu().numpy()) + + if loss_type == PPLM_DISCRIM or loss_type == PPLM_BOW_DISCRIM: + ce_loss = torch.nn.CrossEntropyLoss() + # TODO all there are for (SUMANTH) + # TODO why we need to do this assignment and not just using unpert_past? + curr_unpert_past = unpert_past + # Get the model's token embeddings in order to compute our own embeds from curr_probs: + wte = model.resize_token_embeddings() + # TODO i is never used, why do we need to do this i times instead multiplying + # torch.sum(unpert_hidden, dim=1) * horizon_length? + for i in range(horizon_length): + # TODO the next two lines can be done only one time, and why not using probs instead as they do not change at each iteration? + curr_probs = F.softmax(logits, dim=-1) # get softmax + curr_probs = torch.unsqueeze(curr_probs, dim=1) + inputs_embeds = torch.matmul(curr_probs, wte.weight.data) + _, curr_unpert_past, curr_all_hidden = model( + past=curr_unpert_past, + inputs_embeds=inputs_embeds + ) + # get expected hidden states + unpert_hidden = curr_all_hidden[1] + accumulated_hidden += torch.sum(unpert_hidden, dim=1) + + prediction = classifier( + accumulated_hidden / (curr_length + 1 + horizon_length) + ) + + label = torch.tensor([label_class], device="cuda", dtype=torch.long) + discrim_loss += ce_loss(prediction, label) + print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy()) + + if kl_scale > 0.0: + unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1) + unpert_probs = ( + unpert_probs + SMALL_CONST * + (unpert_probs <= SMALL_CONST).type( + torch.FloatTensor + ).cuda().detach() + ) + + correction = SMALL_CONST * (probs <= SMALL_CONST).type( + torch.FloatTensor + ).cuda().detach() + corrected_probs = probs + correction.detach() + kl_loss += kl_scale * ( + (corrected_probs * (corrected_probs / unpert_probs).log()).sum() + ) + print(' kl_loss', (kl_loss).data.cpu().numpy()) + + loss = bow_loss + discrim_loss + kl_loss + loss_per_iter.append(loss.data.cpu().numpy()) + print(' pplm_loss', (loss - kl_loss).data.cpu().numpy()) + + # compute gradients + loss.backward(retain_graph=True) + + # calculate gradient norms + if grad_norms is not None and loss_type == PPLM_BOW: + grad_norms = [ + torch.max(grad_norms[index], torch.norm(p_.grad * window_mask)) + for index, p_ in enumerate(curr_perturbation) + ] + else: + grad_norms = [ + (torch.norm(p_.grad * window_mask) + SMALL_CONST) + for index, p_ in enumerate(curr_perturbation) + ] + + # normalize gradients + grad = [ + -stepsize + * (p_.grad * window_mask / grad_norms[ + index] ** gamma).data.cpu().numpy() + for index, p_ in enumerate(curr_perturbation) + ] + + # accumulate gradients + grad_accumulator = list(map(add, grad, grad_accumulator)) + + # reset gradients, just to make sure + for p_ in curr_perturbation: + p_.grad.data.zero_() + + # removing past from the graph + new_past = [] + for p_ in past: + new_past.append(p_.detach()) + past = new_past + + # apply the accumulated perturbations to the past + grad_accumulator = [ + to_var(torch.from_numpy(p_), requires_grad=True) + for p_ in grad_accumulator + ] + pert_past = list(map(add, past, grad_accumulator)) + + return pert_past, accumulated_hidden, grad_norms, loss_per_iter + + +def get_classifier( + name: Optional[str], label_class: Union[str, int], device: Union[str, torch.device] +) -> Tuple[Optional[ClassificationHead], Optional[int]]: + if name is None: + return None, None + + params = DISCRIMINATOR_MODELS_PARAMS[name] + classifier = ClassificationHead( + class_size=params['class_size'], + embed_size=params['embed_size'] + ).to(device) + resolved_archive_file = cached_path(params["url"]) + classifier.load_state_dict(torch.load(resolved_archive_file, map_location=device)) + classifier.eval() + + if isinstance(label_class, str): + if label_class in params["class_vocab"]: + label_id = params["class_vocab"][label_class] + else: + label_id = params["default_class"] + print("label_class {} not in class_vocab".format(label_class)) + print("available values are: {}".format(params["class_vocab"])) + print("using default class {}".format(label_id)) + + elif isinstance(label_class, int): + if label_class in set(params["class_vocab"].values()): + label_id = label_class + else: + label_id = params["default_class"] + print("label_class {} not in class_vocab".format(label_class)) + print("available values are: {}".format(params["class_vocab"])) + print("using default class {}".format(label_id)) + + else: + label_id = params["default_class"] + + return classifier, label_id + + +def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str]) -> List[List[List[int]]]: + bow_indices = [] + for id_or_path in bag_of_words_ids_or_paths: + if id_or_path in BAG_OF_WORDS_ARCHIVE_MAP: + filepath = cached_path(BAG_OF_WORDS_ARCHIVE_MAP[id_or_path]) + else: + filepath = id_or_path + with open(filepath, "r") as f: + words = f.read().split("\n") + bow_indices.append([TOKENIZER.encode(word) for word in words]) + return bow_indices + + +def build_bows_one_hot_vectors(bow_indices): + if bow_indices is None: + return None + + one_hot_bows_vectors = [] + for single_bow in bow_indices: + single_bow = list(filter(lambda x: len(x) <= 1, single_bow)) + single_bow = torch.tensor(single_bow).cuda() + num_words = single_bow.shape[0] + one_hot_bow = torch.zeros(num_words, TOKENIZER.vocab_size).cuda() + one_hot_bow.scatter_(1, single_bow, 1) + one_hot_bows_vectors.append(one_hot_bow) + return one_hot_bows_vectors + + +def full_text_generation( + model, + context=None, + num_samples=1, + device="cuda", + sample=True, + discrim=None, + label_class=None, + bag_of_words=None, + length=100, + grad_length=10000, + stepsize=0.02, + num_iterations=3, + temperature=1.0, + gm_scale=0.9, + kl_scale=0.01, + top_k=10, + window_length=0, + horizon_length=1, + decay=False, + gamma=1.5, + **kwargs +): + classifier, class_id = get_classifier( + discrim, + label_class, + device + ) + + bow_indices = [] + if bag_of_words: + bow_indices = get_bag_of_words_indices(bag_of_words.split(";")) + + if bag_of_words and classifier: + print("Both PPLM-BoW and PPLM-Discrim are on. This is not optimized.") + loss_type = PPLM_BOW_DISCRIM + + elif bag_of_words: + loss_type = PPLM_BOW + print("Using PPLM-BoW") + + elif classifier is not None: + loss_type = PPLM_DISCRIM + print("Using PPLM-Discrim") + + else: + raise Exception("Specify either --bag_of_words (-B) or --discrim (-D)") + + unpert_gen_tok_text, _, _ = generate_text_pplm( + model=model, + context=context, + device=device, + length=length, + perturb=False + ) + torch.cuda.empty_cache() + + pert_gen_tok_texts = [] + discrim_losses = [] + losses_in_time = [] + + for i in range(num_samples): + pert_gen_tok_text, discrim_loss, loss_in_time = generate_text_pplm( + model=model, + context=context, + device=device, + sample=sample, + perturb=True, + bow_indices=bow_indices, + classifier=classifier, + label_class=class_id, + loss_type=loss_type, + length=length, + grad_length=grad_length, + stepsize=stepsize, + num_iterations=num_iterations, + temperature=temperature, + gm_scale=gm_scale, + kl_scale=kl_scale, + top_k=top_k, + window_length=window_length, + horizon_length=horizon_length, + decay=decay, + gamma=gamma, + ) + pert_gen_tok_texts.append(pert_gen_tok_text) + if classifier is not None: + discrim_losses.append(discrim_loss.data.cpu().numpy()) + losses_in_time.append(loss_in_time) + + torch.cuda.empty_cache() + + return unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time + + +def generate_text_pplm( + model, + context=None, + past=None, + device="cuda", + sample=True, + perturb=True, + classifier=None, + label_class=None, + bow_indices=None, + loss_type=0, + length=100, + grad_length=10000, + stepsize=0.02, + num_iterations=3, + temperature=1.0, + gm_scale=0.9, + kl_scale=0.01, + top_k=10, + window_length=0, + horizon_length=1, + decay=False, + gamma=1.5, +): + output_so_far = ( + torch.tensor(context, device=device, dtype=torch.long).unsqueeze(0) + if context + else None + ) + + # collect one hot vectors for bags of words + one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices) + + grad_norms = None + last = None + unpert_discrim_loss = 0 + loss_in_time = [] + for i in trange(length, ascii=True): + + # Get past/probs for current output, except for last word + # Note that GPT takes 2 inputs: past + current_token + + # run model forward to obtain unperturbed + if past is None and output_so_far is not None: + last = output_so_far[:, -1:] + if output_so_far.shape[1] > 1: + _, past, _ = model(output_so_far[:, :-1]) + + unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far) + unpert_last_hidden = unpert_all_hidden[-1] + + else: + unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far) + unpert_last_hidden = unpert_all_hidden[-1] + + # check if we are abowe grad max length + if i >= grad_length: + current_stepsize = stepsize * 0 + else: + current_stepsize = stepsize + + # modify the past if necessary + if not perturb or num_iterations == 0: + pert_past = past + + else: + accumulated_hidden = unpert_last_hidden[:, :-1, :] + accumulated_hidden = torch.sum(accumulated_hidden, dim=1) + + if past is not None: + pert_past, _, grad_norms, loss_this_iter = perturb_past( + past, + model, + last, + unpert_past=unpert_past, + unpert_logits=unpert_logits, + accumulated_hidden=accumulated_hidden, + grad_norms=grad_norms, + stepsize=current_stepsize, + classifier=classifier, + label_class=label_class, + one_hot_bows_vectors=one_hot_bows_vectors, + loss_type=loss_type, + num_iterations=num_iterations, + kl_scale=kl_scale, + window_length=window_length, + horizon_length=horizon_length, + decay=decay, + gamma=gamma, + ) + loss_in_time.append(loss_this_iter) + else: + pert_past = past + + pert_logits, past, pert_all_hidden = model(last, past=pert_past) + pert_logits = pert_logits[:, -1, :] / temperature + pert_probs = F.softmax(pert_logits, dim=-1) + + # compute the discriminator loss using unperturbed hidden + if classifier is not None: + prediction = classifier(torch.mean(unpert_last_hidden, dim=1)) + label = torch.tensor([label_class], device="cuda", dtype=torch.long) + unpert_discrim_loss = torch.nn.CrossEntropyLoss()(prediction, label) + print( + "unperturbed discrim loss", + unpert_discrim_loss.data.cpu().numpy() + ) + else: + unpert_discrim_loss = 0 + + # Fuse the modified model and original model probabilities + if perturb: + unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1) + + pert_probs = (pert_probs ** gm_scale) * ( + unpert_probs ** (1 - gm_scale) + ) + + pert_probs = top_k_filter(pert_probs, k=top_k, probs=True) + + # rescale + if torch.sum(pert_probs) <= 1: + pert_probs = pert_probs / torch.sum(pert_probs) + + else: + pert_logits = top_k_filter(pert_logits, k=top_k) + pert_probs = F.softmax(pert_logits, dim=-1) + + # sample or greedy + if sample: + last = torch.multinomial(pert_probs, num_samples=1) + + else: + _, last = torch.topk(pert_probs, k=1, dim=-1) + + # update context/output_so_far appending the new token + output_so_far = ( + last if output_so_far is None + else torch.cat((output_so_far, last), dim=1) + ) + print(TOKENIZER.decode(output_so_far.tolist()[0])) + + return output_so_far, unpert_discrim_loss, loss_in_time + + +def run_model(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--model_path", + "-M", + type=str, + default="gpt2-medium", + help="pretrained model name or path to local checkpoint", + ) + parser.add_argument( + "--bag_of_words", + "-B", + type=str, + default=None, + help="Bags of words used for PPLM-BoW. Either a BOW id (see list in code) or a filepath. Multiple BoWs separated by ;", + ) + parser.add_argument( + "--discrim", + "-D", + type=str, + default=None, + choices=("clickbait", "sentiment", "toxicity"), + help="Discriminator to use for loss-type 2", + ) + parser.add_argument( + "--label_class", + type=int, + default=-1, + help="Class label used for the discriminator", + ) + parser.add_argument("--stepsize", type=float, default=0.02) + parser.add_argument("--length", type=int, default=100) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--temperature", type=float, default=1.0) + parser.add_argument("--top_k", type=int, default=10) + parser.add_argument("--gm_scale", type=float, default=0.9) + parser.add_argument("--kl_scale", type=float, default=0.01) + parser.add_argument("--no_cuda", action="store_true", help="no cuda") + parser.add_argument( + "--uncond", action="store_true", + help="Generate from end-of-text as prefix" + ) + parser.add_argument( + "--cond_text", type=str, default="The lake", + help="Prefix texts to condition on" + ) + parser.add_argument("--num_iterations", type=int, default=3) + parser.add_argument("--grad_length", type=int, default=10000) + parser.add_argument( + "--num_samples", + type=int, + default=1, + help="Number of samples to generate from the modified latents", + ) + parser.add_argument( + "--horizon_length", + type=int, + default=1, + help="Length of future to optimize over", + ) + parser.add_argument( + "--window_length", + type=int, + default=0, + help="Length of past which is being optimized; " + "0 corresponds to infinite window length", + ) + parser.add_argument("--decay", action="store_true", + help="whether to decay or not") + parser.add_argument("--gamma", type=float, default=1.5) + + args = parser.parse_args() + + # set Random seed + torch.manual_seed(args.seed) + np.random.seed(args.seed) + + # set the device + device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") + + # load pretrained model + model = GPT2LMHeadModel.from_pretrained( + args.model_path, + output_hidden_states=True + ) + model.to(device) + model.eval() + + # freeze GPT-2 weights + for param in model.parameters(): + param.requires_grad = False + + # figure out conditioning text + if args.uncond: + tokenized_cond_text = TOKENIZER.encode( + [TOKENIZER.bos_token] + ) + else: + raw_text = args.cond_text + while not raw_text: + print("Did you forget to add `--cond_text`? ") + raw_text = input("Model prompt >>> ") + tokenized_cond_text = TOKENIZER.encode(TOKENIZER.bos_token + raw_text) + + print("= Prefix of sentence =") + print(TOKENIZER.decode(tokenized_cond_text)) + print() + + # generate unperturbed and perturbed texts + + # full_text_generation returns: + # unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time + unpert_gen_tok_text, pert_gen_tok_texts, _, _ = full_text_generation( + model=model, context=tokenized_cond_text, device=device, **vars(args) + ) + + # untokenize unperturbed text + unpert_gen_text = TOKENIZER.decode(unpert_gen_tok_text.tolist()[0]) + + print("=" * 80) + print("= Unperturbed generated text =") + print(unpert_gen_text) + print() + + generated_texts = [] + + # iterate through the perturbed texts + for i, pert_gen_tok_text in enumerate(pert_gen_tok_texts): + try: + # untokenize unperturbed text + unpert_gen_text = TOKENIZER.decode(pert_gen_tok_text.tolist()[0]) + + print("= Perturbed generated text {} =".format(i + 1)) + print(unpert_gen_text) + print() + except: + pass + + # keep the prefix, perturbed seq, original seq for each index + generated_texts.append( + (tokenized_cond_text, pert_gen_tok_text, unpert_gen_tok_text) + ) + + return generated_texts + + +if __name__ == "__main__": + run_model() From 83b1e6ac9e81cbb053ee272a4a4fcb0b6fac06ab Mon Sep 17 00:00:00 2001 From: Rosanne Liu Date: Sun, 3 Nov 2019 04:51:57 +0000 Subject: [PATCH 121/179] fix the loss backward issue (cherry picked from commit 566468cc984c6ec7e10dfc62b5b4191781a99cd2) --- examples/run_pplm.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 30853c68c3..59ae8a9299 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -36,6 +36,7 @@ from tqdm import trange from transformers import GPT2Tokenizer from transformers.file_utils import cached_path from transformers.modeling_gpt2 import GPT2LMHeadModel +from IPython import embed PPLM_BOW = 1 PPLM_DISCRIM = 2 @@ -246,8 +247,8 @@ def perturb_past( inputs_embeds=inputs_embeds ) # get expected hidden states - unpert_hidden = curr_all_hidden[1] - accumulated_hidden += torch.sum(unpert_hidden, dim=1) + unpert_hidden = curr_all_hidden[-1] + accumulated_hidden += torch.sum(unpert_hidden, dim=1).detach() prediction = classifier( accumulated_hidden / (curr_length + 1 + horizon_length) @@ -257,7 +258,7 @@ def perturb_past( discrim_loss += ce_loss(prediction, label) print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy()) - if kl_scale > 0.0: + if kl_scale >= 0.0: unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1) unpert_probs = ( unpert_probs + SMALL_CONST * @@ -270,7 +271,7 @@ def perturb_past( torch.FloatTensor ).cuda().detach() corrected_probs = probs + correction.detach() - kl_loss += kl_scale * ( + kl_loss = kl_scale * ( (corrected_probs * (corrected_probs / unpert_probs).log()).sum() ) print(' kl_loss', (kl_loss).data.cpu().numpy()) @@ -280,7 +281,7 @@ def perturb_past( print(' pplm_loss', (loss - kl_loss).data.cpu().numpy()) # compute gradients - loss.backward(retain_graph=True) + loss.backward() # calculate gradient norms if grad_norms is not None and loss_type == PPLM_BOW: From 0b77d66a6dac359c3473d6d4b7e799af5196ae4d Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Tue, 5 Nov 2019 15:35:51 +0000 Subject: [PATCH 122/179] rm extraneous import --- examples/run_pplm.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 59ae8a9299..2d4dee72a3 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -36,7 +36,6 @@ from tqdm import trange from transformers import GPT2Tokenizer from transformers.file_utils import cached_path from transformers.modeling_gpt2 import GPT2LMHeadModel -from IPython import embed PPLM_BOW = 1 PPLM_DISCRIM = 2 From d5faa74cd6d7de66a058a9b3368e5cbc6dcaf4d6 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Tue, 5 Nov 2019 15:48:00 +0000 Subject: [PATCH 123/179] tokenizer white space: revert to previous behavior --- examples/run_pplm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 2d4dee72a3..4b1a6a2b6f 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -373,7 +373,7 @@ def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str]) -> List[List[ filepath = id_or_path with open(filepath, "r") as f: words = f.read().split("\n") - bow_indices.append([TOKENIZER.encode(word) for word in words]) + bow_indices.append([TOKENIZER.encode(word, add_prefix_space=True) for word in words]) return bow_indices From 34a83faabeb8f1f0e487a70f31a4e6b1cc0185e1 Mon Sep 17 00:00:00 2001 From: Piero Molino Date: Mon, 25 Nov 2019 19:15:25 -0800 Subject: [PATCH 124/179] Let's make PPLM great again --- examples/run_pplm.py | 908 +++++++++++++++++++++---------------------- 1 file changed, 440 insertions(+), 468 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 4b1a6a2b6f..2f853d15c1 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -1,3 +1,4 @@ +#! /usr/bin/env python3 # coding=utf-8 # Copyright 2018 The Uber AI Team Authors. # @@ -37,10 +38,12 @@ from transformers import GPT2Tokenizer from transformers.file_utils import cached_path from transformers.modeling_gpt2 import GPT2LMHeadModel + PPLM_BOW = 1 PPLM_DISCRIM = 2 PPLM_BOW_DISCRIM = 3 SMALL_CONST = 1e-15 +SmallConst = 1e-15 TOKENIZER = GPT2Tokenizer.from_pretrained("gpt2-medium") BAG_OF_WORDS_ARCHIVE_MAP = { @@ -65,7 +68,7 @@ DISCRIMINATOR_MODELS_PARAMS = { "default_class": 1, }, "sentiment": { - "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/sentiment_classifierhead.pt", + "url": "http://s.yosinski.com/SST_classifier_head.pt", "class_size": 5, "embed_size": 1024, "class_vocab": {"very_positive": 2, "very_negative": 3}, @@ -81,6 +84,30 @@ DISCRIMINATOR_MODELS_PARAMS = { } +def to_var(x, requires_grad=False, volatile=False): + if torch.cuda.is_available(): + x = x.cuda() + return Variable(x, requires_grad=requires_grad, volatile=volatile) + + +def top_k_filter(logits, k, probs=False): + """ + Masks everything but the k top entries as -infinity (1e10). + Used to mask logits such that e^-infinity -> 0 won't contribute to the + sum of the denominator. + """ + if k == 0: + return logits + else: + values = torch.topk(logits, k)[0] + batch_mins = values[:, -1].view(-1, 1).expand_as(logits) + if probs: + return torch.where(logits < batch_mins, + torch.ones_like(logits) * 0.0, logits) + return torch.where(logits < batch_mins, torch.ones_like(logits) * -1e10, + logits) + + class ClassificationHead(torch.nn.Module): """ Classification Head for the transformer """ @@ -99,234 +126,175 @@ class ClassificationHead(torch.nn.Module): return logits -def to_var(x, requires_grad=False, volatile=False): - if torch.cuda.is_available(): - x = x.cuda() - return Variable(x, requires_grad=requires_grad, volatile=volatile) +def perturb_past(past, model, prev, args, classifier, good_index=None, + stepsize=0.01, vocab_size=50257, + original_probs=None, accumulated_hidden=None, true_past=None, + grad_norms=None): + window_length = args.window_length + gm_scale, kl_scale = args.gm_scale, args.kl_scale + one_hot_vectors = [] + for good_list in good_index: + good_list = list(filter(lambda x: len(x) <= 1, good_list)) + good_list = torch.tensor(good_list).cuda() + num_good = good_list.shape[0] + one_hot_good = torch.zeros(num_good, vocab_size).cuda() + one_hot_good.scatter_(1, good_list, 1) + one_hot_vectors.append(one_hot_good) - -def top_k_filter(logits, k, probs=False): - """ - Masks everything but the k top entries as -infinity (1e10). - Used to mask logits such that e^-infinity -> 0 won't contribute to the - sum of the denominator. - """ - if k <= 0: - return logits - - else: - values = torch.topk(logits, k)[0] - batch_mins = values[:, -1].view(-1, 1).expand_as(logits) - - if probs: - return torch.where( - logits < batch_mins, - torch.ones_like(logits) * 0.0, - logits - ) - - return torch.where( - logits < batch_mins, - torch.ones_like(logits) * -1e10, - logits - ) - - -def perturb_past( - past, - model, - last, - unpert_past=None, - unpert_logits=None, - accumulated_hidden=None, - grad_norms=None, - stepsize=0.01, - classifier=None, - label_class=None, - one_hot_bows_vectors=None, - loss_type=0, - num_iterations=3, - kl_scale=0.01, - window_length=0, - horizon_length=1, - decay=False, - gamma=1.5, -): - # initializie perturbation accumulator - grad_accumulator = [ - (np.zeros(p.shape).astype("float32")) - for p in past - ] + # Generate inital perturbed past + past_perturb_orig = [ + (np.random.uniform(0.0, 0.0, p.shape).astype('float32')) + for p in past] if accumulated_hidden is None: accumulated_hidden = 0 - if decay: - decay_mask = torch.arange( - 0.0, - 1.0 + SMALL_CONST, - 1.0 / (window_length) - )[1:] + if args.decay: + decay_mask = torch.arange(0., 1.0 + SmallConst, 1.0 / (window_length))[ + 1:] else: decay_mask = 1.0 - # TODO fix this comment (SUMANTH) - # generate a mask if perturbated gradient is based on a past window - _, _, _, curr_length, _ = past[0].shape - if curr_length > window_length and window_length > 0: - ones_key_val_shape = ( - tuple(past[0].shape[:-2]) - + tuple([window_length]) - + tuple(past[0].shape[-1:]) - ) + # Generate a mask is gradient perturbated is based on a past window + _, _, _, current_length, _ = past[0].shape - zeros_key_val_shape = ( - tuple(past[0].shape[:-2]) - + tuple([curr_length - window_length]) - + tuple(past[0].shape[-1:]) - ) + if current_length > window_length and window_length > 0: + ones_key_val_shape = tuple(past[0].shape[:-2]) + tuple( + [window_length]) + tuple( + past[0].shape[-1:]) + + zeros_key_val_shape = tuple(past[0].shape[:-2]) + tuple( + [current_length - window_length]) + tuple( + past[0].shape[-1:]) ones_mask = torch.ones(ones_key_val_shape) ones_mask = decay_mask * ones_mask.permute(0, 1, 2, 4, 3) ones_mask = ones_mask.permute(0, 1, 2, 4, 3) - window_mask = torch.cat( - (ones_mask, torch.zeros(zeros_key_val_shape)), - dim=-2 - ).cuda() - + window_mask = torch.cat((ones_mask, torch.zeros(zeros_key_val_shape)), + dim=-2).cuda() else: window_mask = torch.ones_like(past[0]).cuda() - # accumulate perturbations for num_iterations loss_per_iter = [] - for i in range(num_iterations): + for i in range(args.num_iterations): print("Iteration ", i + 1) + past_perturb = [torch.from_numpy(p_) for p_ in past_perturb_orig] + past_perturb = [to_var(p_, requires_grad=True) for p_ in past_perturb] - curr_perturbation = [ - to_var(torch.from_numpy(p_), requires_grad=True) - for p_ in grad_accumulator - ] + perturbed_past = list(map(add, past, past_perturb)) - # Compute hidden using perturbed past - curr_pert_past = list(map(add, past, curr_perturbation)) - all_logits, _, all_hidden = model(last, past=curr_pert_past) + _, _, _, current_length, _ = past_perturb[0].shape + + # _, future_past = model(prev, past=perturbed_past) + # hidden = model.hidden_states + + # Piero modified model call + logits, _, all_hidden = model(prev, past=perturbed_past) hidden = all_hidden[-1] - accumulated_hidden += torch.sum(hidden, dim=1).detach() - logits = all_logits[:, -1, :] - probs = F.softmax(logits, dim=-1) + new_accumulated_hidden = accumulated_hidden + torch.sum(hidden, + dim=1).detach() - # compute loss - bow_loss = 0.0 - discrim_loss = 0.0 - kl_loss = 0.0 + # TODO: Check the layer-norm consistency of this with trained discriminator + logits = logits[:, -1, :] + probabs = F.softmax(logits, dim=-1) + loss = 0.0 + loss_list = [] + if args.loss_type == 1 or args.loss_type == 3: + for one_hot_good in one_hot_vectors: + good_logits = torch.mm(probabs, torch.t(one_hot_good)) + loss_word = good_logits + loss_word = torch.sum(loss_word) + loss_word = -torch.log(loss_word) + # loss_word = torch.sum(loss_word) /torch.sum(one_hot_good) + loss += loss_word + loss_list.append(loss_word) + print(" pplm_bow_loss:", loss.data.cpu().numpy()) - if loss_type == PPLM_BOW or loss_type == PPLM_BOW_DISCRIM: - for one_hot_bow in one_hot_bows_vectors: - bow_logits = torch.mm(probs, torch.t(one_hot_bow)) - bow_loss += -torch.log(torch.sum(bow_logits)) - print(" pplm_bow_loss:", bow_loss.data.cpu().numpy()) - - if loss_type == PPLM_DISCRIM or loss_type == PPLM_BOW_DISCRIM: + if args.loss_type == 2 or args.loss_type == 3: ce_loss = torch.nn.CrossEntropyLoss() - # TODO all there are for (SUMANTH) - # TODO why we need to do this assignment and not just using unpert_past? - curr_unpert_past = unpert_past - # Get the model's token embeddings in order to compute our own embeds from curr_probs: - wte = model.resize_token_embeddings() - # TODO i is never used, why do we need to do this i times instead multiplying - # torch.sum(unpert_hidden, dim=1) * horizon_length? - for i in range(horizon_length): - # TODO the next two lines can be done only one time, and why not using probs instead as they do not change at each iteration? - curr_probs = F.softmax(logits, dim=-1) # get softmax - curr_probs = torch.unsqueeze(curr_probs, dim=1) - inputs_embeds = torch.matmul(curr_probs, wte.weight.data) - _, curr_unpert_past, curr_all_hidden = model( - past=curr_unpert_past, + new_true_past = true_past + for i in range(args.horizon_length): + future_probabs = F.softmax(logits, dim=-1) # Get softmax + future_probabs = torch.unsqueeze(future_probabs, dim=1) + + # _, new_true_past = model(future_probabs, past=new_true_past) + # future_hidden = model.hidden_states # Get expected hidden states + + # Piero modified model call + wte = model.resize_token_embeddings() + inputs_embeds = torch.matmul(future_probabs, wte.weight.data) + _, new_true_past, future_hidden = model( + past=new_true_past, inputs_embeds=inputs_embeds ) - # get expected hidden states - unpert_hidden = curr_all_hidden[-1] - accumulated_hidden += torch.sum(unpert_hidden, dim=1).detach() + future_hidden = future_hidden[-1] - prediction = classifier( - accumulated_hidden / (curr_length + 1 + horizon_length) - ) + new_accumulated_hidden = new_accumulated_hidden + torch.sum( + future_hidden, dim=1) - label = torch.tensor([label_class], device="cuda", dtype=torch.long) - discrim_loss += ce_loss(prediction, label) + predicted_sentiment = classifier(new_accumulated_hidden / ( + current_length + 1 + args.horizon_length)) + + label = torch.tensor([args.label_class], device='cuda', + dtype=torch.long) + discrim_loss = ce_loss(predicted_sentiment, label) print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy()) + loss += discrim_loss + loss_list.append(discrim_loss) - if kl_scale >= 0.0: - unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1) - unpert_probs = ( - unpert_probs + SMALL_CONST * - (unpert_probs <= SMALL_CONST).type( - torch.FloatTensor - ).cuda().detach() - ) - - correction = SMALL_CONST * (probs <= SMALL_CONST).type( - torch.FloatTensor - ).cuda().detach() - corrected_probs = probs + correction.detach() + kl_loss = 0.0 + if kl_scale > 0.0: + p = (F.softmax(original_probs[:, -1, :], dim=-1)) + p = p + SmallConst * (p <= SmallConst).type( + torch.FloatTensor).cuda().detach() + correction = SmallConst * (probabs <= SmallConst).type( + torch.FloatTensor).cuda().detach() + corrected_probabs = probabs + correction.detach() kl_loss = kl_scale * ( - (corrected_probs * (corrected_probs / unpert_probs).log()).sum() - ) + (corrected_probabs * (corrected_probabs / p).log()).sum()) print(' kl_loss', (kl_loss).data.cpu().numpy()) + loss += kl_loss # + discrim_loss - loss = bow_loss + discrim_loss + kl_loss loss_per_iter.append(loss.data.cpu().numpy()) + print(' pplm_loss', (loss - kl_loss).data.cpu().numpy()) - # compute gradients loss.backward() - - # calculate gradient norms - if grad_norms is not None and loss_type == PPLM_BOW: + if grad_norms is not None and args.loss_type == 1: grad_norms = [ torch.max(grad_norms[index], torch.norm(p_.grad * window_mask)) - for index, p_ in enumerate(curr_perturbation) - ] + for index, p_ in + enumerate(past_perturb)] else: - grad_norms = [ - (torch.norm(p_.grad * window_mask) + SMALL_CONST) - for index, p_ in enumerate(curr_perturbation) - ] + grad_norms = [(torch.norm(p_.grad * window_mask) + SmallConst) for + index, p_ in enumerate(past_perturb)] - # normalize gradients grad = [ - -stepsize - * (p_.grad * window_mask / grad_norms[ - index] ** gamma).data.cpu().numpy() - for index, p_ in enumerate(curr_perturbation) - ] + -stepsize * (p_.grad * window_mask / grad_norms[ + index] ** args.gamma).data.cpu().numpy() + for index, p_ in enumerate(past_perturb)] + past_perturb_orig = list(map(add, grad, past_perturb_orig)) - # accumulate gradients - grad_accumulator = list(map(add, grad, grad_accumulator)) - - # reset gradients, just to make sure - for p_ in curr_perturbation: + for p_ in past_perturb: p_.grad.data.zero_() - # removing past from the graph new_past = [] - for p_ in past: - new_past.append(p_.detach()) + for p in past: + new_past.append(p.detach()) + past = new_past - # apply the accumulated perturbations to the past - grad_accumulator = [ - to_var(torch.from_numpy(p_), requires_grad=True) - for p_ in grad_accumulator - ] - pert_past = list(map(add, past, grad_accumulator)) + past_perturb = [torch.from_numpy(p_) for p_ in past_perturb_orig] + past_perturb = [to_var(p_, requires_grad=True) for p_ in past_perturb] + perturbed_past = list(map(add, past, past_perturb)) - return pert_past, accumulated_hidden, grad_norms, loss_per_iter + return perturbed_past, new_accumulated_hidden, grad_norms, loss_per_iter def get_classifier( - name: Optional[str], label_class: Union[str, int], device: Union[str, torch.device] + name: Optional[str], label_class: Union[str, int], + device: Union[str, torch.device] ) -> Tuple[Optional[ClassificationHead], Optional[int]]: if name is None: return None, None @@ -337,7 +305,8 @@ def get_classifier( embed_size=params['embed_size'] ).to(device) resolved_archive_file = cached_path(params["url"]) - classifier.load_state_dict(torch.load(resolved_archive_file, map_location=device)) + classifier.load_state_dict( + torch.load(resolved_archive_file, map_location=device)) classifier.eval() if isinstance(label_class, str): @@ -364,7 +333,8 @@ def get_classifier( return classifier, label_id -def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str]) -> List[List[List[int]]]: +def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str]) -> List[ + List[List[int]]]: bow_indices = [] for id_or_path in bag_of_words_ids_or_paths: if id_or_path in BAG_OF_WORDS_ARCHIVE_MAP: @@ -372,8 +342,10 @@ def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str]) -> List[List[ else: filepath = id_or_path with open(filepath, "r") as f: - words = f.read().split("\n") - bow_indices.append([TOKENIZER.encode(word, add_prefix_space=True) for word in words]) + words = f.read().strip().split("\n") + bow_indices.append( + [TOKENIZER.encode(word.strip(), add_prefix_space=True) for word in + words]) return bow_indices @@ -392,327 +364,308 @@ def build_bows_one_hot_vectors(bow_indices): return one_hot_bows_vectors -def full_text_generation( - model, - context=None, - num_samples=1, - device="cuda", - sample=True, - discrim=None, - label_class=None, - bag_of_words=None, - length=100, - grad_length=10000, - stepsize=0.02, - num_iterations=3, - temperature=1.0, - gm_scale=0.9, - kl_scale=0.01, - top_k=10, - window_length=0, - horizon_length=1, - decay=False, - gamma=1.5, - **kwargs -): +def latent_perturb(model, args, context=None, sample=True, device='cuda'): classifier, class_id = get_classifier( - discrim, - label_class, + args.discrim, + args.label_class, device ) - bow_indices = [] - if bag_of_words: - bow_indices = get_bag_of_words_indices(bag_of_words.split(";")) + # if args.discrim == 'clickbait': + # classifier = ClassificationHead(class_size=2, embed_size=1024).to(device) + # classifier.load_state_dict(torch.load("discrim_models/clickbait_classifierhead.pt")) + # classifier.eval() + # args.label_class = 1 # clickbaity + # + # elif args.discrim == 'sentiment': + # classifier = ClassificationHead(class_size=5, embed_size=1024).to(device) + # #classifier.load_state_dict(torch.load("discrim_models/sentiment_classifierhead.pt")) + # classifier.load_state_dict(torch.load("discrim_models/SST_classifier_head_epoch_16.pt")) + # classifier.eval() + # if args.label_class < 0: + # raise Exception('Wrong class for sentiment, use --label-class 2 for *very positive*, 3 for *very negative*') + # #args.label_class = 2 # very pos + # #args.label_class = 3 # very neg + # + # elif args.discrim == 'toxicity': + # classifier = ClassificationHead(class_size=2, embed_size=1024).to(device) + # classifier.load_state_dict(torch.load("discrim_models/toxicity_classifierhead.pt")) + # classifier.eval() + # args.label_class = 0 # not toxic + # + # elif args.discrim == 'generic': + # if args.discrim_weights is None: + # raise ValueError('When using a generic discriminator, ' + # 'discrim_weights need to be specified') + # if args.discrim_meta is None: + # raise ValueError('When using a generic discriminator, ' + # 'discrim_meta need to be specified') + # + # with open(args.discrim_meta, 'r') as discrim_meta_file: + # meta = json.load(discrim_meta_file) + # + # classifier = ClassificationHead( + # class_size=meta['class_size'], + # embed_size=meta['embed_size'], + # # todo add tokenizer from meta + # ).to(device) + # classifier.load_state_dict(torch.load(args.discrim_weights)) + # classifier.eval() + # if args.label_class == -1: + # args.label_class = meta['default_class'] + # + # else: + # classifier = None - if bag_of_words and classifier: + # Get tokens for the list of positive words + def list_tokens(word_list): + token_list = [TOKENIZER.encode(word, add_prefix_space=True) for word in + word_list] + # token_list = [] + # for word in word_list: + # token_list.append(TOKENIZER.encode(" " + word)) + return token_list + + # good_index = [] + # if args.bag_of_words: + # bags_of_words = args.bag_of_words.split(";") + # for wordlist in bags_of_words: + # with open(wordlist, "r") as f: + # words = f.read().strip() + # words = words.split('\n') + # good_index.append(list_tokens(words)) + # + # for good_list in good_index: + # good_list = list(filter(lambda x: len(x) <= 1, good_list)) + # actual_words = [(TOKENIZER.decode(ww).strip(),ww) for ww in good_list] + + good_index = [] + actual_words = None + if args.bag_of_words: + good_index = get_bag_of_words_indices(args.bag_of_words.split(";")) + + for good_list in good_index: + good_list = list(filter(lambda x: len(x) <= 1, good_list)) + actual_words = [(TOKENIZER.decode(ww).strip(), ww) for ww in + good_list] + + if args.bag_of_words and classifier: print("Both PPLM-BoW and PPLM-Discrim are on. This is not optimized.") - loss_type = PPLM_BOW_DISCRIM + args.loss_type = PPLM_BOW_DISCRIM - elif bag_of_words: - loss_type = PPLM_BOW + elif args.bag_of_words: + args.loss_type = PPLM_BOW print("Using PPLM-BoW") elif classifier is not None: - loss_type = PPLM_DISCRIM + args.loss_type = PPLM_DISCRIM print("Using PPLM-Discrim") else: raise Exception("Specify either --bag_of_words (-B) or --discrim (-D)") - unpert_gen_tok_text, _, _ = generate_text_pplm( - model=model, - context=context, - device=device, - length=length, - perturb=False - ) + original, _, _ = sample_from_hidden(model=model, args=args, context=context, + device=device, + perturb=False, good_index=good_index, + classifier=classifier) torch.cuda.empty_cache() - pert_gen_tok_texts = [] - discrim_losses = [] - losses_in_time = [] + perturbed_list = [] + discrim_loss_list = [] + loss_in_time_list = [] - for i in range(num_samples): - pert_gen_tok_text, discrim_loss, loss_in_time = generate_text_pplm( - model=model, - context=context, - device=device, - sample=sample, - perturb=True, - bow_indices=bow_indices, - classifier=classifier, - label_class=class_id, - loss_type=loss_type, - length=length, - grad_length=grad_length, - stepsize=stepsize, - num_iterations=num_iterations, - temperature=temperature, - gm_scale=gm_scale, - kl_scale=kl_scale, - top_k=top_k, - window_length=window_length, - horizon_length=horizon_length, - decay=decay, - gamma=gamma, - ) - pert_gen_tok_texts.append(pert_gen_tok_text) + for i in range(args.num_samples): + perturbed, discrim_loss, loss_in_time = sample_from_hidden(model=model, + args=args, + context=context, + device=device, + perturb=True, + good_index=good_index, + classifier=classifier) + perturbed_list.append(perturbed) if classifier is not None: - discrim_losses.append(discrim_loss.data.cpu().numpy()) - losses_in_time.append(loss_in_time) + discrim_loss_list.append(discrim_loss.data.cpu().numpy()) + loss_in_time_list.append(loss_in_time) torch.cuda.empty_cache() - return unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time + return original, perturbed_list, discrim_loss_list, loss_in_time_list, actual_words -def generate_text_pplm( - model, - context=None, - past=None, - device="cuda", - sample=True, - perturb=True, - classifier=None, - label_class=None, - bow_indices=None, - loss_type=0, - length=100, - grad_length=10000, - stepsize=0.02, - num_iterations=3, - temperature=1.0, - gm_scale=0.9, - kl_scale=0.01, - top_k=10, - window_length=0, - horizon_length=1, - decay=False, - gamma=1.5, -): - output_so_far = ( - torch.tensor(context, device=device, dtype=torch.long).unsqueeze(0) - if context - else None - ) - - # collect one hot vectors for bags of words - one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices) +def sample_from_hidden(model, args, classifier, context=None, past=None, + device='cuda', + sample=True, perturb=True, good_index=None): + output = torch.tensor(context, device=device, dtype=torch.long).unsqueeze( + 0) if context else None grad_norms = None - last = None - unpert_discrim_loss = 0 loss_in_time = [] - for i in trange(length, ascii=True): + for i in trange(args.length, ascii=True): # Get past/probs for current output, except for last word - # Note that GPT takes 2 inputs: past + current_token + # Note that GPT takes 2 inputs: past + current-token + # Therefore, use everything from before current i/p token to generate relevant past - # run model forward to obtain unperturbed - if past is None and output_so_far is not None: - last = output_so_far[:, -1:] - if output_so_far.shape[1] > 1: - _, past, _ = model(output_so_far[:, :-1]) + if past is None and output is not None: + prev = output[:, -1:] + # _, past = model(output[:, :-1]) + # original_probs, true_past = model(output) + # true_hidden = model.hidden_states - unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far) - unpert_last_hidden = unpert_all_hidden[-1] + # Piero modified model call + _, past, _ = model(output[:, :-1]) + original_probs, true_past, unpert_all_hidden = model(output) + true_hidden = unpert_all_hidden[-1] else: - unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far) - unpert_last_hidden = unpert_all_hidden[-1] + # original_probs, true_past = model(output) + # true_hidden = model.hidden_states - # check if we are abowe grad max length - if i >= grad_length: - current_stepsize = stepsize * 0 + # Piero modified model call + original_probs, true_past, unpert_all_hidden = model(output) + true_hidden = unpert_all_hidden[-1] + + # Modify the past if necessary + + if i >= args.grad_length: + current_stepsize = args.stepsize * 0 else: - current_stepsize = stepsize + current_stepsize = args.stepsize - # modify the past if necessary - if not perturb or num_iterations == 0: - pert_past = past + if not perturb or args.num_iterations == 0: + perturbed_past = past else: - accumulated_hidden = unpert_last_hidden[:, :-1, :] + # Piero modified model call + # accumulated_hidden = model.hidden_states[:, :-1, :] + accumulated_hidden = true_hidden[:, :-1, :] accumulated_hidden = torch.sum(accumulated_hidden, dim=1) - if past is not None: - pert_past, _, grad_norms, loss_this_iter = perturb_past( - past, - model, - last, - unpert_past=unpert_past, - unpert_logits=unpert_logits, - accumulated_hidden=accumulated_hidden, - grad_norms=grad_norms, - stepsize=current_stepsize, - classifier=classifier, - label_class=label_class, - one_hot_bows_vectors=one_hot_bows_vectors, - loss_type=loss_type, - num_iterations=num_iterations, - kl_scale=kl_scale, - window_length=window_length, - horizon_length=horizon_length, - decay=decay, - gamma=gamma, - ) - loss_in_time.append(loss_this_iter) - else: - pert_past = past + perturbed_past, _, grad_norms, loss_per_iter = perturb_past(past, + model, + prev, + args, + good_index=good_index, + stepsize=current_stepsize, + original_probs=original_probs, + true_past=true_past, + accumulated_hidden=accumulated_hidden, + classifier=classifier, + grad_norms=grad_norms) + loss_in_time.append(loss_per_iter) - pert_logits, past, pert_all_hidden = model(last, past=pert_past) - pert_logits = pert_logits[:, -1, :] / temperature - pert_probs = F.softmax(pert_logits, dim=-1) + # Piero modified model call + logits, past, pert_all_hidden = model(prev, past=perturbed_past) + # test_logits = F.softmax(test_logits[:, -1, :], dim=-1) + # likelywords = torch.topk(test_logits, k=10, dim=-1) + # print(TOKENIZER.decode(likelywords[1].tolist()[0])) - # compute the discriminator loss using unperturbed hidden if classifier is not None: - prediction = classifier(torch.mean(unpert_last_hidden, dim=1)) - label = torch.tensor([label_class], device="cuda", dtype=torch.long) - unpert_discrim_loss = torch.nn.CrossEntropyLoss()(prediction, label) - print( - "unperturbed discrim loss", - unpert_discrim_loss.data.cpu().numpy() - ) + ce_loss = torch.nn.CrossEntropyLoss() + predicted_sentiment = classifier(torch.mean(true_hidden, dim=1)) + label = torch.tensor([args.label_class], device='cuda', + dtype=torch.long) + true_discrim_loss = ce_loss(predicted_sentiment, label) + print("true discrim loss", true_discrim_loss.data.cpu().numpy()) else: - unpert_discrim_loss = 0 + true_discrim_loss = 0 - # Fuse the modified model and original model probabilities + # Piero modified model call + # hidden = model.hidden_states # update hidden + # logits = model.forward_hidden(hidden) + logits = logits[:, -1, :] / args.temperature # + SmallConst + + # logits = top_k_filter(logits, k=args.top_k) # + SmallConst + + log_probs = F.softmax(logits, dim=-1) + + # Fuse the modified model and original model if perturb: - unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1) - pert_probs = (pert_probs ** gm_scale) * ( - unpert_probs ** (1 - gm_scale) - ) + # original_probs = top_k_filter(original_probs[:, -1, :]) #+ SmallConst + original_probs = F.softmax(original_probs[:, -1, :], dim=-1) + # likelywords = torch.topk(original_probs, k=10, dim=-1) + # print(TOKENIZER.decode(likelywords[1].tolist()[0])) - pert_probs = top_k_filter(pert_probs, k=top_k, probs=True) + gm_scale = args.gm_scale + log_probs = ((log_probs ** gm_scale) * ( + original_probs ** (1 - gm_scale))) # + SmallConst - # rescale - if torch.sum(pert_probs) <= 1: - pert_probs = pert_probs / torch.sum(pert_probs) + log_probs = top_k_filter(log_probs, k=args.top_k, + probs=True) # + SmallConst + + if torch.sum(log_probs) <= 1: + log_probs = log_probs / torch.sum(log_probs) else: - pert_logits = top_k_filter(pert_logits, k=top_k) - pert_probs = F.softmax(pert_logits, dim=-1) + logits = top_k_filter(logits, k=args.top_k) # + SmallConst + log_probs = F.softmax(logits, dim=-1) - # sample or greedy if sample: - last = torch.multinomial(pert_probs, num_samples=1) - + # likelywords = torch.topk(log_probs, k=args.top_k, dim=-1) + # print(TOKENIZER.decode(likelywords[1].tolist()[0])) + # print(likelywords[0].tolist()) + prev = torch.multinomial(log_probs, num_samples=1) else: - _, last = torch.topk(pert_probs, k=1, dim=-1) + _, prev = torch.topk(log_probs, k=1, dim=-1) + # if perturb: + # prev = future + output = prev if output is None else torch.cat((output, prev), + dim=1) # update output + print(TOKENIZER.decode(output.tolist()[0])) - # update context/output_so_far appending the new token - output_so_far = ( - last if output_so_far is None - else torch.cat((output_so_far, last), dim=1) - ) - print(TOKENIZER.decode(output_so_far.tolist()[0])) - - return output_so_far, unpert_discrim_loss, loss_in_time + return output, true_discrim_loss, loss_in_time def run_model(): parser = argparse.ArgumentParser() - parser.add_argument( - "--model_path", - "-M", - type=str, - default="gpt2-medium", - help="pretrained model name or path to local checkpoint", - ) - parser.add_argument( - "--bag_of_words", - "-B", - type=str, - default=None, - help="Bags of words used for PPLM-BoW. Either a BOW id (see list in code) or a filepath. Multiple BoWs separated by ;", - ) - parser.add_argument( - "--discrim", - "-D", - type=str, - default=None, - choices=("clickbait", "sentiment", "toxicity"), - help="Discriminator to use for loss-type 2", - ) - parser.add_argument( - "--label_class", - type=int, - default=-1, - help="Class label used for the discriminator", - ) - parser.add_argument("--stepsize", type=float, default=0.02) + parser.add_argument('--model_path', '-M', type=str, default='gpt2-medium', + help='pretrained model name or path to local checkpoint') + parser.add_argument('--bag-of-words', '-B', type=str, default=None, + help='Bags of words used for PPLM-BoW. Multiple BoWs separated by ;') + parser.add_argument('--discrim', '-D', type=str, default=None, + choices=( + 'clickbait', 'sentiment', 'toxicity', 'generic'), + help='Discriminator to use for loss-type 2') + parser.add_argument('--discrim_weights', type=str, default=None, + help='Weights for the generic discriminator') + parser.add_argument('--discrim_meta', type=str, default=None, + help='Meta information for the generic discriminator') + parser.add_argument('--label_class', type=int, default=-1, + help='Class label used for the discriminator') + parser.add_argument('--stepsize', type=float, default=0.02) parser.add_argument("--length", type=int, default=100) parser.add_argument("--seed", type=int, default=0) parser.add_argument("--temperature", type=float, default=1.0) parser.add_argument("--top_k", type=int, default=10) parser.add_argument("--gm_scale", type=float, default=0.9) parser.add_argument("--kl_scale", type=float, default=0.01) - parser.add_argument("--no_cuda", action="store_true", help="no cuda") - parser.add_argument( - "--uncond", action="store_true", - help="Generate from end-of-text as prefix" - ) - parser.add_argument( - "--cond_text", type=str, default="The lake", - help="Prefix texts to condition on" - ) - parser.add_argument("--num_iterations", type=int, default=3) - parser.add_argument("--grad_length", type=int, default=10000) - parser.add_argument( - "--num_samples", - type=int, - default=1, - help="Number of samples to generate from the modified latents", - ) - parser.add_argument( - "--horizon_length", - type=int, - default=1, - help="Length of future to optimize over", - ) - parser.add_argument( - "--window_length", - type=int, - default=0, - help="Length of past which is being optimized; " - "0 corresponds to infinite window length", - ) - parser.add_argument("--decay", action="store_true", - help="whether to decay or not") - parser.add_argument("--gamma", type=float, default=1.5) + parser.add_argument('--nocuda', action='store_true', help='no cuda') + parser.add_argument('--uncond', action='store_true', + help='Generate from end-of-text as prefix') + parser.add_argument("--cond_text", type=str, default='The lake', + help='Prefix texts to condition on') + parser.add_argument('--num_iterations', type=int, default=3) + parser.add_argument('--grad_length', type=int, default=10000) + parser.add_argument('--num_samples', type=int, default=1, + help='Number of samples to generate from the modified latents') + parser.add_argument('--horizon_length', type=int, default=1, + help='Length of future to optimize over') + # parser.add_argument('--force-token', action='store_true', help='no cuda') + parser.add_argument('--window_length', type=int, default=0, + help='Length of past which is being optimizer; 0 corresponds to infinite window length') + parser.add_argument('--decay', action='store_true', + help='whether to decay or not') + parser.add_argument('--gamma', type=float, default=1.5) + parser.add_argument('--colorama', action='store_true', help='no cuda') args = parser.parse_args() - # set Random seed torch.manual_seed(args.seed) np.random.seed(args.seed) - # set the device - device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") + device = 'cpu' if args.nocuda else 'cuda' - # load pretrained model model = GPT2LMHeadModel.from_pretrained( args.model_path, output_hidden_states=True @@ -720,63 +673,82 @@ def run_model(): model.to(device) model.eval() - # freeze GPT-2 weights + # Freeze GPT-2 weights for param in model.parameters(): param.requires_grad = False + pass - # figure out conditioning text if args.uncond: - tokenized_cond_text = TOKENIZER.encode( - [TOKENIZER.bos_token] - ) + seq = [[50256, 50256]] + else: raw_text = args.cond_text while not raw_text: - print("Did you forget to add `--cond_text`? ") + print('Did you forget to add `--cond-text`? ') raw_text = input("Model prompt >>> ") - tokenized_cond_text = TOKENIZER.encode(TOKENIZER.bos_token + raw_text) + seq = [[50256] + TOKENIZER.encode(raw_text)] - print("= Prefix of sentence =") - print(TOKENIZER.decode(tokenized_cond_text)) - print() + collect_gen = dict() + current_index = 0 + for out in seq: - # generate unperturbed and perturbed texts + text = TOKENIZER.decode(out) + print("=" * 40 + " Prefix of sentence " + "=" * 40) + print(text) + print("=" * 80) - # full_text_generation returns: - # unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time - unpert_gen_tok_text, pert_gen_tok_texts, _, _ = full_text_generation( - model=model, context=tokenized_cond_text, device=device, **vars(args) - ) + out1, out_perturb, discrim_loss_list, loss_in_time_list, actual_words = latent_perturb( + model=model, args=args, context=out, + device=device) - # untokenize unperturbed text - unpert_gen_text = TOKENIZER.decode(unpert_gen_tok_text.tolist()[0]) + text_whole = TOKENIZER.decode(out1.tolist()[0]) - print("=" * 80) - print("= Unperturbed generated text =") - print(unpert_gen_text) - print() + print("=" * 80) + print("=" * 40 + " Whole sentence (Original)" + "=" * 40) + print(text_whole) + print("=" * 80) - generated_texts = [] + out_perturb_copy = out_perturb - # iterate through the perturbed texts - for i, pert_gen_tok_text in enumerate(pert_gen_tok_texts): - try: - # untokenize unperturbed text - unpert_gen_text = TOKENIZER.decode(pert_gen_tok_text.tolist()[0]) + for out_perturb in out_perturb_copy: + # try: + # print("=" * 40 + " Whole sentence (Perturbed)" + "=" * 40) + # text_whole = TOKENIZER.decode(out_perturb.tolist()[0]) + # print(text_whole) + # print("=" * 80) + # except: + # pass + # collect_gen[current_index] = [out, out_perturb, out1] + ## Save the prefix, perturbed seq, original seq for each index + print("=" * 40 + " Whole sentence (Perturbed)" + "=" * 40) + keyword_tokens = [aa[-1][0] for aa in + actual_words] if actual_words else [] + output_tokens = out_perturb.tolist()[0] - print("= Perturbed generated text {} =".format(i + 1)) - print(unpert_gen_text) - print() - except: - pass + if args.colorama: + import colorama - # keep the prefix, perturbed seq, original seq for each index - generated_texts.append( - (tokenized_cond_text, pert_gen_tok_text, unpert_gen_tok_text) - ) + text_whole = '' + for out in output_tokens: + if out in keyword_tokens: + text_whole += '%s%s%s' % ( + colorama.Fore.GREEN, TOKENIZER.decode([out]), + colorama.Style.RESET_ALL) + else: + text_whole += TOKENIZER.decode([out]) + else: + text_whole = TOKENIZER.decode(out_perturb.tolist()[0]) - return generated_texts + print(text_whole) + print("=" * 80) + + collect_gen[current_index] = [out, out_perturb, out1] + + current_index = current_index + 1 -if __name__ == "__main__": + return + + +if __name__ == '__main__': run_model() From 0b51fba20bd88c4cc4acbb3e9dce82980719895c Mon Sep 17 00:00:00 2001 From: piero Date: Tue, 26 Nov 2019 13:15:56 -0800 Subject: [PATCH 125/179] Added script for training a discriminator for pplm to use --- examples/run_pplm.py | 19 +- examples/run_pplm_discrim_train.py | 582 +++++++++++++++++++++++++++++ 2 files changed, 583 insertions(+), 18 deletions(-) create mode 100644 examples/run_pplm_discrim_train.py diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 2f853d15c1..217c131b8f 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -34,6 +34,7 @@ import torch.nn.functional as F from torch.autograd import Variable from tqdm import trange +from examples.run_pplm_discrim_train import ClassificationHead from transformers import GPT2Tokenizer from transformers.file_utils import cached_path from transformers.modeling_gpt2 import GPT2LMHeadModel @@ -108,24 +109,6 @@ def top_k_filter(logits, k, probs=False): logits) -class ClassificationHead(torch.nn.Module): - """ Classification Head for the transformer """ - - def __init__(self, class_size=5, embed_size=2048): - super(ClassificationHead, self).__init__() - self.class_size = class_size - self.embed_size = embed_size - # self.mlp1 = torch.nn.Linear(embed_size, embed_size) - # self.mlp2 = (torch.nn.Linear(embed_size, class_size)) - self.mlp = torch.nn.Linear(embed_size, class_size) - - def forward(self, hidden_state): - # hidden_state = F.relu(self.mlp1(hidden_state)) - # hidden_state = self.mlp2(hidden_state) - logits = self.mlp(hidden_state) - return logits - - def perturb_past(past, model, prev, args, classifier, good_index=None, stepsize=0.01, vocab_size=50257, original_probs=None, accumulated_hidden=None, true_past=None, diff --git a/examples/run_pplm_discrim_train.py b/examples/run_pplm_discrim_train.py new file mode 100644 index 0000000000..cc52234281 --- /dev/null +++ b/examples/run_pplm_discrim_train.py @@ -0,0 +1,582 @@ +#! /usr/bin/env python3 +# coding=utf-8 + +# This code is licensed under a non-commercial license. + +import argparse +import csv +import json +import math +import time + +import numpy as np +import torch +import torch.nn.functional as F +import torch.optim +import torch.optim as optim +import torch.utils.data as data +from nltk.tokenize.treebank import TreebankWordDetokenizer +from torchtext import data as torchtext_data +from torchtext import datasets +from transformers import GPT2Tokenizer, GPT2LMHeadModel + +torch.manual_seed(0) +np.random.seed(0) +EPSILON = 1e-10 +device = 'cpu' +example_sentence = "This is incredible! I love it, this is the best chicken I have ever had." +max_length_seq = 100 + + +class ClassificationHead(torch.nn.Module): + """Classification Head for transformer encoders""" + + def __init__(self, class_size, embed_size): + super(ClassificationHead, self).__init__() + self.class_size = class_size + self.embed_size = embed_size + # self.mlp1 = torch.nn.Linear(embed_size, embed_size) + # self.mlp2 = (torch.nn.Linear(embed_size, class_size)) + self.mlp = torch.nn.Linear(embed_size, class_size) + + def forward(self, hidden_state): + # hidden_state = F.relu(self.mlp1(hidden_state)) + # hidden_state = self.mlp2(hidden_state) + logits = self.mlp(hidden_state) + return logits + + +class Discriminator(torch.nn.Module): + """Transformer encoder followed by a Classification Head""" + + def __init__( + self, + class_size, + pretrained_model="gpt2-medium", + cached_mode=False + ): + super(Discriminator, self).__init__() + self.tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model) + self.encoder = GPT2LMHeadModel.from_pretrained(pretrained_model) + self.embed_size = self.encoder.transformer.config.hidden_size + self.classifier_head = ClassificationHead( + class_size=class_size, + embed_size=self.embed_size + ) + self.cached_mode = cached_mode + + def get_classifier(self): + return self.classifier_head + + def train_custom(self): + for param in self.encoder.parameters(): + param.requires_grad = False + pass + self.classifier_head.train() + + def avg_representation(self, x): + mask = x.ne(0).unsqueeze(2).repeat( + 1, 1, self.embed_size + ).float().to(device).detach() + hidden, _ = self.encoder.transformer(x) + masked_hidden = hidden * mask + avg_hidden = torch.sum(masked_hidden, dim=1) / ( + torch.sum(mask, dim=1).detach() + EPSILON + ) + return avg_hidden + + def forward(self, x): + if self.cached_mode: + avg_hidden = x.to(device) + else: + avg_hidden = self.avg_representation(x) + + logits = self.classifier_head(avg_hidden) + probs = F.log_softmax(logits, dim=-1) + + return probs + + +class Dataset(data.Dataset): + def __init__(self, X, y): + """Reads source and target sequences from txt files.""" + self.X = X + self.y = y + + def __len__(self): + return len(self.X) + + def __getitem__(self, index): + """Returns one data pair (source and target).""" + data = {} + data['X'] = self.X[index] + data['y'] = self.y[index] + return data + + +def collate_fn(data): + def pad_sequences(sequences): + lengths = [len(seq) for seq in sequences] + + padded_sequences = torch.zeros( + len(sequences), + max(lengths) + ).long() # padding index 0 + + for i, seq in enumerate(sequences): + end = lengths[i] + padded_sequences[i, :end] = seq[:end] + + return padded_sequences, lengths + + item_info = {} + for key in data[0].keys(): + item_info[key] = [d[key] for d in data] + + x_batch, _ = pad_sequences(item_info['X']) + y_batch = torch.tensor(item_info['y'], dtype=torch.long) + + return x_batch, y_batch + + +def cached_collate_fn(data): + item_info = {} + for key in data[0].keys(): + item_info[key] = [d[key] for d in data] + + x_batch = torch.cat(item_info['X'], 0) + y_batch = torch.tensor(item_info['y'], dtype=torch.long) + + return x_batch, y_batch + + +def train_epoch(data_loader, discriminator, optimizer, + epoch=0, log_interval=10): + samples_so_far = 0 + discriminator.train_custom() + for batch_idx, (input_t, target_t) in enumerate(data_loader): + input_t, target_t = input_t.to(device), target_t.to(device) + + optimizer.zero_grad() + + output_t = discriminator(input_t) + loss = F.nll_loss(output_t, target_t) + loss.backward(retain_graph=True) + optimizer.step() + + samples_so_far += len(input_t) + + if batch_idx % log_interval == 0: + print( + 'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( + epoch + 1, + samples_so_far, len(data_loader.dataset), + 100 * samples_so_far / len(data_loader.dataset), loss.item() + ) + ) + + +def evaluate_performance(data_loader, discriminator): + discriminator.eval() + test_loss = 0 + correct = 0 + with torch.no_grad(): + for input_t, target_t in data_loader: + input_t, target_t = input_t.to(device), target_t.to(device) + output_t = discriminator(input_t) + # sum up batch loss + test_loss += F.nll_loss(output_t, target_t, reduction='sum').item() + # get the index of the max log-probability + pred_t = output_t.argmax(dim=1, keepdim=True) + correct += pred_t.eq(target_t.view_as(pred_t)).sum().item() + + test_loss /= len(data_loader.dataset) + + print( + 'Performance on test set: ' + 'Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format( + test_loss, correct, len(data_loader.dataset), + 100. * correct / len(data_loader.dataset) + ) + ) + + +def predict(input_sentence, model, classes, cached=False): + input_t = model.tokenizer.encode(input_sentence) + input_t = torch.tensor([input_t], dtype=torch.long) + if cached: + input_t = model.avg_representation(input_t) + + log_probs = model(input_t).data.cpu().numpy().flatten().tolist() + print('Input sentence:', input_sentence) + print('Predictions:', ", ".join( + "{}: {:.4f}".format(c, math.exp(log_prob)) for c, log_prob in + zip(classes, log_probs) + )) + + +def get_cached_data_loader(dataset, batch_size, discriminator, shuffle=False): + data_loader = torch.utils.data.DataLoader(dataset=dataset, + batch_size=batch_size, + collate_fn=collate_fn) + + xs = [] + ys = [] + for batch_idx, (x, y) in enumerate(data_loader): + with torch.no_grad(): + x = x.to(device) + avg_rep = discriminator.avg_representation(x).cpu().detach() + avg_rep_list = torch.unbind(avg_rep.unsqueeze(1)) + xs += avg_rep_list + ys += y.cpu().numpy().tolist() + + data_loader = torch.utils.data.DataLoader( + dataset=Dataset(xs, ys), + batch_size=batch_size, + shuffle=shuffle, + collate_fn=cached_collate_fn) + + return data_loader + + +def train_discriminator( + dataset, dataset_fp=None, pretrained_model='gpt2-medium', + epochs=10, batch_size=64, log_interval=10, + save_model=False, cached=False, use_cuda=False): + if use_cuda: + global device + device = 'cuda' + + print('Preprocessing {} dataset...'.format(dataset)) + start = time.time() + + if dataset == 'SST': + idx2class = ["positive", "negative", "very positive", "very negative", + "neutral"] + class2idx = {c: i for i, c in enumerate(idx2class)} + + discriminator = Discriminator( + class_size=len(idx2class), + pretrained_model=pretrained_model, + cached_mode=cached + ).to(device) + + text = torchtext_data.Field() + label = torchtext_data.Field(sequential=False) + train_data, val_data, test_data = datasets.SST.splits( + text, + label, + fine_grained=True, + train_subtrees=True, + ) + + x = [] + y = [] + for i in range(len(train_data)): + seq = TreebankWordDetokenizer().detokenize( + vars(train_data[i])["text"] + ) + seq = discriminator.tokenizer.encode(seq) + seq = torch.tensor([50256] + seq, device=device, dtype=torch.long) + x.append(seq) + y.append(class2idx[vars(train_data[i])["label"]]) + train_dataset = Dataset(x, y) + + test_x = [] + test_y = [] + for i in range(len(test_data)): + seq = TreebankWordDetokenizer().detokenize( + vars(test_data[i])["text"] + ) + seq = discriminator.tokenizer.encode(seq) + seq = torch.tensor([50256] + seq, device=device, dtype=torch.long) + test_x.append(seq) + test_y.append(class2idx[vars(test_data[i])["label"]]) + test_dataset = Dataset(test_x, test_y) + + discriminator_meta = { + "class_size": len(idx2class), + "embed_size": discriminator.embed_size, + "pretrained_model": pretrained_model, + "class_vocab": class2idx, + "default_class": 2, + } + + elif dataset == 'clickbait': + idx2class = ["non_clickbait", "clickbait"] + class2idx = {c: i for i, c in enumerate(idx2class)} + + discriminator = Discriminator( + class_size=len(idx2class), + pretrained_model=pretrained_model, + cached_mode=cached + ).to(device) + + with open("datasets/clickbait/clickbait_train_prefix.txt") as f: + data = [] + for i, line in enumerate(f): + try: + data.append(eval(line)) + except: + print('Error evaluating line {}: {}'.format( + i, line + )) + continue + x = [] + y = [] + y = [] + for i, d in enumerate(data): + try: + seq = discriminator.tokenizer.encode(d["text"]) + + if len(seq) < max_length_seq: + seq = torch.tensor( + [50256] + seq, device=device, dtype=torch.long + ) + else: + print("Line {} is longer than maximum length {}".format( + i, max_length_seq + )) + continue + x.append(seq) + y.append(d['label']) + except: + print("Error tokenizing line {}, skipping it".format(i)) + pass + + full_dataset = Dataset(x, y) + train_size = int(0.9 * len(full_dataset)) + test_size = len(full_dataset) - train_size + train_dataset, test_dataset = torch.utils.data.random_split( + full_dataset, [train_size, test_size] + ) + + discriminator_meta = { + "class_size": len(idx2class), + "embed_size": discriminator.embed_size, + "pretrained_model": pretrained_model, + "class_vocab": class2idx, + "default_class": 1, + } + + elif dataset == 'toxic': + idx2class = ["non_toxic", "toxic"] + class2idx = {c: i for i, c in enumerate(idx2class)} + + discriminator = Discriminator( + class_size=len(idx2class), + pretrained_model=pretrained_model, + cached_mode=cached + ).to(device) + + with open("datasets/toxic/toxic_train.txt") as f: + data = [] + for i, line in enumerate(f): + try: + data.append(eval(line)) + except: + print('Error evaluating line {}: {}'.format( + i, line + )) + continue + + x = [] + y = [] + for i, d in enumerate(data): + try: + seq = discriminator.tokenizer.encode(d["text"]) + + if len(seq) < max_length_seq: + seq = torch.tensor( + [50256] + seq, device=device, dtype=torch.long + ) + else: + print("Line {} is longer than maximum length {}".format( + i, max_length_seq + )) + continue + x.append(seq) + y.append(int(np.sum(d['label']) > 0)) + except: + print("Error tokenizing line {}, skipping it".format(i)) + pass + + full_dataset = Dataset(x, y) + train_size = int(0.9 * len(full_dataset)) + test_size = len(full_dataset) - train_size + train_dataset, test_dataset = torch.utils.data.random_split( + full_dataset, [train_size, test_size] + ) + + discriminator_meta = { + "class_size": len(idx2class), + "embed_size": discriminator.embed_size, + "pretrained_model": pretrained_model, + "class_vocab": class2idx, + "default_class": 0, + } + + else: # if dataset == 'generic': + # This assumes the input dataset is a TSV with the following structure: + # class \t text + + if dataset_fp is None: + raise ValueError('When generic dataset is selected, ' + 'dataset_fp needs to be specified aswell.') + + classes = set() + with open(dataset_fp) as f: + csv_reader = csv.reader(f, delimiter='\t') + for row in csv_reader: + classes.add(row[0]) + + idx2class = sorted(classes) + class2idx = {c: i for i, c in enumerate(idx2class)} + + discriminator = Discriminator( + class_size=len(idx2class), + pretrained_model=pretrained_model, + cached_mode=cached + ).to(device) + + x = [] + y = [] + with open(dataset_fp) as f: + csv_reader = csv.reader(f, delimiter='\t') + for i, row in enumerate(csv_reader): + label = row[0] + text = row[1] + + try: + seq = discriminator.tokenizer.encode(text) + if (len(seq) < max_length_seq): + seq = torch.tensor( + [50256] + seq, + device=device, + dtype=torch.long + ) + + else: + print("Line {} is longer than maximum length {}".format( + i, max_length_seq + )) + continue + + x.append(seq) + y.append(class2idx[label]) + + except: + print("Error tokenizing line {}, skipping it".format(i)) + pass + + full_dataset = Dataset(x, y) + train_size = int(0.9 * len(full_dataset)) + test_size = len(full_dataset) - train_size + train_dataset, test_dataset = torch.utils.data.random_split( + full_dataset, + [train_size, test_size] + ) + + discriminator_meta = { + "class_size": len(idx2class), + "embed_size": discriminator.embed_size, + "pretrained_model": pretrained_model, + "class_vocab": class2idx, + "default_class": 0, + } + + end = time.time() + print('Preprocessed {} data points'.format( + len(train_dataset) + len(test_dataset)) + ) + print("Data preprocessing took: {:.3f}s".format(end - start)) + + if cached: + start = time.time() + + train_loader = get_cached_data_loader( + train_dataset, batch_size, discriminator, shuffle=True + ) + + test_loader = get_cached_data_loader( + test_dataset, batch_size, discriminator + ) + + end = time.time() + print("Building representation cache took: {:.3f}s".format(end - start)) + + else: + train_loader = torch.utils.data.DataLoader(dataset=train_dataset, + batch_size=batch_size, + shuffle=True, + collate_fn=collate_fn) + test_loader = torch.utils.data.DataLoader(dataset=test_dataset, + batch_size=batch_size, + collate_fn=collate_fn) + + if save_model: + with open("{}_classifier_head_meta.json".format(dataset), + "w") as meta_file: + json.dump(discriminator_meta, meta_file) + + optimizer = optim.Adam(discriminator.parameters(), lr=0.0001) + + for epoch in range(epochs): + start = time.time() + print('\nEpoch', epoch + 1) + + train_epoch( + discriminator=discriminator, + data_loader=train_loader, + optimizer=optimizer, + epoch=epoch, + log_interval=log_interval + ) + evaluate_performance( + data_loader=test_loader, + discriminator=discriminator + ) + + end = time.time() + print("Epoch took: {:.3f}s".format(end - start)) + + print("\nExample prediction") + predict(example_sentence, discriminator, idx2class, cached) + + if save_model: + # torch.save(discriminator.state_dict(), + # "{}_discriminator_{}.pt".format( + # args.dataset, epoch + # )) + torch.save(discriminator.get_classifier().state_dict(), + "{}_classifier_head_epoch_{}.pt".format(dataset, epoch)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description='Train a discriminator on top of GPT-2 representations') + parser.add_argument('--dataset', type=str, default='SST', + choices=('SST', 'clickbait', 'toxic', 'generic'), + help='dataset to train the discriminator on.' + 'In case of generic, the dataset is expected' + 'to be a TSBV file with structure: class \\t text') + parser.add_argument('--dataset_fp', type=str, default='', + help='File path of the dataset to use. ' + 'Needed only in case of generic datadset') + parser.add_argument('--pretrained_model', type=str, default='gpt2-medium', + help='Pretrained model to use as encoder') + parser.add_argument('--epochs', type=int, default=10, metavar='N', + help='Number of training epochs') + parser.add_argument('--batch_size', type=int, default=64, metavar='N', + help='input batch size for training (default: 64)') + parser.add_argument('--log_interval', type=int, default=10, metavar='N', + help='how many batches to wait before logging training status') + parser.add_argument('--save_model', action='store_true', + help='whether to save the model') + parser.add_argument('--cached', action='store_true', + help='whether to cache the input representations') + parser.add_argument('--use_cuda', action='store_true', + help='use to turn on cuda') + args = parser.parse_args() + + train_discriminator(**(vars(args))) From 7469d03b1ca3c1c920e4cadc8a007609d17aff50 Mon Sep 17 00:00:00 2001 From: w4nderlust Date: Tue, 26 Nov 2019 21:30:57 -0800 Subject: [PATCH 126/179] Fixed minor bug when running training on cuda --- examples/run_pplm_discrim_train.py | 49 ++++++++++++++++-------------- 1 file changed, 26 insertions(+), 23 deletions(-) diff --git a/examples/run_pplm_discrim_train.py b/examples/run_pplm_discrim_train.py index cc52234281..7f10e861a8 100644 --- a/examples/run_pplm_discrim_train.py +++ b/examples/run_pplm_discrim_train.py @@ -18,6 +18,7 @@ import torch.utils.data as data from nltk.tokenize.treebank import TreebankWordDetokenizer from torchtext import data as torchtext_data from torchtext import datasets + from transformers import GPT2Tokenizer, GPT2LMHeadModel torch.manual_seed(0) @@ -89,7 +90,7 @@ class Discriminator(torch.nn.Module): if self.cached_mode: avg_hidden = x.to(device) else: - avg_hidden = self.avg_representation(x) + avg_hidden = self.avg_representation(x.to(device)) logits = self.classifier_head(avg_hidden) probs = F.log_softmax(logits, dim=-1) @@ -203,7 +204,7 @@ def evaluate_performance(data_loader, discriminator): def predict(input_sentence, model, classes, cached=False): input_t = model.tokenizer.encode(input_sentence) - input_t = torch.tensor([input_t], dtype=torch.long) + input_t = torch.tensor([input_t], dtype=torch.long, device=device) if cached: input_t = model.avg_representation(input_t) @@ -428,7 +429,8 @@ def train_discriminator( with open(dataset_fp) as f: csv_reader = csv.reader(f, delimiter='\t') for row in csv_reader: - classes.add(row[0]) + if row: + classes.add(row[0]) idx2class = sorted(classes) class2idx = {c: i for i, c in enumerate(idx2class)} @@ -444,30 +446,31 @@ def train_discriminator( with open(dataset_fp) as f: csv_reader = csv.reader(f, delimiter='\t') for i, row in enumerate(csv_reader): - label = row[0] - text = row[1] + if row: + label = row[0] + text = row[1] - try: - seq = discriminator.tokenizer.encode(text) - if (len(seq) < max_length_seq): - seq = torch.tensor( - [50256] + seq, - device=device, - dtype=torch.long - ) + try: + seq = discriminator.tokenizer.encode(text) + if (len(seq) < max_length_seq): + seq = torch.tensor( + [50256] + seq, + device=device, + dtype=torch.long + ) - else: - print("Line {} is longer than maximum length {}".format( - i, max_length_seq - )) - continue + else: + print("Line {} is longer than maximum length {}".format( + i, max_length_seq + )) + continue - x.append(seq) - y.append(class2idx[label]) + x.append(seq) + y.append(class2idx[label]) - except: - print("Error tokenizing line {}, skipping it".format(i)) - pass + except: + print("Error tokenizing line {}, skipping it".format(i)) + pass full_dataset = Dataset(x, y) train_size = int(0.9 * len(full_dataset)) From 821de121e86574504ec648f76ccb924e38125b52 Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 15:27:49 -0800 Subject: [PATCH 127/179] Minor changes --- examples/run_pplm_discrim_train.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/run_pplm_discrim_train.py b/examples/run_pplm_discrim_train.py index 7f10e861a8..9438cbbac1 100644 --- a/examples/run_pplm_discrim_train.py +++ b/examples/run_pplm_discrim_train.py @@ -72,7 +72,6 @@ class Discriminator(torch.nn.Module): def train_custom(self): for param in self.encoder.parameters(): param.requires_grad = False - pass self.classifier_head.train() def avg_representation(self, x): @@ -122,7 +121,7 @@ def collate_fn(data): padded_sequences = torch.zeros( len(sequences), max(lengths) - ).long() # padding index 0 + ).long() # padding value = 0 for i, seq in enumerate(sequences): end = lengths[i] From 4f2164e40e803677869865b140b2b3f5a96d4bcd Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 16:32:45 -0800 Subject: [PATCH 128/179] First cleanup step, changing function names and passing parameters all the way through without using args. Identical output as before. --- examples/run_pplm.py | 270 +++++++++++++++++++++++++++++-------------- 1 file changed, 182 insertions(+), 88 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 217c131b8f..0d9ed86f45 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -109,20 +109,40 @@ def top_k_filter(logits, k, probs=False): logits) -def perturb_past(past, model, prev, args, classifier, good_index=None, - stepsize=0.01, vocab_size=50257, - original_probs=None, accumulated_hidden=None, true_past=None, - grad_norms=None): - window_length = args.window_length - gm_scale, kl_scale = args.gm_scale, args.kl_scale - one_hot_vectors = [] - for good_list in good_index: - good_list = list(filter(lambda x: len(x) <= 1, good_list)) - good_list = torch.tensor(good_list).cuda() - num_good = good_list.shape[0] - one_hot_good = torch.zeros(num_good, vocab_size).cuda() - one_hot_good.scatter_(1, good_list, 1) - one_hot_vectors.append(one_hot_good) +def perturb_past( + past, + model, + prev, + unpert_past=None, + unpert_logits=None, + accumulated_hidden=None, + grad_norms=None, + stepsize=0.01, + classifier=None, + label_class=None, + one_hot_bows_vectors=None, + loss_type=0, + num_iterations=3, + kl_scale=0.01, + window_length=0, + horizon_length=1, + decay=False, + gamma=1.5, +): + + #def perturb_past(past, model, prev, classifier, good_index=None, + # stepsize=0.01, vocab_size=50257, + # original_probs=None, accumulated_hidden=None, true_past=None, + # grad_norms=None): + + # one_hot_bows_vectors = [] + # for good_list in good_index: + # good_list = list(filter(lambda x: len(x) <= 1, good_list)) + # good_list = torch.tensor(good_list).cuda() + # num_good = good_list.shape[0] + # one_hot_good = torch.zeros(num_good, vocab_size).cuda() + # one_hot_good.scatter_(1, good_list, 1) + # one_hot_bows_vectors.append(one_hot_good) # Generate inital perturbed past past_perturb_orig = [ @@ -132,7 +152,7 @@ def perturb_past(past, model, prev, args, classifier, good_index=None, if accumulated_hidden is None: accumulated_hidden = 0 - if args.decay: + if decay: decay_mask = torch.arange(0., 1.0 + SmallConst, 1.0 / (window_length))[ 1:] else: @@ -160,7 +180,7 @@ def perturb_past(past, model, prev, args, classifier, good_index=None, window_mask = torch.ones_like(past[0]).cuda() loss_per_iter = [] - for i in range(args.num_iterations): + for i in range(num_iterations): print("Iteration ", i + 1) past_perturb = [torch.from_numpy(p_) for p_ in past_perturb_orig] past_perturb = [to_var(p_, requires_grad=True) for p_ in past_perturb] @@ -183,8 +203,8 @@ def perturb_past(past, model, prev, args, classifier, good_index=None, probabs = F.softmax(logits, dim=-1) loss = 0.0 loss_list = [] - if args.loss_type == 1 or args.loss_type == 3: - for one_hot_good in one_hot_vectors: + if loss_type == 1 or loss_type == 3: + for one_hot_good in one_hot_bows_vectors: good_logits = torch.mm(probabs, torch.t(one_hot_good)) loss_word = good_logits loss_word = torch.sum(loss_word) @@ -194,10 +214,10 @@ def perturb_past(past, model, prev, args, classifier, good_index=None, loss_list.append(loss_word) print(" pplm_bow_loss:", loss.data.cpu().numpy()) - if args.loss_type == 2 or args.loss_type == 3: + if loss_type == 2 or loss_type == 3: ce_loss = torch.nn.CrossEntropyLoss() - new_true_past = true_past - for i in range(args.horizon_length): + new_true_past = unpert_past + for i in range(horizon_length): future_probabs = F.softmax(logits, dim=-1) # Get softmax future_probabs = torch.unsqueeze(future_probabs, dim=1) @@ -217,9 +237,9 @@ def perturb_past(past, model, prev, args, classifier, good_index=None, future_hidden, dim=1) predicted_sentiment = classifier(new_accumulated_hidden / ( - current_length + 1 + args.horizon_length)) + current_length + 1 + horizon_length)) - label = torch.tensor([args.label_class], device='cuda', + label = torch.tensor([label_class], device='cuda', dtype=torch.long) discrim_loss = ce_loss(predicted_sentiment, label) print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy()) @@ -228,7 +248,7 @@ def perturb_past(past, model, prev, args, classifier, good_index=None, kl_loss = 0.0 if kl_scale > 0.0: - p = (F.softmax(original_probs[:, -1, :], dim=-1)) + p = (F.softmax(unpert_logits[:, -1, :], dim=-1)) p = p + SmallConst * (p <= SmallConst).type( torch.FloatTensor).cuda().detach() correction = SmallConst * (probabs <= SmallConst).type( @@ -244,7 +264,7 @@ def perturb_past(past, model, prev, args, classifier, good_index=None, print(' pplm_loss', (loss - kl_loss).data.cpu().numpy()) loss.backward() - if grad_norms is not None and args.loss_type == 1: + if grad_norms is not None and loss_type == 1: grad_norms = [ torch.max(grad_norms[index], torch.norm(p_.grad * window_mask)) for index, p_ in @@ -255,7 +275,7 @@ def perturb_past(past, model, prev, args, classifier, good_index=None, grad = [ -stepsize * (p_.grad * window_mask / grad_norms[ - index] ** args.gamma).data.cpu().numpy() + index] ** gamma).data.cpu().numpy() for index, p_ in enumerate(past_perturb)] past_perturb_orig = list(map(add, grad, past_perturb_orig)) @@ -347,10 +367,32 @@ def build_bows_one_hot_vectors(bow_indices): return one_hot_bows_vectors -def latent_perturb(model, args, context=None, sample=True, device='cuda'): +def full_text_generation( + model, + context=None, + num_samples=1, + device="cuda", + sample=True, + discrim=None, + label_class=None, + bag_of_words=None, + length=100, + grad_length=10000, + stepsize=0.02, + num_iterations=3, + temperature=1.0, + gm_scale=0.9, + kl_scale=0.01, + top_k=10, + window_length=0, + horizon_length=1, + decay=False, + gamma=1.5, + **kwargs + ): classifier, class_id = get_classifier( - args.discrim, - args.label_class, + discrim, + label_class, device ) @@ -422,49 +464,68 @@ def latent_perturb(model, args, context=None, sample=True, device='cuda'): # good_list = list(filter(lambda x: len(x) <= 1, good_list)) # actual_words = [(TOKENIZER.decode(ww).strip(),ww) for ww in good_list] - good_index = [] + bow_indices = [] actual_words = None - if args.bag_of_words: - good_index = get_bag_of_words_indices(args.bag_of_words.split(";")) + if bag_of_words: + bow_indices = get_bag_of_words_indices(bag_of_words.split(";")) - for good_list in good_index: + for good_list in bow_indices: good_list = list(filter(lambda x: len(x) <= 1, good_list)) actual_words = [(TOKENIZER.decode(ww).strip(), ww) for ww in good_list] - if args.bag_of_words and classifier: + if bag_of_words and classifier: print("Both PPLM-BoW and PPLM-Discrim are on. This is not optimized.") - args.loss_type = PPLM_BOW_DISCRIM + loss_type = PPLM_BOW_DISCRIM - elif args.bag_of_words: - args.loss_type = PPLM_BOW + elif bag_of_words: + loss_type = PPLM_BOW print("Using PPLM-BoW") elif classifier is not None: - args.loss_type = PPLM_DISCRIM + loss_type = PPLM_DISCRIM print("Using PPLM-Discrim") else: raise Exception("Specify either --bag_of_words (-B) or --discrim (-D)") - original, _, _ = sample_from_hidden(model=model, args=args, context=context, - device=device, - perturb=False, good_index=good_index, - classifier=classifier) + original, _, _ = generate_text_pplm( + model=model, + context=context, + device=device, + length=length, + perturb=False + ) torch.cuda.empty_cache() perturbed_list = [] discrim_loss_list = [] loss_in_time_list = [] - for i in range(args.num_samples): - perturbed, discrim_loss, loss_in_time = sample_from_hidden(model=model, - args=args, - context=context, - device=device, - perturb=True, - good_index=good_index, - classifier=classifier) + for i in range(num_samples): + perturbed, discrim_loss, loss_in_time = generate_text_pplm( + model=model, + context=context, + device=device, + sample=sample, + perturb=True, + bow_indices=bow_indices, + classifier=classifier, + label_class=class_id, + loss_type=loss_type, + length=length, + grad_length=grad_length, + stepsize=stepsize, + num_iterations=num_iterations, + temperature=temperature, + gm_scale=gm_scale, + kl_scale=kl_scale, + top_k=top_k, + window_length=window_length, + horizon_length=horizon_length, + decay=decay, + gamma=gamma, + ) perturbed_list.append(perturbed) if classifier is not None: discrim_loss_list.append(discrim_loss.data.cpu().numpy()) @@ -475,15 +536,40 @@ def latent_perturb(model, args, context=None, sample=True, device='cuda'): return original, perturbed_list, discrim_loss_list, loss_in_time_list, actual_words -def sample_from_hidden(model, args, classifier, context=None, past=None, - device='cuda', - sample=True, perturb=True, good_index=None): + +def generate_text_pplm( + model, + context=None, + past=None, + device="cuda", + sample=True, + perturb=True, + classifier=None, + label_class=None, + bow_indices=None, + loss_type=0, + length=100, + grad_length=10000, + stepsize=0.02, + num_iterations=3, + temperature=1.0, + gm_scale=0.9, + kl_scale=0.01, + top_k=10, + window_length=0, + horizon_length=1, + decay=False, + gamma=1.5, +): output = torch.tensor(context, device=device, dtype=torch.long).unsqueeze( 0) if context else None + # collect one hot vectors for bags of words + one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices) + grad_norms = None loss_in_time = [] - for i in trange(args.length, ascii=True): + for i in trange(length, ascii=True): # Get past/probs for current output, except for last word # Note that GPT takes 2 inputs: past + current-token @@ -497,7 +583,7 @@ def sample_from_hidden(model, args, classifier, context=None, past=None, # Piero modified model call _, past, _ = model(output[:, :-1]) - original_probs, true_past, unpert_all_hidden = model(output) + unpert_logits, unpert_past, unpert_all_hidden = model(output) true_hidden = unpert_all_hidden[-1] else: @@ -505,17 +591,17 @@ def sample_from_hidden(model, args, classifier, context=None, past=None, # true_hidden = model.hidden_states # Piero modified model call - original_probs, true_past, unpert_all_hidden = model(output) + unpert_logits, unpert_past, unpert_all_hidden = model(output) true_hidden = unpert_all_hidden[-1] # Modify the past if necessary - if i >= args.grad_length: - current_stepsize = args.stepsize * 0 + if i >= grad_length: + current_stepsize = stepsize * 0 else: - current_stepsize = args.stepsize + current_stepsize = stepsize - if not perturb or args.num_iterations == 0: + if not perturb or num_iterations == 0: perturbed_past = past else: @@ -524,17 +610,26 @@ def sample_from_hidden(model, args, classifier, context=None, past=None, accumulated_hidden = true_hidden[:, :-1, :] accumulated_hidden = torch.sum(accumulated_hidden, dim=1) - perturbed_past, _, grad_norms, loss_per_iter = perturb_past(past, - model, - prev, - args, - good_index=good_index, - stepsize=current_stepsize, - original_probs=original_probs, - true_past=true_past, - accumulated_hidden=accumulated_hidden, - classifier=classifier, - grad_norms=grad_norms) + perturbed_past, _, grad_norms, loss_per_iter = perturb_past( + past, + model, + prev, + unpert_past=unpert_past, + unpert_logits=unpert_logits, + accumulated_hidden=accumulated_hidden, + grad_norms=grad_norms, + stepsize=current_stepsize, + classifier=classifier, + label_class=label_class, + one_hot_bows_vectors=one_hot_bows_vectors, + loss_type=loss_type, + num_iterations=num_iterations, + kl_scale=kl_scale, + window_length=window_length, + horizon_length=horizon_length, + decay=decay, + gamma=gamma, + ) loss_in_time.append(loss_per_iter) # Piero modified model call @@ -546,7 +641,7 @@ def sample_from_hidden(model, args, classifier, context=None, past=None, if classifier is not None: ce_loss = torch.nn.CrossEntropyLoss() predicted_sentiment = classifier(torch.mean(true_hidden, dim=1)) - label = torch.tensor([args.label_class], device='cuda', + label = torch.tensor([label_class], device='cuda', dtype=torch.long) true_discrim_loss = ce_loss(predicted_sentiment, label) print("true discrim loss", true_discrim_loss.data.cpu().numpy()) @@ -556,7 +651,7 @@ def sample_from_hidden(model, args, classifier, context=None, past=None, # Piero modified model call # hidden = model.hidden_states # update hidden # logits = model.forward_hidden(hidden) - logits = logits[:, -1, :] / args.temperature # + SmallConst + logits = logits[:, -1, :] / temperature # + SmallConst # logits = top_k_filter(logits, k=args.top_k) # + SmallConst @@ -566,22 +661,21 @@ def sample_from_hidden(model, args, classifier, context=None, past=None, if perturb: # original_probs = top_k_filter(original_probs[:, -1, :]) #+ SmallConst - original_probs = F.softmax(original_probs[:, -1, :], dim=-1) + unpert_logits = F.softmax(unpert_logits[:, -1, :], dim=-1) # likelywords = torch.topk(original_probs, k=10, dim=-1) # print(TOKENIZER.decode(likelywords[1].tolist()[0])) - gm_scale = args.gm_scale log_probs = ((log_probs ** gm_scale) * ( - original_probs ** (1 - gm_scale))) # + SmallConst + unpert_logits ** (1 - gm_scale))) # + SmallConst - log_probs = top_k_filter(log_probs, k=args.top_k, + log_probs = top_k_filter(log_probs, k=top_k, probs=True) # + SmallConst if torch.sum(log_probs) <= 1: log_probs = log_probs / torch.sum(log_probs) else: - logits = top_k_filter(logits, k=args.top_k) # + SmallConst + logits = top_k_filter(logits, k=top_k) # + SmallConst log_probs = F.softmax(logits, dim=-1) if sample: @@ -673,16 +767,16 @@ def run_model(): collect_gen = dict() current_index = 0 - for out in seq: + for tokenized_cond_text in seq: - text = TOKENIZER.decode(out) + text = TOKENIZER.decode(tokenized_cond_text) print("=" * 40 + " Prefix of sentence " + "=" * 40) print(text) print("=" * 80) - out1, out_perturb, discrim_loss_list, loss_in_time_list, actual_words = latent_perturb( - model=model, args=args, context=out, - device=device) + out1, out_perturb, discrim_loss_list, loss_in_time_list, actual_words = full_text_generation( + model=model, context=tokenized_cond_text, device=device, **vars(args) + ) text_whole = TOKENIZER.decode(out1.tolist()[0]) @@ -712,20 +806,20 @@ def run_model(): import colorama text_whole = '' - for out in output_tokens: - if out in keyword_tokens: + for tokenized_cond_text in output_tokens: + if tokenized_cond_text in keyword_tokens: text_whole += '%s%s%s' % ( - colorama.Fore.GREEN, TOKENIZER.decode([out]), + colorama.Fore.GREEN, TOKENIZER.decode([tokenized_cond_text]), colorama.Style.RESET_ALL) else: - text_whole += TOKENIZER.decode([out]) + text_whole += TOKENIZER.decode([tokenized_cond_text]) else: text_whole = TOKENIZER.decode(out_perturb.tolist()[0]) print(text_whole) print("=" * 80) - collect_gen[current_index] = [out, out_perturb, out1] + collect_gen[current_index] = [tokenized_cond_text, out_perturb, out1] current_index = current_index + 1 From 7ffe47c88861ccea7e4b28538ea3dcb504c497a2 Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 16:39:49 -0800 Subject: [PATCH 129/179] Improved device specification --- examples/run_pplm_discrim_train.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/examples/run_pplm_discrim_train.py b/examples/run_pplm_discrim_train.py index 9438cbbac1..519e2de29a 100644 --- a/examples/run_pplm_discrim_train.py +++ b/examples/run_pplm_discrim_train.py @@ -242,10 +242,9 @@ def get_cached_data_loader(dataset, batch_size, discriminator, shuffle=False): def train_discriminator( dataset, dataset_fp=None, pretrained_model='gpt2-medium', epochs=10, batch_size=64, log_interval=10, - save_model=False, cached=False, use_cuda=False): - if use_cuda: - global device - device = 'cuda' + save_model=False, cached=False, no_cuda=False): + global device + device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu" print('Preprocessing {} dataset...'.format(dataset)) start = time.time() @@ -577,8 +576,8 @@ if __name__ == '__main__': help='whether to save the model') parser.add_argument('--cached', action='store_true', help='whether to cache the input representations') - parser.add_argument('--use_cuda', action='store_true', - help='use to turn on cuda') + parser.add_argument('--no_cuda', action='store_true', + help='use to turn off cuda') args = parser.parse_args() train_discriminator(**(vars(args))) From 6c9c1317800499bd5aaf3f1a7492a72961bbfa91 Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 17:27:39 -0800 Subject: [PATCH 130/179] More cleanup for run_model. Identical output as before. --- examples/run_pplm.py | 308 ++++++++++++++++++++++++------------------- 1 file changed, 171 insertions(+), 137 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 0d9ed86f45..27ead3c3c5 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -39,7 +39,6 @@ from transformers import GPT2Tokenizer from transformers.file_utils import cached_path from transformers.modeling_gpt2 import GPT2LMHeadModel - PPLM_BOW = 1 PPLM_DISCRIM = 2 PPLM_BOW_DISCRIM = 3 @@ -129,8 +128,7 @@ def perturb_past( decay=False, gamma=1.5, ): - - #def perturb_past(past, model, prev, classifier, good_index=None, + # def perturb_past(past, model, prev, classifier, good_index=None, # stepsize=0.01, vocab_size=50257, # original_probs=None, accumulated_hidden=None, true_past=None, # grad_norms=None): @@ -237,7 +235,7 @@ def perturb_past( future_hidden, dim=1) predicted_sentiment = classifier(new_accumulated_hidden / ( - current_length + 1 + horizon_length)) + current_length + 1 + horizon_length)) label = torch.tensor([label_class], device='cuda', dtype=torch.long) @@ -349,6 +347,13 @@ def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str]) -> List[ bow_indices.append( [TOKENIZER.encode(word.strip(), add_prefix_space=True) for word in words]) + + #bow_words = set() + #for bow_list in bow_indices: + # bow_list = list(filter(lambda x: len(x) <= 1, bow_list)) + # bow_words.update( + # (TOKENIZER.decode(word).strip(), word) for word in bow_list) + return bow_indices @@ -368,28 +373,28 @@ def build_bows_one_hot_vectors(bow_indices): def full_text_generation( - model, - context=None, - num_samples=1, - device="cuda", - sample=True, - discrim=None, - label_class=None, - bag_of_words=None, - length=100, - grad_length=10000, - stepsize=0.02, - num_iterations=3, - temperature=1.0, - gm_scale=0.9, - kl_scale=0.01, - top_k=10, - window_length=0, - horizon_length=1, - decay=False, - gamma=1.5, - **kwargs - ): + model, + context=None, + num_samples=1, + device="cuda", + sample=True, + discrim=None, + label_class=None, + bag_of_words=None, + length=100, + grad_length=10000, + stepsize=0.02, + num_iterations=3, + temperature=1.0, + gm_scale=0.9, + kl_scale=0.01, + top_k=10, + window_length=0, + horizon_length=1, + decay=False, + gamma=1.5, + **kwargs +): classifier, class_id = get_classifier( discrim, label_class, @@ -465,15 +470,9 @@ def full_text_generation( # actual_words = [(TOKENIZER.decode(ww).strip(),ww) for ww in good_list] bow_indices = [] - actual_words = None if bag_of_words: bow_indices = get_bag_of_words_indices(bag_of_words.split(";")) - for good_list in bow_indices: - good_list = list(filter(lambda x: len(x) <= 1, good_list)) - actual_words = [(TOKENIZER.decode(ww).strip(), ww) for ww in - good_list] - if bag_of_words and classifier: print("Both PPLM-BoW and PPLM-Discrim are on. This is not optimized.") loss_type = PPLM_BOW_DISCRIM @@ -533,8 +532,7 @@ def full_text_generation( torch.cuda.empty_cache() - return original, perturbed_list, discrim_loss_list, loss_in_time_list, actual_words - + return original, perturbed_list, discrim_loss_list, loss_in_time_list def generate_text_pplm( @@ -611,25 +609,25 @@ def generate_text_pplm( accumulated_hidden = torch.sum(accumulated_hidden, dim=1) perturbed_past, _, grad_norms, loss_per_iter = perturb_past( - past, - model, - prev, - unpert_past=unpert_past, - unpert_logits=unpert_logits, - accumulated_hidden=accumulated_hidden, - grad_norms=grad_norms, - stepsize=current_stepsize, - classifier=classifier, - label_class=label_class, - one_hot_bows_vectors=one_hot_bows_vectors, - loss_type=loss_type, - num_iterations=num_iterations, - kl_scale=kl_scale, - window_length=window_length, - horizon_length=horizon_length, - decay=decay, - gamma=gamma, - ) + past, + model, + prev, + unpert_past=unpert_past, + unpert_logits=unpert_logits, + accumulated_hidden=accumulated_hidden, + grad_norms=grad_norms, + stepsize=current_stepsize, + classifier=classifier, + label_class=label_class, + one_hot_bows_vectors=one_hot_bows_vectors, + loss_type=loss_type, + num_iterations=num_iterations, + kl_scale=kl_scale, + window_length=window_length, + horizon_length=horizon_length, + decay=decay, + gamma=gamma, + ) loss_in_time.append(loss_per_iter) # Piero modified model call @@ -666,7 +664,7 @@ def generate_text_pplm( # print(TOKENIZER.decode(likelywords[1].tolist()[0])) log_probs = ((log_probs ** gm_scale) * ( - unpert_logits ** (1 - gm_scale))) # + SmallConst + unpert_logits ** (1 - gm_scale))) # + SmallConst log_probs = top_k_filter(log_probs, k=top_k, probs=True) # + SmallConst @@ -696,53 +694,88 @@ def generate_text_pplm( def run_model(): parser = argparse.ArgumentParser() - parser.add_argument('--model_path', '-M', type=str, default='gpt2-medium', - help='pretrained model name or path to local checkpoint') - parser.add_argument('--bag-of-words', '-B', type=str, default=None, - help='Bags of words used for PPLM-BoW. Multiple BoWs separated by ;') - parser.add_argument('--discrim', '-D', type=str, default=None, - choices=( - 'clickbait', 'sentiment', 'toxicity', 'generic'), - help='Discriminator to use for loss-type 2') - parser.add_argument('--discrim_weights', type=str, default=None, - help='Weights for the generic discriminator') - parser.add_argument('--discrim_meta', type=str, default=None, - help='Meta information for the generic discriminator') - parser.add_argument('--label_class', type=int, default=-1, - help='Class label used for the discriminator') - parser.add_argument('--stepsize', type=float, default=0.02) + parser.add_argument( + "--model_path", + "-M", + type=str, + default="gpt2-medium", + help="pretrained model name or path to local checkpoint", + ) + parser.add_argument( + "--bag_of_words", + "-B", + type=str, + default=None, + help="Bags of words used for PPLM-BoW. " + "Either a BOW id (see list in code) or a filepath. " + "Multiple BoWs separated by ;", + ) + parser.add_argument( + "--discrim", + "-D", + type=str, + default=None, + choices=("clickbait", "sentiment", "toxicity"), + help="Discriminator to use for loss-type 2", + ) + parser.add_argument( + "--label_class", + type=int, + default=-1, + help="Class label used for the discriminator", + ) + parser.add_argument("--stepsize", type=float, default=0.02) parser.add_argument("--length", type=int, default=100) parser.add_argument("--seed", type=int, default=0) parser.add_argument("--temperature", type=float, default=1.0) parser.add_argument("--top_k", type=int, default=10) parser.add_argument("--gm_scale", type=float, default=0.9) parser.add_argument("--kl_scale", type=float, default=0.01) - parser.add_argument('--nocuda', action='store_true', help='no cuda') - parser.add_argument('--uncond', action='store_true', - help='Generate from end-of-text as prefix') - parser.add_argument("--cond_text", type=str, default='The lake', - help='Prefix texts to condition on') - parser.add_argument('--num_iterations', type=int, default=3) - parser.add_argument('--grad_length', type=int, default=10000) - parser.add_argument('--num_samples', type=int, default=1, - help='Number of samples to generate from the modified latents') - parser.add_argument('--horizon_length', type=int, default=1, - help='Length of future to optimize over') - # parser.add_argument('--force-token', action='store_true', help='no cuda') - parser.add_argument('--window_length', type=int, default=0, - help='Length of past which is being optimizer; 0 corresponds to infinite window length') - parser.add_argument('--decay', action='store_true', - help='whether to decay or not') - parser.add_argument('--gamma', type=float, default=1.5) - parser.add_argument('--colorama', action='store_true', help='no cuda') + parser.add_argument("--no_cuda", action="store_true", help="no cuda") + parser.add_argument( + "--uncond", action="store_true", + help="Generate from end-of-text as prefix" + ) + parser.add_argument( + "--cond_text", type=str, default="The lake", + help="Prefix texts to condition on" + ) + parser.add_argument("--num_iterations", type=int, default=3) + parser.add_argument("--grad_length", type=int, default=10000) + parser.add_argument( + "--num_samples", + type=int, + default=1, + help="Number of samples to generate from the modified latents", + ) + parser.add_argument( + "--horizon_length", + type=int, + default=1, + help="Length of future to optimize over", + ) + parser.add_argument( + "--window_length", + type=int, + default=0, + help="Length of past which is being optimized; " + "0 corresponds to infinite window length", + ) + parser.add_argument("--decay", action="store_true", + help="whether to decay or not") + parser.add_argument("--gamma", type=float, default=1.5) + parser.add_argument("--colorama", action="store_true", help="colors keywords") args = parser.parse_args() + # set Random seed torch.manual_seed(args.seed) np.random.seed(args.seed) - device = 'cpu' if args.nocuda else 'cuda' + # set the device + device = "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" + # load pretrained model model = GPT2LMHeadModel.from_pretrained( args.model_path, output_hidden_states=True @@ -753,76 +786,77 @@ def run_model(): # Freeze GPT-2 weights for param in model.parameters(): param.requires_grad = False - pass + # figure out conditioning text if args.uncond: - seq = [[50256, 50256]] - + tokenized_cond_text = TOKENIZER.encode( + [TOKENIZER.bos_token] + ) else: raw_text = args.cond_text while not raw_text: - print('Did you forget to add `--cond-text`? ') + print("Did you forget to add `--cond_text`? ") raw_text = input("Model prompt >>> ") - seq = [[50256] + TOKENIZER.encode(raw_text)] + tokenized_cond_text = TOKENIZER.encode(TOKENIZER.bos_token + raw_text) - collect_gen = dict() - current_index = 0 - for tokenized_cond_text in seq: + print("= Prefix of sentence =") + print(TOKENIZER.decode(tokenized_cond_text)) + print() - text = TOKENIZER.decode(tokenized_cond_text) - print("=" * 40 + " Prefix of sentence " + "=" * 40) - print(text) - print("=" * 80) + # generate unperturbed and perturbed texts - out1, out_perturb, discrim_loss_list, loss_in_time_list, actual_words = full_text_generation( - model=model, context=tokenized_cond_text, device=device, **vars(args) - ) + # full_text_generation returns: + # unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time + unpert_gen_tok_text, pert_gen_tok_texts, _, _ = full_text_generation( + model=model, context=tokenized_cond_text, device=device, **vars(args) + ) - text_whole = TOKENIZER.decode(out1.tolist()[0]) + # untokenize unperturbed text + unpert_gen_text = TOKENIZER.decode(unpert_gen_tok_text.tolist()[0]) - print("=" * 80) - print("=" * 40 + " Whole sentence (Original)" + "=" * 40) - print(text_whole) - print("=" * 80) + print("=" * 80) + print("= Unperturbed generated text =") + print(unpert_gen_text) + print() - out_perturb_copy = out_perturb + generated_texts = [] - for out_perturb in out_perturb_copy: - # try: - # print("=" * 40 + " Whole sentence (Perturbed)" + "=" * 40) - # text_whole = TOKENIZER.decode(out_perturb.tolist()[0]) - # print(text_whole) - # print("=" * 80) - # except: - # pass - # collect_gen[current_index] = [out, out_perturb, out1] - ## Save the prefix, perturbed seq, original seq for each index - print("=" * 40 + " Whole sentence (Perturbed)" + "=" * 40) - keyword_tokens = [aa[-1][0] for aa in - actual_words] if actual_words else [] - output_tokens = out_perturb.tolist()[0] + bow_words = set() + bow_indices = get_bag_of_words_indices(args.bag_of_words.split(";")) + for bow_list in bow_indices: + filtered = list(filter(lambda x: len(x) <= 1, bow_list)) + bow_words.update(w[0] for w in filtered) + # iterate through the perturbed texts + for i, pert_gen_tok_text in enumerate(pert_gen_tok_texts): + try: + # untokenize unperturbed text if args.colorama: import colorama - text_whole = '' - for tokenized_cond_text in output_tokens: - if tokenized_cond_text in keyword_tokens: - text_whole += '%s%s%s' % ( - colorama.Fore.GREEN, TOKENIZER.decode([tokenized_cond_text]), - colorama.Style.RESET_ALL) + pert_gen_text = '' + for word_id in pert_gen_tok_text.tolist()[0]: + if word_id in bow_words: + pert_gen_text += '{}{}{}'.format( + colorama.Fore.RED, + TOKENIZER.decode([word_id]), + colorama.Style.RESET_ALL + ) else: - text_whole += TOKENIZER.decode([tokenized_cond_text]) + pert_gen_text += TOKENIZER.decode([word_id]) else: - text_whole = TOKENIZER.decode(out_perturb.tolist()[0]) + pert_gen_text = TOKENIZER.decode(pert_gen_tok_text.tolist()[0]) - print(text_whole) - print("=" * 80) - - collect_gen[current_index] = [tokenized_cond_text, out_perturb, out1] - - current_index = current_index + 1 + print("= Perturbed generated text {} =".format(i + 1)) + print(pert_gen_text) + print() + except: + pass + # keep the prefix, perturbed seq, original seq for each index + generated_texts.append( + (tokenized_cond_text, pert_gen_tok_text, unpert_gen_tok_text) + ) return From 08c6e456a391cd463b0a52d125166aa766a2c7e4 Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 17:48:46 -0800 Subject: [PATCH 131/179] Cleaned full_text_generation. Identical output as before. --- examples/run_pplm.py | 103 ++++++++----------------------------------- 1 file changed, 19 insertions(+), 84 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 27ead3c3c5..b85998d706 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -401,74 +401,6 @@ def full_text_generation( device ) - # if args.discrim == 'clickbait': - # classifier = ClassificationHead(class_size=2, embed_size=1024).to(device) - # classifier.load_state_dict(torch.load("discrim_models/clickbait_classifierhead.pt")) - # classifier.eval() - # args.label_class = 1 # clickbaity - # - # elif args.discrim == 'sentiment': - # classifier = ClassificationHead(class_size=5, embed_size=1024).to(device) - # #classifier.load_state_dict(torch.load("discrim_models/sentiment_classifierhead.pt")) - # classifier.load_state_dict(torch.load("discrim_models/SST_classifier_head_epoch_16.pt")) - # classifier.eval() - # if args.label_class < 0: - # raise Exception('Wrong class for sentiment, use --label-class 2 for *very positive*, 3 for *very negative*') - # #args.label_class = 2 # very pos - # #args.label_class = 3 # very neg - # - # elif args.discrim == 'toxicity': - # classifier = ClassificationHead(class_size=2, embed_size=1024).to(device) - # classifier.load_state_dict(torch.load("discrim_models/toxicity_classifierhead.pt")) - # classifier.eval() - # args.label_class = 0 # not toxic - # - # elif args.discrim == 'generic': - # if args.discrim_weights is None: - # raise ValueError('When using a generic discriminator, ' - # 'discrim_weights need to be specified') - # if args.discrim_meta is None: - # raise ValueError('When using a generic discriminator, ' - # 'discrim_meta need to be specified') - # - # with open(args.discrim_meta, 'r') as discrim_meta_file: - # meta = json.load(discrim_meta_file) - # - # classifier = ClassificationHead( - # class_size=meta['class_size'], - # embed_size=meta['embed_size'], - # # todo add tokenizer from meta - # ).to(device) - # classifier.load_state_dict(torch.load(args.discrim_weights)) - # classifier.eval() - # if args.label_class == -1: - # args.label_class = meta['default_class'] - # - # else: - # classifier = None - - # Get tokens for the list of positive words - def list_tokens(word_list): - token_list = [TOKENIZER.encode(word, add_prefix_space=True) for word in - word_list] - # token_list = [] - # for word in word_list: - # token_list.append(TOKENIZER.encode(" " + word)) - return token_list - - # good_index = [] - # if args.bag_of_words: - # bags_of_words = args.bag_of_words.split(";") - # for wordlist in bags_of_words: - # with open(wordlist, "r") as f: - # words = f.read().strip() - # words = words.split('\n') - # good_index.append(list_tokens(words)) - # - # for good_list in good_index: - # good_list = list(filter(lambda x: len(x) <= 1, good_list)) - # actual_words = [(TOKENIZER.decode(ww).strip(),ww) for ww in good_list] - bow_indices = [] if bag_of_words: bow_indices = get_bag_of_words_indices(bag_of_words.split(";")) @@ -486,9 +418,9 @@ def full_text_generation( print("Using PPLM-Discrim") else: - raise Exception("Specify either --bag_of_words (-B) or --discrim (-D)") + raise Exception("Specify either a bag of words or a discriminator") - original, _, _ = generate_text_pplm( + unpert_gen_tok_text, _, _ = generate_text_pplm( model=model, context=context, device=device, @@ -497,12 +429,12 @@ def full_text_generation( ) torch.cuda.empty_cache() - perturbed_list = [] - discrim_loss_list = [] - loss_in_time_list = [] + pert_gen_tok_texts = [] + discrim_losses = [] + losses_in_time = [] for i in range(num_samples): - perturbed, discrim_loss, loss_in_time = generate_text_pplm( + pert_gen_tok_text, discrim_loss, loss_in_time = generate_text_pplm( model=model, context=context, device=device, @@ -525,14 +457,14 @@ def full_text_generation( decay=decay, gamma=gamma, ) - perturbed_list.append(perturbed) + pert_gen_tok_texts.append(pert_gen_tok_text) if classifier is not None: - discrim_loss_list.append(discrim_loss.data.cpu().numpy()) - loss_in_time_list.append(loss_in_time) + discrim_losses.append(discrim_loss.data.cpu().numpy()) + losses_in_time.append(loss_in_time) torch.cuda.empty_cache() - return original, perturbed_list, discrim_loss_list, loss_in_time_list + return unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time def generate_text_pplm( @@ -821,11 +753,14 @@ def run_model(): generated_texts = [] - bow_words = set() - bow_indices = get_bag_of_words_indices(args.bag_of_words.split(";")) - for bow_list in bow_indices: - filtered = list(filter(lambda x: len(x) <= 1, bow_list)) - bow_words.update(w[0] for w in filtered) + bow_word_ids = set() + if args.bag_of_words and args.colorama: + bow_indices = get_bag_of_words_indices(args.bag_of_words.split(";")) + for single_bow_list in bow_indices: + # filtering all words in the list composed of more than 1 token + filtered = list(filter(lambda x: len(x) <= 1, single_bow_list)) + # w[0] because we are sure w has only 1 item because previous fitler + bow_word_ids.update(w[0] for w in filtered) # iterate through the perturbed texts for i, pert_gen_tok_text in enumerate(pert_gen_tok_texts): @@ -836,7 +771,7 @@ def run_model(): pert_gen_text = '' for word_id in pert_gen_tok_text.tolist()[0]: - if word_id in bow_words: + if word_id in bow_word_ids: pert_gen_text += '{}{}{}'.format( colorama.Fore.RED, TOKENIZER.decode([word_id]), From 7ea12db3f539af414579c0e27b87e399645c58ed Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 17:49:39 -0800 Subject: [PATCH 132/179] Removed commented code. Identical output as before. --- examples/run_pplm.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index b85998d706..f4d697de3b 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -347,13 +347,6 @@ def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str]) -> List[ bow_indices.append( [TOKENIZER.encode(word.strip(), add_prefix_space=True) for word in words]) - - #bow_words = set() - #for bow_list in bow_indices: - # bow_list = list(filter(lambda x: len(x) <= 1, bow_list)) - # bow_words.update( - # (TOKENIZER.decode(word).strip(), word) for word in bow_list) - return bow_indices From ef47b2c03ad3d901f4b7454b004eb44cb01cc3e3 Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 17:50:21 -0800 Subject: [PATCH 133/179] Removed commented code. Identical output as before. --- examples/run_pplm.py | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index f4d697de3b..b18e97b5a8 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -128,20 +128,6 @@ def perturb_past( decay=False, gamma=1.5, ): - # def perturb_past(past, model, prev, classifier, good_index=None, - # stepsize=0.01, vocab_size=50257, - # original_probs=None, accumulated_hidden=None, true_past=None, - # grad_norms=None): - - # one_hot_bows_vectors = [] - # for good_list in good_index: - # good_list = list(filter(lambda x: len(x) <= 1, good_list)) - # good_list = torch.tensor(good_list).cuda() - # num_good = good_list.shape[0] - # one_hot_good = torch.zeros(num_good, vocab_size).cuda() - # one_hot_good.scatter_(1, good_list, 1) - # one_hot_bows_vectors.append(one_hot_good) - # Generate inital perturbed past past_perturb_orig = [ (np.random.uniform(0.0, 0.0, p.shape).astype('float32')) From 61a12f790de9795af1c3dff5fad7e2c4f6808d05 Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 17:54:49 -0800 Subject: [PATCH 134/179] Renamed SmallConst to SMALL_CONST and introduced BIG_CONST. Identical output as before. --- examples/run_pplm.py | 25 +++++++++++++------------ 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index b18e97b5a8..4d335a9241 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -43,7 +43,7 @@ PPLM_BOW = 1 PPLM_DISCRIM = 2 PPLM_BOW_DISCRIM = 3 SMALL_CONST = 1e-15 -SmallConst = 1e-15 +BIG_CONST = 1e10 TOKENIZER = GPT2Tokenizer.from_pretrained("gpt2-medium") BAG_OF_WORDS_ARCHIVE_MAP = { @@ -104,7 +104,8 @@ def top_k_filter(logits, k, probs=False): if probs: return torch.where(logits < batch_mins, torch.ones_like(logits) * 0.0, logits) - return torch.where(logits < batch_mins, torch.ones_like(logits) * -1e10, + return torch.where(logits < batch_mins, + torch.ones_like(logits) * -BIG_CONST, logits) @@ -137,7 +138,7 @@ def perturb_past( accumulated_hidden = 0 if decay: - decay_mask = torch.arange(0., 1.0 + SmallConst, 1.0 / (window_length))[ + decay_mask = torch.arange(0., 1.0 + SMALL_CONST, 1.0 / (window_length))[ 1:] else: decay_mask = 1.0 @@ -233,9 +234,9 @@ def perturb_past( kl_loss = 0.0 if kl_scale > 0.0: p = (F.softmax(unpert_logits[:, -1, :], dim=-1)) - p = p + SmallConst * (p <= SmallConst).type( + p = p + SMALL_CONST * (p <= SMALL_CONST).type( torch.FloatTensor).cuda().detach() - correction = SmallConst * (probabs <= SmallConst).type( + correction = SMALL_CONST * (probabs <= SMALL_CONST).type( torch.FloatTensor).cuda().detach() corrected_probabs = probabs + correction.detach() kl_loss = kl_scale * ( @@ -254,7 +255,7 @@ def perturb_past( for index, p_ in enumerate(past_perturb)] else: - grad_norms = [(torch.norm(p_.grad * window_mask) + SmallConst) for + grad_norms = [(torch.norm(p_.grad * window_mask) + SMALL_CONST) for index, p_ in enumerate(past_perturb)] grad = [ @@ -560,31 +561,31 @@ def generate_text_pplm( # Piero modified model call # hidden = model.hidden_states # update hidden # logits = model.forward_hidden(hidden) - logits = logits[:, -1, :] / temperature # + SmallConst + logits = logits[:, -1, :] / temperature # + SMALL_CONST - # logits = top_k_filter(logits, k=args.top_k) # + SmallConst + # logits = top_k_filter(logits, k=args.top_k) # + SMALL_CONST log_probs = F.softmax(logits, dim=-1) # Fuse the modified model and original model if perturb: - # original_probs = top_k_filter(original_probs[:, -1, :]) #+ SmallConst + # original_probs = top_k_filter(original_probs[:, -1, :]) #+ SMALL_CONST unpert_logits = F.softmax(unpert_logits[:, -1, :], dim=-1) # likelywords = torch.topk(original_probs, k=10, dim=-1) # print(TOKENIZER.decode(likelywords[1].tolist()[0])) log_probs = ((log_probs ** gm_scale) * ( - unpert_logits ** (1 - gm_scale))) # + SmallConst + unpert_logits ** (1 - gm_scale))) # + SMALL_CONST log_probs = top_k_filter(log_probs, k=top_k, - probs=True) # + SmallConst + probs=True) # + SMALL_CONST if torch.sum(log_probs) <= 1: log_probs = log_probs / torch.sum(log_probs) else: - logits = top_k_filter(logits, k=top_k) # + SmallConst + logits = top_k_filter(logits, k=top_k) # + SMALL_CONST log_probs = F.softmax(logits, dim=-1) if sample: From 9f693a0c4831d09ba2c177128452984091fda619 Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 18:16:30 -0800 Subject: [PATCH 135/179] Cleaned generate_text_pplm. Identical output as before. --- examples/run_pplm.py | 125 ++++++++++++++++++------------------------- 1 file changed, 53 insertions(+), 72 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 4d335a9241..bd03bbe5e0 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -471,59 +471,49 @@ def generate_text_pplm( decay=False, gamma=1.5, ): - output = torch.tensor(context, device=device, dtype=torch.long).unsqueeze( - 0) if context else None + output_so_far = ( + torch.tensor(context, device=device, dtype=torch.long).unsqueeze(0) + if context + else None + ) # collect one hot vectors for bags of words one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices) grad_norms = None + unpert_discrim_loss = 0 loss_in_time = [] for i in trange(length, ascii=True): # Get past/probs for current output, except for last word - # Note that GPT takes 2 inputs: past + current-token - # Therefore, use everything from before current i/p token to generate relevant past + # Note that GPT takes 2 inputs: past + current_token - if past is None and output is not None: - prev = output[:, -1:] - # _, past = model(output[:, :-1]) - # original_probs, true_past = model(output) - # true_hidden = model.hidden_states + # run model forward to obtain unperturbed + if past is None and output_so_far is not None: + last = output_so_far[:, -1:] + _, past, _ = model(output_so_far[:, :-1]) - # Piero modified model call - _, past, _ = model(output[:, :-1]) - unpert_logits, unpert_past, unpert_all_hidden = model(output) - true_hidden = unpert_all_hidden[-1] - - else: - # original_probs, true_past = model(output) - # true_hidden = model.hidden_states - - # Piero modified model call - unpert_logits, unpert_past, unpert_all_hidden = model(output) - true_hidden = unpert_all_hidden[-1] - - # Modify the past if necessary + unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far) + unpert_last_hidden = unpert_all_hidden[-1] + # check if we are abowe grad max length if i >= grad_length: current_stepsize = stepsize * 0 else: current_stepsize = stepsize + # modify the past if necessary if not perturb or num_iterations == 0: - perturbed_past = past + pert_past = past else: - # Piero modified model call - # accumulated_hidden = model.hidden_states[:, :-1, :] - accumulated_hidden = true_hidden[:, :-1, :] + accumulated_hidden = unpert_last_hidden[:, :-1, :] accumulated_hidden = torch.sum(accumulated_hidden, dim=1) - perturbed_past, _, grad_norms, loss_per_iter = perturb_past( + pert_past, _, grad_norms, loss_this_iter = perturb_past( past, model, - prev, + last, unpert_past=unpert_past, unpert_logits=unpert_logits, accumulated_hidden=accumulated_hidden, @@ -540,68 +530,59 @@ def generate_text_pplm( decay=decay, gamma=gamma, ) - loss_in_time.append(loss_per_iter) + loss_in_time.append(loss_this_iter) - # Piero modified model call - logits, past, pert_all_hidden = model(prev, past=perturbed_past) - # test_logits = F.softmax(test_logits[:, -1, :], dim=-1) - # likelywords = torch.topk(test_logits, k=10, dim=-1) - # print(TOKENIZER.decode(likelywords[1].tolist()[0])) + pert_logits, past, pert_all_hidden = model(last, past=pert_past) + pert_logits = pert_logits[:, -1, :] / temperature # + SMALL_CONST + pert_probs = F.softmax(pert_logits, dim=-1) if classifier is not None: ce_loss = torch.nn.CrossEntropyLoss() - predicted_sentiment = classifier(torch.mean(true_hidden, dim=1)) + prediction = classifier(torch.mean(unpert_last_hidden, dim=1)) label = torch.tensor([label_class], device='cuda', dtype=torch.long) - true_discrim_loss = ce_loss(predicted_sentiment, label) - print("true discrim loss", true_discrim_loss.data.cpu().numpy()) + unpert_discrim_loss = ce_loss(prediction, label) + print( + "unperturbed discrim loss", + unpert_discrim_loss.data.cpu().numpy() + ) else: - true_discrim_loss = 0 - - # Piero modified model call - # hidden = model.hidden_states # update hidden - # logits = model.forward_hidden(hidden) - logits = logits[:, -1, :] / temperature # + SMALL_CONST - - # logits = top_k_filter(logits, k=args.top_k) # + SMALL_CONST - - log_probs = F.softmax(logits, dim=-1) + unpert_discrim_loss = 0 # Fuse the modified model and original model if perturb: - # original_probs = top_k_filter(original_probs[:, -1, :]) #+ SMALL_CONST - unpert_logits = F.softmax(unpert_logits[:, -1, :], dim=-1) - # likelywords = torch.topk(original_probs, k=10, dim=-1) - # print(TOKENIZER.decode(likelywords[1].tolist()[0])) + unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1) - log_probs = ((log_probs ** gm_scale) * ( - unpert_logits ** (1 - gm_scale))) # + SMALL_CONST - - log_probs = top_k_filter(log_probs, k=top_k, + pert_probs = ((pert_probs ** gm_scale) * ( + unpert_probs ** (1 - gm_scale))) # + SMALL_CONST + pert_probs = top_k_filter(pert_probs, k=top_k, probs=True) # + SMALL_CONST - if torch.sum(log_probs) <= 1: - log_probs = log_probs / torch.sum(log_probs) + # rescale + if torch.sum(pert_probs) <= 1: + pert_probs = pert_probs / torch.sum(pert_probs) else: - logits = top_k_filter(logits, k=top_k) # + SMALL_CONST - log_probs = F.softmax(logits, dim=-1) + pert_logits = top_k_filter(pert_logits, k=top_k) # + SMALL_CONST + pert_probs = F.softmax(pert_logits, dim=-1) + # sample or greedy if sample: - # likelywords = torch.topk(log_probs, k=args.top_k, dim=-1) - # print(TOKENIZER.decode(likelywords[1].tolist()[0])) - # print(likelywords[0].tolist()) - prev = torch.multinomial(log_probs, num_samples=1) - else: - _, prev = torch.topk(log_probs, k=1, dim=-1) - # if perturb: - # prev = future - output = prev if output is None else torch.cat((output, prev), - dim=1) # update output - print(TOKENIZER.decode(output.tolist()[0])) + last = torch.multinomial(pert_probs, num_samples=1) - return output, true_discrim_loss, loss_in_time + else: + _, last = torch.topk(pert_probs, k=1, dim=-1) + + # update context/output_so_far appending the new token + output_so_far = ( + last if output_so_far is None + else torch.cat((output_so_far, last), dim=1) + ) + + print(TOKENIZER.decode(output_so_far.tolist()[0])) + + return output_so_far, unpert_discrim_loss, loss_in_time def run_model(): From ffc29354051ccd4fa3fd12010abacb0ff2e0733e Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 18:30:42 -0800 Subject: [PATCH 136/179] Fix for making unditioned generation work. Identical output as before. --- examples/run_pplm.py | 49 ++++++++++++++++++++++++-------------------- 1 file changed, 27 insertions(+), 22 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index bd03bbe5e0..e337add46d 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -481,6 +481,7 @@ def generate_text_pplm( one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices) grad_norms = None + last = None unpert_discrim_loss = 0 loss_in_time = [] for i in trange(length, ascii=True): @@ -491,7 +492,8 @@ def generate_text_pplm( # run model forward to obtain unperturbed if past is None and output_so_far is not None: last = output_so_far[:, -1:] - _, past, _ = model(output_so_far[:, :-1]) + if output_so_far.shape[1] > 1: + _, past, _ = model(output_so_far[:, :-1]) unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far) unpert_last_hidden = unpert_all_hidden[-1] @@ -510,27 +512,30 @@ def generate_text_pplm( accumulated_hidden = unpert_last_hidden[:, :-1, :] accumulated_hidden = torch.sum(accumulated_hidden, dim=1) - pert_past, _, grad_norms, loss_this_iter = perturb_past( - past, - model, - last, - unpert_past=unpert_past, - unpert_logits=unpert_logits, - accumulated_hidden=accumulated_hidden, - grad_norms=grad_norms, - stepsize=current_stepsize, - classifier=classifier, - label_class=label_class, - one_hot_bows_vectors=one_hot_bows_vectors, - loss_type=loss_type, - num_iterations=num_iterations, - kl_scale=kl_scale, - window_length=window_length, - horizon_length=horizon_length, - decay=decay, - gamma=gamma, - ) - loss_in_time.append(loss_this_iter) + if past is not None: + pert_past, _, grad_norms, loss_this_iter = perturb_past( + past, + model, + last, + unpert_past=unpert_past, + unpert_logits=unpert_logits, + accumulated_hidden=accumulated_hidden, + grad_norms=grad_norms, + stepsize=current_stepsize, + classifier=classifier, + label_class=label_class, + one_hot_bows_vectors=one_hot_bows_vectors, + loss_type=loss_type, + num_iterations=num_iterations, + kl_scale=kl_scale, + window_length=window_length, + horizon_length=horizon_length, + decay=decay, + gamma=gamma, + ) + loss_in_time.append(loss_this_iter) + else: + pert_past = past pert_logits, past, pert_all_hidden = model(last, past=pert_past) pert_logits = pert_logits[:, -1, :] / temperature # + SMALL_CONST From 61399e5afe15915f5e074fd25faf85a5346e093a Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 19:51:42 -0800 Subject: [PATCH 137/179] Cleaned perturb_past. Identical output as before. --- examples/run_pplm.py | 202 ++++++++++++++++++++++++------------------- 1 file changed, 111 insertions(+), 91 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index e337add46d..77758759d9 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -112,7 +112,7 @@ def top_k_filter(logits, k, probs=False): def perturb_past( past, model, - prev, + last, unpert_past=None, unpert_logits=None, accumulated_hidden=None, @@ -128,156 +128,174 @@ def perturb_past( horizon_length=1, decay=False, gamma=1.5, + device='cuda' ): # Generate inital perturbed past - past_perturb_orig = [ - (np.random.uniform(0.0, 0.0, p.shape).astype('float32')) - for p in past] + grad_accumulator = [ + (np.zeros(p.shape).astype("float32")) + for p in past + ] if accumulated_hidden is None: accumulated_hidden = 0 if decay: - decay_mask = torch.arange(0., 1.0 + SMALL_CONST, 1.0 / (window_length))[ - 1:] + decay_mask = torch.arange( + 0., + 1.0 + SMALL_CONST, + 1.0 / (window_length) + )[1:] else: decay_mask = 1.0 + # TODO fix this comment (SUMANTH) # Generate a mask is gradient perturbated is based on a past window - _, _, _, current_length, _ = past[0].shape + _, _, _, curr_length, _ = past[0].shape - if current_length > window_length and window_length > 0: - ones_key_val_shape = tuple(past[0].shape[:-2]) + tuple( - [window_length]) + tuple( - past[0].shape[-1:]) + if curr_length > window_length and window_length > 0: + ones_key_val_shape = ( + tuple(past[0].shape[:-2]) + + tuple([window_length]) + + tuple(past[0].shape[-1:]) + ) - zeros_key_val_shape = tuple(past[0].shape[:-2]) + tuple( - [current_length - window_length]) + tuple( - past[0].shape[-1:]) + zeros_key_val_shape = ( + tuple(past[0].shape[:-2]) + + tuple([curr_length - window_length]) + + tuple(past[0].shape[-1:]) + ) ones_mask = torch.ones(ones_key_val_shape) ones_mask = decay_mask * ones_mask.permute(0, 1, 2, 4, 3) ones_mask = ones_mask.permute(0, 1, 2, 4, 3) - window_mask = torch.cat((ones_mask, torch.zeros(zeros_key_val_shape)), - dim=-2).cuda() + window_mask = torch.cat( + (ones_mask, torch.zeros(zeros_key_val_shape)), + dim=-2 + ).to(device) else: - window_mask = torch.ones_like(past[0]).cuda() + window_mask = torch.ones_like(past[0]).to(device) + # accumulate perturbations for num_iterations loss_per_iter = [] + new_accumulated_hidden = None for i in range(num_iterations): print("Iteration ", i + 1) - past_perturb = [torch.from_numpy(p_) for p_ in past_perturb_orig] - past_perturb = [to_var(p_, requires_grad=True) for p_ in past_perturb] + curr_perturbation = [ + to_var(torch.from_numpy(p_), requires_grad=True) + for p_ in grad_accumulator + ] - perturbed_past = list(map(add, past, past_perturb)) - - _, _, _, current_length, _ = past_perturb[0].shape - - # _, future_past = model(prev, past=perturbed_past) - # hidden = model.hidden_states - - # Piero modified model call - logits, _, all_hidden = model(prev, past=perturbed_past) + # Compute hidden using perturbed past + perturbed_past = list(map(add, past, curr_perturbation)) + _, _, _, curr_length, _ = curr_perturbation[0].shape + all_logits, _, all_hidden = model(last, past=perturbed_past) hidden = all_hidden[-1] - new_accumulated_hidden = accumulated_hidden + torch.sum(hidden, - dim=1).detach() + new_accumulated_hidden = accumulated_hidden + torch.sum( + hidden, + dim=1 + ).detach() + # TODO: Check the layer-norm consistency of this with trained discriminator (Sumanth) + logits = all_logits[:, -1, :] + probs = F.softmax(logits, dim=-1) - # TODO: Check the layer-norm consistency of this with trained discriminator - logits = logits[:, -1, :] - probabs = F.softmax(logits, dim=-1) loss = 0.0 loss_list = [] - if loss_type == 1 or loss_type == 3: - for one_hot_good in one_hot_bows_vectors: - good_logits = torch.mm(probabs, torch.t(one_hot_good)) - loss_word = good_logits - loss_word = torch.sum(loss_word) - loss_word = -torch.log(loss_word) - # loss_word = torch.sum(loss_word) /torch.sum(one_hot_good) - loss += loss_word - loss_list.append(loss_word) + if loss_type == PPLM_BOW or loss_type == PPLM_BOW_DISCRIM: + for one_hot_bow in one_hot_bows_vectors: + bow_logits = torch.mm(probs, torch.t(one_hot_bow)) + bow_loss = -torch.log(torch.sum(bow_logits)) + loss += bow_loss + loss_list.append(bow_loss) print(" pplm_bow_loss:", loss.data.cpu().numpy()) if loss_type == 2 or loss_type == 3: ce_loss = torch.nn.CrossEntropyLoss() - new_true_past = unpert_past - for i in range(horizon_length): - future_probabs = F.softmax(logits, dim=-1) # Get softmax - future_probabs = torch.unsqueeze(future_probabs, dim=1) - - # _, new_true_past = model(future_probabs, past=new_true_past) - # future_hidden = model.hidden_states # Get expected hidden states - - # Piero modified model call - wte = model.resize_token_embeddings() - inputs_embeds = torch.matmul(future_probabs, wte.weight.data) - _, new_true_past, future_hidden = model( - past=new_true_past, + # TODO why we need to do this assignment and not just using unpert_past? (Sumanth) + curr_unpert_past = unpert_past + curr_probs = torch.unsqueeze(probs, dim=1) + wte = model.resize_token_embeddings() + for _ in range(horizon_length): + inputs_embeds = torch.matmul(curr_probs, wte.weight.data) + _, curr_unpert_past, curr_all_hidden = model( + past=curr_unpert_past, inputs_embeds=inputs_embeds ) - future_hidden = future_hidden[-1] - + curr_hidden = curr_all_hidden[-1] new_accumulated_hidden = new_accumulated_hidden + torch.sum( - future_hidden, dim=1) + curr_hidden, dim=1) - predicted_sentiment = classifier(new_accumulated_hidden / ( - current_length + 1 + horizon_length)) + prediction = classifier(new_accumulated_hidden / + (curr_length + 1 + horizon_length)) - label = torch.tensor([label_class], device='cuda', + label = torch.tensor([label_class], device=device, dtype=torch.long) - discrim_loss = ce_loss(predicted_sentiment, label) + discrim_loss = ce_loss(prediction, label) print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy()) loss += discrim_loss loss_list.append(discrim_loss) kl_loss = 0.0 if kl_scale > 0.0: - p = (F.softmax(unpert_logits[:, -1, :], dim=-1)) - p = p + SMALL_CONST * (p <= SMALL_CONST).type( - torch.FloatTensor).cuda().detach() - correction = SMALL_CONST * (probabs <= SMALL_CONST).type( - torch.FloatTensor).cuda().detach() - corrected_probabs = probabs + correction.detach() + unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1) + unpert_probs = ( + unpert_probs + SMALL_CONST * + (unpert_probs <= SMALL_CONST).float().to(device).detach() + ) + correction = SMALL_CONST * (probs <= SMALL_CONST).float().to(device).detach() + corrected_probs = probs + correction.detach() kl_loss = kl_scale * ( - (corrected_probabs * (corrected_probabs / p).log()).sum()) - print(' kl_loss', (kl_loss).data.cpu().numpy()) - loss += kl_loss # + discrim_loss + (corrected_probs * (corrected_probs / unpert_probs).log()).sum() + ) + print(' kl_loss', kl_loss.data.cpu().numpy()) + loss += kl_loss loss_per_iter.append(loss.data.cpu().numpy()) - print(' pplm_loss', (loss - kl_loss).data.cpu().numpy()) + # compute gradients loss.backward() - if grad_norms is not None and loss_type == 1: + + # calculate gradient norms + if grad_norms is not None and loss_type == PPLM_BOW: grad_norms = [ torch.max(grad_norms[index], torch.norm(p_.grad * window_mask)) - for index, p_ in - enumerate(past_perturb)] + for index, p_ in enumerate(curr_perturbation) + ] else: - grad_norms = [(torch.norm(p_.grad * window_mask) + SMALL_CONST) for - index, p_ in enumerate(past_perturb)] + grad_norms = [ + (torch.norm(p_.grad * window_mask) + SMALL_CONST) + for index, p_ in enumerate(curr_perturbation) + ] + # normalize gradients grad = [ - -stepsize * (p_.grad * window_mask / grad_norms[ - index] ** gamma).data.cpu().numpy() - for index, p_ in enumerate(past_perturb)] - past_perturb_orig = list(map(add, grad, past_perturb_orig)) + -stepsize * + (p_.grad * window_mask / grad_norms[index] ** gamma).data.cpu().numpy() + for index, p_ in enumerate(curr_perturbation) + ] - for p_ in past_perturb: + # accumulate gradient + grad_accumulator = list(map(add, grad, grad_accumulator)) + + # reset gradients, just to make sure + for p_ in curr_perturbation: p_.grad.data.zero_() + # removing past from the graph new_past = [] - for p in past: - new_past.append(p.detach()) - + for p_ in past: + new_past.append(p_.detach()) past = new_past - past_perturb = [torch.from_numpy(p_) for p_ in past_perturb_orig] - past_perturb = [to_var(p_, requires_grad=True) for p_ in past_perturb] - perturbed_past = list(map(add, past, past_perturb)) + # apply the accumulated perturbations to the past + grad_accumulator = [ + to_var(torch.from_numpy(p_), requires_grad=True) + for p_ in grad_accumulator + ] + pert_past = list(map(add, past, grad_accumulator)) - return perturbed_past, new_accumulated_hidden, grad_norms, loss_per_iter + return pert_past, new_accumulated_hidden, grad_norms, loss_per_iter def get_classifier( @@ -532,6 +550,7 @@ def generate_text_pplm( horizon_length=horizon_length, decay=decay, gamma=gamma, + device=device ) loss_in_time.append(loss_this_iter) else: @@ -562,7 +581,7 @@ def generate_text_pplm( pert_probs = ((pert_probs ** gm_scale) * ( unpert_probs ** (1 - gm_scale))) # + SMALL_CONST pert_probs = top_k_filter(pert_probs, k=top_k, - probs=True) # + SMALL_CONST + probs=True) # + SMALL_CONST # rescale if torch.sum(pert_probs) <= 1: @@ -662,7 +681,8 @@ def run_model(): parser.add_argument("--decay", action="store_true", help="whether to decay or not") parser.add_argument("--gamma", type=float, default=1.5) - parser.add_argument("--colorama", action="store_true", help="colors keywords") + parser.add_argument("--colorama", action="store_true", + help="colors keywords") args = parser.parse_args() From afc7dcd94d2480e1fa6ef91ec8e9029142566612 Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 20:08:53 -0800 Subject: [PATCH 138/179] Now run_pplm works on cpu. Identical output as before (when using gpu). --- examples/run_pplm.py | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 77758759d9..0d6b0d635d 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -84,9 +84,11 @@ DISCRIMINATOR_MODELS_PARAMS = { } -def to_var(x, requires_grad=False, volatile=False): - if torch.cuda.is_available(): +def to_var(x, requires_grad=False, volatile=False, device='cuda'): + if torch.cuda.is_available() and device == 'cuda': x = x.cuda() + elif device != 'cuda': + x = x.to(device) return Variable(x, requires_grad=requires_grad, volatile=volatile) @@ -182,7 +184,7 @@ def perturb_past( for i in range(num_iterations): print("Iteration ", i + 1) curr_perturbation = [ - to_var(torch.from_numpy(p_), requires_grad=True) + to_var(torch.from_numpy(p_), requires_grad=True, device=device) for p_ in grad_accumulator ] @@ -290,7 +292,7 @@ def perturb_past( # apply the accumulated perturbations to the past grad_accumulator = [ - to_var(torch.from_numpy(p_), requires_grad=True) + to_var(torch.from_numpy(p_), requires_grad=True, device=device) for p_ in grad_accumulator ] pert_past = list(map(add, past, grad_accumulator)) @@ -300,7 +302,7 @@ def perturb_past( def get_classifier( name: Optional[str], label_class: Union[str, int], - device: Union[str, torch.device] + device: str ) -> Tuple[Optional[ClassificationHead], Optional[int]]: if name is None: return None, None @@ -355,16 +357,16 @@ def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str]) -> List[ return bow_indices -def build_bows_one_hot_vectors(bow_indices): +def build_bows_one_hot_vectors(bow_indices, device='cuda'): if bow_indices is None: return None one_hot_bows_vectors = [] for single_bow in bow_indices: single_bow = list(filter(lambda x: len(x) <= 1, single_bow)) - single_bow = torch.tensor(single_bow).cuda() + single_bow = torch.tensor(single_bow).to(device) num_words = single_bow.shape[0] - one_hot_bow = torch.zeros(num_words, TOKENIZER.vocab_size).cuda() + one_hot_bow = torch.zeros(num_words, TOKENIZER.vocab_size).to(device) one_hot_bow.scatter_(1, single_bow, 1) one_hot_bows_vectors.append(one_hot_bow) return one_hot_bows_vectors @@ -425,7 +427,8 @@ def full_text_generation( length=length, perturb=False ) - torch.cuda.empty_cache() + if device == 'cuda': + torch.cuda.empty_cache() pert_gen_tok_texts = [] discrim_losses = [] @@ -460,7 +463,8 @@ def full_text_generation( discrim_losses.append(discrim_loss.data.cpu().numpy()) losses_in_time.append(loss_in_time) - torch.cuda.empty_cache() + if device == 'cuda': + torch.cuda.empty_cache() return unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time @@ -496,7 +500,7 @@ def generate_text_pplm( ) # collect one hot vectors for bags of words - one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices) + one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices, device) grad_norms = None last = None @@ -563,7 +567,7 @@ def generate_text_pplm( if classifier is not None: ce_loss = torch.nn.CrossEntropyLoss() prediction = classifier(torch.mean(unpert_last_hidden, dim=1)) - label = torch.tensor([label_class], device='cuda', + label = torch.tensor([label_class], device=device, dtype=torch.long) unpert_discrim_loss = ce_loss(prediction, label) print( From 611961ade71042ff759712e6f680544ec5ff68b9 Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 21:34:49 -0800 Subject: [PATCH 139/179] Added tqdm to preprocessing --- examples/run_pplm_discrim_train.py | 206 ++++++++++++++--------------- 1 file changed, 102 insertions(+), 104 deletions(-) diff --git a/examples/run_pplm_discrim_train.py b/examples/run_pplm_discrim_train.py index 519e2de29a..5291ad4b51 100644 --- a/examples/run_pplm_discrim_train.py +++ b/examples/run_pplm_discrim_train.py @@ -18,13 +18,14 @@ import torch.utils.data as data from nltk.tokenize.treebank import TreebankWordDetokenizer from torchtext import data as torchtext_data from torchtext import datasets +from tqdm import tqdm, trange from transformers import GPT2Tokenizer, GPT2LMHeadModel torch.manual_seed(0) np.random.seed(0) EPSILON = 1e-10 -device = 'cpu' +device = "cpu" example_sentence = "This is incredible! I love it, this is the best chicken I have ever had." max_length_seq = 100 @@ -109,8 +110,8 @@ class Dataset(data.Dataset): def __getitem__(self, index): """Returns one data pair (source and target).""" data = {} - data['X'] = self.X[index] - data['y'] = self.y[index] + data["X"] = self.X[index] + data["y"] = self.y[index] return data @@ -133,8 +134,8 @@ def collate_fn(data): for key in data[0].keys(): item_info[key] = [d[key] for d in data] - x_batch, _ = pad_sequences(item_info['X']) - y_batch = torch.tensor(item_info['y'], dtype=torch.long) + x_batch, _ = pad_sequences(item_info["X"]) + y_batch = torch.tensor(item_info["y"], dtype=torch.long) return x_batch, y_batch @@ -144,8 +145,8 @@ def cached_collate_fn(data): for key in data[0].keys(): item_info[key] = [d[key] for d in data] - x_batch = torch.cat(item_info['X'], 0) - y_batch = torch.tensor(item_info['y'], dtype=torch.long) + x_batch = torch.cat(item_info["X"], 0) + y_batch = torch.tensor(item_info["y"], dtype=torch.long) return x_batch, y_batch @@ -168,7 +169,7 @@ def train_epoch(data_loader, discriminator, optimizer, if batch_idx % log_interval == 0: print( - 'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( + "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( epoch + 1, samples_so_far, len(data_loader.dataset), 100 * samples_so_far / len(data_loader.dataset), loss.item() @@ -185,7 +186,7 @@ def evaluate_performance(data_loader, discriminator): input_t, target_t = input_t.to(device), target_t.to(device) output_t = discriminator(input_t) # sum up batch loss - test_loss += F.nll_loss(output_t, target_t, reduction='sum').item() + test_loss += F.nll_loss(output_t, target_t, reduction="sum").item() # get the index of the max log-probability pred_t = output_t.argmax(dim=1, keepdim=True) correct += pred_t.eq(target_t.view_as(pred_t)).sum().item() @@ -193,8 +194,8 @@ def evaluate_performance(data_loader, discriminator): test_loss /= len(data_loader.dataset) print( - 'Performance on test set: ' - 'Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format( + "Performance on test set: " + "Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format( test_loss, correct, len(data_loader.dataset), 100. * correct / len(data_loader.dataset) ) @@ -208,8 +209,8 @@ def predict(input_sentence, model, classes, cached=False): input_t = model.avg_representation(input_t) log_probs = model(input_t).data.cpu().numpy().flatten().tolist() - print('Input sentence:', input_sentence) - print('Predictions:', ", ".join( + print("Input sentence:", input_sentence) + print("Predictions:", ", ".join( "{}: {:.4f}".format(c, math.exp(log_prob)) for c, log_prob in zip(classes, log_probs) )) @@ -222,7 +223,7 @@ def get_cached_data_loader(dataset, batch_size, discriminator, shuffle=False): xs = [] ys = [] - for batch_idx, (x, y) in enumerate(data_loader): + for batch_idx, (x, y) in enumerate(tqdm(data_loader, ascii=True)): with torch.no_grad(): x = x.to(device) avg_rep = discriminator.avg_representation(x).cpu().detach() @@ -240,16 +241,16 @@ def get_cached_data_loader(dataset, batch_size, discriminator, shuffle=False): def train_discriminator( - dataset, dataset_fp=None, pretrained_model='gpt2-medium', + dataset, dataset_fp=None, pretrained_model="gpt2-medium", epochs=10, batch_size=64, log_interval=10, save_model=False, cached=False, no_cuda=False): global device device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu" - print('Preprocessing {} dataset...'.format(dataset)) + print("Preprocessing {} dataset...".format(dataset)) start = time.time() - if dataset == 'SST': + if dataset == "SST": idx2class = ["positive", "negative", "very positive", "very negative", "neutral"] class2idx = {c: i for i, c in enumerate(idx2class)} @@ -271,7 +272,7 @@ def train_discriminator( x = [] y = [] - for i in range(len(train_data)): + for i in trange(len(train_data), ascii=True): seq = TreebankWordDetokenizer().detokenize( vars(train_data[i])["text"] ) @@ -283,7 +284,7 @@ def train_discriminator( test_x = [] test_y = [] - for i in range(len(test_data)): + for i in trange(len(test_data), ascii=True): seq = TreebankWordDetokenizer().detokenize( vars(test_data[i])["text"] ) @@ -301,7 +302,7 @@ def train_discriminator( "default_class": 2, } - elif dataset == 'clickbait': + elif dataset == "clickbait": idx2class = ["non_clickbait", "clickbait"] class2idx = {c: i for i, c in enumerate(idx2class)} @@ -317,31 +318,33 @@ def train_discriminator( try: data.append(eval(line)) except: - print('Error evaluating line {}: {}'.format( + print("Error evaluating line {}: {}".format( i, line )) continue x = [] y = [] - y = [] - for i, d in enumerate(data): - try: - seq = discriminator.tokenizer.encode(d["text"]) + with open("datasets/clickbait/clickbait_train_prefix.txt") as f: + for i, line in enumerate(tqdm(f, ascii=True)): + try: + d = eval(line) + seq = discriminator.tokenizer.encode(d["text"]) - if len(seq) < max_length_seq: - seq = torch.tensor( - [50256] + seq, device=device, dtype=torch.long - ) - else: - print("Line {} is longer than maximum length {}".format( - i, max_length_seq - )) - continue - x.append(seq) - y.append(d['label']) - except: - print("Error tokenizing line {}, skipping it".format(i)) - pass + if len(seq) < max_length_seq: + seq = torch.tensor( + [50256] + seq, device=device, dtype=torch.long + ) + else: + print("Line {} is longer than maximum length {}".format( + i, max_length_seq + )) + continue + x.append(seq) + y.append(d["label"]) + except: + print("Error evaluating / tokenizing" + " line {}, skipping it".format(i)) + pass full_dataset = Dataset(x, y) train_size = int(0.9 * len(full_dataset)) @@ -358,7 +361,7 @@ def train_discriminator( "default_class": 1, } - elif dataset == 'toxic': + elif dataset == "toxic": idx2class = ["non_toxic", "toxic"] class2idx = {c: i for i, c in enumerate(idx2class)} @@ -368,37 +371,29 @@ def train_discriminator( cached_mode=cached ).to(device) - with open("datasets/toxic/toxic_train.txt") as f: - data = [] - for i, line in enumerate(f): - try: - data.append(eval(line)) - except: - print('Error evaluating line {}: {}'.format( - i, line - )) - continue - x = [] y = [] - for i, d in enumerate(data): - try: - seq = discriminator.tokenizer.encode(d["text"]) + with open("datasets/toxic/toxic_train.txt") as f: + for i, line in enumerate(tqdm(f, ascii=True)): + try: + d = eval(line) + seq = discriminator.tokenizer.encode(d["text"]) - if len(seq) < max_length_seq: - seq = torch.tensor( - [50256] + seq, device=device, dtype=torch.long - ) - else: - print("Line {} is longer than maximum length {}".format( - i, max_length_seq - )) - continue - x.append(seq) - y.append(int(np.sum(d['label']) > 0)) - except: - print("Error tokenizing line {}, skipping it".format(i)) - pass + if len(seq) < max_length_seq: + seq = torch.tensor( + [50256] + seq, device=device, dtype=torch.long + ) + else: + print("Line {} is longer than maximum length {}".format( + i, max_length_seq + )) + continue + x.append(seq) + y.append(int(np.sum(d["label"]) > 0)) + except: + print("Error evaluating / tokenizing" + " line {}, skipping it".format(i)) + pass full_dataset = Dataset(x, y) train_size = int(0.9 * len(full_dataset)) @@ -415,18 +410,18 @@ def train_discriminator( "default_class": 0, } - else: # if dataset == 'generic': + else: # if dataset == "generic": # This assumes the input dataset is a TSV with the following structure: # class \t text if dataset_fp is None: - raise ValueError('When generic dataset is selected, ' - 'dataset_fp needs to be specified aswell.') + raise ValueError("When generic dataset is selected, " + "dataset_fp needs to be specified aswell.") classes = set() with open(dataset_fp) as f: - csv_reader = csv.reader(f, delimiter='\t') - for row in csv_reader: + csv_reader = csv.reader(f, delimiter="\t") + for row in tqdm(csv_reader, ascii=True): if row: classes.add(row[0]) @@ -442,8 +437,8 @@ def train_discriminator( x = [] y = [] with open(dataset_fp) as f: - csv_reader = csv.reader(f, delimiter='\t') - for i, row in enumerate(csv_reader): + csv_reader = csv.reader(f, delimiter="\t") + for i, row in enumerate(tqdm(csv_reader, ascii=True)): if row: label = row[0] text = row[1] @@ -458,9 +453,10 @@ def train_discriminator( ) else: - print("Line {} is longer than maximum length {}".format( - i, max_length_seq - )) + print( + "Line {} is longer than maximum length {}".format( + i, max_length_seq + )) continue x.append(seq) @@ -487,12 +483,14 @@ def train_discriminator( } end = time.time() - print('Preprocessed {} data points'.format( + print("Preprocessed {} data points".format( len(train_dataset) + len(test_dataset)) ) print("Data preprocessing took: {:.3f}s".format(end - start)) if cached: + print("Building representation cache...") + start = time.time() train_loader = get_cached_data_loader( @@ -524,7 +522,7 @@ def train_discriminator( for epoch in range(epochs): start = time.time() - print('\nEpoch', epoch + 1) + print("\nEpoch", epoch + 1) train_epoch( discriminator=discriminator, @@ -553,31 +551,31 @@ def train_discriminator( "{}_classifier_head_epoch_{}.pt".format(dataset, epoch)) -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser( - description='Train a discriminator on top of GPT-2 representations') - parser.add_argument('--dataset', type=str, default='SST', - choices=('SST', 'clickbait', 'toxic', 'generic'), - help='dataset to train the discriminator on.' - 'In case of generic, the dataset is expected' - 'to be a TSBV file with structure: class \\t text') - parser.add_argument('--dataset_fp', type=str, default='', - help='File path of the dataset to use. ' - 'Needed only in case of generic datadset') - parser.add_argument('--pretrained_model', type=str, default='gpt2-medium', - help='Pretrained model to use as encoder') - parser.add_argument('--epochs', type=int, default=10, metavar='N', - help='Number of training epochs') - parser.add_argument('--batch_size', type=int, default=64, metavar='N', - help='input batch size for training (default: 64)') - parser.add_argument('--log_interval', type=int, default=10, metavar='N', - help='how many batches to wait before logging training status') - parser.add_argument('--save_model', action='store_true', - help='whether to save the model') - parser.add_argument('--cached', action='store_true', - help='whether to cache the input representations') - parser.add_argument('--no_cuda', action='store_true', - help='use to turn off cuda') + description="Train a discriminator on top of GPT-2 representations") + parser.add_argument("--dataset", type=str, default="SST", + choices=("SST", "clickbait", "toxic", "generic"), + help="dataset to train the discriminator on." + "In case of generic, the dataset is expected" + "to be a TSBV file with structure: class \\t text") + parser.add_argument("--dataset_fp", type=str, default="", + help="File path of the dataset to use. " + "Needed only in case of generic datadset") + parser.add_argument("--pretrained_model", type=str, default="gpt2-medium", + help="Pretrained model to use as encoder") + parser.add_argument("--epochs", type=int, default=10, metavar="N", + help="Number of training epochs") + parser.add_argument("--batch_size", type=int, default=64, metavar="N", + help="input batch size for training (default: 64)") + parser.add_argument("--log_interval", type=int, default=10, metavar="N", + help="how many batches to wait before logging training status") + parser.add_argument("--save_model", action="store_true", + help="whether to save the model") + parser.add_argument("--cached", action="store_true", + help="whether to cache the input representations") + parser.add_argument("--no_cuda", action="store_true", + help="use to turn off cuda") args = parser.parse_args() train_discriminator(**(vars(args))) From b0eaff36e6aefd45c0fe89bb2ab86a495e4e735f Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 21:43:43 -0800 Subject: [PATCH 140/179] Added a +1 to epoch when saving weights --- examples/run_pplm_discrim_train.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/run_pplm_discrim_train.py b/examples/run_pplm_discrim_train.py index 5291ad4b51..fccfb14426 100644 --- a/examples/run_pplm_discrim_train.py +++ b/examples/run_pplm_discrim_train.py @@ -545,10 +545,11 @@ def train_discriminator( if save_model: # torch.save(discriminator.state_dict(), # "{}_discriminator_{}.pt".format( - # args.dataset, epoch + # args.dataset, epoch + 1 # )) torch.save(discriminator.get_classifier().state_dict(), - "{}_classifier_head_epoch_{}.pt".format(dataset, epoch)) + "{}_classifier_head_epoch_{}.pt".format(dataset, + epoch + 1)) if __name__ == "__main__": From 7fd54b55a3f7c3134f8cc5a62f4cc447a5cd34de Mon Sep 17 00:00:00 2001 From: piero Date: Wed, 27 Nov 2019 21:45:19 -0800 Subject: [PATCH 141/179] Added support for generic discriminators --- examples/run_pplm.py | 77 +++++++++++++++++++++++++++++--------------- 1 file changed, 51 insertions(+), 26 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 0d6b0d635d..28aa66cc7d 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -14,17 +14,16 @@ # See the License for the specific language governing permissions and # limitations under the License. -# TODO: add code for training a custom discriminator - """ Example command with bag of words: python examples/run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95 Example command with discriminator: -python examples/run_pplm.py -D sentiment --label_class 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 30 --num_samples 10 --stepsize 0.01 --kl_scale 0.01 --gm_scale 0.95 +python examples/run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 30 --num_samples 10 --stepsize 0.01 --kl_scale 0.01 --gm_scale 0.95 """ import argparse +import json from operator import add from typing import List, Optional, Tuple, Union @@ -121,7 +120,7 @@ def perturb_past( grad_norms=None, stepsize=0.01, classifier=None, - label_class=None, + class_label=None, one_hot_bows_vectors=None, loss_type=0, num_iterations=3, @@ -230,7 +229,7 @@ def perturb_past( prediction = classifier(new_accumulated_hidden / (curr_length + 1 + horizon_length)) - label = torch.tensor([label_class], device=device, + label = torch.tensor([class_label], device=device, dtype=torch.long) discrim_loss = ce_loss(prediction, label) print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy()) @@ -244,7 +243,8 @@ def perturb_past( unpert_probs + SMALL_CONST * (unpert_probs <= SMALL_CONST).float().to(device).detach() ) - correction = SMALL_CONST * (probs <= SMALL_CONST).float().to(device).detach() + correction = SMALL_CONST * (probs <= SMALL_CONST).float().to( + device).detach() corrected_probs = probs + correction.detach() kl_loss = kl_scale * ( (corrected_probs * (corrected_probs / unpert_probs).log()).sum() @@ -273,7 +273,8 @@ def perturb_past( # normalize gradients grad = [ -stepsize * - (p_.grad * window_mask / grad_norms[index] ** gamma).data.cpu().numpy() + (p_.grad * window_mask / grad_norms[ + index] ** gamma).data.cpu().numpy() for index, p_ in enumerate(curr_perturbation) ] @@ -301,7 +302,7 @@ def perturb_past( def get_classifier( - name: Optional[str], label_class: Union[str, int], + name: Optional[str], class_label: Union[str, int], device: str ) -> Tuple[Optional[ClassificationHead], Optional[int]]: if name is None: @@ -312,26 +313,29 @@ def get_classifier( class_size=params['class_size'], embed_size=params['embed_size'] ).to(device) - resolved_archive_file = cached_path(params["url"]) + if "url" in params: + resolved_archive_file = cached_path(params["url"]) + else: + resolved_archive_file = params["path"] classifier.load_state_dict( torch.load(resolved_archive_file, map_location=device)) classifier.eval() - if isinstance(label_class, str): - if label_class in params["class_vocab"]: - label_id = params["class_vocab"][label_class] + if isinstance(class_label, str): + if class_label in params["class_vocab"]: + label_id = params["class_vocab"][class_label] else: label_id = params["default_class"] - print("label_class {} not in class_vocab".format(label_class)) + print("class_label {} not in class_vocab".format(class_label)) print("available values are: {}".format(params["class_vocab"])) print("using default class {}".format(label_id)) - elif isinstance(label_class, int): - if label_class in set(params["class_vocab"].values()): - label_id = label_class + elif isinstance(class_label, int): + if class_label in set(params["class_vocab"].values()): + label_id = class_label else: label_id = params["default_class"] - print("label_class {} not in class_vocab".format(label_class)) + print("class_label {} not in class_vocab".format(class_label)) print("available values are: {}".format(params["class_vocab"])) print("using default class {}".format(label_id)) @@ -379,7 +383,7 @@ def full_text_generation( device="cuda", sample=True, discrim=None, - label_class=None, + class_label=None, bag_of_words=None, length=100, grad_length=10000, @@ -397,7 +401,7 @@ def full_text_generation( ): classifier, class_id = get_classifier( discrim, - label_class, + class_label, device ) @@ -443,7 +447,7 @@ def full_text_generation( perturb=True, bow_indices=bow_indices, classifier=classifier, - label_class=class_id, + class_label=class_id, loss_type=loss_type, length=length, grad_length=grad_length, @@ -477,7 +481,7 @@ def generate_text_pplm( sample=True, perturb=True, classifier=None, - label_class=None, + class_label=None, bow_indices=None, loss_type=0, length=100, @@ -545,7 +549,7 @@ def generate_text_pplm( grad_norms=grad_norms, stepsize=current_stepsize, classifier=classifier, - label_class=label_class, + class_label=class_label, one_hot_bows_vectors=one_hot_bows_vectors, loss_type=loss_type, num_iterations=num_iterations, @@ -567,7 +571,7 @@ def generate_text_pplm( if classifier is not None: ce_loss = torch.nn.CrossEntropyLoss() prediction = classifier(torch.mean(unpert_last_hidden, dim=1)) - label = torch.tensor([label_class], device=device, + label = torch.tensor([class_label], device=device, dtype=torch.long) unpert_discrim_loss = ce_loss(prediction, label) print( @@ -613,6 +617,20 @@ def generate_text_pplm( return output_so_far, unpert_discrim_loss, loss_in_time +def set_generic_model_params(discrim_weights, discrim_meta): + if discrim_weights is None: + raise ValueError('When using a generic discriminator, ' + 'discrim_weights need to be specified') + if discrim_meta is None: + raise ValueError('When using a generic discriminator, ' + 'discrim_meta need to be specified') + + with open(discrim_meta, 'r') as discrim_meta_file: + meta = json.load(discrim_meta_file) + meta['path'] = discrim_weights + DISCRIMINATOR_MODELS_PARAMS['generic'] = meta + + def run_model(): parser = argparse.ArgumentParser() parser.add_argument( @@ -636,11 +654,15 @@ def run_model(): "-D", type=str, default=None, - choices=("clickbait", "sentiment", "toxicity"), - help="Discriminator to use for loss-type 2", + choices=("clickbait", "sentiment", "toxicity", "generic"), + help="Discriminator to use", ) + parser.add_argument('--discrim_weights', type=str, default=None, + help='Weights for the generic discriminator') + parser.add_argument('--discrim_meta', type=str, default=None, + help='Meta information for the generic discriminator') parser.add_argument( - "--label_class", + "--class_label", type=int, default=-1, help="Class label used for the discriminator", @@ -697,6 +719,9 @@ def run_model(): # set the device device = "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" + if args.discrim == 'generic': + set_generic_model_params(args.discrim_weights, args.discrim_meta) + # load pretrained model model = GPT2LMHeadModel.from_pretrained( args.model_path, From 75904dae669249c9f5d4d4d57890fb6c537d1639 Mon Sep 17 00:00:00 2001 From: w4nderlust Date: Fri, 29 Nov 2019 18:51:27 -0800 Subject: [PATCH 142/179] Removed global variable device --- examples/run_pplm_discrim_train.py | 47 ++++++++++++++++++------------ 1 file changed, 28 insertions(+), 19 deletions(-) diff --git a/examples/run_pplm_discrim_train.py b/examples/run_pplm_discrim_train.py index fccfb14426..db081e1a17 100644 --- a/examples/run_pplm_discrim_train.py +++ b/examples/run_pplm_discrim_train.py @@ -25,7 +25,6 @@ from transformers import GPT2Tokenizer, GPT2LMHeadModel torch.manual_seed(0) np.random.seed(0) EPSILON = 1e-10 -device = "cpu" example_sentence = "This is incredible! I love it, this is the best chicken I have ever had." max_length_seq = 100 @@ -55,7 +54,8 @@ class Discriminator(torch.nn.Module): self, class_size, pretrained_model="gpt2-medium", - cached_mode=False + cached_mode=False, + device='cpu' ): super(Discriminator, self).__init__() self.tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model) @@ -66,6 +66,7 @@ class Discriminator(torch.nn.Module): embed_size=self.embed_size ) self.cached_mode = cached_mode + self.device = device def get_classifier(self): return self.classifier_head @@ -78,7 +79,7 @@ class Discriminator(torch.nn.Module): def avg_representation(self, x): mask = x.ne(0).unsqueeze(2).repeat( 1, 1, self.embed_size - ).float().to(device).detach() + ).float().to(self.device).detach() hidden, _ = self.encoder.transformer(x) masked_hidden = hidden * mask avg_hidden = torch.sum(masked_hidden, dim=1) / ( @@ -88,9 +89,9 @@ class Discriminator(torch.nn.Module): def forward(self, x): if self.cached_mode: - avg_hidden = x.to(device) + avg_hidden = x.to(self.device) else: - avg_hidden = self.avg_representation(x.to(device)) + avg_hidden = self.avg_representation(x.to(self.device)) logits = self.classifier_head(avg_hidden) probs = F.log_softmax(logits, dim=-1) @@ -152,7 +153,7 @@ def cached_collate_fn(data): def train_epoch(data_loader, discriminator, optimizer, - epoch=0, log_interval=10): + epoch=0, log_interval=10, device='cpu'): samples_so_far = 0 discriminator.train_custom() for batch_idx, (input_t, target_t) in enumerate(data_loader): @@ -177,7 +178,7 @@ def train_epoch(data_loader, discriminator, optimizer, ) -def evaluate_performance(data_loader, discriminator): +def evaluate_performance(data_loader, discriminator, device='cpu'): discriminator.eval() test_loss = 0 correct = 0 @@ -202,7 +203,7 @@ def evaluate_performance(data_loader, discriminator): ) -def predict(input_sentence, model, classes, cached=False): +def predict(input_sentence, model, classes, cached=False, device='cpu'): input_t = model.tokenizer.encode(input_sentence) input_t = torch.tensor([input_t], dtype=torch.long, device=device) if cached: @@ -216,7 +217,8 @@ def predict(input_sentence, model, classes, cached=False): )) -def get_cached_data_loader(dataset, batch_size, discriminator, shuffle=False): +def get_cached_data_loader(dataset, batch_size, discriminator, + shuffle=False, device='cpu'): data_loader = torch.utils.data.DataLoader(dataset=dataset, batch_size=batch_size, collate_fn=collate_fn) @@ -244,7 +246,6 @@ def train_discriminator( dataset, dataset_fp=None, pretrained_model="gpt2-medium", epochs=10, batch_size=64, log_interval=10, save_model=False, cached=False, no_cuda=False): - global device device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu" print("Preprocessing {} dataset...".format(dataset)) @@ -258,7 +259,8 @@ def train_discriminator( discriminator = Discriminator( class_size=len(idx2class), pretrained_model=pretrained_model, - cached_mode=cached + cached_mode=cached, + device=device ).to(device) text = torchtext_data.Field() @@ -309,7 +311,8 @@ def train_discriminator( discriminator = Discriminator( class_size=len(idx2class), pretrained_model=pretrained_model, - cached_mode=cached + cached_mode=cached, + device=device ).to(device) with open("datasets/clickbait/clickbait_train_prefix.txt") as f: @@ -368,7 +371,8 @@ def train_discriminator( discriminator = Discriminator( class_size=len(idx2class), pretrained_model=pretrained_model, - cached_mode=cached + cached_mode=cached, + device=device ).to(device) x = [] @@ -431,7 +435,8 @@ def train_discriminator( discriminator = Discriminator( class_size=len(idx2class), pretrained_model=pretrained_model, - cached_mode=cached + cached_mode=cached, + device=device ).to(device) x = [] @@ -494,11 +499,12 @@ def train_discriminator( start = time.time() train_loader = get_cached_data_loader( - train_dataset, batch_size, discriminator, shuffle=True + train_dataset, batch_size, discriminator, + shuffle=True, device=device ) test_loader = get_cached_data_loader( - test_dataset, batch_size, discriminator + test_dataset, batch_size, discriminator, device=device ) end = time.time() @@ -529,18 +535,21 @@ def train_discriminator( data_loader=train_loader, optimizer=optimizer, epoch=epoch, - log_interval=log_interval + log_interval=log_interval, + device=device ) evaluate_performance( data_loader=test_loader, - discriminator=discriminator + discriminator=discriminator, + device=device ) end = time.time() print("Epoch took: {:.3f}s".format(end - start)) print("\nExample prediction") - predict(example_sentence, discriminator, idx2class, cached) + predict(example_sentence, discriminator, idx2class, + cached=cached, device=device) if save_model: # torch.save(discriminator.state_dict(), From f10b925015b03612877fc2213e118d9507dd3ff2 Mon Sep 17 00:00:00 2001 From: w4nderlust Date: Fri, 29 Nov 2019 19:59:02 -0800 Subject: [PATCH 143/179] Imrpovements: model_path renamed pretrained_model, tokenizer loaded from pretrained_model, pretrained_model set to discriminator's when discrim is specified, sample = False by default but cli parameter introduced. To obtain identical samples call the cli with --sample --- examples/run_pplm.py | 300 ++++++++++++++++++++++++++----------------- 1 file changed, 185 insertions(+), 115 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 28aa66cc7d..8516454f86 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -43,7 +43,6 @@ PPLM_DISCRIM = 2 PPLM_BOW_DISCRIM = 3 SMALL_CONST = 1e-15 BIG_CONST = 1e10 -TOKENIZER = GPT2Tokenizer.from_pretrained("gpt2-medium") BAG_OF_WORDS_ARCHIVE_MAP = { 'kitchen': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/kitchen.txt", @@ -65,6 +64,7 @@ DISCRIMINATOR_MODELS_PARAMS = { "embed_size": 1024, "class_vocab": {"non_clickbait": 0, "clickbait": 1}, "default_class": 1, + "pretrained_model": "gpt2-medium", }, "sentiment": { "url": "http://s.yosinski.com/SST_classifier_head.pt", @@ -72,6 +72,7 @@ DISCRIMINATOR_MODELS_PARAMS = { "embed_size": 1024, "class_vocab": {"very_positive": 2, "very_negative": 3}, "default_class": 3, + "pretrained_model": "gpt2-medium", }, "toxicity": { "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/toxicity_classifierhead.pt", @@ -79,6 +80,7 @@ DISCRIMINATOR_MODELS_PARAMS = { "embed_size": 1024, "class_vocab": {"non_toxic": 0, "toxic": 1}, "default_class": 0, + "pretrained_model": "gpt2-medium", }, } @@ -345,8 +347,9 @@ def get_classifier( return classifier, label_id -def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str]) -> List[ - List[List[int]]]: +def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str], tokenizer) -> \ + List[ + List[List[int]]]: bow_indices = [] for id_or_path in bag_of_words_ids_or_paths: if id_or_path in BAG_OF_WORDS_ARCHIVE_MAP: @@ -356,12 +359,12 @@ def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str]) -> List[ with open(filepath, "r") as f: words = f.read().strip().split("\n") bow_indices.append( - [TOKENIZER.encode(word.strip(), add_prefix_space=True) for word in + [tokenizer.encode(word.strip(), add_prefix_space=True) for word in words]) return bow_indices -def build_bows_one_hot_vectors(bow_indices, device='cuda'): +def build_bows_one_hot_vectors(bow_indices, tokenizer, device='cuda'): if bow_indices is None: return None @@ -370,7 +373,7 @@ def build_bows_one_hot_vectors(bow_indices, device='cuda'): single_bow = list(filter(lambda x: len(x) <= 1, single_bow)) single_bow = torch.tensor(single_bow).to(device) num_words = single_bow.shape[0] - one_hot_bow = torch.zeros(num_words, TOKENIZER.vocab_size).to(device) + one_hot_bow = torch.zeros(num_words, tokenizer.vocab_size).to(device) one_hot_bow.scatter_(1, single_bow, 1) one_hot_bows_vectors.append(one_hot_bow) return one_hot_bows_vectors @@ -378,10 +381,11 @@ def build_bows_one_hot_vectors(bow_indices, device='cuda'): def full_text_generation( model, + tokenizer, context=None, num_samples=1, device="cuda", - sample=True, + sample=False, discrim=None, class_label=None, bag_of_words=None, @@ -407,7 +411,8 @@ def full_text_generation( bow_indices = [] if bag_of_words: - bow_indices = get_bag_of_words_indices(bag_of_words.split(";")) + bow_indices = get_bag_of_words_indices(bag_of_words.split(";"), + tokenizer) if bag_of_words and classifier: print("Both PPLM-BoW and PPLM-Discrim are on. This is not optimized.") @@ -426,9 +431,11 @@ def full_text_generation( unpert_gen_tok_text, _, _ = generate_text_pplm( model=model, + tokenizer=tokenizer, context=context, device=device, length=length, + sample=sample, perturb=False ) if device == 'cuda': @@ -441,6 +448,7 @@ def full_text_generation( for i in range(num_samples): pert_gen_tok_text, discrim_loss, loss_in_time = generate_text_pplm( model=model, + tokenizer=tokenizer, context=context, device=device, sample=sample, @@ -475,10 +483,11 @@ def full_text_generation( def generate_text_pplm( model, + tokenizer, context=None, past=None, device="cuda", - sample=True, + sample=False, perturb=True, classifier=None, class_label=None, @@ -504,7 +513,8 @@ def generate_text_pplm( ) # collect one hot vectors for bags of words - one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices, device) + one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices, tokenizer, + device) grad_norms = None last = None @@ -612,7 +622,7 @@ def generate_text_pplm( else torch.cat((output_so_far, last), dim=1) ) - print(TOKENIZER.decode(output_so_far.tolist()[0])) + print(tokenizer.decode(output_so_far.tolist()[0])) return output_so_far, unpert_discrim_loss, loss_in_time @@ -631,10 +641,167 @@ def set_generic_model_params(discrim_weights, discrim_meta): DISCRIMINATOR_MODELS_PARAMS['generic'] = meta -def run_model(): +def run_pplm_example( + pretrained_model="gpt2-medium", + cond_text="", + uncond=False, + num_samples=1, + bag_of_words=None, + discrim=None, + discrim_weights=None, + discrim_meta=None, + class_label=-1, + length=100, + stepsize=0.02, + temperature=1.0, + top_k=10, + sample=False, + num_iterations=3, + grad_length=10000, + horizon_length=1, + window_length=0, + decay=False, + gamma=1.5, + gm_scale=0.9, + kl_scale=0.01, + seed=0, + no_cuda=False, + colorama=False +): + # set Random seed + torch.manual_seed(seed) + np.random.seed(seed) + + # set the device + device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu" + + if discrim == 'generic': + set_generic_model_params(discrim_weights, discrim_meta) + + if discrim is not None: + pretrained_model = DISCRIMINATOR_MODELS_PARAMS[discrim][ + "pretrained_model" + ] + print("discrim = {}, setting pretrained_model " + "to discriminator's = {}".format(discrim, pretrained_model)) + + # load pretrained model + model = GPT2LMHeadModel.from_pretrained( + pretrained_model, + output_hidden_states=True + ) + model.to(device) + model.eval() + + # load tokenizer + tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model) + + # Freeze GPT-2 weights + for param in model.parameters(): + param.requires_grad = False + + # figure out conditioning text + if uncond: + tokenized_cond_text = tokenizer.encode( + [tokenizer.bos_token] + ) + else: + raw_text = cond_text + while not raw_text: + print("Did you forget to add `--cond_text`? ") + raw_text = input("Model prompt >>> ") + tokenized_cond_text = tokenizer.encode(tokenizer.bos_token + raw_text) + + print("= Prefix of sentence =") + print(tokenizer.decode(tokenized_cond_text)) + print() + + # generate unperturbed and perturbed texts + + # full_text_generation returns: + # unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time + unpert_gen_tok_text, pert_gen_tok_texts, _, _ = full_text_generation( + model=model, + tokenizer=tokenizer, + context=tokenized_cond_text, + device=device, + num_samples=num_samples, + bag_of_words=bag_of_words, + discrim=discrim, + class_label=class_label, + length=length, + stepsize=stepsize, + temperature=temperature, + top_k=top_k, + sample=sample, + num_iterations=num_iterations, + grad_length=grad_length, + horizon_length=horizon_length, + window_length=window_length, + decay=decay, + gamma=gamma, + gm_scale=gm_scale, + kl_scale=kl_scale, + ) + + # untokenize unperturbed text + unpert_gen_text = tokenizer.decode(unpert_gen_tok_text.tolist()[0]) + + print("=" * 80) + print("= Unperturbed generated text =") + print(unpert_gen_text) + print() + + generated_texts = [] + + bow_word_ids = set() + if bag_of_words and colorama: + bow_indices = get_bag_of_words_indices(bag_of_words.split(";"), + tokenizer) + for single_bow_list in bow_indices: + # filtering all words in the list composed of more than 1 token + filtered = list(filter(lambda x: len(x) <= 1, single_bow_list)) + # w[0] because we are sure w has only 1 item because previous fitler + bow_word_ids.update(w[0] for w in filtered) + + # iterate through the perturbed texts + for i, pert_gen_tok_text in enumerate(pert_gen_tok_texts): + try: + # untokenize unperturbed text + if colorama: + import colorama + + pert_gen_text = '' + for word_id in pert_gen_tok_text.tolist()[0]: + if word_id in bow_word_ids: + pert_gen_text += '{}{}{}'.format( + colorama.Fore.RED, + tokenizer.decode([word_id]), + colorama.Style.RESET_ALL + ) + else: + pert_gen_text += tokenizer.decode([word_id]) + else: + pert_gen_text = tokenizer.decode(pert_gen_tok_text.tolist()[0]) + + print("= Perturbed generated text {} =".format(i + 1)) + print(pert_gen_text) + print() + except: + pass + + # keep the prefix, perturbed seq, original seq for each index + generated_texts.append( + (tokenized_cond_text, pert_gen_tok_text, unpert_gen_tok_text) + ) + + return + + +if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( - "--model_path", + "--pretrained_model", "-M", type=str, default="gpt2-medium", @@ -675,6 +842,10 @@ def run_model(): parser.add_argument("--gm_scale", type=float, default=0.9) parser.add_argument("--kl_scale", type=float, default=0.01) parser.add_argument("--no_cuda", action="store_true", help="no cuda") + parser.add_argument( + "--sample", action="store_true", + help="Generate from end-of-text as prefix" + ) parser.add_argument( "--uncond", action="store_true", help="Generate from end-of-text as prefix" @@ -711,105 +882,4 @@ def run_model(): help="colors keywords") args = parser.parse_args() - - # set Random seed - torch.manual_seed(args.seed) - np.random.seed(args.seed) - - # set the device - device = "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" - - if args.discrim == 'generic': - set_generic_model_params(args.discrim_weights, args.discrim_meta) - - # load pretrained model - model = GPT2LMHeadModel.from_pretrained( - args.model_path, - output_hidden_states=True - ) - model.to(device) - model.eval() - - # Freeze GPT-2 weights - for param in model.parameters(): - param.requires_grad = False - - # figure out conditioning text - if args.uncond: - tokenized_cond_text = TOKENIZER.encode( - [TOKENIZER.bos_token] - ) - else: - raw_text = args.cond_text - while not raw_text: - print("Did you forget to add `--cond_text`? ") - raw_text = input("Model prompt >>> ") - tokenized_cond_text = TOKENIZER.encode(TOKENIZER.bos_token + raw_text) - - print("= Prefix of sentence =") - print(TOKENIZER.decode(tokenized_cond_text)) - print() - - # generate unperturbed and perturbed texts - - # full_text_generation returns: - # unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time - unpert_gen_tok_text, pert_gen_tok_texts, _, _ = full_text_generation( - model=model, context=tokenized_cond_text, device=device, **vars(args) - ) - - # untokenize unperturbed text - unpert_gen_text = TOKENIZER.decode(unpert_gen_tok_text.tolist()[0]) - - print("=" * 80) - print("= Unperturbed generated text =") - print(unpert_gen_text) - print() - - generated_texts = [] - - bow_word_ids = set() - if args.bag_of_words and args.colorama: - bow_indices = get_bag_of_words_indices(args.bag_of_words.split(";")) - for single_bow_list in bow_indices: - # filtering all words in the list composed of more than 1 token - filtered = list(filter(lambda x: len(x) <= 1, single_bow_list)) - # w[0] because we are sure w has only 1 item because previous fitler - bow_word_ids.update(w[0] for w in filtered) - - # iterate through the perturbed texts - for i, pert_gen_tok_text in enumerate(pert_gen_tok_texts): - try: - # untokenize unperturbed text - if args.colorama: - import colorama - - pert_gen_text = '' - for word_id in pert_gen_tok_text.tolist()[0]: - if word_id in bow_word_ids: - pert_gen_text += '{}{}{}'.format( - colorama.Fore.RED, - TOKENIZER.decode([word_id]), - colorama.Style.RESET_ALL - ) - else: - pert_gen_text += TOKENIZER.decode([word_id]) - else: - pert_gen_text = TOKENIZER.decode(pert_gen_tok_text.tolist()[0]) - - print("= Perturbed generated text {} =".format(i + 1)) - print(pert_gen_text) - print() - except: - pass - - # keep the prefix, perturbed seq, original seq for each index - generated_texts.append( - (tokenized_cond_text, pert_gen_tok_text, unpert_gen_tok_text) - ) - - return - - -if __name__ == '__main__': - run_model() + run_pplm_example(**vars(args)) From f42816e7fca8280927790f74c6e280c37d49b280 Mon Sep 17 00:00:00 2001 From: w4nderlust Date: Fri, 29 Nov 2019 20:00:43 -0800 Subject: [PATCH 144/179] Added additional check for url and path in discriminator model params --- examples/run_pplm.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 8516454f86..9ddd42681e 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -317,8 +317,11 @@ def get_classifier( ).to(device) if "url" in params: resolved_archive_file = cached_path(params["url"]) - else: + elif "path" in params: resolved_archive_file = params["path"] + else: + raise ValueError("Either url or path have to be specified " + "in the discriminator model parameters") classifier.load_state_dict( torch.load(resolved_archive_file, map_location=device)) classifier.eval() From 893d0d64fe008a63eca89b8750502fa5cb684439 Mon Sep 17 00:00:00 2001 From: w4nderlust Date: Fri, 29 Nov 2019 20:19:33 -0800 Subject: [PATCH 145/179] Changed order of some parameters to be more consistent. Identical results. --- examples/run_pplm.py | 111 +++++++++++++++++++++---------------------- 1 file changed, 55 insertions(+), 56 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 9ddd42681e..57bed3890f 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -121,17 +121,17 @@ def perturb_past( accumulated_hidden=None, grad_norms=None, stepsize=0.01, + one_hot_bows_vectors=None, classifier=None, class_label=None, - one_hot_bows_vectors=None, loss_type=0, num_iterations=3, - kl_scale=0.01, - window_length=0, horizon_length=1, + window_length=0, decay=False, gamma=1.5, - device='cuda' + kl_scale=0.01, + device='cuda', ): # Generate inital perturbed past grad_accumulator = [ @@ -351,8 +351,7 @@ def get_classifier( def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str], tokenizer) -> \ - List[ - List[List[int]]]: + List[List[List[int]]]: bow_indices = [] for id_or_path in bag_of_words_ids_or_paths: if id_or_path in BAG_OF_WORDS_ARCHIVE_MAP: @@ -388,22 +387,22 @@ def full_text_generation( context=None, num_samples=1, device="cuda", - sample=False, + bag_of_words=None, discrim=None, class_label=None, - bag_of_words=None, length=100, - grad_length=10000, stepsize=0.02, - num_iterations=3, temperature=1.0, - gm_scale=0.9, - kl_scale=0.01, top_k=10, - window_length=0, + sample=False, + num_iterations=3, + grad_length=10000, horizon_length=1, + window_length=0, decay=False, gamma=1.5, + gm_scale=0.9, + kl_scale=0.01, **kwargs ): classifier, class_id = get_classifier( @@ -454,24 +453,24 @@ def full_text_generation( tokenizer=tokenizer, context=context, device=device, - sample=sample, perturb=True, bow_indices=bow_indices, classifier=classifier, class_label=class_id, loss_type=loss_type, length=length, - grad_length=grad_length, stepsize=stepsize, - num_iterations=num_iterations, temperature=temperature, - gm_scale=gm_scale, - kl_scale=kl_scale, top_k=top_k, - window_length=window_length, + sample=sample, + num_iterations=num_iterations, + grad_length=grad_length, horizon_length=horizon_length, + window_length=window_length, decay=decay, gamma=gamma, + gm_scale=gm_scale, + kl_scale=kl_scale, ) pert_gen_tok_texts.append(pert_gen_tok_text) if classifier is not None: @@ -490,24 +489,24 @@ def generate_text_pplm( context=None, past=None, device="cuda", - sample=False, perturb=True, + bow_indices=None, classifier=None, class_label=None, - bow_indices=None, loss_type=0, length=100, - grad_length=10000, stepsize=0.02, - num_iterations=3, temperature=1.0, - gm_scale=0.9, - kl_scale=0.01, top_k=10, - window_length=0, + sample=False, + num_iterations=3, + grad_length=10000, horizon_length=1, + window_length=0, decay=False, gamma=1.5, + gm_scale=0.9, + kl_scale=0.01, ): output_so_far = ( torch.tensor(context, device=device, dtype=torch.long).unsqueeze(0) @@ -561,17 +560,17 @@ def generate_text_pplm( accumulated_hidden=accumulated_hidden, grad_norms=grad_norms, stepsize=current_stepsize, + one_hot_bows_vectors=one_hot_bows_vectors, classifier=classifier, class_label=class_label, - one_hot_bows_vectors=one_hot_bows_vectors, loss_type=loss_type, num_iterations=num_iterations, - kl_scale=kl_scale, - window_length=window_length, horizon_length=horizon_length, + window_length=window_length, decay=decay, gamma=gamma, - device=device + kl_scale=kl_scale, + device=device, ) loss_in_time.append(loss_this_iter) else: @@ -685,7 +684,7 @@ def run_pplm_example( pretrained_model = DISCRIMINATOR_MODELS_PARAMS[discrim][ "pretrained_model" ] - print("discrim = {}, setting pretrained_model " + print("discrim = {}, pretrained_model set " "to discriminator's = {}".format(discrim, pretrained_model)) # load pretrained model @@ -810,6 +809,20 @@ if __name__ == '__main__': default="gpt2-medium", help="pretrained model name or path to local checkpoint", ) + parser.add_argument( + "--cond_text", type=str, default="The lake", + help="Prefix texts to condition on" + ) + parser.add_argument( + "--uncond", action="store_true", + help="Generate from end-of-text as prefix" + ) + parser.add_argument( + "--num_samples", + type=int, + default=1, + help="Number of samples to generate from the modified latents", + ) parser.add_argument( "--bag_of_words", "-B", @@ -837,40 +850,16 @@ if __name__ == '__main__': default=-1, help="Class label used for the discriminator", ) - parser.add_argument("--stepsize", type=float, default=0.02) parser.add_argument("--length", type=int, default=100) - parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--stepsize", type=float, default=0.02) parser.add_argument("--temperature", type=float, default=1.0) parser.add_argument("--top_k", type=int, default=10) - parser.add_argument("--gm_scale", type=float, default=0.9) - parser.add_argument("--kl_scale", type=float, default=0.01) - parser.add_argument("--no_cuda", action="store_true", help="no cuda") parser.add_argument( "--sample", action="store_true", help="Generate from end-of-text as prefix" ) - parser.add_argument( - "--uncond", action="store_true", - help="Generate from end-of-text as prefix" - ) - parser.add_argument( - "--cond_text", type=str, default="The lake", - help="Prefix texts to condition on" - ) parser.add_argument("--num_iterations", type=int, default=3) parser.add_argument("--grad_length", type=int, default=10000) - parser.add_argument( - "--num_samples", - type=int, - default=1, - help="Number of samples to generate from the modified latents", - ) - parser.add_argument( - "--horizon_length", - type=int, - default=1, - help="Length of future to optimize over", - ) parser.add_argument( "--window_length", type=int, @@ -878,9 +867,19 @@ if __name__ == '__main__': help="Length of past which is being optimized; " "0 corresponds to infinite window length", ) + parser.add_argument( + "--horizon_length", + type=int, + default=1, + help="Length of future to optimize over", + ) parser.add_argument("--decay", action="store_true", help="whether to decay or not") parser.add_argument("--gamma", type=float, default=1.5) + parser.add_argument("--gm_scale", type=float, default=0.9) + parser.add_argument("--kl_scale", type=float, default=0.01) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--no_cuda", action="store_true", help="no cuda") parser.add_argument("--colorama", action="store_true", help="colors keywords") From a59fdd162703009e5774683ec65887a2ce419c1f Mon Sep 17 00:00:00 2001 From: Piero Molino Date: Sun, 1 Dec 2019 15:48:33 -0800 Subject: [PATCH 146/179] generate_text_pplm now works with batch_size > 1 --- examples/run_pplm.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/examples/run_pplm.py b/examples/run_pplm.py index 57bed3890f..5e09427879 100644 --- a/examples/run_pplm.py +++ b/examples/run_pplm.py @@ -231,7 +231,8 @@ def perturb_past( prediction = classifier(new_accumulated_hidden / (curr_length + 1 + horizon_length)) - label = torch.tensor([class_label], device=device, + label = torch.tensor(prediction.shape[0] * [class_label], + device=device, dtype=torch.long) discrim_loss = ce_loss(prediction, label) print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy()) @@ -508,11 +509,12 @@ def generate_text_pplm( gm_scale=0.9, kl_scale=0.01, ): - output_so_far = ( - torch.tensor(context, device=device, dtype=torch.long).unsqueeze(0) - if context - else None - ) + output_so_far = None + if context: + context_t = torch.tensor(context, device=device, dtype=torch.long) + while len(context_t.shape) < 2: + context_t = context_t.unsqueeze(0) + output_so_far = context_t # collect one hot vectors for bags of words one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices, tokenizer, From 1efb2ae7fc4b1350b2bd46bd9898b9e5a31f8381 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Mon, 2 Dec 2019 16:01:57 -0500 Subject: [PATCH 147/179] [pplm] move scripts under examples/pplm/ --- examples/{ => pplm}/run_pplm.py | 0 examples/{ => pplm}/run_pplm_discrim_train.py | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename examples/{ => pplm}/run_pplm.py (100%) rename examples/{ => pplm}/run_pplm_discrim_train.py (100%) diff --git a/examples/run_pplm.py b/examples/pplm/run_pplm.py similarity index 100% rename from examples/run_pplm.py rename to examples/pplm/run_pplm.py diff --git a/examples/run_pplm_discrim_train.py b/examples/pplm/run_pplm_discrim_train.py similarity index 100% rename from examples/run_pplm_discrim_train.py rename to examples/pplm/run_pplm_discrim_train.py From 0cb2c9089074cb1bf158da701c9e9c027ed08258 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Mon, 2 Dec 2019 18:17:28 -0500 Subject: [PATCH 148/179] readme Co-Authored-By: Rosanne Liu --- examples/pplm/README.md | 48 ++++++++++++++++++++++++++++++ examples/pplm/imgs/headfigure.png | Bin 0 -> 668261 bytes examples/pplm/imgs/wooly.png | Bin 0 -> 679776 bytes 3 files changed, 48 insertions(+) create mode 100644 examples/pplm/README.md create mode 100644 examples/pplm/imgs/headfigure.png create mode 100644 examples/pplm/imgs/wooly.png diff --git a/examples/pplm/README.md b/examples/pplm/README.md new file mode 100644 index 0000000000..6eb040a442 --- /dev/null +++ b/examples/pplm/README.md @@ -0,0 +1,48 @@ +# PPLM + +This folder contains the original code used to run the Plug and Play Language Model (PPLM). +![header image](./imgs/headfigure.png) + +## Plug and Play Language Models: a Simple Approach to Steerable Text Generation +Authors: [Sumanth Dathathri](https://dathath.github.io/), Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, [Piero Molino](), [Jason Yosinski](http://yosinski.com/), and [Rosanne Liu](http://www.rosanneliu.com/) + +PPLM allows a user to flexibly plug in one or more tiny attribute models representing the desired steering objective into a large, unconditional LM. The method has the key property that it uses the LM _as is_---no training or fine-tuning is required---which enables researchers to leverage best-in-class LMs even if they do not have the extensive hardware required to train them. + +Paper link: + +Blog link: https://eng.uber.com/pplm + + +## Setup +TODO + +## PPLM-BoW + +### Example command for bag-of-words control +``` +python run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 1 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95 +``` + +### Tuning hyperparameters for bag-of-words control +1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model. + +2. If the language being generated is repetitive (For e.g. "science science experiment experiment"), there are several options to consider:
+ a) Reduce the `--stepsize`
+ b) Increase `--kl_scale` (the KL-loss coefficient) or decrease `--gm_scale` (the gm-scaling term)
+ c) Add `--grad-length xx` where xx is an (integer <= length, e.g. `--grad-length 30`).
+ + +## PPLM-Discrim +### Example command for discriminator based sentiment control +``` +python run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 10 --num_samples 1 --stepsize 0.03 --kl_scale 0.01 --gm_scale 0.95 +``` + +### Tuning hyperparameters for discriminator control +1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model. + +2. 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zar)0|-vWsj`k)G{1*fVG1d-`BczpuzqN#j>jo)PYaJlEMCID0{ih$RFW z(HF*thFEA+H6f|*_@EfPsu4d+EV>fKo|QJ!bMqAl;TxXw@ziM2o#3EjnZ<#&_>vIS z?^|t#ls}1b4A3Lp?AfJZK!mrfImn9J{fHDAa-bdpZJpor6)4k&tM<*mQ(fKr- zEe(e)G^9;h(-#y8v@9c@VJQA#JM?aOF<+?_6 zx@X>5(#Mw`Egh@&$@-`1S=p4NXDw?4FZ$D3lHn#+#_V~@t%Lpyi=SXuWET9U@|rUq((~uYk3x z7@@rXe~@`PVdin%*ydcyEi3q;aqxGp!Ta_Gd$01Md&3vZf^2TR#N1*D*tefw8!I=Ia{XubbYzkLV& zeAfGiV%C=Cp(`yd7LgZ`<03bptd;ir|LfvS4?5`YhdF<{VTt+DWk%+QcEUeno4<4X K_V#*D_WuAET58w; literal 0 HcmV?d00001 From 3b48806f753b41b8d0540e001d4217cdc054b602 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Tue, 3 Dec 2019 10:11:47 -0500 Subject: [PATCH 149/179] [pplm] README: add setup + tweaks --- examples/pplm/README.md | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/examples/pplm/README.md b/examples/pplm/README.md index 6eb040a442..103218ae72 100644 --- a/examples/pplm/README.md +++ b/examples/pplm/README.md @@ -4,7 +4,7 @@ This folder contains the original code used to run the Plug and Play Language Mo ![header image](./imgs/headfigure.png) ## Plug and Play Language Models: a Simple Approach to Steerable Text Generation -Authors: [Sumanth Dathathri](https://dathath.github.io/), Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, [Piero Molino](), [Jason Yosinski](http://yosinski.com/), and [Rosanne Liu](http://www.rosanneliu.com/) +Authors: [Sumanth Dathathri](https://dathath.github.io/), Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, [Piero Molino](https://w4nderlu.st/), [Jason Yosinski](http://yosinski.com/), and [Rosanne Liu](http://www.rosanneliu.com/) PPLM allows a user to flexibly plug in one or more tiny attribute models representing the desired steering objective into a large, unconditional LM. The method has the key property that it uses the LM _as is_---no training or fine-tuning is required---which enables researchers to leverage best-in-class LMs even if they do not have the extensive hardware required to train them. @@ -14,16 +14,24 @@ Blog link: https://eng.uber.com/pplm ## Setup -TODO + +```bash +git clone https://github.com/huggingface/transformers && cd transformers +pip install [--editable] . +pip install nltk torchtext # additional requirements. +cd examples/pplm +``` ## PPLM-BoW ### Example command for bag-of-words control -``` + +```bash python run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 1 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95 ``` ### Tuning hyperparameters for bag-of-words control + 1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model. 2. If the language being generated is repetitive (For e.g. "science science experiment experiment"), there are several options to consider:
@@ -33,16 +41,21 @@ python run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 ## PPLM-Discrim + ### Example command for discriminator based sentiment control -``` + +```bash python run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 10 --num_samples 1 --stepsize 0.03 --kl_scale 0.01 --gm_scale 0.95 ``` ### Tuning hyperparameters for discriminator control + 1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model. 2. Use `--class_label 3` for negative, and `--class_label 2` for positive ### Example command for detoxificiation: -python run_pplm.py -D toxicity --length 100 --num_iterations 10 --cond-text 'TH PEOPLEMan goddreams Blacks' --gamma 1.0 --num_samples 10 --stepsize 0.02 +```bash +python run_pplm.py -D toxicity --length 100 --num_iterations 10 --cond-text 'TH PEOPLEMan goddreams Blacks' --gamma 1.0 --num_samples 10 --stepsize 0.02 +``` From 96e83506d1ddee8e19b07118668be73d175decb6 Mon Sep 17 00:00:00 2001 From: Ethan Perez Date: Fri, 29 Nov 2019 18:22:34 -0600 Subject: [PATCH 150/179] Always use SequentialSampler during evaluation When evaluating, shouldn't we always use the SequentialSampler instead of DistributedSampler? Evaluation only runs on 1 GPU no matter what, so if you use the DistributedSampler with N GPUs, I think you'll only evaluate on 1/N of the evaluation set. That's at least what I'm finding when I run an older/modified version of this repo. --- examples/run_squad.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_squad.py b/examples/run_squad.py index 59683c0668..32d807b3ad 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -216,7 +216,7 @@ def evaluate(args, model, tokenizer, prefix=""): args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly - eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset) + eval_sampler = SequentialSampler(dataset) eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu evaluate From f434bfc623de9e535d49f0c095c0b4af3e88e22a Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Tue, 3 Dec 2019 10:53:02 -0500 Subject: [PATCH 151/179] [pplm] Update S3 links Co-Authored-By: Piero Molino --- examples/pplm/run_pplm.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/pplm/run_pplm.py b/examples/pplm/run_pplm.py index 5e09427879..f626a43f4f 100644 --- a/examples/pplm/run_pplm.py +++ b/examples/pplm/run_pplm.py @@ -59,7 +59,7 @@ BAG_OF_WORDS_ARCHIVE_MAP = { DISCRIMINATOR_MODELS_PARAMS = { "clickbait": { - "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/clickbait_classifierhead.pt", + "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/clickbait_classifier_head.pt", "class_size": 2, "embed_size": 1024, "class_vocab": {"non_clickbait": 0, "clickbait": 1}, @@ -67,7 +67,7 @@ DISCRIMINATOR_MODELS_PARAMS = { "pretrained_model": "gpt2-medium", }, "sentiment": { - "url": "http://s.yosinski.com/SST_classifier_head.pt", + "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/SST_classifier_head.pt", "class_size": 5, "embed_size": 1024, "class_vocab": {"very_positive": 2, "very_negative": 3}, @@ -75,7 +75,7 @@ DISCRIMINATOR_MODELS_PARAMS = { "pretrained_model": "gpt2-medium", }, "toxicity": { - "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/toxicity_classifierhead.pt", + "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/toxic_classifier_head.pt", "class_size": 2, "embed_size": 1024, "class_vocab": {"non_toxic": 0, "toxic": 1}, From 48cbf267c988b56c71a2380f748a3e6092ccaed3 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Tue, 3 Dec 2019 11:01:37 -0500 Subject: [PATCH 152/179] Use full dataset for eval (SequentialSampler in Distributed setting) --- examples/run_glue.py | 2 +- examples/run_lm_finetuning.py | 2 +- examples/run_multiple_choice.py | 2 +- examples/run_xnli.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/run_glue.py b/examples/run_glue.py index 601e9a34c2..369a7110ab 100644 --- a/examples/run_glue.py +++ b/examples/run_glue.py @@ -231,7 +231,7 @@ def evaluate(args, model, tokenizer, prefix=""): args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly - eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) + eval_sampler = SequentialSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu eval diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py index 4acea00c55..0bb7460353 100644 --- a/examples/run_lm_finetuning.py +++ b/examples/run_lm_finetuning.py @@ -300,7 +300,7 @@ def evaluate(args, model, tokenizer, prefix=""): args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly - eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) + eval_sampler = SequentialSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu evaluate diff --git a/examples/run_multiple_choice.py b/examples/run_multiple_choice.py index 30c3332929..9d1ca7f300 100644 --- a/examples/run_multiple_choice.py +++ b/examples/run_multiple_choice.py @@ -226,7 +226,7 @@ def evaluate(args, model, tokenizer, prefix="", test=False): args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly - eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) + eval_sampler = SequentialSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu evaluate diff --git a/examples/run_xnli.py b/examples/run_xnli.py index a3bc0d4604..42d134a43a 100644 --- a/examples/run_xnli.py +++ b/examples/run_xnli.py @@ -206,7 +206,7 @@ def evaluate(args, model, tokenizer, prefix=""): args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly - eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) + eval_sampler = SequentialSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu eval From 8101924a6812ffb09c54c2af85d2182f9a81db20 Mon Sep 17 00:00:00 2001 From: LysandreJik Date: Tue, 3 Dec 2019 11:20:26 -0500 Subject: [PATCH 153/179] Patch: v2.2.1 --- README.md | 2 +- docs/source/conf.py | 2 +- setup.py | 2 +- transformers/__init__.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index dd20b80590..3173b6cf10 100644 --- a/README.md +++ b/README.md @@ -58,7 +58,7 @@ Choose the right framework for every part of a model's lifetime | [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation | | [Migrating from pytorch-transformers to transformers](#Migrating-from-pytorch-transformers-to-transformers) | Migrating your code from pytorch-transformers to transformers | | [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-transformers) | Migrating your code from pytorch-pretrained-bert to transformers | -| [Documentation][(v2.2.0)](https://huggingface.co/transformers/v2.2.0) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) [(master)](https://huggingface.co/transformers) | Full API documentation and more | +| [Documentation][(v2.2.0/v2.2.1)](https://huggingface.co/transformers/v2.2.0) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) [(master)](https://huggingface.co/transformers) | Full API documentation and more | ## Installation diff --git a/docs/source/conf.py b/docs/source/conf.py index f762a89cd2..2f8505ab3a 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -26,7 +26,7 @@ author = u'huggingface' # The short X.Y version version = u'' # The full version, including alpha/beta/rc tags -release = u'2.2.0' +release = u'2.2.1' # -- General configuration --------------------------------------------------- diff --git a/setup.py b/setup.py index d8dcf7b898..c07920520d 100644 --- a/setup.py +++ b/setup.py @@ -38,7 +38,7 @@ from setuptools import find_packages, setup setup( name="transformers", - version="2.2.0", + version="2.2.1", author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors", author_email="thomas@huggingface.co", description="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch", diff --git a/transformers/__init__.py b/transformers/__init__.py index de25c24b9e..970bdf0cf1 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -1,4 +1,4 @@ -__version__ = "2.2.0" +__version__ = "2.2.1" # Work around to update TensorFlow's absl.logging threshold which alters the # default Python logging output behavior when present. From 7edb51f3a516ca533797fb2bb2f2b7ce86e0df70 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Tue, 3 Dec 2019 22:07:25 +0000 Subject: [PATCH 154/179] [pplm] split classif head into its own file --- examples/pplm/pplm_classification_head.py | 18 ++++++++++++++++++ examples/pplm/run_pplm.py | 2 +- examples/pplm/run_pplm_discrim_train.py | 17 +---------------- 3 files changed, 20 insertions(+), 17 deletions(-) create mode 100644 examples/pplm/pplm_classification_head.py diff --git a/examples/pplm/pplm_classification_head.py b/examples/pplm/pplm_classification_head.py new file mode 100644 index 0000000000..9aae0f17e9 --- /dev/null +++ b/examples/pplm/pplm_classification_head.py @@ -0,0 +1,18 @@ +import torch + +class ClassificationHead(torch.nn.Module): + """Classification Head for transformer encoders""" + + def __init__(self, class_size, embed_size): + super(ClassificationHead, self).__init__() + self.class_size = class_size + self.embed_size = embed_size + # self.mlp1 = torch.nn.Linear(embed_size, embed_size) + # self.mlp2 = (torch.nn.Linear(embed_size, class_size)) + self.mlp = torch.nn.Linear(embed_size, class_size) + + def forward(self, hidden_state): + # hidden_state = F.relu(self.mlp1(hidden_state)) + # hidden_state = self.mlp2(hidden_state) + logits = self.mlp(hidden_state) + return logits diff --git a/examples/pplm/run_pplm.py b/examples/pplm/run_pplm.py index f626a43f4f..dda5d85ae7 100644 --- a/examples/pplm/run_pplm.py +++ b/examples/pplm/run_pplm.py @@ -33,10 +33,10 @@ import torch.nn.functional as F from torch.autograd import Variable from tqdm import trange -from examples.run_pplm_discrim_train import ClassificationHead from transformers import GPT2Tokenizer from transformers.file_utils import cached_path from transformers.modeling_gpt2 import GPT2LMHeadModel +from pplm_classification_head import ClassificationHead PPLM_BOW = 1 PPLM_DISCRIM = 2 diff --git a/examples/pplm/run_pplm_discrim_train.py b/examples/pplm/run_pplm_discrim_train.py index db081e1a17..9d36b79bc4 100644 --- a/examples/pplm/run_pplm_discrim_train.py +++ b/examples/pplm/run_pplm_discrim_train.py @@ -21,6 +21,7 @@ from torchtext import datasets from tqdm import tqdm, trange from transformers import GPT2Tokenizer, GPT2LMHeadModel +from pplm_classification_head import ClassificationHead torch.manual_seed(0) np.random.seed(0) @@ -29,22 +30,6 @@ example_sentence = "This is incredible! I love it, this is the best chicken I ha max_length_seq = 100 -class ClassificationHead(torch.nn.Module): - """Classification Head for transformer encoders""" - - def __init__(self, class_size, embed_size): - super(ClassificationHead, self).__init__() - self.class_size = class_size - self.embed_size = embed_size - # self.mlp1 = torch.nn.Linear(embed_size, embed_size) - # self.mlp2 = (torch.nn.Linear(embed_size, class_size)) - self.mlp = torch.nn.Linear(embed_size, class_size) - - def forward(self, hidden_state): - # hidden_state = F.relu(self.mlp1(hidden_state)) - # hidden_state = self.mlp2(hidden_state) - logits = self.mlp(hidden_state) - return logits class Discriminator(torch.nn.Module): From e4fbf3e2cc26b1476b3333ec0b1ffef949277262 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Wed, 4 Dec 2019 00:52:23 -0500 Subject: [PATCH 155/179] CLI for authenticated file sharing --- setup.py | 10 ++ transformers-cli | 23 +++++ transformers/commands/__init__.py | 12 +++ transformers/commands/user.py | 122 ++++++++++++++++++++++++ transformers/hf_api.py | 152 ++++++++++++++++++++++++++++++ transformers/tests/hf_api_test.py | 94 ++++++++++++++++++ 6 files changed, 413 insertions(+) create mode 100644 transformers-cli create mode 100644 transformers/commands/__init__.py create mode 100644 transformers/commands/user.py create mode 100644 transformers/hf_api.py create mode 100644 transformers/tests/hf_api_test.py diff --git a/setup.py b/setup.py index c07920520d..25f503f8d0 100644 --- a/setup.py +++ b/setup.py @@ -36,6 +36,12 @@ To create the package for pypi. from io import open from setuptools import find_packages, setup + +extras = { + 'serving': ['uvicorn', 'fastapi'] +} +extras['all'] = [package for package in extras.values()] + setup( name="transformers", version="2.2.1", @@ -61,6 +67,10 @@ setup( "transformers=transformers.__main__:main", ] }, + extras_require=extras, + scripts=[ + 'transformers-cli' + ], # python_requires='>=3.5.0', tests_require=['pytest'], classifiers=[ diff --git a/transformers-cli b/transformers-cli new file mode 100644 index 0000000000..ef00d15aa3 --- /dev/null +++ b/transformers-cli @@ -0,0 +1,23 @@ +#!/usr/bin/env python +from argparse import ArgumentParser + +from transformers.commands.user import UserCommands + + +if __name__ == '__main__': + parser = ArgumentParser(description='Transformers CLI tool', usage='transformers-cli []') + commands_parser = parser.add_subparsers(help='transformers-cli command helpers') + + # Register commands + UserCommands.register_subcommand(commands_parser) + + # Let's go + args = parser.parse_args() + + if not hasattr(args, 'func'): + parser.print_help() + exit(1) + + # Run + service = args.func(args) + service.run() diff --git a/transformers/commands/__init__.py b/transformers/commands/__init__.py new file mode 100644 index 0000000000..bbdd5655fc --- /dev/null +++ b/transformers/commands/__init__.py @@ -0,0 +1,12 @@ +from abc import ABC, abstractmethod +from argparse import ArgumentParser + +class BaseTransformersCLICommand(ABC): + @staticmethod + @abstractmethod + def register_subcommand(parser: ArgumentParser): + raise NotImplementedError() + + @abstractmethod + def run(self): + raise NotImplementedError() diff --git a/transformers/commands/user.py b/transformers/commands/user.py new file mode 100644 index 0000000000..4b826f4dc6 --- /dev/null +++ b/transformers/commands/user.py @@ -0,0 +1,122 @@ +from argparse import ArgumentParser +from getpass import getpass +import os + +from transformers.commands import BaseTransformersCLICommand +from transformers.hf_api import HfApi, HfFolder, HTTPError + + +class UserCommands(BaseTransformersCLICommand): + @staticmethod + def register_subcommand(parser: ArgumentParser): + login_parser = parser.add_parser('login') + login_parser.set_defaults(func=lambda args: LoginCommand(args)) + whoami_parser = parser.add_parser('whoami') + whoami_parser.set_defaults(func=lambda args: WhoamiCommand(args)) + logout_parser = parser.add_parser('logout') + logout_parser.set_defaults(func=lambda args: LogoutCommand(args)) + list_parser = parser.add_parser('ls') + list_parser.set_defaults(func=lambda args: ListObjsCommand(args)) + # upload + upload_parser = parser.add_parser('upload') + upload_parser.add_argument('file', type=str, help='Local filepath of the file to upload.') + upload_parser.add_argument('--filename', type=str, default=None, help='Optional: override object filename on S3.') + upload_parser.set_defaults(func=lambda args: UploadCommand(args)) + + + +class BaseUserCommand: + def __init__(self, args): + self.args = args + self._api = HfApi() + + +class LoginCommand(BaseUserCommand): + def run(self): + print(""" + _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_| + _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _| + _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_| + _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _| + _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_| + + """) + username = input("Username: ") + password = getpass() + try: + token = self._api.login(username, password) + except HTTPError as e: + # probably invalid credentials, display error message. + print(e) + exit(1) + HfFolder.save_token(token) + print("Login successful") + print("Your token:", token, "\n") + print("Your token has been saved to", HfFolder.path_token) + + +class WhoamiCommand(BaseUserCommand): + def run(self): + token = HfFolder.get_token() + if token is None: + print("Not logged in") + exit() + try: + user = self._api.whoami(token) + print(user) + except HTTPError as e: + print(e) + + +class LogoutCommand(BaseUserCommand): + def run(self): + token = HfFolder.get_token() + if token is None: + print("Not logged in") + exit() + HfFolder.delete_token() + self._api.logout(token) + print("Successfully logged out.") + + +class ListObjsCommand(BaseUserCommand): + def run(self): + token = HfFolder.get_token() + if token is None: + print("Not logged in") + exit(1) + try: + objs = self._api.list_objs(token) + except HTTPError as e: + print(e) + exit(1) + if len(objs) == 0: + print("No shared file yet") + for obj in objs: + print( + obj.filename, + obj.LastModified, + obj.ETag, + obj.Size + ) + + +class UploadCommand(BaseUserCommand): + def run(self): + token = HfFolder.get_token() + if token is None: + print("Not logged in") + exit(1) + filepath = os.path.join(os.getcwd(), self.args.file) + filename = self.args.filename if self.args.filename is not None else os.path.basename(filepath) + print("About to upload file {} to S3 under filename {}".format(filepath, filename)) + choice = input("Proceed? [Y/n] ").lower() + if not(choice == "" or choice == "y" or choice == "yes"): + print("Abort") + exit() + print("Uploading...") + access_url = self._api.presign_and_upload( + token=token, filename=filename, filepath=filepath + ) + print("Your file now lives at:") + print(access_url) diff --git a/transformers/hf_api.py b/transformers/hf_api.py new file mode 100644 index 0000000000..238762ebf8 --- /dev/null +++ b/transformers/hf_api.py @@ -0,0 +1,152 @@ +# coding=utf-8 +# Copyright 2019-present, the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import absolute_import, division, print_function + +from typing import List, NamedTuple +import os +from os.path import expanduser + +import requests +from requests.exceptions import HTTPError + +ENDPOINT = "https://huggingface.co" + +class S3Obj: + def __init__(self, filename: str, LastModified: str, ETag: str, Size: int): + self.filename = filename + self.LastModified = LastModified + self.ETag = ETag + self.Size = Size + + +class PresignedUrl(NamedTuple): + write: str + access: str + + +class HfApi: + def __init__(self, endpoint=None): + self.endpoint = endpoint if endpoint is not None else ENDPOINT + + def login(self, username: str, password: str) -> str: + """ + Call HF API to sign in a user and get a token if credentials are valid. + + Outputs: + token if credentials are valid + + Throws: + requests.exceptions.HTTPError if credentials are invalid + """ + path = "{}/api/login".format(self.endpoint) + r = requests.post(path, json={"username": username, "password": password}) + r.raise_for_status() + d = r.json() + return d["token"] + + def whoami(self, token: str) -> str: + """ + Call HF API to know "whoami" + """ + path = "{}/api/whoami".format(self.endpoint) + r = requests.get(path, headers={"authorization": "Bearer {}".format(token)}) + r.raise_for_status() + d = r.json() + return d["user"] + + def logout(self, token: str): + """ + Call HF API to log out. + """ + path = "{}/api/logout".format(self.endpoint) + r = requests.post(path, headers={"authorization": "Bearer {}".format(token)}) + r.raise_for_status() + + def presign(self, token: str, filename: str) -> PresignedUrl: + """ + Call HF API to get a presigned url to upload `filename` to S3. + """ + path = "{}/api/presign".format(self.endpoint) + r = requests.post( + path, + headers={"authorization": "Bearer {}".format(token)}, + json={"filename": filename}, + ) + r.raise_for_status() + d = r.json() + return PresignedUrl(**d) + + def presign_and_upload(self, token: str, filename: str, filepath: str) -> str: + """ + Get a presigned url, then upload file to S3. + + Outputs: + url: Read-only url for the stored file on S3. + """ + urls = self.presign(token, filename=filename) + # streaming upload: + # https://2.python-requests.org/en/master/user/advanced/#streaming-uploads + with open(filepath, "rb") as f: + r = requests.put(urls.write, data=f) + r.raise_for_status() + return urls.access + + def list_objs(self, token: str) -> List[S3Obj]: + """ + Call HF API to list all stored files for user. + """ + path = "{}/api/listObjs".format(self.endpoint) + r = requests.get(path, headers={"authorization": "Bearer {}".format(token)}) + r.raise_for_status() + d = r.json() + return [S3Obj(**x) for x in d] + + + + + +class HfFolder: + path_token = expanduser("~/.huggingface/token") + + @classmethod + def save_token(cls, token: str): + """ + Save token, creating folder as needed. + """ + os.makedirs(os.path.dirname(cls.path_token), exist_ok=True) + with open(cls.path_token, 'w+') as f: + f.write(token) + + @classmethod + def get_token(cls): + """ + Get token or None if not existent. + """ + try: + with open(cls.path_token, 'r') as f: + return f.read() + except FileNotFoundError: + return None + + @classmethod + def delete_token(cls): + """ + Delete token. + Do not fail if token does not exist. + """ + try: + os.remove(cls.path_token) + except: + return diff --git a/transformers/tests/hf_api_test.py b/transformers/tests/hf_api_test.py new file mode 100644 index 0000000000..59822344ba --- /dev/null +++ b/transformers/tests/hf_api_test.py @@ -0,0 +1,94 @@ +# coding=utf-8 +# Copyright 2019-present, the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import absolute_import, division, print_function + +import os +import time +import unittest + +from transformers.hf_api import HfApi, S3Obj, PresignedUrl, HfFolder, HTTPError + +USER = "__DUMMY_TRANSFORMERS_USER__" +PASS = "__DUMMY_TRANSFORMERS_PASS__" +FILE_KEY = "Test-{}.txt".format(int(time.time())) +FILE_PATH = os.path.join( + os.path.dirname(os.path.abspath(__file__)), "fixtures/input.txt" +) + + + +class HfApiCommonTest(unittest.TestCase): + _api = HfApi(endpoint="https://moon-staging.huggingface.co") + + +class HfApiLoginTest(HfApiCommonTest): + def test_login_invalid(self): + with self.assertRaises(HTTPError): + self._api.login(username=USER, password="fake") + + def test_login_valid(self): + token = self._api.login(username=USER, password=PASS) + self.assertIsInstance(token, str) + + +class HfApiEndpointsTest(HfApiCommonTest): + @classmethod + def setUpClass(cls): + """ + Share this valid token in all tests below. + """ + cls._token = cls._api.login(username=USER, password=PASS) + + def test_whoami(self): + user = self._api.whoami(token=self._token) + self.assertEqual(user, USER) + + def test_presign(self): + url = self._api.presign(token=self._token, filename=FILE_KEY) + self.assertIsInstance(url, PresignedUrl) + + def test_presign_and_upload(self): + access_url = self._api.presign_and_upload( + token=self._token, filename=FILE_KEY, filepath=FILE_PATH + ) + self.assertIsInstance(access_url, str) + + def test_list_objs(self): + objs = self._api.list_objs(token=self._token) + o = objs[-1] + self.assertIsInstance(o, S3Obj) + + + +class HfFolderTest(unittest.TestCase): + def test_token_workflow(self): + """ + Test the whole token save/get/delete workflow, + with the desired behavior with respect to non-existent tokens. + """ + token = "token-{}".format(int(time.time())) + HfFolder.save_token(token) + self.assertEqual( + HfFolder.get_token(), + token + ) + HfFolder.delete_token() + HfFolder.delete_token() + # ^^ not an error, we test that the + # second call does not fail. + self.assertEqual( + HfFolder.get_token(), + None + ) From 40255ab00207f343d5e913c616c20f0f79504bfe Mon Sep 17 00:00:00 2001 From: Aymeric Augustin Date: Wed, 4 Dec 2019 08:21:02 +0100 Subject: [PATCH 156/179] Remove dead code in tests. --- transformers/tests/modeling_tf_common_test.py | 169 ------------------ 1 file changed, 169 deletions(-) diff --git a/transformers/tests/modeling_tf_common_test.py b/transformers/tests/modeling_tf_common_test.py index ea8cd1aecd..7445ce826a 100644 --- a/transformers/tests/modeling_tf_common_test.py +++ b/transformers/tests/modeling_tf_common_test.py @@ -233,80 +233,6 @@ class TFCommonTestCases: self.model_tester.seq_length, self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length]) - def test_headmasking(self): - pass - # config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() - - # config.output_attentions = True - # config.output_hidden_states = True - # configs_no_init = _config_zero_init(config) # To be sure we have no Nan - # for model_class in self.all_model_classes: - # model = model_class(config=configs_no_init) - # model.eval() - - # # Prepare head_mask - # # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior) - # head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads) - # head_mask[0, 0] = 0 - # head_mask[-1, :-1] = 0 - # head_mask.requires_grad_(requires_grad=True) - # inputs = inputs_dict.copy() - # inputs['head_mask'] = head_mask - - # outputs = model(**inputs) - - # # Test that we can get a gradient back for importance score computation - # output = sum(t.sum() for t in outputs[0]) - # output = output.sum() - # output.backward() - # multihead_outputs = head_mask.grad - - # attentions = outputs[-1] - # hidden_states = outputs[-2] - - # # Remove Nan - - # self.assertIsNotNone(multihead_outputs) - # self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers) - # self.assertAlmostEqual( - # attentions[0][..., 0, :, :].flatten().sum().item(), 0.0) - # self.assertNotEqual( - # attentions[0][..., -1, :, :].flatten().sum().item(), 0.0) - # self.assertNotEqual( - # attentions[1][..., 0, :, :].flatten().sum().item(), 0.0) - # self.assertAlmostEqual( - # attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0) - # self.assertNotEqual( - # attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0) - - - def test_head_pruning(self): - pass - # if not self.test_pruning: - # return - - # config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() - - # for model_class in self.all_model_classes: - # config.output_attentions = True - # config.output_hidden_states = False - # model = model_class(config=config) - # model.eval() - # heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)), - # -1: [0]} - # model.prune_heads(heads_to_prune) - # outputs = model(**inputs_dict) - - # attentions = outputs[-1] - - # self.assertEqual( - # attentions[0].shape[-3], 1) - # self.assertEqual( - # attentions[1].shape[-3], self.model_tester.num_attention_heads) - # self.assertEqual( - # attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) - - def test_hidden_states_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() @@ -323,43 +249,6 @@ class TFCommonTestCases: list(hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size]) - - def test_resize_tokens_embeddings(self): - pass - # original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() - # if not self.test_resize_embeddings: - # return - - # for model_class in self.all_model_classes: - # config = copy.deepcopy(original_config) - # model = model_class(config) - - # model_vocab_size = config.vocab_size - # # Retrieve the embeddings and clone theme - # model_embed = model.resize_token_embeddings(model_vocab_size) - # cloned_embeddings = model_embed.weight.clone() - - # # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size - # model_embed = model.resize_token_embeddings(model_vocab_size + 10) - # self.assertEqual(model.config.vocab_size, model_vocab_size + 10) - # # Check that it actually resizes the embeddings matrix - # self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) - - # # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size - # model_embed = model.resize_token_embeddings(model_vocab_size - 15) - # self.assertEqual(model.config.vocab_size, model_vocab_size - 15) - # # Check that it actually resizes the embeddings matrix - # self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) - - # # Check that adding and removing tokens has not modified the first part of the embedding matrix. - # models_equal = True - # for p1, p2 in zip(cloned_embeddings, model_embed.weight): - # if p1.data.ne(p2.data).sum() > 0: - # models_equal = False - - # self.assertTrue(models_equal) - - def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() @@ -369,40 +258,6 @@ class TFCommonTestCases: x = model.get_output_embeddings() assert x is None or isinstance(x, tf.keras.layers.Layer) - - def test_tie_model_weights(self): - pass - # config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() - - # def check_same_values(layer_1, layer_2): - # equal = True - # for p1, p2 in zip(layer_1.weight, layer_2.weight): - # if p1.data.ne(p2.data).sum() > 0: - # equal = False - # return equal - - # for model_class in self.all_model_classes: - # if not hasattr(model_class, 'tie_weights'): - # continue - - # config.torchscript = True - # model_not_tied = model_class(config) - # params_not_tied = list(model_not_tied.parameters()) - - # config_tied = copy.deepcopy(config) - # config_tied.torchscript = False - # model_tied = model_class(config_tied) - # params_tied = list(model_tied.parameters()) - - # # Check that the embedding layer and decoding layer are the same in size and in value - # self.assertGreater(len(params_not_tied), len(params_tied)) - - # # Check that after resize they remain tied. - # model_tied.resize_token_embeddings(config.vocab_size + 10) - # params_tied_2 = list(model_tied.parameters()) - # self.assertGreater(len(params_not_tied), len(params_tied)) - # self.assertEqual(len(params_tied_2), len(params_tied)) - def test_determinism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() @@ -461,29 +316,5 @@ def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None): return output -class TFModelUtilsTest(unittest.TestCase): - @pytest.mark.skipif('tensorflow' not in sys.modules, reason="requires TensorFlow") - def test_model_from_pretrained(self): - pass - # logging.basicConfig(level=logging.INFO) - # for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: - # config = BertConfig.from_pretrained(model_name) - # self.assertIsNotNone(config) - # self.assertIsInstance(config, PretrainedConfig) - - # model = BertModel.from_pretrained(model_name) - # model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True) - # self.assertIsNotNone(model) - # self.assertIsInstance(model, PreTrainedModel) - # for value in loading_info.values(): - # self.assertEqual(len(value), 0) - - # config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True) - # model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True) - # self.assertEqual(model.config.output_attentions, True) - # self.assertEqual(model.config.output_hidden_states, True) - # self.assertEqual(model.config, config) - - if __name__ == "__main__": unittest.main() From ecb923da9cea390742a1262327a139852c5493e9 Mon Sep 17 00:00:00 2001 From: Julien Plu Date: Wed, 4 Dec 2019 09:43:15 +0100 Subject: [PATCH 157/179] Create a NER example similar to the Pytorch one. It takes the same options, and can be run the same way. --- examples/run_tf_ner.py | 612 +++++++++++++++++++++++++ transformers/__init__.py | 3 + transformers/modeling_tf_distilbert.py | 47 ++ transformers/optimization_tf.py | 254 ++++++++++ 4 files changed, 916 insertions(+) create mode 100644 examples/run_tf_ner.py create mode 100644 transformers/optimization_tf.py diff --git a/examples/run_tf_ner.py b/examples/run_tf_ner.py new file mode 100644 index 0000000000..ef1fcf6aa4 --- /dev/null +++ b/examples/run_tf_ner.py @@ -0,0 +1,612 @@ +# coding=utf-8 +import datetime +import os +import math +import glob +import re +import tensorflow as tf +import collections +import numpy as np +from seqeval import metrics +import _pickle as pickle +from absl import logging +from transformers import TF2_WEIGHTS_NAME, BertConfig, BertTokenizer, TFBertForTokenClassification +from transformers import RobertaConfig, RobertaTokenizer, TFRobertaForTokenClassification +from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForTokenClassification +from transformers import create_optimizer, GradientAccumulator +from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file +from fastprogress import master_bar, progress_bar +from absl import flags +from absl import app + + +ALL_MODELS = sum( + (tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, RobertaConfig, DistilBertConfig)), + ()) + +MODEL_CLASSES = { + "bert": (BertConfig, TFBertForTokenClassification, BertTokenizer), + "roberta": (RobertaConfig, TFRobertaForTokenClassification, RobertaTokenizer), + "distilbert": (DistilBertConfig, TFDistilBertForTokenClassification, DistilBertTokenizer) +} + + +flags.DEFINE_string( + "data_dir", None, + "The input data dir. Should contain the .conll files (or other data files) " + "for the task.") + +flags.DEFINE_string( + "model_type", None, + "Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys())) + +flags.DEFINE_string( + "model_name_or_path", None, + "Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS)) + +flags.DEFINE_string( + "output_dir", None, + "The output directory where the model checkpoints will be written.") + +flags.DEFINE_string( + "labels", "", + "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.") + +flags.DEFINE_string( + "config_name", "", + "Pretrained config name or path if not the same as model_name") + +flags.DEFINE_string( + "tokenizer_name", "", + "Pretrained tokenizer name or path if not the same as model_name") + +flags.DEFINE_string( + "cache_dir", "", + "Where do you want to store the pre-trained models downloaded from s3") + +flags.DEFINE_integer( + "max_seq_length", 128, + "The maximum total input sentence length after tokenization. " + "Sequences longer than this will be truncated, sequences shorter " + "will be padded.") + +flags.DEFINE_string( + "tpu", None, + "The Cloud TPU to use for training. This should be either the name " + "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " + "url.") + +flags.DEFINE_integer( + "num_tpu_cores", 8, + "Total number of TPU cores to use.") + +flags.DEFINE_boolean( + "do_train", False, + "Whether to run training.") + +flags.DEFINE_boolean( + "do_eval", False, + "Whether to run eval on the dev set.") + +flags.DEFINE_boolean( + "do_predict", False, + "Whether to run predictions on the test set.") + +flags.DEFINE_boolean( + "evaluate_during_training", False, + "Whether to run evaluation during training at each logging step.") + +flags.DEFINE_boolean( + "do_lower_case", False, + "Set this flag if you are using an uncased model.") + +flags.DEFINE_integer( + "per_device_train_batch_size", 8, + "Batch size per GPU/CPU/TPU for training.") + +flags.DEFINE_integer( + "per_device_eval_batch_size", 8, + "Batch size per GPU/CPU/TPU for evaluation.") + +flags.DEFINE_integer( + "gradient_accumulation_steps", 1, + "Number of updates steps to accumulate before performing a backward/update pass.") + +flags.DEFINE_float( + "learning_rate", 5e-5, + "The initial learning rate for Adam.") + +flags.DEFINE_float( + "weight_decay", 0.0, + "Weight decay if we apply some.") + +flags.DEFINE_float( + "adam_epsilon", 1e-8, + "Epsilon for Adam optimizer.") + +flags.DEFINE_float( + "max_grad_norm", 1.0, + "Max gradient norm.") + +flags.DEFINE_integer( + "num_train_epochs", 3, + "Total number of training epochs to perform.") + +flags.DEFINE_integer( + "max_steps", -1, + "If > 0: set total number of training steps to perform. Override num_train_epochs.") + +flags.DEFINE_integer( + "warmup_steps", 0, + "Linear warmup over warmup_steps.") + +flags.DEFINE_integer( + "logging_steps", 50, + "Log every X updates steps.") + +flags.DEFINE_integer( + "save_steps", 50, + "Save checkpoint every X updates steps.") + +flags.DEFINE_boolean( + "eval_all_checkpoints", False, + "Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number") + +flags.DEFINE_boolean( + "no_cuda", False, + "Avoid using CUDA when available") + +flags.DEFINE_boolean( + "overwrite_output_dir", False, + "Overwrite the content of the output directory") + +flags.DEFINE_boolean( + "overwrite_cache", False, + "Overwrite the cached training and evaluation sets") + +flags.DEFINE_integer( + "seed", 42, + "random seed for initialization") + +flags.DEFINE_boolean( + "fp16", False, + "Whether to use 16-bit (mixed) precision instead of 32-bit") + +flags.DEFINE_string( + "gpus", "0", + "Comma separated list of gpus devices. If only one, switch to single " + "gpu strategy, if None takes all the gpus available.") + + +def train(args, strategy, train_dataset, tokenizer, model, num_train_examples, labels, train_batch_size, pad_token_label_id): + if args['max_steps'] > 0: + num_train_steps = args['max_steps'] * args['gradient_accumulation_steps'] + args['num_train_epochs'] = 1 + else: + num_train_steps = math.ceil(num_train_examples / train_batch_size) // args['gradient_accumulation_steps'] * args['num_train_epochs'] + + writer = tf.summary.create_file_writer("/tmp/mylogs") + + with strategy.scope(): + loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE) + optimizer = create_optimizer(args['learning_rate'], num_train_steps, args['warmup_steps']) + + if args['fp16']: + optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(optimizer, 'dynamic') + + loss_metric = tf.keras.metrics.Mean(name='loss', dtype=tf.float32) + gradient_accumulator = GradientAccumulator() + + logging.info("***** Running training *****") + logging.info(" Num examples = %d", num_train_examples) + logging.info(" Num Epochs = %d", args['num_train_epochs']) + logging.info(" Instantaneous batch size per device = %d", args['per_device_train_batch_size']) + logging.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", + train_batch_size * args['gradient_accumulation_steps']) + logging.info(" Gradient Accumulation steps = %d", args['gradient_accumulation_steps']) + logging.info(" Total training steps = %d", num_train_steps) + + model.summary() + + @tf.function + def apply_gradients(): + grads_and_vars = [] + + for gradient, variable in zip(gradient_accumulator.gradients, model.trainable_variables): + if gradient is not None: + scaled_gradient = gradient / (args['n_device'] * args['gradient_accumulation_steps']) + grads_and_vars.append((scaled_gradient, variable)) + else: + grads_and_vars.append((gradient, variable)) + + optimizer.apply_gradients(grads_and_vars, args['max_grad_norm']) + gradient_accumulator.reset() + + @tf.function + def train_step(train_features, train_labels): + def step_fn(train_features, train_labels): + inputs = {'attention_mask': train_features['input_mask'], 'training': True} + + if args['model_type'] != "distilbert": + inputs["token_type_ids"] = train_features['segment_ids'] if args['model_type'] in ["bert", "xlnet"] else None + + with tf.GradientTape() as tape: + logits = model(train_features['input_ids'], **inputs)[0] + logits = tf.reshape(logits, (-1, len(labels) + 1)) + active_loss = tf.reshape(train_features['input_mask'], (-1,)) + active_logits = tf.boolean_mask(logits, active_loss) + train_labels = tf.reshape(train_labels, (-1,)) + active_labels = tf.boolean_mask(train_labels, active_loss) + cross_entropy = loss_fct(active_labels, active_logits) + loss = tf.reduce_sum(cross_entropy) * (1.0 / train_batch_size) + grads = tape.gradient(loss, model.trainable_variables) + + gradient_accumulator(grads) + + return cross_entropy + + per_example_losses = strategy.experimental_run_v2(step_fn, args=(train_features, train_labels)) + mean_loss = strategy.reduce(tf.distribute.ReduceOp.MEAN, per_example_losses, axis=0) + + return mean_loss + + current_time = datetime.datetime.now() + train_iterator = master_bar(range(args['num_train_epochs'])) + global_step = 0 + logging_loss = 0.0 + + for epoch in train_iterator: + epoch_iterator = progress_bar(train_dataset, total=num_train_steps, parent=train_iterator, display=args['n_device'] > 1) + step = 1 + + with strategy.scope(): + for train_features, train_labels in epoch_iterator: + loss = train_step(train_features, train_labels) + + if step % args['gradient_accumulation_steps'] == 0: + strategy.experimental_run_v2(apply_gradients) + + loss_metric(loss) + + global_step += 1 + + if args['logging_steps'] > 0 and global_step % args['logging_steps'] == 0: + # Log metrics + if args['n_device'] == 1 and args['evaluate_during_training']: # Only evaluate when single GPU otherwise metrics may not average well + y_true, y_pred, eval_loss = evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode="dev") + report = metrics.classification_report(y_true, y_pred, digits=4) + + logging.info("Eval at step " + str(global_step) + "\n" + report) + logging.info("eval_loss: " + str(eval_loss)) + + precision = metrics.precision_score(y_true, y_pred) + recall = metrics.recall_score(y_true, y_pred) + f1 = metrics.f1_score(y_true, y_pred) + + with writer.as_default(): + tf.summary.scalar("eval_loss", eval_loss, global_step) + tf.summary.scalar("precision", precision, global_step) + tf.summary.scalar("recall", recall, global_step) + tf.summary.scalar("f1", f1, global_step) + + lr = optimizer.learning_rate + learning_rate = lr(step) + + with writer.as_default(): + tf.summary.scalar("lr", learning_rate, global_step) + tf.summary.scalar("loss", (loss_metric.result() - logging_loss) / args['logging_steps'], global_step) + + logging_loss = loss_metric.result() + + with writer.as_default(): + tf.summary.scalar("loss", loss_metric.result(), step=step) + + if args['save_steps'] > 0 and global_step % args['save_steps'] == 0: + # Save model checkpoint + output_dir = os.path.join(args['output_dir'], "checkpoint-{}".format(global_step)) + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + model.save_pretrained(output_dir) + logging.info("Saving model checkpoint to %s", output_dir) + + train_iterator.child.comment = f'loss : {loss_metric.result()}' + step += 1 + + train_iterator.write(f'loss epoch {epoch + 1}: {loss_metric.result()}') + + loss_metric.reset_states() + + logging.info(" Training took time = {}".format(datetime.datetime.now() - current_time)) + + +def evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode): + eval_batch_size = args['per_device_eval_batch_size'] * args['n_device'] + eval_dataset, size = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, eval_batch_size, mode=mode) + eval_dataset = strategy.experimental_distribute_dataset(eval_dataset) + preds = None + num_eval_steps = math.ceil(size / eval_batch_size) + master = master_bar(range(1)) + eval_iterator = progress_bar(eval_dataset, total=num_eval_steps, parent=master, display=args['n_device'] > 1) + loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE) + loss = 0.0 + + logging.info("***** Running evaluation *****") + logging.info(" Num examples = %d", size) + logging.info(" Batch size = %d", eval_batch_size) + + for eval_features, eval_labels in eval_iterator: + inputs = {'attention_mask': eval_features['input_mask'], 'training': False} + + if args['model_type'] != "distilbert": + inputs["token_type_ids"] = eval_features['segment_ids'] if args['model_type'] in ["bert", "xlnet"] else None + + with strategy.scope(): + logits = model(eval_features['input_ids'], **inputs)[0] + tmp_logits = tf.reshape(logits, (-1, len(labels) + 1)) + active_loss = tf.reshape(eval_features['input_mask'], (-1,)) + active_logits = tf.boolean_mask(tmp_logits, active_loss) + tmp_eval_labels = tf.reshape(eval_labels, (-1,)) + active_labels = tf.boolean_mask(tmp_eval_labels, active_loss) + cross_entropy = loss_fct(active_labels, active_logits) + loss += tf.reduce_sum(cross_entropy) * (1.0 / eval_batch_size) + + if preds is None: + preds = logits.numpy() + label_ids = eval_labels.numpy() + else: + preds = np.append(preds, logits.numpy(), axis=0) + label_ids = np.append(label_ids, eval_labels.numpy(), axis=0) + + preds = np.argmax(preds, axis=2) + y_pred = [[] for _ in range(label_ids.shape[0])] + y_true = [[] for _ in range(label_ids.shape[0])] + loss = loss / num_eval_steps + + for i in range(label_ids.shape[0]): + for j in range(label_ids.shape[1]): + if label_ids[i, j] != pad_token_label_id: + y_pred[i].append(labels[preds[i, j] - 1]) + y_true[i].append(labels[label_ids[i, j] - 1]) + + return y_true, y_pred, loss.numpy() + + +def load_cache(cached_file, max_seq_length): + name_to_features = { + "input_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64), + "input_mask": tf.io.FixedLenFeature([max_seq_length], tf.int64), + "segment_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64), + "label_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64), + } + + def _decode_record(record): + example = tf.io.parse_single_example(record, name_to_features) + features = {} + features['input_ids'] = example['input_ids'] + features['input_mask'] = example['input_mask'] + features['segment_ids'] = example['segment_ids'] + + return features, example['label_ids'] + + d = tf.data.TFRecordDataset(cached_file) + d = d.map(_decode_record, num_parallel_calls=4) + count = d.reduce(0, lambda x, _: x + 1) + + return d, count.numpy() + + +def save_cache(features, cached_features_file): + writer = tf.io.TFRecordWriter(cached_features_file) + + for (ex_index, feature) in enumerate(features): + if ex_index % 5000 == 0: + logging.info("Writing example %d of %d" % (ex_index, len(features))) + + def create_int_feature(values): + f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) + return f + + record_feature = collections.OrderedDict() + record_feature["input_ids"] = create_int_feature(feature.input_ids) + record_feature["input_mask"] = create_int_feature(feature.input_mask) + record_feature["segment_ids"] = create_int_feature(feature.segment_ids) + record_feature["label_ids"] = create_int_feature(feature.label_ids) + + tf_example = tf.train.Example(features=tf.train.Features(feature=record_feature)) + + writer.write(tf_example.SerializeToString()) + + writer.close() + + +def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, batch_size, mode): + drop_remainder = True if args['tpu'] or mode == 'train' else False + + # Load data features from cache or dataset file + cached_features_file = os.path.join(args['data_dir'], "cached_{}_{}_{}.tf_record".format(mode, + list(filter(None, args['model_name_or_path'].split("/"))).pop(), + str(args['max_seq_length']))) + if os.path.exists(cached_features_file) and not args['overwrite_cache']: + logging.info("Loading features from cached file %s", cached_features_file) + dataset, size = load_cache(cached_features_file, args['max_seq_length']) + else: + logging.info("Creating features from dataset file at %s", args['data_dir']) + examples = read_examples_from_file(args['data_dir'], mode) + features = convert_examples_to_features(examples, labels, args['max_seq_length'], tokenizer, + cls_token_at_end=bool(args['model_type'] in ["xlnet"]), + # xlnet has a cls token at the end + cls_token=tokenizer.cls_token, + cls_token_segment_id=2 if args['model_type'] in ["xlnet"] else 0, + sep_token=tokenizer.sep_token, + sep_token_extra=bool(args['model_type'] in ["roberta"]), + # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805 + pad_on_left=bool(args['model_type'] in ["xlnet"]), + # pad on the left for xlnet + pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0], + pad_token_segment_id=4 if args['model_type'] in ["xlnet"] else 0, + pad_token_label_id=pad_token_label_id + ) + logging.info("Saving features into cached file %s", cached_features_file) + save_cache(features, cached_features_file) + dataset, size = load_cache(cached_features_file, args['max_seq_length']) + + if mode == 'train': + dataset = dataset.repeat() + dataset = dataset.shuffle(buffer_size=8192, seed=args['seed']) + + dataset = dataset.batch(batch_size, drop_remainder) + dataset = dataset.prefetch(buffer_size=batch_size) + + return dataset, size + + +def main(_): + logging.set_verbosity(logging.INFO) + args = flags.FLAGS.flag_values_dict() + + if os.path.exists(args['output_dir']) and os.listdir( + args['output_dir']) and args['do_train'] and not args['overwrite_output_dir']: + raise ValueError( + "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( + args['output_dir'])) + + if args['fp16']: + tf.config.optimizer.set_experimental_options({"auto_mixed_precision": True}) + + if args['tpu']: + resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=args['tpu']) + tf.config.experimental_connect_to_cluster(resolver) + tf.tpu.experimental.initialize_tpu_system(resolver) + strategy = tf.distribute.experimental.TPUStrategy(resolver) + args['n_device'] = args['num_tpu_cores'] + elif len(args['gpus'].split(',')) > 1: + args['n_device'] = len([f"/gpu:{gpu}" for gpu in args['gpus'].split(',')]) + strategy = tf.distribute.MirroredStrategy(devices=[f"/gpu:{gpu}" for gpu in args['gpus'].split(',')]) + elif args['no_cuda']: + args['n_device'] = 1 + strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0") + else: + args['n_device'] = len(args['gpus'].split(',')) + strategy = tf.distribute.OneDeviceStrategy(device="/gpu:" + args['gpus'].split(',')[0]) + + logging.warning("n_device: %s, distributed training: %s, 16-bits training: %s", + args['n_device'], bool(args['n_device'] > 1), args['fp16']) + + labels = get_labels(args['labels']) + num_labels = len(labels) + 1 + pad_token_label_id = 0 + config_class, model_class, tokenizer_class = MODEL_CLASSES[args['model_type']] + config = config_class.from_pretrained(args['config_name'] if args['config_name'] else args['model_name_or_path'], + num_labels=num_labels, + cache_dir=args['cache_dir'] if args['cache_dir'] else None) + + logging.info("Training/evaluation parameters %s", args) + + # Training + if args['do_train']: + tokenizer = tokenizer_class.from_pretrained(args['tokenizer_name'] if args['tokenizer_name'] else args['model_name_or_path'], + do_lower_case=args['do_lower_case'], + cache_dir=args['cache_dir'] if args['cache_dir'] else None) + + with strategy.scope(): + model = model_class.from_pretrained(args['model_name_or_path'], + from_pt=bool(".bin" in args['model_name_or_path']), + config=config, + cache_dir=args['cache_dir'] if args['cache_dir'] else None) + model.layers[-1].activation = tf.keras.activations.softmax + + train_batch_size = args['per_device_train_batch_size'] * args['n_device'] + train_dataset, num_train_examples = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, train_batch_size, mode="train") + train_dataset = strategy.experimental_distribute_dataset(train_dataset) + train(args, strategy, train_dataset, tokenizer, model, num_train_examples, labels, train_batch_size, pad_token_label_id) + + if not os.path.exists(args['output_dir']): + os.makedirs(args['output_dir']) + + logging.info("Saving model to %s", args['output_dir']) + + model.save_pretrained(args['output_dir']) + tokenizer.save_pretrained(args['output_dir']) + + # Evaluation + if args['do_eval']: + tokenizer = tokenizer_class.from_pretrained(args['output_dir'], do_lower_case=args['do_lower_case']) + checkpoints = [] + results = [] + + if args['eval_all_checkpoints']: + checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args['output_dir'] + "/**/" + TF2_WEIGHTS_NAME, recursive=True), key=lambda f: int(''.join(filter(str.isdigit, f)) or -1))) + + logging.info("Evaluate the following checkpoints: %s", checkpoints) + + for checkpoint in checkpoints: + global_step = checkpoint.split("-")[-1] if re.match(".*checkpoint-[0-9]", checkpoint) else "final" + + with strategy.scope(): + model = model_class.from_pretrained(checkpoint) + + y_true, y_pred, eval_loss = evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode="dev") + report = metrics.classification_report(y_true, y_pred, digits=4) + + if global_step: + results.append({global_step + "_report": report, global_step + "_loss": eval_loss}) + + output_eval_file = os.path.join(args['output_dir'], "eval_results.txt") + + with tf.io.gfile.GFile(output_eval_file, "w") as writer: + for res in results: + for key, val in res.items(): + if "loss" in key: + logging.info(key + " = " + str(val)) + writer.write(key + " = " + str(val)) + writer.write("\n") + else: + logging.info(key) + logging.info("\n" + report) + writer.write(key + "\n") + writer.write(report) + writer.write("\n") + + if args['do_predict']: + tokenizer = tokenizer_class.from_pretrained(args['output_dir'], do_lower_case=args['do_lower_case']) + model = model_class.from_pretrained(args['output_dir']) + eval_batch_size = args['per_gpu_eval_batch_size'] * args['n_device'] + predict_dataset, _ = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, eval_batch_size, mode="test") + y_true, y_pred, pred_loss = evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode="test") + output_test_results_file = os.path.join(args.output_dir, "test_results.txt") + output_test_predictions_file = os.path.join(args['output_dir'], "test_predictions.txt") + report = metrics.classification_report(y_true, y_pred, digits=4) + + with tf.io.gfile.GFile(output_test_results_file, "w") as writer: + report = metrics.classification_report(y_true, y_pred, digits=4) + + logging.info("\n" + report) + + writer.write(report) + writer.write("\n\nloss = " + str(pred_loss)) + + with tf.io.gfile.GFile(output_test_predictions_file, "w") as writer: + with tf.io.gfile.GFile(os.path.join(args['data_dir'], "test.txt"), "r") as f: + example_id = 0 + + for line in f: + if line.startswith("-DOCSTART-") or line == "" or line == "\n": + writer.write(line) + + if not y_pred[example_id]: + example_id += 1 + elif y_pred[example_id]: + output_line = line.split()[0] + " " + y_pred[example_id].pop(0) + "\n" + writer.write(output_line) + else: + logging.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]) + + +if __name__ == "__main__": + flags.mark_flag_as_required("data_dir") + flags.mark_flag_as_required("output_dir") + flags.mark_flag_as_required("model_name_or_path") + flags.mark_flag_as_required("model_type") + app.run(main) diff --git a/transformers/__init__.py b/transformers/__init__.py index 970bdf0cf1..2f74b7e79c 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -162,6 +162,7 @@ if is_tf_available(): from .modeling_tf_distilbert import (TFDistilBertPreTrainedModel, TFDistilBertMainLayer, TFDistilBertModel, TFDistilBertForMaskedLM, TFDistilBertForSequenceClassification, + TFDistilBertForTokenClassification TFDistilBertForQuestionAnswering, TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP) @@ -172,6 +173,8 @@ if is_tf_available(): from .modeling_tf_albert import (TFAlbertPreTrainedModel, TFAlbertModel, TFAlbertForMaskedLM, TFAlbertForSequenceClassification, TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) + # Optimization + from .optimization_tf import (WarmUp, create_optimizer, AdamWeightDecay, GradientAccumulator) # TF 2.0 <=> PyTorch conversion utilities from .modeling_tf_pytorch_utils import (convert_tf_weight_name_to_pt_weight_name, diff --git a/transformers/modeling_tf_distilbert.py b/transformers/modeling_tf_distilbert.py index b3d4889475..8e1aef7462 100644 --- a/transformers/modeling_tf_distilbert.py +++ b/transformers/modeling_tf_distilbert.py @@ -703,6 +703,53 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel): return outputs # logits, (hidden_states), (attentions) +@add_start_docstrings("""DistilBert Model with a token classification head on top (a linear layer on top of + the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, + DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING) +class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel): + r""" + Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: + **scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.num_labels)`` + Classification scores (before SoftMax). + **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) + list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings) + of shape ``(batch_size, sequence_length, hidden_size)``: + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + **attentions**: (`optional`, returned when ``config.output_attentions=True``) + list of ``Numpy array`` or ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + Examples:: + import tensorflow as tf + from transformers import DistilBertTokenizer, TFDistilBertForTokenClassification + tokenizer = DistilBertTokenizer.from_pretrained('bert-base-uncased') + model = TFDistilBertForTokenClassification.from_pretrained('bert-base-uncased') + input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 + outputs = model(input_ids) + scores = outputs[0] + """ + def __init__(self, config, *inputs, **kwargs): + super(TFDistilBertForTokenClassification, self).__init__(config, *inputs, **kwargs) + self.num_labels = config.num_labels + + self.distilbert = TFDistilBertMainLayer(config, name='distilbert') + self.dropout = tf.keras.layers.Dropout(config.dropout) + self.classifier = tf.keras.layers.Dense(config.num_labels, + kernel_initializer=get_initializer(config.initializer_range), + name='classifier') + + def call(self, inputs, **kwargs): + outputs = self.distilbert(inputs, **kwargs) + + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output, training=kwargs.get('training', False)) + logits = self.classifier(sequence_output) + + outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here + + return outputs # scores, (hidden_states), (attentions) + + @add_start_docstrings("""DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING) diff --git a/transformers/optimization_tf.py b/transformers/optimization_tf.py new file mode 100644 index 0000000000..c5fa248083 --- /dev/null +++ b/transformers/optimization_tf.py @@ -0,0 +1,254 @@ +# Copyright 2019 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Functions and classes related to optimization (weight updates).""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import re + +import tensorflow as tf + + +class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule): + """Applys a warmup schedule on a given learning rate decay schedule.""" + + def __init__( + self, + initial_learning_rate, + decay_schedule_fn, + warmup_steps, + power=1.0, + name=None): + super(WarmUp, self).__init__() + self.initial_learning_rate = initial_learning_rate + self.warmup_steps = warmup_steps + self.power = power + self.decay_schedule_fn = decay_schedule_fn + self.name = name + + def __call__(self, step): + with tf.name_scope(self.name or 'WarmUp') as name: + # Implements polynomial warmup. i.e., if global_step < warmup_steps, the + # learning rate will be `global_step/num_warmup_steps * init_lr`. + global_step_float = tf.cast(step, tf.float32) + warmup_steps_float = tf.cast(self.warmup_steps, tf.float32) + warmup_percent_done = global_step_float / warmup_steps_float + warmup_learning_rate = ( + self.initial_learning_rate * + tf.math.pow(warmup_percent_done, self.power)) + return tf.cond(global_step_float < warmup_steps_float, + lambda: warmup_learning_rate, + lambda: self.decay_schedule_fn(step), + name=name) + + def get_config(self): + return { + 'initial_learning_rate': self.initial_learning_rate, + 'decay_schedule_fn': self.decay_schedule_fn, + 'warmup_steps': self.warmup_steps, + 'power': self.power, + 'name': self.name + } + + +def create_optimizer(init_lr, num_train_steps, num_warmup_steps): + """Creates an optimizer with learning rate schedule.""" + # Implements linear decay of the learning rate. + learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay( + initial_learning_rate=init_lr, + decay_steps=num_train_steps, + end_learning_rate=0.0) + if num_warmup_steps: + learning_rate_fn = WarmUp(initial_learning_rate=init_lr, + decay_schedule_fn=learning_rate_fn, + warmup_steps=num_warmup_steps) + optimizer = AdamWeightDecay( + learning_rate=learning_rate_fn, + weight_decay_rate=0.01, + beta_1=0.9, + beta_2=0.999, + epsilon=1e-6, + exclude_from_weight_decay=['layer_norm', 'bias']) + return optimizer + + +class AdamWeightDecay(tf.keras.optimizers.Adam): + """Adam enables L2 weight decay and clip_by_global_norm on gradients. + + Just adding the square of the weights to the loss function is *not* the + correct way of using L2 regularization/weight decay with Adam, since that will + interact with the m and v parameters in strange ways. + + Instead we want ot decay the weights in a manner that doesn't interact with + the m/v parameters. This is equivalent to adding the square of the weights to + the loss with plain (non-momentum) SGD. + """ + + def __init__(self, + learning_rate=0.001, + beta_1=0.9, + beta_2=0.999, + epsilon=1e-7, + amsgrad=False, + weight_decay_rate=0.0, + include_in_weight_decay=None, + exclude_from_weight_decay=None, + name='AdamWeightDecay', + **kwargs): + super(AdamWeightDecay, self).__init__( + learning_rate, beta_1, beta_2, epsilon, amsgrad, name, **kwargs) + self.weight_decay_rate = weight_decay_rate + self._include_in_weight_decay = include_in_weight_decay + self._exclude_from_weight_decay = exclude_from_weight_decay + + @classmethod + def from_config(cls, config): + """Creates an optimizer from its config with WarmUp custom object.""" + custom_objects = {'WarmUp': WarmUp} + return super(AdamWeightDecay, cls).from_config( + config, custom_objects=custom_objects) + + def _prepare_local(self, var_device, var_dtype, apply_state): + super(AdamWeightDecay, self)._prepare_local(var_device, var_dtype, + apply_state) + apply_state['weight_decay_rate'] = tf.constant( + self.weight_decay_rate, name='adam_weight_decay_rate') + + def _decay_weights_op(self, var, learning_rate, apply_state): + do_decay = self._do_use_weight_decay(var.name) + if do_decay: + return var.assign_sub( + learning_rate * var * + apply_state['weight_decay_rate'], + use_locking=self._use_locking) + return tf.no_op() + + def apply_gradients(self, grads_and_vars, clip_norm, name=None): + grads, tvars = list(zip(*grads_and_vars)) + (grads, _) = tf.clip_by_global_norm(grads, clip_norm=clip_norm) + return super(AdamWeightDecay, self).apply_gradients(zip(grads, tvars)) + + def _get_lr(self, var_device, var_dtype, apply_state): + """Retrieves the learning rate with the given state.""" + if apply_state is None: + return self._decayed_lr_t[var_dtype], {} + + apply_state = apply_state or {} + coefficients = apply_state.get((var_device, var_dtype)) + if coefficients is None: + coefficients = self._fallback_apply_state(var_device, var_dtype) + apply_state[(var_device, var_dtype)] = coefficients + + return coefficients['lr_t'], dict(apply_state=apply_state) + + def _resource_apply_dense(self, grad, var, apply_state=None): + lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state) + decay = self._decay_weights_op(var, lr_t, apply_state) + with tf.control_dependencies([decay]): + return super(AdamWeightDecay, self)._resource_apply_dense( + grad, var, **kwargs) + + def _resource_apply_sparse(self, grad, var, indices, apply_state=None): + lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state) + decay = self._decay_weights_op(var, lr_t, apply_state) + with tf.control_dependencies([decay]): + return super(AdamWeightDecay, self)._resource_apply_sparse( + grad, var, indices, **kwargs) + + def get_config(self): + config = super(AdamWeightDecay, self).get_config() + config.update({ + 'weight_decay_rate': self.weight_decay_rate, + }) + return config + + def _do_use_weight_decay(self, param_name): + """Whether to use L2 weight decay for `param_name`.""" + if self.weight_decay_rate == 0: + return False + + if self._include_in_weight_decay: + for r in self._include_in_weight_decay: + if re.search(r, param_name) is not None: + return True + + if self._exclude_from_weight_decay: + for r in self._exclude_from_weight_decay: + if re.search(r, param_name) is not None: + return False + return True + + +## Inspired from https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/optimizers/utils.py +class GradientAccumulator(object): + """Distribution strategies-aware gradient accumulation utility.""" + + def __init__(self): + """Initializes the accumulator.""" + self._gradients = [] + self._accum_steps = tf.Variable( + initial_value=0, + dtype=tf.int64, + trainable=False, + aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA) + + @property + def step(self): + """Number of accumulated steps.""" + return self._accum_steps.value() + + @property + def gradients(self): + """The accumulated gradients.""" + return list(gradient.value() if gradient is not None else gradient for gradient in self._get_replica_gradients()) + + def __call__(self, gradients): + """Accumulates :obj:`gradients`.""" + if not self._gradients: + self._gradients.extend([tf.Variable(tf.zeros_like(gradient), trainable=False) if gradient is not None else gradient for gradient in gradients]) + + if len(gradients) != len(self._gradients): + raise ValueError("Expected %s gradients, but got %d" % (len(self._gradients), len(gradients))) + + for accum_gradient, gradient in zip(self._get_replica_gradients(), gradients): + if accum_gradient is not None: + accum_gradient.assign_add(gradient) + + self._accum_steps.assign_add(1) + + def reset(self): + """Resets the accumulated gradients.""" + if self._gradients: + self._accum_steps.assign(0) + + for gradient in self._get_replica_gradients(): + if gradient is not None: + gradient.assign(tf.zeros_like(gradient)) + + def _get_replica_gradients(self): + if tf.distribute.has_strategy(): + # In a replica context, we want to accumulate gradients on each replica + # without synchronization, so we directly assign the value of the + # current replica. + replica_context = tf.distribute.get_replica_context() + + if replica_context is None or tf.distribute.get_strategy().num_replicas_in_sync == 1: + return self._gradients + + return (gradient.device_map.select_for_current_replica(gradient.values, replica_context) for gradient in self._gradients) + else: + return self._gradients From 254ebb979c09d2e8f7efeb11d46bd1196f856699 Mon Sep 17 00:00:00 2001 From: Julien Plu Date: Wed, 4 Dec 2019 10:00:25 +0100 Subject: [PATCH 158/179] Bugfix on init file. Missing comma. --- transformers/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/__init__.py b/transformers/__init__.py index 2f74b7e79c..e4f5984c70 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -162,7 +162,7 @@ if is_tf_available(): from .modeling_tf_distilbert import (TFDistilBertPreTrainedModel, TFDistilBertMainLayer, TFDistilBertModel, TFDistilBertForMaskedLM, TFDistilBertForSequenceClassification, - TFDistilBertForTokenClassification + TFDistilBertForTokenClassification, TFDistilBertForQuestionAnswering, TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP) From 5bfcd0485ece086ebcbed2d008813037968a9e58 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Wed, 4 Dec 2019 14:53:11 +0100 Subject: [PATCH 159/179] fix #1991 --- examples/run_lm_finetuning.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py index 0bb7460353..a5eaf524ac 100644 --- a/examples/run_lm_finetuning.py +++ b/examples/run_lm_finetuning.py @@ -217,7 +217,10 @@ def train(args, train_dataset, model, tokenizer): global_step = 0 tr_loss, logging_loss = 0.0, 0.0 - model.resize_token_embeddings(len(tokenizer)) + + model_to_resize = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training + model_to_resize.resize_token_embeddings(len(tokenizer)) + model.zero_grad() train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) set_seed(args) # Added here for reproducibility (even between python 2 and 3) From ff98b041da4b992a87d8b6258b30e47310ec8430 Mon Sep 17 00:00:00 2001 From: Julien Plu Date: Wed, 4 Dec 2019 16:53:06 +0100 Subject: [PATCH 160/179] Fix whitespace issue --- transformers/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/__init__.py b/transformers/__init__.py index e4f5984c70..6d18f11722 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -162,7 +162,7 @@ if is_tf_available(): from .modeling_tf_distilbert import (TFDistilBertPreTrainedModel, TFDistilBertMainLayer, TFDistilBertModel, TFDistilBertForMaskedLM, TFDistilBertForSequenceClassification, - TFDistilBertForTokenClassification, + TFDistilBertForTokenClassification, TFDistilBertForQuestionAnswering, TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP) From 96fa9a8a70a52221446b0b887f99c90c5ce31eeb Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Wed, 4 Dec 2019 17:22:50 -0500 Subject: [PATCH 161/179] Python 2 + Post mime-type to S3 --- transformers/hf_api.py | 74 ++++++++++++++++++++++++------- transformers/tests/hf_api_test.py | 20 ++++++--- 2 files changed, 73 insertions(+), 21 deletions(-) diff --git a/transformers/hf_api.py b/transformers/hf_api.py index 238762ebf8..c21592a838 100644 --- a/transformers/hf_api.py +++ b/transformers/hf_api.py @@ -14,9 +14,9 @@ # limitations under the License. from __future__ import absolute_import, division, print_function -from typing import List, NamedTuple import os from os.path import expanduser +import six import requests from requests.exceptions import HTTPError @@ -24,23 +24,43 @@ from requests.exceptions import HTTPError ENDPOINT = "https://huggingface.co" class S3Obj: - def __init__(self, filename: str, LastModified: str, ETag: str, Size: int): + def __init__( + self, + filename, # type: str + LastModified, # type: str + ETag, # type: str + Size, # type: int + **kwargs + ): self.filename = filename self.LastModified = LastModified self.ETag = ETag self.Size = Size -class PresignedUrl(NamedTuple): - write: str - access: str +class PresignedUrl: + def __init__( + self, + write, # type: str + access, # type: str + type, # type: str + **kwargs + ): + self.write = write + self.access = access + self.type = type # mime-type to send to S3. class HfApi: def __init__(self, endpoint=None): self.endpoint = endpoint if endpoint is not None else ENDPOINT - def login(self, username: str, password: str) -> str: + def login( + self, + username, # type: str + password, # type: str + ): + # type: (...) -> str """ Call HF API to sign in a user and get a token if credentials are valid. @@ -56,7 +76,11 @@ class HfApi: d = r.json() return d["token"] - def whoami(self, token: str) -> str: + def whoami( + self, + token, # type: str + ): + # type: (...) -> str """ Call HF API to know "whoami" """ @@ -66,7 +90,8 @@ class HfApi: d = r.json() return d["user"] - def logout(self, token: str): + def logout(self, token): + # type: (...) -> void """ Call HF API to log out. """ @@ -74,7 +99,8 @@ class HfApi: r = requests.post(path, headers={"authorization": "Bearer {}".format(token)}) r.raise_for_status() - def presign(self, token: str, filename: str) -> PresignedUrl: + def presign(self, token, filename): + # type: (...) -> PresignedUrl """ Call HF API to get a presigned url to upload `filename` to S3. """ @@ -88,7 +114,8 @@ class HfApi: d = r.json() return PresignedUrl(**d) - def presign_and_upload(self, token: str, filename: str, filepath: str) -> str: + def presign_and_upload(self, token, filename, filepath): + # type: (...) -> str """ Get a presigned url, then upload file to S3. @@ -98,12 +125,18 @@ class HfApi: urls = self.presign(token, filename=filename) # streaming upload: # https://2.python-requests.org/en/master/user/advanced/#streaming-uploads + # + # Even though we presign with the correct content-type, + # the client still has to specify it when uploading the file. with open(filepath, "rb") as f: - r = requests.put(urls.write, data=f) + r = requests.put(urls.write, data=f, headers={ + "content-type": urls.type, + }) r.raise_for_status() return urls.access - def list_objs(self, token: str) -> List[S3Obj]: + def list_objs(self, token): + # type: (...) -> List[S3Obj] """ Call HF API to list all stored files for user. """ @@ -121,11 +154,20 @@ class HfFolder: path_token = expanduser("~/.huggingface/token") @classmethod - def save_token(cls, token: str): + def save_token(cls, token): """ Save token, creating folder as needed. """ - os.makedirs(os.path.dirname(cls.path_token), exist_ok=True) + if six.PY3: + os.makedirs(os.path.dirname(cls.path_token), exist_ok=True) + else: + # Python 2 + try: + os.makedirs(os.path.dirname(cls.path_token)) + except OSError as e: + if e.errno != os.errno.EEXIST: + raise e + pass with open(cls.path_token, 'w+') as f: f.write(token) @@ -137,7 +179,9 @@ class HfFolder: try: with open(cls.path_token, 'r') as f: return f.read() - except FileNotFoundError: + except: + # this is too wide. When Py2 is dead use: + # `except FileNotFoundError:` instead return None @classmethod diff --git a/transformers/tests/hf_api_test.py b/transformers/tests/hf_api_test.py index 59822344ba..92d41b6dff 100644 --- a/transformers/tests/hf_api_test.py +++ b/transformers/tests/hf_api_test.py @@ -15,6 +15,7 @@ from __future__ import absolute_import, division, print_function import os +import six import time import unittest @@ -40,7 +41,7 @@ class HfApiLoginTest(HfApiCommonTest): def test_login_valid(self): token = self._api.login(username=USER, password=PASS) - self.assertIsInstance(token, str) + self.assertIsInstance(token, six.string_types) class HfApiEndpointsTest(HfApiCommonTest): @@ -56,19 +57,22 @@ class HfApiEndpointsTest(HfApiCommonTest): self.assertEqual(user, USER) def test_presign(self): - url = self._api.presign(token=self._token, filename=FILE_KEY) - self.assertIsInstance(url, PresignedUrl) + urls = self._api.presign(token=self._token, filename=FILE_KEY) + self.assertIsInstance(urls, PresignedUrl) + self.assertEqual(urls.type, "text/plain") def test_presign_and_upload(self): access_url = self._api.presign_and_upload( token=self._token, filename=FILE_KEY, filepath=FILE_PATH ) - self.assertIsInstance(access_url, str) + self.assertIsInstance(access_url, six.string_types) def test_list_objs(self): objs = self._api.list_objs(token=self._token) - o = objs[-1] - self.assertIsInstance(o, S3Obj) + self.assertIsInstance(objs, list) + if len(objs) > 0: + o = objs[-1] + self.assertIsInstance(o, S3Obj) @@ -92,3 +96,7 @@ class HfFolderTest(unittest.TestCase): HfFolder.get_token(), None ) + + +if __name__ == "__main__": + unittest.main() From 3ba417e1a877d2c5f2170b12a8cc39f16dbf46aa Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Wed, 4 Dec 2019 18:40:52 -0500 Subject: [PATCH 162/179] [cli] ls: Tabular formatting --- transformers/commands/user.py | 61 +++++++++++++++++++++++++++++------ 1 file changed, 52 insertions(+), 9 deletions(-) diff --git a/transformers/commands/user.py b/transformers/commands/user.py index 4b826f4dc6..d79922ed8a 100644 --- a/transformers/commands/user.py +++ b/transformers/commands/user.py @@ -25,6 +25,17 @@ class UserCommands(BaseTransformersCLICommand): +class ANSI: + """ + Helper for en.wikipedia.org/wiki/ANSI_escape_code + """ + _bold = u"\u001b[1m" + _reset = u"\u001b[0m" + @classmethod + def bold(cls, s): + return "{}{}{}".format(cls._bold, s, cls._reset) + + class BaseUserCommand: def __init__(self, args): self.args = args @@ -80,6 +91,28 @@ class LogoutCommand(BaseUserCommand): class ListObjsCommand(BaseUserCommand): + def tabulate(self, rows, headers): + # type: (List[List[Union[str, int]]], List[str]) -> str + """ + Inspired by: + stackoverflow.com/a/8356620/593036 + stackoverflow.com/questions/9535954/printing-lists-as-tabular-data + """ + col_widths = [max(len(str(x)) for x in col) for col in zip(*rows, headers)] + row_format = ("{{:{}}} " * len(headers)).format(*col_widths) + lines = [] + lines.append( + row_format.format(*headers) + ) + lines.append( + row_format.format(*["-" * w for w in col_widths]) + ) + for row in rows: + lines.append( + row_format.format(*row) + ) + return "\n".join(lines) + def run(self): token = HfFolder.get_token() if token is None: @@ -92,13 +125,16 @@ class ListObjsCommand(BaseUserCommand): exit(1) if len(objs) == 0: print("No shared file yet") - for obj in objs: - print( - obj.filename, - obj.LastModified, - obj.ETag, - obj.Size - ) + exit() + rows = [ [ + obj.filename, + obj.LastModified, + obj.ETag, + obj.Size + ] for obj in objs ] + print( + self.tabulate(rows, headers=["Filename", "LastModified", "ETag", "Size"]) + ) class UploadCommand(BaseUserCommand): @@ -109,12 +145,19 @@ class UploadCommand(BaseUserCommand): exit(1) filepath = os.path.join(os.getcwd(), self.args.file) filename = self.args.filename if self.args.filename is not None else os.path.basename(filepath) - print("About to upload file {} to S3 under filename {}".format(filepath, filename)) + print( + "About to upload file {} to S3 under filename {}".format( + ANSI.bold(filepath), ANSI.bold(filename) + ) + ) + choice = input("Proceed? [Y/n] ").lower() if not(choice == "" or choice == "y" or choice == "yes"): print("Abort") exit() - print("Uploading...") + print( + ANSI.bold("Uploading... This might take a while if file is large") + ) access_url = self._api.presign_and_upload( token=token, filename=filename, filepath=filepath ) From fb0d2f1da102d699c6457fd98be35f89852d08b9 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Thu, 5 Dec 2019 03:00:16 -0500 Subject: [PATCH 163/179] preparing release distil-mBERT --- transformers/modeling_tf_distilbert.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/transformers/modeling_tf_distilbert.py b/transformers/modeling_tf_distilbert.py index b3d4889475..79ae49d811 100644 --- a/transformers/modeling_tf_distilbert.py +++ b/transformers/modeling_tf_distilbert.py @@ -37,7 +37,8 @@ logger = logging.getLogger(__name__) TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = { 'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-tf_model.h5", - 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-tf_model.h5" + 'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-tf_model.h5", + 'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-tf_model.h5", } From 8b388827b509e0c117c53803f2ee030ead0e5a81 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 5 Dec 2019 11:18:43 +0100 Subject: [PATCH 164/179] fix #1920 --- transformers/tokenization_ctrl.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/transformers/tokenization_ctrl.py b/transformers/tokenization_ctrl.py index 3d67fa2c5b..9454cbbaf3 100644 --- a/transformers/tokenization_ctrl.py +++ b/transformers/tokenization_ctrl.py @@ -192,9 +192,9 @@ class CTRLTokenizer(PreTrainedTokenizer): """ split_tokens = [] - text = text.split(' ') + words = re.findall(r'\S+\n?', text) - for token in text: + for token in words: split_tokens.extend([t for t in self.bpe(token).split(' ')]) return split_tokens From 75a97af6bc3b842df22e3bc12e530c22c5e15482 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 5 Dec 2019 11:26:55 +0100 Subject: [PATCH 165/179] fix #1450 - add doc --- examples/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/examples/README.md b/examples/README.md index 960b218f11..25bbcf2246 100644 --- a/examples/README.md +++ b/examples/README.md @@ -4,12 +4,14 @@ In this section a few examples are put together. All of these examples work for similar API between the different models. **Important** -To run the latest versions of the examples, you have to install from source. Execute the following steps in a new virtual environment: +To run the latest versions of the examples, you have to install from source and install some specific requirements for the examples. +Execute the following steps in a new virtual environment: ```bash git clone https://github.com/huggingface/transformers cd transformers pip install [--editable] . +pip install -r ./examples/requirements.txt ``` | Section | Description | From 71e4693f087271053d5c188319cfb5217836cca4 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 5 Dec 2019 12:14:24 +0100 Subject: [PATCH 166/179] fix #1968 --- transformers/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/transformers/__init__.py b/transformers/__init__.py index f06ee3f35d..ab5090723d 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -98,7 +98,7 @@ if is_torch_available(): RobertaForSequenceClassification, RobertaForMultipleChoice, RobertaForTokenClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP) - from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel, + from .modeling_distilbert import (DistilBertPreTrainedModel, DistilBertForMaskedLM, DistilBertModel, DistilBertForSequenceClassification, DistilBertForQuestionAnswering, DistilBertForTokenClassification, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP) @@ -108,7 +108,7 @@ if is_torch_available(): CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model - from .modeling_albert import (AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, + from .modeling_albert import (AlbertPreTrainedModel, AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, AlbertForQuestionAnswering, load_tf_weights_in_albert, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) From 9200a759d782a87530765fb32f52b6248c7f4d03 Mon Sep 17 00:00:00 2001 From: Julien Plu Date: Thu, 5 Dec 2019 12:56:43 +0100 Subject: [PATCH 167/179] Add few tests on the TF optimization file with some info in the documentation. Complete the README. --- .../main_classes/optimizer_schedules.rst | 24 +++++ examples/README.md | 77 +++++++++++++++- examples/run_tf_ner.py | 7 +- transformers/tests/optimization_tf_test.py | 89 +++++++++++++++++++ 4 files changed, 191 insertions(+), 6 deletions(-) create mode 100644 transformers/tests/optimization_tf_test.py diff --git a/docs/source/main_classes/optimizer_schedules.rst b/docs/source/main_classes/optimizer_schedules.rst index b30a2e0e2e..22ed1b28fb 100644 --- a/docs/source/main_classes/optimizer_schedules.rst +++ b/docs/source/main_classes/optimizer_schedules.rst @@ -5,6 +5,7 @@ The ``.optimization`` module provides: - an optimizer with weight decay fixed that can be used to fine-tuned models, and - several schedules in the form of schedule objects that inherit from ``_LRSchedule``: +- a gradient accumulation class to accumulate the gradients of multiple batches ``AdamW`` ~~~~~~~~~~~~~~~~ @@ -12,6 +13,15 @@ The ``.optimization`` module provides: .. autoclass:: transformers.AdamW :members: +``AdamWeightDecay`` +~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.AdamWeightDecay + :members: + +.. autofunction:: transformers.create_optimizer + :members: + Schedules ---------------------------------------------------- @@ -49,3 +59,17 @@ Learning Rate Schedules .. image:: /imgs/warmup_linear_schedule.png :target: /imgs/warmup_linear_schedule.png :alt: + +``Warmup`` +~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.Warmup + :members: + +Gradient Strategies +---------------------------------------------------- + +``GradientAccumulator`` +~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: transformers.GradientAccumulator diff --git a/examples/README.md b/examples/README.md index 960b218f11..2dd6653916 100644 --- a/examples/README.md +++ b/examples/README.md @@ -465,7 +465,8 @@ Training with the previously defined hyper-parameters yields the following resul ## Named Entity Recognition -Based on the script [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py). +Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) for Pytorch and +[`run_tf_ner.py`(https://github.com/huggingface/transformers/blob/master/examples/run_tf_ner.py)] for Tensorflow 2. This example fine-tune Bert Multilingual on GermEval 2014 (German NER). Details and results for the fine-tuning provided by @stefan-it. @@ -510,7 +511,7 @@ The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt ``` -### Training +### Prepare the run Additional environment variables must be set: @@ -522,6 +523,8 @@ export SAVE_STEPS=750 export SEED=1 ``` +### Run the Pytorch version + To start training, just run: ```bash @@ -542,7 +545,7 @@ python3 run_ner.py --data_dir ./ \ If your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets. -### Evaluation +#### Evaluation Evaluation on development dataset outputs the following for our example: @@ -564,7 +567,7 @@ On the test dataset the following results could be achieved: 10/04/2019 00:42:42 - INFO - __main__ - recall = 0.8624150210424085 ``` -### Comparing BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased) +#### Comparing BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased) Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased) with the same hyperparameters as specified in the [example documentation](https://huggingface.co/transformers/examples.html#named-entity-recognition) (one run): @@ -574,6 +577,72 @@ Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) a | `roberta-large` | 95.96 | 91.87 | `distilbert-base-uncased` | 94.34 | 90.32 +### Run the Tensorflow 2 version + +To start training, just run: + +```bash +python3 run_tf_ner.py --data_dir ./ \ +--model_type bert \ +--labels ./labels.txt \ +--model_name_or_path $BERT_MODEL \ +--output_dir $OUTPUT_DIR \ +--max_seq_length $MAX_LENGTH \ +--num_train_epochs $NUM_EPOCHS \ +--per_device_train_batch_size $BATCH_SIZE \ +--save_steps $SAVE_STEPS \ +--seed $SEED \ +--do_train \ +--do_eval \ +--do_predict +``` + +Such as the Pytorch version, if your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets. + +#### Evaluation + +Evaluation on development dataset outputs the following for our example: +```bash + precision recall f1-score support + + LOCderiv 0.7619 0.6154 0.6809 52 + PERpart 0.8724 0.8997 0.8858 4057 + OTHpart 0.9360 0.9466 0.9413 711 + ORGpart 0.7015 0.6989 0.7002 269 + LOCpart 0.7668 0.8488 0.8057 496 + LOC 0.8745 0.9191 0.8963 235 + ORGderiv 0.7723 0.8571 0.8125 91 + OTHderiv 0.4800 0.6667 0.5581 18 + OTH 0.5789 0.6875 0.6286 16 + PERderiv 0.5385 0.3889 0.4516 18 + PER 0.5000 0.5000 0.5000 2 + ORG 0.0000 0.0000 0.0000 3 + +micro avg 0.8574 0.8862 0.8715 5968 +macro avg 0.8575 0.8862 0.8713 5968 +``` + +On the test dataset the following results could be achieved: +```bash + precision recall f1-score support + + PERpart 0.8847 0.8944 0.8896 9397 + OTHpart 0.9376 0.9353 0.9365 1639 + ORGpart 0.7307 0.7044 0.7173 697 + LOC 0.9133 0.9394 0.9262 561 + LOCpart 0.8058 0.8157 0.8107 1150 + ORG 0.0000 0.0000 0.0000 8 + OTHderiv 0.5882 0.4762 0.5263 42 + PERderiv 0.6571 0.5227 0.5823 44 + OTH 0.4906 0.6667 0.5652 39 + ORGderiv 0.7016 0.7791 0.7383 172 + LOCderiv 0.8256 0.6514 0.7282 109 + PER 0.0000 0.0000 0.0000 11 + +micro avg 0.8722 0.8774 0.8748 13869 +macro avg 0.8712 0.8774 0.8740 13869 +``` + ## Abstractive summarization Based on the script diff --git a/examples/run_tf_ner.py b/examples/run_tf_ner.py index ef1fcf6aa4..eb284f4c2a 100644 --- a/examples/run_tf_ner.py +++ b/examples/run_tf_ner.py @@ -540,6 +540,9 @@ def main(_): checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args['output_dir'] + "/**/" + TF2_WEIGHTS_NAME, recursive=True), key=lambda f: int(''.join(filter(str.isdigit, f)) or -1))) logging.info("Evaluate the following checkpoints: %s", checkpoints) + + if len(checkpoints) == 0: + checkpoints.append(args['output_dir']) for checkpoint in checkpoints: global_step = checkpoint.split("-")[-1] if re.match(".*checkpoint-[0-9]", checkpoint) else "final" @@ -572,10 +575,10 @@ def main(_): if args['do_predict']: tokenizer = tokenizer_class.from_pretrained(args['output_dir'], do_lower_case=args['do_lower_case']) model = model_class.from_pretrained(args['output_dir']) - eval_batch_size = args['per_gpu_eval_batch_size'] * args['n_device'] + eval_batch_size = args['per_device_eval_batch_size'] * args['n_device'] predict_dataset, _ = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, eval_batch_size, mode="test") y_true, y_pred, pred_loss = evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode="test") - output_test_results_file = os.path.join(args.output_dir, "test_results.txt") + output_test_results_file = os.path.join(args['output_dir'], "test_results.txt") output_test_predictions_file = os.path.join(args['output_dir'], "test_predictions.txt") report = metrics.classification_report(y_true, y_pred, digits=4) diff --git a/transformers/tests/optimization_tf_test.py b/transformers/tests/optimization_tf_test.py new file mode 100644 index 0000000000..ac5109cb56 --- /dev/null +++ b/transformers/tests/optimization_tf_test.py @@ -0,0 +1,89 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import unittest +import pytest + +from transformers import is_tf_available + +if is_tf_available(): + import tensorflow as tf + from tensorflow.python.eager import context + from tensorflow.python.framework import ops + from transformers import (create_optimizer, GradientAccumulator) +else: + pytestmark = pytest.mark.skip("Require TensorFlow") + +class OptimizationFTest(unittest.TestCase): + def assertListAlmostEqual(self, list1, list2, tol): + self.assertEqual(len(list1), len(list2)) + for a, b in zip(list1, list2): + self.assertAlmostEqual(a, b, delta=tol) + + def testGradientAccumulator(self): + accumulator = GradientAccumulator() + accumulator([tf.constant([1.0, 2.0])]) + accumulator([tf.constant([-2.0, 1.0])]) + accumulator([tf.constant([-1.0, 2.0])]) + with self.assertRaises(ValueError): + accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])]) + self.assertEqual(accumulator.step, 3) + self.assertEqual(len(accumulator.gradients), 1) + self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [-2.0, 5.0], tol=1e-2) + accumulator.reset() + self.assertEqual(accumulator.step, 0) + self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [0.0, 0.0], tol=1e-2) + + def testGradientAccumulatorDistributionStrategy(self): + context._context = None + ops.enable_eager_execution_internal() + physical_devices = tf.config.experimental.list_physical_devices("CPU") + tf.config.experimental.set_virtual_device_configuration( + physical_devices[0], + [tf.config.experimental.VirtualDeviceConfiguration(), + tf.config.experimental.VirtualDeviceConfiguration()]) + + devices = tf.config.experimental.list_logical_devices(device_type="CPU") + strategy = tf.distribute.MirroredStrategy(devices=[device.name for device in devices]) + + with strategy.scope(): + accumulator = GradientAccumulator() + variable = tf.Variable([4.0, 3.0]) + optimizer = create_optimizer(5e-5, 10, 5) + gradient_placeholder = tf.Variable([0.0, 0.0], trainable=False) + + def accumulate_on_replica(gradient): + accumulator([gradient]) + + def apply_on_replica(): + optimizer.apply_gradients(list(zip(accumulator.gradients, [variable])), 1.0) + + @tf.function + def accumulate(grad1, grad2): + with strategy.scope(): + gradient_placeholder.values[0].assign(grad1) + gradient_placeholder.values[1].assign(grad2) + strategy.experimental_run_v2(accumulate_on_replica, args=(gradient_placeholder,)) + + @tf.function + def apply_grad(): + with strategy.scope(): + strategy.experimental_run_v2(apply_on_replica) + + accumulate([1.0, 2.0], [-1.0, 1.0]) + accumulate([3.0, -1.0], [-1.0, -1.0]) + accumulate([-2.0, 2.0], [3.0, -2.0]) + self.assertEqual(accumulator.step, 3) + self.assertListAlmostEqual(accumulator._gradients[0].values[0].value().numpy().tolist(), [2.0, 3.0], tol=1e-2) + self.assertListAlmostEqual(accumulator._gradients[0].values[1].value().numpy().tolist(), [1.0, -2.0], tol=1e-2) + apply_grad() + self.assertListAlmostEqual(variable.value().numpy().tolist(), [4.0, 3.0], tol=1e-2) + accumulator.reset() + self.assertEqual(accumulator.step, 0) + self.assertListAlmostEqual(accumulator._gradients[0].values[0].value().numpy().tolist(), [0.0, 0.0], tol=1e-2) + self.assertListAlmostEqual(accumulator._gradients[0].values[1].value().numpy().tolist(), [0.0, 0.0], tol=1e-2) + + +if __name__ == "__main__": + unittest.main() \ No newline at end of file From 6c5297a42384c9234ad5fd2d044a5aa61d82ce97 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 5 Dec 2019 13:27:58 +0100 Subject: [PATCH 168/179] Fixing camembert tokenization --- transformers/tokenization_camembert.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/transformers/tokenization_camembert.py b/transformers/tokenization_camembert.py index bf2a6fe993..b4091558e1 100644 --- a/transformers/tokenization_camembert.py +++ b/transformers/tokenization_camembert.py @@ -51,7 +51,7 @@ class CamembertTokenizer(PreTrainedTokenizer): def __init__(self, vocab_file, bos_token="", eos_token="", sep_token="", cls_token="", unk_token="", pad_token='', mask_token='', - additional_special_tokens=['NOTUSED', 'NOTUSED'], **kwargs): + additional_special_tokens=['NOTUSED', 'NOTUSED'], **kwargs): super(CamembertTokenizer, self).__init__(max_len=512, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, additional_special_tokens=additional_special_tokens, @@ -125,7 +125,7 @@ class CamembertTokenizer(PreTrainedTokenizer): @property def vocab_size(self): - return self.fairseq_offset + len(self.sp_model) + return len(self.fairseq_tokens_to_ids) + len(self.sp_model) def _tokenize(self, text): return self.sp_model.EncodeAsPieces(text) @@ -134,6 +134,9 @@ class CamembertTokenizer(PreTrainedTokenizer): """ Converts a token (str/unicode) in an id using the vocab. """ if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] + elif self.sp_model.PieceToId(token) == 0: + # Convert sentence piece unk token to fairseq unk token index + return self.unk_token_id return self.fairseq_offset + self.sp_model.PieceToId(token) def _convert_id_to_token(self, index): From 3268ebd2290800036fce0b931dc6c9b87b76e098 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 5 Dec 2019 13:35:29 +0100 Subject: [PATCH 169/179] fix xlnet test --- transformers/tests/modeling_xlnet_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/tests/modeling_xlnet_test.py b/transformers/tests/modeling_xlnet_test.py index 3f0ef6793c..38888d4488 100644 --- a/transformers/tests/modeling_xlnet_test.py +++ b/transformers/tests/modeling_xlnet_test.py @@ -167,7 +167,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers) def create_and_check_xlnet_base_model_with_att_output(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, - target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels): + target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): model = XLNetModel(config) model.eval() From 2d5d86e03779b4b316698438caff0f675ee54abd Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 5 Dec 2019 14:06:29 +0100 Subject: [PATCH 170/179] fix #2031 --- transformers/tokenization_albert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformers/tokenization_albert.py b/transformers/tokenization_albert.py index 2f9af0b0bc..40a4b29206 100644 --- a/transformers/tokenization_albert.py +++ b/transformers/tokenization_albert.py @@ -66,7 +66,7 @@ class AlbertTokenizer(PreTrainedTokenizer): def __init__(self, vocab_file, do_lower_case=True, remove_space=True, keep_accents=False, bos_token="[CLS]", eos_token="[SEP]", unk_token="", sep_token="[SEP]", - pad_token="", cls_token="[CLS]", mask_token="[MASK]>", **kwargs): + pad_token="", cls_token="[CLS]", mask_token="[MASK]", **kwargs): super(AlbertTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, From 18fb93530ba0c1f6a45240270b24dc5c5da340ae Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 5 Dec 2019 14:36:34 +0100 Subject: [PATCH 171/179] fixing #2042 - Nicer error message --- transformers/modeling_bert.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/transformers/modeling_bert.py b/transformers/modeling_bert.py index 5f92fb96a3..1ee3e3f097 100644 --- a/transformers/modeling_bert.py +++ b/transformers/modeling_bert.py @@ -667,11 +667,10 @@ class BertModel(BertPreTrainedModel): # ourselves in which case we just need to make it broadcastable to all heads. if attention_mask.dim() == 3: extended_attention_mask = attention_mask[:, None, :, :] - - # Provided a padding mask of dimensions [batch_size, seq_length] - # - if the model is a decoder, apply a causal mask in addition to the padding mask - # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] - if attention_mask.dim() == 2: + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder: batch_size, seq_length = input_shape seq_ids = torch.arange(seq_length, device=device) @@ -679,6 +678,8 @@ class BertModel(BertPreTrainedModel): extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] else: extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError("Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(input_shape, attention_mask.shape)) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for @@ -696,8 +697,11 @@ class BertModel(BertPreTrainedModel): if encoder_attention_mask.dim() == 3: encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] - if encoder_attention_mask.dim() == 2: + elif encoder_attention_mask.dim() == 2: encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] + else: + raise ValueError("Wrong shape for input_ids (shape {}) or encoder_attention_mask (shape {})".format(input_shape, + encoder_attention_mask.shape)) encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 From ee53de7aac8312140e87d452718e15e3d42e27dd Mon Sep 17 00:00:00 2001 From: Rosanne Liu Date: Thu, 5 Dec 2019 06:20:07 -0800 Subject: [PATCH 172/179] Pr for pplm (#2060) * license * changes * ok * Update paper link and commands to run * pointer to uber repo --- examples/pplm/README.md | 23 +++++++----------- examples/pplm/run_pplm.py | 32 +++++++++---------------- examples/pplm/run_pplm_discrim_train.py | 14 ++++++++++- 3 files changed, 32 insertions(+), 37 deletions(-) diff --git a/examples/pplm/README.md b/examples/pplm/README.md index 103218ae72..b12205854a 100644 --- a/examples/pplm/README.md +++ b/examples/pplm/README.md @@ -1,17 +1,15 @@ -# PPLM +# Plug and Play Language Models: a Simple Approach to Controlled Text Generation + +Authors: [Sumanth Dathathri](https://dathath.github.io/), [Andrea Madotto](https://andreamad8.github.io/), Janice Lan, Jane Hung, Eric Frank, [Piero Molino](https://w4nderlu.st/), [Jason Yosinski](http://yosinski.com/), and [Rosanne Liu](http://www.rosanneliu.com/) This folder contains the original code used to run the Plug and Play Language Model (PPLM). -![header image](./imgs/headfigure.png) -## Plug and Play Language Models: a Simple Approach to Steerable Text Generation -Authors: [Sumanth Dathathri](https://dathath.github.io/), Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, [Piero Molino](https://w4nderlu.st/), [Jason Yosinski](http://yosinski.com/), and [Rosanne Liu](http://www.rosanneliu.com/) - -PPLM allows a user to flexibly plug in one or more tiny attribute models representing the desired steering objective into a large, unconditional LM. The method has the key property that it uses the LM _as is_---no training or fine-tuning is required---which enables researchers to leverage best-in-class LMs even if they do not have the extensive hardware required to train them. - -Paper link: +Paper link: https://arxiv.org/abs/1912.02164 Blog link: https://eng.uber.com/pplm +Please check out the repo under uber-research for more information: https://github.com/uber-research/PPLM + ## Setup @@ -27,7 +25,7 @@ cd examples/pplm ### Example command for bag-of-words control ```bash -python run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 1 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95 +python run_pplm.py -B military --cond_text "The potato" --length 50 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.03 --window_length 5 --kl_scale 0.01 --gm_scale 0.99 --colorama --sample ``` ### Tuning hyperparameters for bag-of-words control @@ -45,7 +43,7 @@ python run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 ### Example command for discriminator based sentiment control ```bash -python run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 10 --num_samples 1 --stepsize 0.03 --kl_scale 0.01 --gm_scale 0.95 +python run_pplm.py -D sentiment --class_label 2 --cond_text "My dog died" --length 50 --gamma 1.0 --num_iterations 10 --num_samples 10 --stepsize 0.04 --kl_scale 0.01 --gm_scale 0.95 --sample ``` ### Tuning hyperparameters for discriminator control @@ -54,8 +52,3 @@ python run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 2. Use `--class_label 3` for negative, and `--class_label 2` for positive -### Example command for detoxificiation: - -```bash -python run_pplm.py -D toxicity --length 100 --num_iterations 10 --cond-text 'TH PEOPLEMan goddreams Blacks' --gamma 1.0 --num_samples 10 --stepsize 0.02 -``` diff --git a/examples/pplm/run_pplm.py b/examples/pplm/run_pplm.py index dda5d85ae7..095dc39a74 100644 --- a/examples/pplm/run_pplm.py +++ b/examples/pplm/run_pplm.py @@ -1,18 +1,19 @@ #! /usr/bin/env python3 # coding=utf-8 -# Copyright 2018 The Uber AI Team Authors. + +#Copyright (c) 2019 Uber Technologies, Inc. # -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at # -# http://www.apache.org/licenses/LICENSE-2.0 +#http://www.apache.org/licenses/LICENSE-2.0 # -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. """ Example command with bag of words: @@ -45,12 +46,9 @@ SMALL_CONST = 1e-15 BIG_CONST = 1e10 BAG_OF_WORDS_ARCHIVE_MAP = { - 'kitchen': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/kitchen.txt", 'legal': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/legal.txt", 'military': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/military.txt", - 'monsters': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/monsters.txt", 'politics': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/politics.txt", - 'positive_words': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/positive_words.txt", 'religion': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/religion.txt", 'science': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/science.txt", 'space': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/space.txt", @@ -74,14 +72,6 @@ DISCRIMINATOR_MODELS_PARAMS = { "default_class": 3, "pretrained_model": "gpt2-medium", }, - "toxicity": { - "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/toxic_classifier_head.pt", - "class_size": 2, - "embed_size": 1024, - "class_vocab": {"non_toxic": 0, "toxic": 1}, - "default_class": 0, - "pretrained_model": "gpt2-medium", - }, } diff --git a/examples/pplm/run_pplm_discrim_train.py b/examples/pplm/run_pplm_discrim_train.py index 9d36b79bc4..3055139d8c 100644 --- a/examples/pplm/run_pplm_discrim_train.py +++ b/examples/pplm/run_pplm_discrim_train.py @@ -1,7 +1,19 @@ #! /usr/bin/env python3 # coding=utf-8 -# This code is licensed under a non-commercial license. +#Copyright (c) 2019 Uber Technologies, Inc. +# +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at +# +#http://www.apache.org/licenses/LICENSE-2.0 +# +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. import argparse import csv From 552c44a9b19fa4f5593ac3bc8de0e9f46495ecd0 Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Thu, 5 Dec 2019 10:14:58 -0500 Subject: [PATCH 173/179] release distilm-bert --- README.md | 2 +- docs/source/pretrained_models.rst | 4 ++++ examples/distillation/README.md | 20 ++++++++++++++++---- 3 files changed, 21 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 3173b6cf10..ddeabe08d6 100644 --- a/README.md +++ b/README.md @@ -131,7 +131,7 @@ At some point in the future, you'll be able to seamlessly move from pre-training 5. **[XLNet](https://github.com/zihangdai/xlnet/)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le. 6. **[XLM](https://github.com/facebookresearch/XLM/)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau. 7. **[RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. -8. **[DistilBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation). +8. **[DistilBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT. 9. **[CTRL](https://github.com/salesforce/ctrl/)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher. 10. **[CamemBERT](https://camembert-model.fr)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot. 11. **[ALBERT](https://github.com/google-research/google-research/tree/master/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. diff --git a/docs/source/pretrained_models.rst b/docs/source/pretrained_models.rst index 0cd794a678..090cb75808 100644 --- a/docs/source/pretrained_models.rst +++ b/docs/source/pretrained_models.rst @@ -155,6 +155,10 @@ Here is the full list of the currently provided pretrained models together with | | ``distilbert-base-german-cased`` | | 6-layer, 768-hidden, 12-heads, 66M parameters | | | | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint. | | | | (see `details `__) | +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``distilbert-base-multilingual-cased`` | | 6-layer, 768-hidden, 12-heads, 134M parameters | +| | | | The multilingual DistilBERT model distilled from the Multilingual BERT model `bert-base-multilingual-cased` checkpoint. | +| | | (see `details `__) | +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ | CTRL | ``ctrl`` | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters | | | | | Salesforce's Large-sized CTRL English model | diff --git a/examples/distillation/README.md b/examples/distillation/README.md index c2765c28ff..24a1677db1 100644 --- a/examples/distillation/README.md +++ b/examples/distillation/README.md @@ -2,6 +2,8 @@ This folder contains the original code used to train Distil* as well as examples showcasing how to use DistilBERT, DistilRoBERTa and DistilGPT2. +**December 6th, 2019 - Update** We release **DistilmBERT**: 92% of `bert-base-multilingual-cased` on XNLI. The model supports 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). + **November 19th, 2019 - Update** We release German **DistilBERT**: 98.8% of `bert-base-german-dbmdz-cased` on NER tasks. **October 23rd, 2019 - Update** We release **DistilRoBERTa**: 95% of `RoBERTa-base`'s performance on GLUE, twice as fast as RoBERTa while being 35% smaller. @@ -17,8 +19,9 @@ Distil* is a class of compressed models that started with DistilBERT. DistilBERT We have applied the same method to other Transformer architectures and released the weights: - GPT2: on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test set of 15.0 compared to 18.5 for **DistilGPT2** (after fine-tuning on the train set). -- RoBERTa: **DistilRoBERTa** reaches 95% of `RoBERTa-base` performance on GLUE while being twice faster and 35% smaller. -- and more to come! 🤗🤗🤗 +- RoBERTa: **DistilRoBERTa** reaches 95% of `RoBERTa-base`'s performance on GLUE while being twice faster and 35% smaller. +- German BERT: **German DistilBERT** reaches 99% of `bert-base-german-dbmdz-cased`'s performance on German NER (CoNLL-2003). +- Multilingual BERT: **DistilmBERT** reaches 92% of Multilingual BERT's performance on XNLI while being twice faster and 25% smaller. The model supports 104 languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). For more information on DistilBERT, please refer to our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108). @@ -29,7 +32,7 @@ Here are the results on the dev sets of GLUE: | BERT-base | **77.6** | 48.9 | 84.3 | 88.6 | 89.3 | 89.5 | 71.3 | 91.7 | 91.2 | 43.7 | | DistilBERT | **76.8** | 49.1 | 81.8 | 90.2 | 90.2 | 89.2 | 62.9 | 92.7 | 90.7 | 44.4 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | -| RoBERTa-base (reported) | **83.2**/**86.4**2 | 63.6 | 87.6 | 90.2 | 92.8 | 91.9 | 78.7 | 94.8 | 91.2 | 57.73 | +| RoBERTa-base (reported) | **83.2**/**86.4**2 | 63.6 | 87.6 | 90.2 | 92.8 | 91.9 | 78.7 | 94.8 | 91.2 | 57.73 | | DistilRoBERTa1 | **79.0**/**82.3**2 | 59.4 | 83.9 | 86.6 | 90.8 | 89.4 | 67.9 | 92.5 | 88.3 | 52.1 | 1 We did not use the MNLI checkpoint for fine-tuning but directy perform transfer learning on the pre-trained DistilRoBERTa. @@ -38,6 +41,14 @@ Here are the results on the dev sets of GLUE: 3 We compute this score ourselves for completeness. +Here are the results on the *test* sets for 6 of the languages available in XNLI. The results are computed in the zero shot setting (trained on the English portion and evaluated on the target language portion): + +| Model | English | Spanish | Chinese | German | Arabic | Urdu | +| :---: | :---: | :---: | :---: | :---: | :---: | :---:| +| mBERT base cased (computed) | 82.1 | 74.6 | 69.1 | 72.3 | 66.4 | 58.5 | +| mBERT base uncased (reported)| 81.4 | 74.3 | 63.8 | 70.5 | 62.1 | 58.3 | +| DistilmBERT | 78.2 | 69.1 | 64.0 | 66.3 | 59.1 | 54.7 | + ## Setup This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`. @@ -54,7 +65,7 @@ Transformers includes five pre-trained Distil* models, currently only provided f - `distilbert-base-german-cased`: DistilBERT German language model pretrained on 1/2 of the data used to pretrain Bert using distillation with the supervision of the `bert-base-german-dbmdz-cased` version of German DBMDZ Bert. For NER tasks the model reaches a F1 score of 83.49 on the CoNLL-2003 test set (for comparison, `bert-base-german-dbmdz-cased` reaches a 84.52 F1 score), and a F1 score of 85.23 on the GermEval 2014 test set (`bert-base-german-dbmdz-cased` reaches a 86.89 F1 score). - `distilgpt2`: DistilGPT2 English language model pretrained with the supervision of `gpt2` (the smallest version of GPT2) on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 124M parameters for GPT2). On average, DistilGPT2 is two times faster than GPT2. - `distilroberta-base`: DistilRoBERTa English language model pretrained with the supervision of `roberta-base` solely on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset (it is ~4 times less training data than the teacher RoBERTa). The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base. -- and more to come! 🤗🤗🤗 +- `distilbert-base-multilingual-cased`: DistilmBERT multilingual model pretrained with the supervision of `bert-base-multilingual-cased` on the concatenation of Wikipedia in 104 different languages. The model supports the 104 languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). The model has 6 layers, 768 dimension and 12 heads, totalizing 134M parameters (compared to 177M parameters for mBERT-base). On average DistilmBERT is twice as fast as mBERT-base. Using DistilBERT is very similar to using BERT. DistilBERT share the same tokenizer as BERT's `bert-base-uncased` even though we provide a link to this tokenizer under the `DistilBertTokenizer` name to have a consistent naming between the library models. @@ -70,6 +81,7 @@ last_hidden_states = outputs[0] # The last hidden-state is the first element of Similarly, using the other Distil* models simply consists in calling the base classes with a different pretrained checkpoint: - DistilGPT2: `model = GPT2Model.from_pretrained('distilgpt2')` - DistilRoBERTa: `model = RobertaModel.from_pretrained('distilroberta-base')` +- DistilmBERT: `model = DistilBertModel.from_pretrained('distilbert-base-multilingual-cased')` ## How to train Distil* From 35ff345fc9df9e777b27903f11fa213e4052595b Mon Sep 17 00:00:00 2001 From: VictorSanh Date: Thu, 5 Dec 2019 12:07:04 -0500 Subject: [PATCH 174/179] update requirements --- examples/distillation/distiller.py | 1 - examples/distillation/requirements.txt | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/distillation/distiller.py b/examples/distillation/distiller.py index 0442072e84..1e33190aca 100644 --- a/examples/distillation/distiller.py +++ b/examples/distillation/distiller.py @@ -21,7 +21,6 @@ import psutil import time from tqdm import trange, tqdm import numpy as np -import psutil import torch import torch.nn as nn diff --git a/examples/distillation/requirements.txt b/examples/distillation/requirements.txt index d76273b34a..491924ee2c 100644 --- a/examples/distillation/requirements.txt +++ b/examples/distillation/requirements.txt @@ -3,4 +3,4 @@ tensorboard>=1.14.0 tensorboardX==1.8 psutil==5.6.3 scipy==1.3.1 -transformers==2.0.0 +transformers From d0383e4daf44557e56cd4cbc5dc95b1d35457768 Mon Sep 17 00:00:00 2001 From: patrickvonplaten Date: Fri, 6 Dec 2019 01:24:22 +0100 Subject: [PATCH 175/179] corrected documentation for past tensor shape for ctrl and gpt2 model --- transformers/modeling_ctrl.py | 4 ++-- transformers/modeling_gpt2.py | 6 +++--- transformers/modeling_tf_ctrl.py | 4 ++-- transformers/modeling_tf_gpt2.py | 6 +++--- 4 files changed, 10 insertions(+), 10 deletions(-) diff --git a/transformers/modeling_ctrl.py b/transformers/modeling_ctrl.py index 3a252941ac..97bcb14434 100644 --- a/transformers/modeling_ctrl.py +++ b/transformers/modeling_ctrl.py @@ -252,7 +252,7 @@ class CTRLModel(CTRLPreTrainedModel): **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)`` Sequence of hidden-states at the last layer of the model. **past**: - list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``torch.FloatTensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``: that contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. @@ -438,7 +438,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel): **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). **past**: - list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``torch.FloatTensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``: that contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. diff --git a/transformers/modeling_gpt2.py b/transformers/modeling_gpt2.py index 35bc5c8d6e..96fd1c0607 100644 --- a/transformers/modeling_gpt2.py +++ b/transformers/modeling_gpt2.py @@ -329,7 +329,7 @@ class GPT2Model(GPT2PreTrainedModel): **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)`` Sequence of hidden-states at the last layer of the model. **past**: - list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``torch.FloatTensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``: that contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. @@ -503,7 +503,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel): **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). **past**: - list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``torch.FloatTensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``: that contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. @@ -596,7 +596,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel): **mc_prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)`` Prediction scores of the multiplechoice classification head (scores for each choice before SoftMax). **past**: - list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``torch.FloatTensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``: that contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. diff --git a/transformers/modeling_tf_ctrl.py b/transformers/modeling_tf_ctrl.py index 6d0d6a57ad..29ee5113a4 100644 --- a/transformers/modeling_tf_ctrl.py +++ b/transformers/modeling_tf_ctrl.py @@ -400,7 +400,7 @@ class TFCTRLModel(TFCTRLPreTrainedModel): **last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)`` Sequence of hidden-states at the last layer of the model. **past**: - list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``tf.Tensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``: that contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) @@ -462,7 +462,7 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel): **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). **past**: - list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``tf.Tensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``: that contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) diff --git a/transformers/modeling_tf_gpt2.py b/transformers/modeling_tf_gpt2.py index aebe790114..c738e5e8e3 100644 --- a/transformers/modeling_tf_gpt2.py +++ b/transformers/modeling_tf_gpt2.py @@ -436,7 +436,7 @@ class TFGPT2Model(TFGPT2PreTrainedModel): **last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)`` Sequence of hidden-states at the last layer of the model. **past**: - list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of ``tf.Tensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``: that contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) @@ -476,7 +476,7 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel): **prediction_scores**: `tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). **past**: - list of `tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of `tf.Tensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``: that contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) @@ -535,7 +535,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel): **mc_prediction_scores**: `tf.Tensor`` of shape ``(batch_size, num_choices)`` Prediction scores of the multiplechoice classification head (scores for each choice before SoftMax). **past**: - list of `tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: + list of `tf.Tensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``: that contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) to speed up sequential decoding. **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) From e4679cddced7d746427066a78e8079fb40e51528 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Fri, 6 Dec 2019 11:56:23 -0500 Subject: [PATCH 176/179] [cli] Uploads: add progress bar (#2078) * [cli] Uploads: add progress bar see https://github.com/huggingface/transformers/pull/2044#discussion_r354057827 for context * rename + documentation * Add auto-referential comment --- transformers/hf_api.py | 34 +++++++++++++++++++++++++++++++++- 1 file changed, 33 insertions(+), 1 deletion(-) diff --git a/transformers/hf_api.py b/transformers/hf_api.py index c21592a838..3bbb6c567a 100644 --- a/transformers/hf_api.py +++ b/transformers/hf_api.py @@ -16,10 +16,11 @@ from __future__ import absolute_import, division, print_function import os from os.path import expanduser -import six import requests +import six from requests.exceptions import HTTPError +from tqdm import tqdm ENDPOINT = "https://huggingface.co" @@ -129,10 +130,13 @@ class HfApi: # Even though we presign with the correct content-type, # the client still has to specify it when uploading the file. with open(filepath, "rb") as f: + pf = TqdmProgressFileReader(f) + r = requests.put(urls.write, data=f, headers={ "content-type": urls.type, }) r.raise_for_status() + pf.close() return urls.access def list_objs(self, token): @@ -148,6 +152,34 @@ class HfApi: +class TqdmProgressFileReader: + """ + Wrap an io.BufferedReader `f` (such as the output of `open(…, "rb")`) + and override `f.read()` so as to display a tqdm progress bar. + + see github.com/huggingface/transformers/pull/2078#discussion_r354739608 + for implementation details. + """ + def __init__( + self, + f # type: io.BufferedReader + ): + self.f = f + self.total_size = os.fstat(f.fileno()).st_size # type: int + self.pbar = tqdm(total=self.total_size, leave=False) + if six.PY3: + # does not work unless PY3 + # no big deal as the CLI does not currently support PY2 anyways. + self.read = f.read + f.read = self._read + + def _read(self, n=-1): + self.pbar.update(n) + return self.read(n) + + def close(self): + self.pbar.close() + class HfFolder: From 35401fe50fa3e460b2a4422630b017f106c79e03 Mon Sep 17 00:00:00 2001 From: Aymeric Augustin Date: Fri, 6 Dec 2019 19:57:38 +0100 Subject: [PATCH 177/179] Remove dependency on pytest for running tests (#2055) * Switch to plain unittest for skipping slow tests. Add a RUN_SLOW environment variable for running them. * Switch to plain unittest for PyTorch dependency. * Switch to plain unittest for TensorFlow dependency. * Avoid leaking open files in the test suite. This prevents spurious warnings when running tests. * Fix unicode warning on Python 2 when running tests. The warning was: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal * Support running PyTorch tests on a GPU. Reverts 27e015bd. * Tests no longer require pytest. * Make tests pass on cuda --- README.md | 11 +++- docs/source/installation.md | 11 +++- setup.py | 1 - .../tests/modeling_tf_xxx_test.py | 7 +- .../tests/modeling_xxx_test.py | 12 ++-- transformers/modeling_openai.py | 6 +- transformers/tests/conftest.py | 31 --------- transformers/tests/modeling_albert_test.py | 11 ++-- transformers/tests/modeling_auto_test.py | 14 ++-- transformers/tests/modeling_bert_test.py | 38 +++++------ transformers/tests/modeling_common_test.py | 43 ++++++++++--- transformers/tests/modeling_ctrl_test.py | 9 +-- .../tests/modeling_distilbert_test.py | 12 ++-- .../tests/modeling_encoder_decoder_test.py | 7 +- transformers/tests/modeling_gpt2_test.py | 10 +-- transformers/tests/modeling_openai_test.py | 10 +-- transformers/tests/modeling_roberta_test.py | 22 ++++--- transformers/tests/modeling_tf_albert_test.py | 7 +- transformers/tests/modeling_tf_auto_test.py | 14 ++-- transformers/tests/modeling_tf_bert_test.py | 7 +- transformers/tests/modeling_tf_common_test.py | 8 +-- transformers/tests/modeling_tf_ctrl_test.py | 7 +- .../tests/modeling_tf_distilbert_test.py | 7 +- transformers/tests/modeling_tf_gpt2_test.py | 7 +- .../tests/modeling_tf_openai_gpt_test.py | 7 +- .../tests/modeling_tf_roberta_test.py | 19 +++--- .../tests/modeling_tf_transfo_xl_test.py | 7 +- transformers/tests/modeling_tf_xlm_test.py | 7 +- transformers/tests/modeling_tf_xlnet_test.py | 10 +-- .../tests/modeling_transfo_xl_test.py | 10 +-- transformers/tests/modeling_xlm_test.py | 12 ++-- transformers/tests/modeling_xlnet_test.py | 21 ++++-- transformers/tests/optimization_test.py | 6 +- transformers/tests/tokenization_auto_test.py | 5 +- transformers/tests/tokenization_bert_test.py | 4 +- .../tests/tokenization_distilbert_test.py | 4 +- .../tests/tokenization_roberta_test.py | 4 +- .../tests/tokenization_tests_commons.py | 6 +- .../tests/tokenization_transfo_xl_test.py | 6 +- transformers/tests/tokenization_utils_test.py | 6 +- transformers/tests/tokenization_xlm_test.py | 4 +- transformers/tests/tokenization_xlnet_test.py | 4 +- transformers/tests/utils.py | 64 +++++++++++++++++++ transformers/tokenization_albert.py | 8 +-- transformers/tokenization_ctrl.py | 6 +- transformers/tokenization_gpt2.py | 12 ++-- transformers/tokenization_openai.py | 6 +- transformers/tokenization_utils.py | 13 ++-- transformers/tokenization_xlm.py | 8 ++- transformers/tokenization_xlnet.py | 4 +- 50 files changed, 344 insertions(+), 231 deletions(-) delete mode 100644 transformers/tests/conftest.py create mode 100644 transformers/tests/utils.py diff --git a/README.md b/README.md index ddeabe08d6..64ec631651 100644 --- a/README.md +++ b/README.md @@ -101,17 +101,26 @@ pip install [--editable] . A series of tests are included for the library and the example scripts. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/transformers/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples). -These tests can be run using `pytest` (install pytest if needed with `pip install pytest`). +These tests can be run using `unittest` or `pytest` (install pytest if needed with `pip install pytest`). Depending on which framework is installed (TensorFlow 2.0 and/or PyTorch), the irrelevant tests will be skipped. Ensure that both frameworks are installed if you want to execute all tests. You can run the tests from the root of the cloned repository with the commands: +```bash +python -m unittest discover -s transformers/tests -p "*test.py" -t . +python -m unittest discover -s examples -p "*test.py" -t examples +``` + +or + ```bash python -m pytest -sv ./transformers/tests/ python -m pytest -sv ./examples/ ``` +By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to `yes` to run them. + ### Do you want to run a Transformer model on a mobile device? You should check out our [`swift-coreml-transformers`](https://github.com/huggingface/swift-coreml-transformers) repo. diff --git a/docs/source/installation.md b/docs/source/installation.md index 11beb1ab3a..6263f7604d 100644 --- a/docs/source/installation.md +++ b/docs/source/installation.md @@ -24,15 +24,24 @@ pip install [--editable] . An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/transformers/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples). -Tests can be run using `pytest` (install pytest if needed with `pip install pytest`). +Tests can be run using `unittest` or `pytest` (install pytest if needed with `pip install pytest`). Run all the tests from the root of the cloned repository with the commands: +```bash +python -m unittest discover -s transformers/tests -p "*test.py" -t . +python -m unittest discover -s examples -p "*test.py" -t examples +``` + +or + ``` bash python -m pytest -sv ./transformers/tests/ python -m pytest -sv ./examples/ ``` +By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to `yes` to run them. + ## OpenAI GPT original tokenization workflow If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install `ftfy` (use version 4.4.3 if you are using Python 2) and `SpaCy`: diff --git a/setup.py b/setup.py index 25f503f8d0..c4af32df83 100644 --- a/setup.py +++ b/setup.py @@ -72,7 +72,6 @@ setup( 'transformers-cli' ], # python_requires='>=3.5.0', - tests_require=['pytest'], classifiers=[ 'Intended Audience :: Science/Research', 'License :: OSI Approved :: Apache Software License', diff --git a/templates/adding_a_new_model/tests/modeling_tf_xxx_test.py b/templates/adding_a_new_model/tests/modeling_tf_xxx_test.py index 90837ca1ea..d7e576bf8b 100644 --- a/templates/adding_a_new_model/tests/modeling_tf_xxx_test.py +++ b/templates/adding_a_new_model/tests/modeling_tf_xxx_test.py @@ -18,11 +18,11 @@ from __future__ import print_function import unittest import shutil -import pytest import sys from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_tf, slow from transformers import XxxConfig, is_tf_available @@ -33,10 +33,9 @@ if is_tf_available(): TFXxxForTokenClassification, TFXxxForQuestionAnswering, TF_XXX_PRETRAINED_MODEL_ARCHIVE_MAP) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") +@require_tf class TFXxxModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes = (TFXxxModel, TFXxxForMaskedLM, TFXxxForQuestionAnswering, @@ -244,7 +243,7 @@ class TFXxxModelTest(TFCommonTestCases.TFCommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xxx_for_token_classification(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in ['xxx-base-uncased']: diff --git a/templates/adding_a_new_model/tests/modeling_xxx_test.py b/templates/adding_a_new_model/tests/modeling_xxx_test.py index 8c0cc3cf32..bfc70921cd 100644 --- a/templates/adding_a_new_model/tests/modeling_xxx_test.py +++ b/templates/adding_a_new_model/tests/modeling_xxx_test.py @@ -18,12 +18,12 @@ from __future__ import print_function import unittest import shutil -import pytest from transformers import is_torch_available from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_torch, slow, torch_device if is_torch_available(): from transformers import (XxxConfig, XxxModel, XxxForMaskedLM, @@ -31,10 +31,9 @@ if is_torch_available(): XxxForQuestionAnswering, XxxForSequenceClassification, XxxForTokenClassification, XxxForMultipleChoice) from transformers.modeling_xxx import XXX_PRETRAINED_MODEL_ARCHIVE_MAP -else: - pytestmark = pytest.mark.skip("Require Torch") +@require_torch class XxxModelTest(CommonTestCases.CommonModelTester): all_model_classes = (XxxModel, XxxForMaskedLM, XxxForQuestionAnswering, @@ -131,6 +130,7 @@ class XxxModelTest(CommonTestCases.CommonModelTester): def create_and_check_xxx_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = XxxModel(config=config) + model.to(torch_device) model.eval() sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids) @@ -148,6 +148,7 @@ class XxxModelTest(CommonTestCases.CommonModelTester): def create_and_check_xxx_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = XxxForMaskedLM(config=config) + model.to(torch_device) model.eval() loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels) result = { @@ -162,6 +163,7 @@ class XxxModelTest(CommonTestCases.CommonModelTester): def create_and_check_xxx_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = XxxForQuestionAnswering(config=config) + model.to(torch_device) model.eval() loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels) @@ -182,6 +184,7 @@ class XxxModelTest(CommonTestCases.CommonModelTester): def create_and_check_xxx_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): config.num_labels = self.num_labels model = XxxForSequenceClassification(config) + model.to(torch_device) model.eval() loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) result = { @@ -197,6 +200,7 @@ class XxxModelTest(CommonTestCases.CommonModelTester): def create_and_check_xxx_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): config.num_labels = self.num_labels model = XxxForTokenClassification(config=config) + model.to(torch_device) model.eval() loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) result = { @@ -243,7 +247,7 @@ class XxxModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xxx_for_token_classification(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(XXX_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/modeling_openai.py b/transformers/modeling_openai.py index e88f55c3ea..4fe7ffee8b 100644 --- a/transformers/modeling_openai.py +++ b/transformers/modeling_openai.py @@ -50,8 +50,10 @@ def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path): logger.info("Loading weights from {}".format(openai_checkpoint_folder_path)) - names = json.load(open(openai_checkpoint_folder_path + '/parameters_names.json', "r", encoding='utf-8')) - shapes = json.load(open(openai_checkpoint_folder_path + '/params_shapes.json', "r", encoding='utf-8')) + with open(openai_checkpoint_folder_path + '/parameters_names.json', "r", encoding='utf-8') as names_handle: + names = json.load(names_handle) + with open(openai_checkpoint_folder_path + '/params_shapes.json', "r", encoding='utf-8') as shapes_handle: + shapes = json.load(shapes_handle) offsets = np.cumsum([np.prod(shape) for shape in shapes]) init_params = [np.load(openai_checkpoint_folder_path + '/params_{}.npy'.format(n)) for n in range(10)] init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1] diff --git a/transformers/tests/conftest.py b/transformers/tests/conftest.py deleted file mode 100644 index f809234cd5..0000000000 --- a/transformers/tests/conftest.py +++ /dev/null @@ -1,31 +0,0 @@ -# content of conftest.py - -import pytest - - -def pytest_addoption(parser): - parser.addoption( - "--runslow", action="store_true", default=False, help="run slow tests" - ) - parser.addoption( - "--use_cuda", action="store_true", default=False, help="run tests on gpu" - ) - - -def pytest_configure(config): - config.addinivalue_line("markers", "slow: mark test as slow to run") - - -def pytest_collection_modifyitems(config, items): - if config.getoption("--runslow"): - # --runslow given in cli: do not skip slow tests - return - skip_slow = pytest.mark.skip(reason="need --runslow option to run") - for item in items: - if "slow" in item.keywords: - item.add_marker(skip_slow) - -@pytest.fixture -def use_cuda(request): - """ Run test on gpu """ - return request.config.getoption("--use_cuda") diff --git a/transformers/tests/modeling_albert_test.py b/transformers/tests/modeling_albert_test.py index 976feff9db..a14d66ae8f 100644 --- a/transformers/tests/modeling_albert_test.py +++ b/transformers/tests/modeling_albert_test.py @@ -18,22 +18,21 @@ from __future__ import print_function import unittest import shutil -import pytest from transformers import is_torch_available from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_torch, slow, torch_device if is_torch_available(): from transformers import (AlbertConfig, AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, AlbertForQuestionAnswering, ) from transformers.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP -else: - pytestmark = pytest.mark.skip("Require Torch") +@require_torch class AlbertModelTest(CommonTestCases.CommonModelTester): all_model_classes = (AlbertModel, AlbertForMaskedLM) if is_torch_available() else () @@ -133,6 +132,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): def create_and_check_albert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = AlbertModel(config=config) + model.to(torch_device) model.eval() sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids) @@ -150,6 +150,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): def create_and_check_albert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = AlbertForMaskedLM(config=config) + model.to(torch_device) model.eval() loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels) result = { @@ -163,6 +164,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): def create_and_check_albert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = AlbertForQuestionAnswering(config=config) + model.to(torch_device) model.eval() loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels) @@ -183,6 +185,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): def create_and_check_albert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): config.num_labels = self.num_labels model = AlbertForSequenceClassification(config) + model.to(torch_device) model.eval() loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) result = { @@ -225,7 +228,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_albert_for_sequence_classification(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_auto_test.py b/transformers/tests/modeling_auto_test.py index 6d2c7ec979..9b7d920bc8 100644 --- a/transformers/tests/modeling_auto_test.py +++ b/transformers/tests/modeling_auto_test.py @@ -18,11 +18,12 @@ from __future__ import print_function import unittest import shutil -import pytest import logging from transformers import is_torch_available +from .utils import require_torch, slow + if is_torch_available(): from transformers import (AutoConfig, BertConfig, AutoModel, BertModel, @@ -33,12 +34,11 @@ if is_torch_available(): from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester -else: - pytestmark = pytest.mark.skip("Require Torch") +@require_torch class AutoModelTest(unittest.TestCase): - @pytest.mark.slow + @slow def test_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: @@ -53,7 +53,7 @@ class AutoModelTest(unittest.TestCase): for value in loading_info.values(): self.assertEqual(len(value), 0) - @pytest.mark.slow + @slow def test_lmhead_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: @@ -66,7 +66,7 @@ class AutoModelTest(unittest.TestCase): self.assertIsNotNone(model) self.assertIsInstance(model, BertForMaskedLM) - @pytest.mark.slow + @slow def test_sequence_classification_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: @@ -79,7 +79,7 @@ class AutoModelTest(unittest.TestCase): self.assertIsNotNone(model) self.assertIsInstance(model, BertForSequenceClassification) - @pytest.mark.slow + @slow def test_question_answering_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_bert_test.py b/transformers/tests/modeling_bert_test.py index 6c93c9a187..539f66cd3f 100644 --- a/transformers/tests/modeling_bert_test.py +++ b/transformers/tests/modeling_bert_test.py @@ -18,12 +18,12 @@ from __future__ import print_function import unittest import shutil -import pytest from transformers import is_torch_available from .modeling_common_test import (CommonTestCases, ids_tensor, floats_tensor) from .configuration_common_test import ConfigTester +from .utils import require_torch, slow, torch_device if is_torch_available(): from transformers import (BertConfig, BertModel, BertForMaskedLM, @@ -31,11 +31,9 @@ if is_torch_available(): BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertForMultipleChoice) from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP -else: - pytestmark = pytest.mark.skip("Require Torch") -@pytest.mark.usefixtures("use_cuda") +@require_torch class BertModelTest(CommonTestCases.CommonModelTester): all_model_classes = (BertModel, BertForMaskedLM, BertForNextSentencePrediction, @@ -67,7 +65,6 @@ class BertModelTest(CommonTestCases.CommonModelTester): num_labels=3, num_choices=4, scope=None, - device='cpu', ): self.parent = parent self.batch_size = batch_size @@ -91,26 +88,25 @@ class BertModelTest(CommonTestCases.CommonModelTester): self.num_labels = num_labels self.num_choices = num_choices self.scope = scope - self.device = device def prepare_config_and_inputs(self): - input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).to(self.device) + input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: - input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2).to(self.device) + input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) token_type_ids = None if self.use_token_type_ids: - token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size).to(self.device) + token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: - sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size).to(self.device) - token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels).to(self.device) - choice_labels = ids_tensor([self.batch_size], self.num_choices).to(self.device) + sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) + token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) + choice_labels = ids_tensor([self.batch_size], self.num_choices) config = BertConfig( vocab_size_or_config_json_file=self.vocab_size, @@ -144,7 +140,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): def create_and_check_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = BertModel(config=config) - model.to(input_ids.device) + model.to(torch_device) model.eval() sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids) @@ -161,6 +157,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): def create_and_check_bert_model_as_decoder(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask): model = BertModel(config) + model.to(torch_device) model.eval() sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask) sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) @@ -177,6 +174,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): def create_and_check_bert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = BertForMaskedLM(config=config) + model.to(torch_device) model.eval() loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels) result = { @@ -190,6 +188,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): def create_and_check_bert_model_for_masked_lm_as_decoder(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask): model = BertForMaskedLM(config=config) + model.to(torch_device) model.eval() loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask) loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels, encoder_hidden_states=encoder_hidden_states) @@ -204,6 +203,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): def create_and_check_bert_for_next_sequence_prediction(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = BertForNextSentencePrediction(config=config) + model.to(torch_device) model.eval() loss, seq_relationship_score = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, next_sentence_label=sequence_labels) result = { @@ -217,6 +217,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): def create_and_check_bert_for_pretraining(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = BertForPreTraining(config=config) + model.to(torch_device) model.eval() loss, prediction_scores, seq_relationship_score = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels, next_sentence_label=sequence_labels) @@ -235,6 +236,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): def create_and_check_bert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = BertForQuestionAnswering(config=config) + model.to(torch_device) model.eval() loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels) @@ -254,6 +256,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): def create_and_check_bert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): config.num_labels = self.num_labels model = BertForSequenceClassification(config) + model.to(torch_device) model.eval() loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) result = { @@ -268,6 +271,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): def create_and_check_bert_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): config.num_labels = self.num_labels model = BertForTokenClassification(config=config) + model.to(torch_device) model.eval() loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) result = { @@ -282,6 +286,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): def create_and_check_bert_for_multiple_choice(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): config.num_choices = self.num_choices model = BertForMultipleChoice(config=config) + model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() @@ -313,10 +318,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): def test_config(self): self.config_tester.run_common_tests() - def test_bert_model(self, use_cuda=False): - # ^^ This could be a real fixture - if use_cuda: - self.model_tester.device = "cuda" + def test_bert_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_bert_model(*config_and_inputs) @@ -356,7 +358,7 @@ class BertModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_bert_for_token_classification(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_common_test.py b/transformers/tests/modeling_common_test.py index baf1531403..80d5d95455 100644 --- a/transformers/tests/modeling_common_test.py +++ b/transformers/tests/modeling_common_test.py @@ -27,10 +27,11 @@ import uuid import unittest import logging -import pytest from transformers import is_torch_available +from .utils import require_torch, slow, torch_device + if is_torch_available(): import torch import numpy as np @@ -38,8 +39,6 @@ if is_torch_available(): from transformers import (AdaptiveEmbedding, PretrainedConfig, PreTrainedModel, BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2LMHeadModel, GPT2Config, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP) -else: - pytestmark = pytest.mark.skip("Require Torch") if sys.version_info[0] == 2: import cPickle as pickle @@ -65,6 +64,7 @@ def _config_zero_init(config): class CommonTestCases: + @require_torch class CommonModelTester(unittest.TestCase): model_tester = None @@ -79,6 +79,7 @@ class CommonTestCases: for model_class in self.all_model_classes: model = model_class(config) + model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**inputs_dict) @@ -86,12 +87,13 @@ class CommonTestCases: with TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname) + model.to(torch_device) with torch.no_grad(): after_outputs = model(**inputs_dict) # Make sure we don't have nans - out_1 = after_outputs[0].numpy() - out_2 = outputs[0].numpy() + out_1 = after_outputs[0].cpu().numpy() + out_2 = outputs[0].cpu().numpy() out_1 = out_1[~np.isnan(out_1)] out_2 = out_2[~np.isnan(out_2)] max_diff = np.amax(np.abs(out_1 - out_2)) @@ -113,6 +115,7 @@ class CommonTestCases: for model_class in self.all_model_classes: model = model_class(config) + model.to(torch_device) model.eval() first, second = model(inputs_dict["input_ids"])[0], model(inputs_dict["input_ids"])[0] self.assertEqual(first.ne(second).sum().item(), 0) @@ -125,6 +128,7 @@ class CommonTestCases: config.output_attentions = True config.output_hidden_states = False model = model_class(config) + model.to(torch_device) model.eval() outputs = model(**inputs_dict) attentions = outputs[-1] @@ -142,6 +146,7 @@ class CommonTestCases: config.output_attentions = True config.output_hidden_states = True model = model_class(config) + model.to(torch_device) model.eval() outputs = model(**inputs_dict) self.assertEqual(out_len+1, len(outputs)) @@ -181,6 +186,7 @@ class CommonTestCases: configs_no_init.torchscript = True for model_class in self.all_model_classes: model = model_class(config=configs_no_init) + model.to(torch_device) model.eval() inputs = inputs_dict['input_ids'] # Let's keep only input_ids @@ -201,7 +207,10 @@ class CommonTestCases: except ValueError: self.fail("Couldn't load module.") + model.to(torch_device) model.eval() + + loaded_model.to(torch_device) loaded_model.eval() model_params = model.parameters() @@ -228,11 +237,12 @@ class CommonTestCases: configs_no_init = _config_zero_init(config) # To be sure we have no Nan for model_class in self.all_model_classes: model = model_class(config=configs_no_init) + model.to(torch_device) model.eval() # Prepare head_mask # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior) - head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads) + head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads, device=torch_device) head_mask[0, 0] = 0 head_mask[-1, :-1] = 0 head_mask.requires_grad_(requires_grad=True) @@ -282,6 +292,7 @@ class CommonTestCases: config.output_attentions = True config.output_hidden_states = False model = model_class(config=config) + model.to(torch_device) model.eval() heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)), -1: [0]} @@ -310,6 +321,7 @@ class CommonTestCases: config.output_attentions = True config.output_hidden_states = False model = model_class(config=config) + model.to(torch_device) model.eval() heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)), -1: [0]} @@ -319,6 +331,7 @@ class CommonTestCases: os.makedirs(directory) model.save_pretrained(directory) model = model_class.from_pretrained(directory) + model.to(torch_device) outputs = model(**inputs_dict) attentions = outputs[-1] @@ -346,6 +359,7 @@ class CommonTestCases: config.pruned_heads = heads_to_prune model = model_class(config=config) + model.to(torch_device) model.eval() outputs = model(**inputs_dict) @@ -372,6 +386,7 @@ class CommonTestCases: config.pruned_heads = heads_to_prune model = model_class(config=config) + model.to(torch_device) model.eval() outputs = model(**inputs_dict) @@ -388,6 +403,7 @@ class CommonTestCases: os.makedirs(directory) model.save_pretrained(directory) model = model_class.from_pretrained(directory) + model.to(torch_device) shutil.rmtree(directory) outputs = model(**inputs_dict) @@ -419,6 +435,7 @@ class CommonTestCases: config.output_hidden_states = True config.output_attentions = False model = model_class(config) + model.to(torch_device) model.eval() outputs = model(**inputs_dict) hidden_states = outputs[-1] @@ -538,6 +555,7 @@ class CommonTestCases: for model_class in self.all_model_classes: model = model_class(config) + model.to(torch_device) model.eval() wte = model.get_input_embeddings() @@ -628,6 +646,7 @@ class CommonTestCases: def create_and_check_base_model(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): model = self.base_model_class(config) + model.to(torch_device) model.eval() outputs = model(input_ids, position_ids, token_type_ids) @@ -643,6 +662,7 @@ class CommonTestCases: def create_and_check_lm_head(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): model = self.lm_head_model_class(config) + model.to(torch_device) model.eval() outputs = model(input_ids, position_ids, token_type_ids, lm_labels) loss, lm_logits = outputs[:2] @@ -659,6 +679,7 @@ class CommonTestCases: mc_labels, lm_labels, mc_token_ids): for model_class in self.all_model_classes: model = model_class(config) + model.to(torch_device) model.eval() outputs = model(input_ids) presents = outputs[-1] @@ -671,6 +692,7 @@ class CommonTestCases: def create_and_check_double_heads(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): model = self.double_head_model_class(config) + model.to(torch_device) model.eval() outputs = model(input_ids, mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels, token_type_ids=token_type_ids, position_ids=position_ids) @@ -716,7 +738,7 @@ class CommonTestCases: config_and_inputs = self.prepare_config_and_inputs() self.create_and_check_presents(*config_and_inputs) - @pytest.mark.slow + @slow def run_slow_tests(self): self.create_and_check_model_from_pretrained() @@ -770,7 +792,7 @@ def ids_tensor(shape, vocab_size, rng=None, name=None): for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) - return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous() + return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous() def floats_tensor(shape, scale=1.0, rng=None, name=None): @@ -786,11 +808,12 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None): for _ in range(total_dims): values.append(rng.random() * scale) - return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous() + return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous() +@require_torch class ModelUtilsTest(unittest.TestCase): - @pytest.mark.slow + @slow def test_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_ctrl_test.py b/transformers/tests/modeling_ctrl_test.py index 47ff8d8d51..8c14578a5c 100644 --- a/transformers/tests/modeling_ctrl_test.py +++ b/transformers/tests/modeling_ctrl_test.py @@ -16,7 +16,6 @@ from __future__ import division from __future__ import print_function import unittest -import pytest import shutil import pdb @@ -25,13 +24,13 @@ from transformers import is_torch_available if is_torch_available(): from transformers import (CTRLConfig, CTRLModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, CTRLLMHeadModel) -else: - pytestmark = pytest.mark.skip("Require Torch") from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_torch, slow, torch_device +@require_torch class CTRLModelTest(CommonTestCases.CommonModelTester): all_model_classes = (CTRLModel, CTRLLMHeadModel) if is_torch_available() else () @@ -140,6 +139,7 @@ class CTRLModelTest(CommonTestCases.CommonModelTester): def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = CTRLModel(config=config) + model.to(torch_device) model.eval() model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask) @@ -157,6 +157,7 @@ class CTRLModelTest(CommonTestCases.CommonModelTester): def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = CTRLLMHeadModel(config) + model.to(torch_device) model.eval() loss, lm_logits, _ = model(input_ids, token_type_ids=token_type_ids, labels=input_ids) @@ -202,7 +203,7 @@ class CTRLModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lm_head_model(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_distilbert_test.py b/transformers/tests/modeling_distilbert_test.py index 8099c03586..4b8f64327d 100644 --- a/transformers/tests/modeling_distilbert_test.py +++ b/transformers/tests/modeling_distilbert_test.py @@ -17,7 +17,6 @@ from __future__ import division from __future__ import print_function import unittest -import pytest from transformers import is_torch_available @@ -25,13 +24,13 @@ if is_torch_available(): from transformers import (DistilBertConfig, DistilBertModel, DistilBertForMaskedLM, DistilBertForTokenClassification, DistilBertForQuestionAnswering, DistilBertForSequenceClassification) -else: - pytestmark = pytest.mark.skip("Require Torch") from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_torch, slow, torch_device +@require_torch class DistilBertModelTest(CommonTestCases.CommonModelTester): all_model_classes = (DistilBertModel, DistilBertForMaskedLM, DistilBertForQuestionAnswering, @@ -126,6 +125,7 @@ class DistilBertModelTest(CommonTestCases.CommonModelTester): def create_and_check_distilbert_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = DistilBertModel(config=config) + model.to(torch_device) model.eval() (sequence_output,) = model(input_ids, input_mask) (sequence_output,) = model(input_ids) @@ -139,6 +139,7 @@ class DistilBertModelTest(CommonTestCases.CommonModelTester): def create_and_check_distilbert_for_masked_lm(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = DistilBertForMaskedLM(config=config) + model.to(torch_device) model.eval() loss, prediction_scores = model(input_ids, attention_mask=input_mask, masked_lm_labels=token_labels) result = { @@ -152,6 +153,7 @@ class DistilBertModelTest(CommonTestCases.CommonModelTester): def create_and_check_distilbert_for_question_answering(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = DistilBertForQuestionAnswering(config=config) + model.to(torch_device) model.eval() loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, start_positions=sequence_labels, end_positions=sequence_labels) result = { @@ -170,6 +172,7 @@ class DistilBertModelTest(CommonTestCases.CommonModelTester): def create_and_check_distilbert_for_sequence_classification(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): config.num_labels = self.num_labels model = DistilBertForSequenceClassification(config) + model.to(torch_device) model.eval() loss, logits = model(input_ids, attention_mask=input_mask, labels=sequence_labels) result = { @@ -184,6 +187,7 @@ class DistilBertModelTest(CommonTestCases.CommonModelTester): def create_and_check_distilbert_for_token_classification(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): config.num_labels = self.num_labels model = DistilBertForTokenClassification(config=config) + model.to(torch_device) model.eval() loss, logits = model(input_ids, attention_mask=input_mask, labels=token_labels) @@ -229,7 +233,7 @@ class DistilBertModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_distilbert_for_token_classification(*config_and_inputs) - # @pytest.mark.slow + # @slow # def test_model_from_pretrained(self): # cache_dir = "/tmp/transformers_test/" # for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_encoder_decoder_test.py b/transformers/tests/modeling_encoder_decoder_test.py index a6c88ed9a9..64e86df8f5 100644 --- a/transformers/tests/modeling_encoder_decoder_test.py +++ b/transformers/tests/modeling_encoder_decoder_test.py @@ -15,19 +15,18 @@ import logging import unittest -import pytest from transformers import is_torch_available +from .utils import require_torch, slow if is_torch_available(): from transformers import BertModel, BertForMaskedLM, Model2Model from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP -else: - pytestmark = pytest.mark.skip("Require Torch") +@require_torch class EncoderDecoderModelTest(unittest.TestCase): - @pytest.mark.slow + @slow def test_model2model_from_pretrained(self): logging.basicConfig(level=logging.INFO) for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_gpt2_test.py b/transformers/tests/modeling_gpt2_test.py index 4263e51bc9..ecaa2a4bd0 100644 --- a/transformers/tests/modeling_gpt2_test.py +++ b/transformers/tests/modeling_gpt2_test.py @@ -17,7 +17,6 @@ from __future__ import division from __future__ import print_function import unittest -import pytest import shutil from transformers import is_torch_available @@ -25,13 +24,13 @@ from transformers import is_torch_available if is_torch_available(): from transformers import (GPT2Config, GPT2Model, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2LMHeadModel, GPT2DoubleHeadsModel) -else: - pytestmark = pytest.mark.skip("Require Torch") from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_torch, slow, torch_device +@require_torch class GPT2ModelTest(CommonTestCases.CommonModelTester): all_model_classes = (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else () @@ -136,6 +135,7 @@ class GPT2ModelTest(CommonTestCases.CommonModelTester): def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = GPT2Model(config=config) + model.to(torch_device) model.eval() model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask) @@ -153,6 +153,7 @@ class GPT2ModelTest(CommonTestCases.CommonModelTester): def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = GPT2LMHeadModel(config) + model.to(torch_device) model.eval() loss, lm_logits, _ = model(input_ids, token_type_ids=token_type_ids, labels=input_ids) @@ -171,6 +172,7 @@ class GPT2ModelTest(CommonTestCases.CommonModelTester): def create_and_check_double_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args): model = GPT2DoubleHeadsModel(config) + model.to(torch_device) model.eval() @@ -235,7 +237,7 @@ class GPT2ModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_openai_test.py b/transformers/tests/modeling_openai_test.py index 33218288a0..8e4d13438d 100644 --- a/transformers/tests/modeling_openai_test.py +++ b/transformers/tests/modeling_openai_test.py @@ -17,7 +17,6 @@ from __future__ import division from __future__ import print_function import unittest -import pytest import shutil from transformers import is_torch_available @@ -25,13 +24,13 @@ from transformers import is_torch_available if is_torch_available(): from transformers import (OpenAIGPTConfig, OpenAIGPTModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel) -else: - pytestmark = pytest.mark.skip("Require Torch") from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_torch, slow, torch_device +@require_torch class OpenAIGPTModelTest(CommonTestCases.CommonModelTester): all_model_classes = (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel) if is_torch_available() else () @@ -124,6 +123,7 @@ class OpenAIGPTModelTest(CommonTestCases.CommonModelTester): def create_and_check_openai_gpt_model(self, config, input_ids, head_mask, token_type_ids, *args): model = OpenAIGPTModel(config=config) + model.to(torch_device) model.eval() model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask) @@ -139,6 +139,7 @@ class OpenAIGPTModelTest(CommonTestCases.CommonModelTester): def create_and_check_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args): model = OpenAIGPTLMHeadModel(config) + model.to(torch_device) model.eval() loss, lm_logits = model(input_ids, token_type_ids=token_type_ids, labels=input_ids) @@ -157,6 +158,7 @@ class OpenAIGPTModelTest(CommonTestCases.CommonModelTester): def create_and_check_double_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args): model = OpenAIGPTDoubleHeadsModel(config) + model.to(torch_device) model.eval() loss, lm_logits, mc_logits = model(input_ids, token_type_ids=token_type_ids, lm_labels=input_ids) @@ -203,7 +205,7 @@ class OpenAIGPTModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_roberta_test.py b/transformers/tests/modeling_roberta_test.py index 0620ddf630..7a3553b164 100644 --- a/transformers/tests/modeling_roberta_test.py +++ b/transformers/tests/modeling_roberta_test.py @@ -18,7 +18,6 @@ from __future__ import print_function import unittest import shutil -import pytest from transformers import is_torch_available @@ -27,13 +26,13 @@ if is_torch_available(): from transformers import (RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification, RobertaForTokenClassification) from transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP -else: - pytestmark = pytest.mark.skip("Require Torch") from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_torch, slow, torch_device +@require_torch class RobertaModelTest(CommonTestCases.CommonModelTester): all_model_classes = (RobertaForMaskedLM, RobertaModel) if is_torch_available() else () @@ -129,6 +128,7 @@ class RobertaModelTest(CommonTestCases.CommonModelTester): def create_and_check_roberta_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = RobertaModel(config=config) + model.to(torch_device) model.eval() sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids) @@ -146,6 +146,7 @@ class RobertaModelTest(CommonTestCases.CommonModelTester): def create_and_check_roberta_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = RobertaForMaskedLM(config=config) + model.to(torch_device) model.eval() loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels) result = { @@ -161,6 +162,7 @@ class RobertaModelTest(CommonTestCases.CommonModelTester): sequence_labels, token_labels, choice_labels): config.num_labels = self.num_labels model = RobertaForTokenClassification(config=config) + model.to(torch_device) model.eval() loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) @@ -195,7 +197,7 @@ class RobertaModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_roberta_for_masked_lm(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: @@ -207,10 +209,10 @@ class RobertaModelTest(CommonTestCases.CommonModelTester): class RobertaModelIntegrationTest(unittest.TestCase): - @pytest.mark.slow + @slow def test_inference_masked_lm(self): model = RobertaForMaskedLM.from_pretrained('roberta-base') - + input_ids = torch.tensor([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] expected_shape = torch.Size((1, 11, 50265)) @@ -228,10 +230,10 @@ class RobertaModelIntegrationTest(unittest.TestCase): torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3) ) - @pytest.mark.slow + @slow def test_inference_no_head(self): model = RobertaModel.from_pretrained('roberta-base') - + input_ids = torch.tensor([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] # compare the actual values for a slice. @@ -244,10 +246,10 @@ class RobertaModelIntegrationTest(unittest.TestCase): torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3) ) - @pytest.mark.slow + @slow def test_inference_classification_head(self): model = RobertaForSequenceClassification.from_pretrained('roberta-large-mnli') - + input_ids = torch.tensor([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] expected_shape = torch.Size((1, 3)) diff --git a/transformers/tests/modeling_tf_albert_test.py b/transformers/tests/modeling_tf_albert_test.py index fbd519b8f6..7d3325b70b 100644 --- a/transformers/tests/modeling_tf_albert_test.py +++ b/transformers/tests/modeling_tf_albert_test.py @@ -18,11 +18,11 @@ from __future__ import print_function import unittest import shutil -import pytest import sys from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_tf, slow from transformers import AlbertConfig, is_tf_available @@ -31,10 +31,9 @@ if is_tf_available(): from transformers.modeling_tf_albert import (TFAlbertModel, TFAlbertForMaskedLM, TFAlbertForSequenceClassification, TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") +@require_tf class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes = ( @@ -216,7 +215,7 @@ class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester): self.model_tester.create_and_check_albert_for_sequence_classification( *config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" # for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_tf_auto_test.py b/transformers/tests/modeling_tf_auto_test.py index fa90906e86..7ea48015d9 100644 --- a/transformers/tests/modeling_tf_auto_test.py +++ b/transformers/tests/modeling_tf_auto_test.py @@ -18,11 +18,12 @@ from __future__ import print_function import unittest import shutil -import pytest import logging from transformers import is_tf_available +from .utils import require_tf, slow + if is_tf_available(): from transformers import (AutoConfig, BertConfig, TFAutoModel, TFBertModel, @@ -33,12 +34,11 @@ if is_tf_available(): from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester -else: - pytestmark = pytest.mark.skip("Require TensorFlow") +@require_tf class TFAutoModelTest(unittest.TestCase): - @pytest.mark.slow + @slow def test_model_from_pretrained(self): import h5py self.assertTrue(h5py.version.hdf5_version.startswith("1.10")) @@ -54,7 +54,7 @@ class TFAutoModelTest(unittest.TestCase): self.assertIsNotNone(model) self.assertIsInstance(model, TFBertModel) - @pytest.mark.slow + @slow def test_lmhead_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: @@ -67,7 +67,7 @@ class TFAutoModelTest(unittest.TestCase): self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) - @pytest.mark.slow + @slow def test_sequence_classification_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: @@ -80,7 +80,7 @@ class TFAutoModelTest(unittest.TestCase): self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForSequenceClassification) - @pytest.mark.slow + @slow def test_question_answering_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_tf_bert_test.py b/transformers/tests/modeling_tf_bert_test.py index bcee97435e..d7a86fecb9 100644 --- a/transformers/tests/modeling_tf_bert_test.py +++ b/transformers/tests/modeling_tf_bert_test.py @@ -18,11 +18,11 @@ from __future__ import print_function import unittest import shutil -import pytest import sys from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_tf, slow from transformers import BertConfig, is_tf_available @@ -36,10 +36,9 @@ if is_tf_available(): TFBertForTokenClassification, TFBertForQuestionAnswering, TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") +@require_tf class TFBertModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes = (TFBertModel, TFBertForMaskedLM, TFBertForNextSentencePrediction, @@ -309,7 +308,7 @@ class TFBertModelTest(TFCommonTestCases.TFCommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_bert_for_token_classification(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_tf_common_test.py b/transformers/tests/modeling_tf_common_test.py index 7445ce826a..439360ba35 100644 --- a/transformers/tests/modeling_tf_common_test.py +++ b/transformers/tests/modeling_tf_common_test.py @@ -25,18 +25,17 @@ import unittest import uuid import tempfile -import pytest import sys from transformers import is_tf_available, is_torch_available +from .utils import require_tf, slow + if is_tf_available(): import tensorflow as tf import numpy as np from transformers import TFPreTrainedModel # from transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP -else: - pytestmark = pytest.mark.skip("Require TensorFlow") if sys.version_info[0] == 2: import cPickle as pickle @@ -62,6 +61,7 @@ def _config_zero_init(config): class TFCommonTestCases: + @require_tf class TFCommonModelTester(unittest.TestCase): model_tester = None @@ -164,7 +164,7 @@ class TFCommonTestCases: for model_class in self.all_model_classes: # Prepare our model model = model_class(config) - + # Let's load it from the disk to be sure we can use pretrained weights with TemporaryDirectory() as tmpdirname: outputs = model(inputs_dict) # build the model diff --git a/transformers/tests/modeling_tf_ctrl_test.py b/transformers/tests/modeling_tf_ctrl_test.py index a57c882169..0b421c20c9 100644 --- a/transformers/tests/modeling_tf_ctrl_test.py +++ b/transformers/tests/modeling_tf_ctrl_test.py @@ -18,11 +18,11 @@ from __future__ import print_function import unittest import shutil -import pytest import sys from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_tf, slow from transformers import CTRLConfig, is_tf_available @@ -30,10 +30,9 @@ if is_tf_available(): import tensorflow as tf from transformers.modeling_tf_ctrl import (TFCTRLModel, TFCTRLLMHeadModel, TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") +@require_tf class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes = (TFCTRLModel, TFCTRLLMHeadModel) if is_tf_available() else () @@ -188,7 +187,7 @@ class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_ctrl_lm_head(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_tf_distilbert_test.py b/transformers/tests/modeling_tf_distilbert_test.py index e6d3795914..0ec45150ca 100644 --- a/transformers/tests/modeling_tf_distilbert_test.py +++ b/transformers/tests/modeling_tf_distilbert_test.py @@ -17,10 +17,10 @@ from __future__ import division from __future__ import print_function import unittest -import pytest from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_tf, slow from transformers import DistilBertConfig, is_tf_available @@ -30,10 +30,9 @@ if is_tf_available(): TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering, TFDistilBertForSequenceClassification) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") +@require_tf class TFDistilBertModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes = (TFDistilBertModel, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering, @@ -210,7 +209,7 @@ class TFDistilBertModelTest(TFCommonTestCases.TFCommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_distilbert_for_sequence_classification(*config_and_inputs) - # @pytest.mark.slow + # @slow # def test_model_from_pretrained(self): # cache_dir = "/tmp/transformers_test/" # for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_tf_gpt2_test.py b/transformers/tests/modeling_tf_gpt2_test.py index 76e9ee2298..e070b72e65 100644 --- a/transformers/tests/modeling_tf_gpt2_test.py +++ b/transformers/tests/modeling_tf_gpt2_test.py @@ -18,11 +18,11 @@ from __future__ import print_function import unittest import shutil -import pytest import sys from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_tf, slow from transformers import GPT2Config, is_tf_available @@ -31,10 +31,9 @@ if is_tf_available(): from transformers.modeling_tf_gpt2 import (TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel, TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") +@require_tf class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel, @@ -219,7 +218,7 @@ class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_double_head(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_tf_openai_gpt_test.py b/transformers/tests/modeling_tf_openai_gpt_test.py index d470c8862d..675e806c12 100644 --- a/transformers/tests/modeling_tf_openai_gpt_test.py +++ b/transformers/tests/modeling_tf_openai_gpt_test.py @@ -18,11 +18,11 @@ from __future__ import print_function import unittest import shutil -import pytest import sys from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_tf, slow from transformers import OpenAIGPTConfig, is_tf_available @@ -31,10 +31,9 @@ if is_tf_available(): from transformers.modeling_tf_openai import (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") +@require_tf class TFOpenAIGPTModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes = (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, @@ -218,7 +217,7 @@ class TFOpenAIGPTModelTest(TFCommonTestCases.TFCommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_openai_gpt_double_head(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_tf_roberta_test.py b/transformers/tests/modeling_tf_roberta_test.py index edbfa4e205..42440bf1b7 100644 --- a/transformers/tests/modeling_tf_roberta_test.py +++ b/transformers/tests/modeling_tf_roberta_test.py @@ -18,10 +18,10 @@ from __future__ import print_function import unittest import shutil -import pytest from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_tf, slow from transformers import RobertaConfig, is_tf_available @@ -32,10 +32,9 @@ if is_tf_available(): TFRobertaForSequenceClassification, TFRobertaForTokenClassification, TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") +@require_tf class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes = (TFRobertaModel,TFRobertaForMaskedLM, @@ -191,7 +190,7 @@ class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_roberta_for_masked_lm(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: @@ -203,10 +202,10 @@ class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester): class TFRobertaModelIntegrationTest(unittest.TestCase): - @pytest.mark.slow + @slow def test_inference_masked_lm(self): model = TFRobertaForMaskedLM.from_pretrained('roberta-base') - + input_ids = tf.constant([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] expected_shape = [1, 11, 50265] @@ -224,10 +223,10 @@ class TFRobertaModelIntegrationTest(unittest.TestCase): numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3) ) - @pytest.mark.slow + @slow def test_inference_no_head(self): model = TFRobertaModel.from_pretrained('roberta-base') - + input_ids = tf.constant([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] # compare the actual values for a slice. @@ -240,10 +239,10 @@ class TFRobertaModelIntegrationTest(unittest.TestCase): numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3) ) - @pytest.mark.slow + @slow def test_inference_classification_head(self): model = TFRobertaForSequenceClassification.from_pretrained('roberta-large-mnli') - + input_ids = tf.constant([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] expected_shape = [1, 3] diff --git a/transformers/tests/modeling_tf_transfo_xl_test.py b/transformers/tests/modeling_tf_transfo_xl_test.py index 534fe39646..03e332bdc1 100644 --- a/transformers/tests/modeling_tf_transfo_xl_test.py +++ b/transformers/tests/modeling_tf_transfo_xl_test.py @@ -19,10 +19,10 @@ from __future__ import print_function import unittest import random import shutil -import pytest from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_tf, slow from transformers import TransfoXLConfig, is_tf_available @@ -31,10 +31,9 @@ if is_tf_available(): from transformers.modeling_tf_transfo_xl import (TFTransfoXLModel, TFTransfoXLLMHeadModel, TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") +@require_tf class TFTransfoXLModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes = (TFTransfoXLModel, TFTransfoXLLMHeadModel) if is_tf_available() else () @@ -204,7 +203,7 @@ class TFTransfoXLModelTest(TFCommonTestCases.TFCommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_transfo_xl_lm_head(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_tf_xlm_test.py b/transformers/tests/modeling_tf_xlm_test.py index 1bd661bebf..a680b70367 100644 --- a/transformers/tests/modeling_tf_xlm_test.py +++ b/transformers/tests/modeling_tf_xlm_test.py @@ -18,7 +18,6 @@ from __future__ import print_function import unittest import shutil -import pytest from transformers import is_tf_available @@ -29,13 +28,13 @@ if is_tf_available(): TFXLMForSequenceClassification, TFXLMForQuestionAnsweringSimple, TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_tf, slow +@require_tf class TFXLMModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes = (TFXLMModel, TFXLMWithLMHeadModel, @@ -251,7 +250,7 @@ class TFXLMModelTest(TFCommonTestCases.TFCommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_tf_xlnet_test.py b/transformers/tests/modeling_tf_xlnet_test.py index a00a965570..94864b86f2 100644 --- a/transformers/tests/modeling_tf_xlnet_test.py +++ b/transformers/tests/modeling_tf_xlnet_test.py @@ -21,7 +21,6 @@ import unittest import json import random import shutil -import pytest from transformers import XLNetConfig, is_tf_available @@ -33,12 +32,13 @@ if is_tf_available(): TFXLNetForTokenClassification, TFXLNetForQuestionAnsweringSimple, TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_tf, slow + +@require_tf class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester): all_model_classes=(TFXLNetModel, TFXLNetLMHeadModel, @@ -304,7 +304,7 @@ class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester): def test_xlnet_lm_head(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() - self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs) + self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs) def test_xlnet_sequence_classif(self): self.model_tester.set_seed() @@ -320,7 +320,7 @@ class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_qa(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_transfo_xl_test.py b/transformers/tests/modeling_transfo_xl_test.py index f7b913da5b..647dd3724d 100644 --- a/transformers/tests/modeling_transfo_xl_test.py +++ b/transformers/tests/modeling_transfo_xl_test.py @@ -19,7 +19,6 @@ from __future__ import print_function import unittest import random import shutil -import pytest from transformers import is_torch_available @@ -27,12 +26,13 @@ if is_torch_available(): import torch from transformers import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel) from transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP -else: - pytestmark = pytest.mark.skip("Require Torch") from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_torch, slow, torch_device + +@require_torch class TransfoXLModelTest(CommonTestCases.CommonModelTester): all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel) if is_torch_available() else () @@ -111,6 +111,7 @@ class TransfoXLModelTest(CommonTestCases.CommonModelTester): def create_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels): model = TransfoXLModel(config) + model.to(torch_device) model.eval() hidden_states_1, mems_1 = model(input_ids_1) @@ -140,6 +141,7 @@ class TransfoXLModelTest(CommonTestCases.CommonModelTester): def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels): model = TransfoXLLMHeadModel(config) + model.to(torch_device) model.eval() lm_logits_1, mems_1 = model(input_ids_1) @@ -204,7 +206,7 @@ class TransfoXLModelTest(CommonTestCases.CommonModelTester): output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs) self.model_tester.check_transfo_xl_lm_head_output(output_result) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_xlm_test.py b/transformers/tests/modeling_xlm_test.py index 0133febb58..f6b980767c 100644 --- a/transformers/tests/modeling_xlm_test.py +++ b/transformers/tests/modeling_xlm_test.py @@ -18,7 +18,6 @@ from __future__ import print_function import unittest import shutil -import pytest from transformers import is_torch_available @@ -26,13 +25,13 @@ if is_torch_available(): from transformers import (XLMConfig, XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering, XLMForSequenceClassification, XLMForQuestionAnsweringSimple) from transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_MAP -else: - pytestmark = pytest.mark.skip("Require Torch") from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_torch, slow, torch_device +@require_torch class XLMModelTest(CommonTestCases.CommonModelTester): all_model_classes = (XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering, @@ -148,6 +147,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester): def create_and_check_xlm_model(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask): model = XLMModel(config=config) + model.to(torch_device) model.eval() outputs = model(input_ids, lengths=input_lengths, langs=token_type_ids) outputs = model(input_ids, langs=token_type_ids) @@ -163,6 +163,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester): def create_and_check_xlm_lm_head(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask): model = XLMWithLMHeadModel(config) + model.to(torch_device) model.eval() loss, logits = model(input_ids, token_type_ids=token_type_ids, labels=token_labels) @@ -182,6 +183,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester): def create_and_check_xlm_simple_qa(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask): model = XLMForQuestionAnsweringSimple(config) + model.to(torch_device) model.eval() outputs = model(input_ids) @@ -206,6 +208,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester): def create_and_check_xlm_qa(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask): model = XLMForQuestionAnswering(config) + model.to(torch_device) model.eval() outputs = model(input_ids) @@ -260,6 +263,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester): def create_and_check_xlm_sequence_classif(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask): model = XLMForSequenceClassification(config) + model.to(torch_device) model.eval() (logits,) = model(input_ids) @@ -312,7 +316,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/modeling_xlnet_test.py b/transformers/tests/modeling_xlnet_test.py index 38888d4488..56b6bb3f4d 100644 --- a/transformers/tests/modeling_xlnet_test.py +++ b/transformers/tests/modeling_xlnet_test.py @@ -21,7 +21,6 @@ import unittest import json import random import shutil -import pytest from transformers import is_torch_available @@ -31,12 +30,13 @@ if is_torch_available(): from transformers import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForTokenClassification, XLNetForQuestionAnswering) from transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_MAP -else: - pytestmark = pytest.mark.skip("Require Torch") from .modeling_common_test import (CommonTestCases, ids_tensor) from .configuration_common_test import ConfigTester +from .utils import require_torch, slow, torch_device + +@require_torch class XLNetModelTest(CommonTestCases.CommonModelTester): all_model_classes=(XLNetModel, XLNetLMHeadModel, XLNetForTokenClassification, @@ -100,9 +100,9 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): input_mask = ids_tensor([self.batch_size, self.seq_length], 2).float() input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size) - perm_mask = torch.zeros(self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float) + perm_mask = torch.zeros(self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float, device=torch_device) perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token - target_mapping = torch.zeros(self.batch_size, 1, self.seq_length + 1, dtype=torch.float) + target_mapping = torch.zeros(self.batch_size, 1, self.seq_length + 1, dtype=torch.float, device=torch_device) target_mapping[:, 0, -1] = 1.0 # predict last token sequence_labels = None @@ -141,6 +141,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): def create_and_check_xlnet_base_model(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): model = XLNetModel(config) + model.to(torch_device) model.eval() _, _ = model(input_ids_1, input_mask=input_mask) @@ -155,6 +156,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): config.mem_len = 0 model = XLNetModel(config) + model.to(torch_device) model.eval() no_mems_outputs = model(input_ids_1) self.parent.assertEqual(len(no_mems_outputs), 1) @@ -169,6 +171,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): def create_and_check_xlnet_base_model_with_att_output(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): model = XLNetModel(config) + model.to(torch_device) model.eval() _, _, attentions = model(input_ids_1, target_mapping=target_mapping) @@ -181,6 +184,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): def create_and_check_xlnet_lm_head(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): model = XLNetLMHeadModel(config) + model.to(torch_device) model.eval() loss_1, all_logits_1, mems_1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels) @@ -221,6 +225,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): def create_and_check_xlnet_qa(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): model = XLNetForQuestionAnswering(config) + model.to(torch_device) model.eval() outputs = model(input_ids_1) @@ -279,6 +284,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): def create_and_check_xlnet_token_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): model = XLNetForTokenClassification(config) + model.to(torch_device) model.eval() logits, mems_1 = model(input_ids_1) @@ -311,6 +317,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): def create_and_check_xlnet_sequence_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask, target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels): model = XLNetForSequenceClassification(config) + model.to(torch_device) model.eval() logits, mems_1 = model(input_ids_1) @@ -362,7 +369,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): def test_xlnet_lm_head(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() - self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs) + self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs) def test_xlnet_sequence_classif(self): self.model_tester.set_seed() @@ -379,7 +386,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlnet_qa(*config_and_inputs) - @pytest.mark.slow + @slow def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/optimization_test.py b/transformers/tests/optimization_test.py index ab9afbfcf7..cc10ad5908 100644 --- a/transformers/tests/optimization_test.py +++ b/transformers/tests/optimization_test.py @@ -18,7 +18,6 @@ from __future__ import print_function import unittest import os -import pytest from transformers import is_torch_available @@ -31,10 +30,9 @@ if is_torch_available(): get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup) -else: - pytestmark = pytest.mark.skip("Require Torch") from .tokenization_tests_commons import TemporaryDirectory +from .utils import require_torch def unwrap_schedule(scheduler, num_steps=10): @@ -58,6 +56,7 @@ def unwrap_and_save_reload_schedule(scheduler, num_steps=10): scheduler.load_state_dict(state_dict) return lrs +@require_torch class OptimizationTest(unittest.TestCase): def assertListAlmostEqual(self, list1, list2, tol): @@ -80,6 +79,7 @@ class OptimizationTest(unittest.TestCase): self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2) +@require_torch class ScheduleInitTest(unittest.TestCase): m = torch.nn.Linear(50, 50) if is_torch_available() else None optimizer = AdamW(m.parameters(), lr=10.) if is_torch_available() else None diff --git a/transformers/tests/tokenization_auto_test.py b/transformers/tests/tokenization_auto_test.py index 79370811e8..18346d2768 100644 --- a/transformers/tests/tokenization_auto_test.py +++ b/transformers/tests/tokenization_auto_test.py @@ -18,15 +18,16 @@ from __future__ import print_function import unittest import shutil -import pytest import logging from transformers import AutoTokenizer, BertTokenizer, AutoTokenizer, GPT2Tokenizer from transformers import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP +from .utils import slow + class AutoTokenizerTest(unittest.TestCase): - @pytest.mark.slow + @slow def test_tokenizer_from_pretrained(self): logging.basicConfig(level=logging.INFO) for model_name in list(BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys())[:1]: diff --git a/transformers/tests/tokenization_bert_test.py b/transformers/tests/tokenization_bert_test.py index 73ea38e20a..f390248956 100644 --- a/transformers/tests/tokenization_bert_test.py +++ b/transformers/tests/tokenization_bert_test.py @@ -16,7 +16,6 @@ from __future__ import absolute_import, division, print_function, unicode_litera import os import unittest -import pytest from io import open from transformers.tokenization_bert import (BasicTokenizer, @@ -26,6 +25,7 @@ from transformers.tokenization_bert import (BasicTokenizer, _is_whitespace, VOCAB_FILES_NAMES) from .tokenization_tests_commons import CommonTestCases +from .utils import slow class BertTokenizationTest(CommonTestCases.CommonTokenizerTester): @@ -126,7 +126,7 @@ class BertTokenizationTest(CommonTestCases.CommonTokenizerTester): self.assertFalse(_is_punctuation(u"A")) self.assertFalse(_is_punctuation(u" ")) - @pytest.mark.slow + @slow def test_sequence_builders(self): tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased") diff --git a/transformers/tests/tokenization_distilbert_test.py b/transformers/tests/tokenization_distilbert_test.py index 77a487651d..e815eca672 100644 --- a/transformers/tests/tokenization_distilbert_test.py +++ b/transformers/tests/tokenization_distilbert_test.py @@ -16,13 +16,13 @@ from __future__ import absolute_import, division, print_function, unicode_litera import os import unittest -import pytest from io import open from transformers.tokenization_distilbert import (DistilBertTokenizer) from .tokenization_tests_commons import CommonTestCases from .tokenization_bert_test import BertTokenizationTest +from .utils import slow class DistilBertTokenizationTest(BertTokenizationTest): @@ -31,7 +31,7 @@ class DistilBertTokenizationTest(BertTokenizationTest): def get_tokenizer(self, **kwargs): return DistilBertTokenizer.from_pretrained(self.tmpdirname, **kwargs) - @pytest.mark.slow + @slow def test_sequence_builders(self): tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased") diff --git a/transformers/tests/tokenization_roberta_test.py b/transformers/tests/tokenization_roberta_test.py index a27bf7d654..8ad0b59511 100644 --- a/transformers/tests/tokenization_roberta_test.py +++ b/transformers/tests/tokenization_roberta_test.py @@ -17,11 +17,11 @@ from __future__ import absolute_import, division, print_function, unicode_litera import os import json import unittest -import pytest from io import open from transformers.tokenization_roberta import RobertaTokenizer, VOCAB_FILES_NAMES from .tokenization_tests_commons import CommonTestCases +from .utils import slow class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester): @@ -79,7 +79,7 @@ class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester): [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2] ) - @pytest.mark.slow + @slow def test_sequence_builders(self): tokenizer = RobertaTokenizer.from_pretrained("roberta-base") diff --git a/transformers/tests/tokenization_tests_commons.py b/transformers/tests/tokenization_tests_commons.py index 97cd555df3..faff003f4b 100644 --- a/transformers/tests/tokenization_tests_commons.py +++ b/transformers/tests/tokenization_tests_commons.py @@ -102,9 +102,11 @@ class CommonTestCases: with TemporaryDirectory() as tmpdirname: filename = os.path.join(tmpdirname, u"tokenizer.bin") - pickle.dump(tokenizer, open(filename, "wb")) + with open(filename, "wb") as handle: + pickle.dump(tokenizer, handle) - tokenizer_new = pickle.load(open(filename, "rb")) + with open(filename, "rb") as handle: + tokenizer_new = pickle.load(handle) subwords_loaded = tokenizer_new.tokenize(text) diff --git a/transformers/tests/tokenization_transfo_xl_test.py b/transformers/tests/tokenization_transfo_xl_test.py index 4e99484b0c..5495ebd3a6 100644 --- a/transformers/tests/tokenization_transfo_xl_test.py +++ b/transformers/tests/tokenization_transfo_xl_test.py @@ -16,7 +16,6 @@ from __future__ import absolute_import, division, print_function, unicode_litera import os import unittest -import pytest from io import open from transformers import is_torch_available @@ -24,11 +23,12 @@ from transformers import is_torch_available if is_torch_available(): import torch from transformers.tokenization_transfo_xl import TransfoXLTokenizer, VOCAB_FILES_NAMES -else: - pytestmark = pytest.mark.skip("Require Torch") # TODO: untangle Transfo-XL tokenizer from torch.load and torch.save from .tokenization_tests_commons import CommonTestCases +from .utils import require_torch + +@require_torch class TransfoXLTokenizationTest(CommonTestCases.CommonTokenizerTester): tokenizer_class = TransfoXLTokenizer if is_torch_available() else None diff --git a/transformers/tests/tokenization_utils_test.py b/transformers/tests/tokenization_utils_test.py index 8630191c69..ff3f80ff7d 100644 --- a/transformers/tests/tokenization_utils_test.py +++ b/transformers/tests/tokenization_utils_test.py @@ -18,13 +18,14 @@ from __future__ import print_function import unittest import six -import pytest from transformers import PreTrainedTokenizer from transformers.tokenization_gpt2 import GPT2Tokenizer +from .utils import slow + class TokenizerUtilsTest(unittest.TestCase): - @pytest.mark.slow + def check_tokenizer_from_pretrained(self, tokenizer_class): s3_models = list(tokenizer_class.max_model_input_sizes.keys()) for model_name in s3_models[:1]: @@ -41,6 +42,7 @@ class TokenizerUtilsTest(unittest.TestCase): special_tok_id = tokenizer.convert_tokens_to_ids(special_tok) self.assertIsInstance(special_tok_id, int) + @slow def test_pretrained_tokenizers(self): self.check_tokenizer_from_pretrained(GPT2Tokenizer) diff --git a/transformers/tests/tokenization_xlm_test.py b/transformers/tests/tokenization_xlm_test.py index 3ff6564e34..7582a46662 100644 --- a/transformers/tests/tokenization_xlm_test.py +++ b/transformers/tests/tokenization_xlm_test.py @@ -17,11 +17,11 @@ from __future__ import absolute_import, division, print_function, unicode_litera import os import unittest import json -import pytest from transformers.tokenization_xlm import XLMTokenizer, VOCAB_FILES_NAMES from .tokenization_tests_commons import CommonTestCases +from .utils import slow class XLMTokenizationTest(CommonTestCases.CommonTokenizerTester): @@ -67,7 +67,7 @@ class XLMTokenizationTest(CommonTestCases.CommonTokenizerTester): self.assertListEqual( tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) - @pytest.mark.slow + @slow def test_sequence_builders(self): tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048") diff --git a/transformers/tests/tokenization_xlnet_test.py b/transformers/tests/tokenization_xlnet_test.py index 2e14ffeb82..b68495a796 100644 --- a/transformers/tests/tokenization_xlnet_test.py +++ b/transformers/tests/tokenization_xlnet_test.py @@ -16,11 +16,11 @@ from __future__ import absolute_import, division, print_function, unicode_litera import os import unittest -import pytest from transformers.tokenization_xlnet import (XLNetTokenizer, SPIECE_UNDERLINE) from .tokenization_tests_commons import CommonTestCases +from .utils import slow SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'fixtures/test_sentencepiece.model') @@ -90,7 +90,7 @@ class XLNetTokenizationTest(CommonTestCases.CommonTokenizerTester): u'9', u'2', u'0', u'0', u'0', u',', SPIECE_UNDERLINE + u'and', SPIECE_UNDERLINE + u'this', SPIECE_UNDERLINE + u'is', SPIECE_UNDERLINE + u'f', u'al', u'se', u'.']) - @pytest.mark.slow + @slow def test_sequence_builders(self): tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased") diff --git a/transformers/tests/utils.py b/transformers/tests/utils.py new file mode 100644 index 0000000000..7a51ab612b --- /dev/null +++ b/transformers/tests/utils.py @@ -0,0 +1,64 @@ +import os +import unittest + +from distutils.util import strtobool + +from transformers.file_utils import _tf_available, _torch_available + + +try: + run_slow = os.environ["RUN_SLOW"] +except KeyError: + # RUN_SLOW isn't set, default to skipping slow tests. + _run_slow_tests = False +else: + # RUN_SLOW is set, convert it to True or False. + try: + _run_slow_tests = strtobool(run_slow) + except ValueError: + # More values are supported, but let's keep the message simple. + raise ValueError("If set, RUN_SLOW must be yes or no.") + + +def slow(test_case): + """ + Decorator marking a test as slow. + + Slow tests are skipped by default. Set the RUN_SLOW environment variable + to a truthy value to run them. + + """ + if not _run_slow_tests: + test_case = unittest.skip("test is slow")(test_case) + return test_case + + +def require_torch(test_case): + """ + Decorator marking a test that requires PyTorch. + + These tests are skipped when PyTorch isn't installed. + + """ + if not _torch_available: + test_case = unittest.skip("test requires PyTorch")(test_case) + return test_case + + +def require_tf(test_case): + """ + Decorator marking a test that requires TensorFlow. + + These tests are skipped when TensorFlow isn't installed. + + """ + if not _tf_available: + test_case = unittest.skip("test requires TensorFlow")(test_case) + return test_case + + +if _torch_available: + # Set the USE_CUDA environment variable to select a GPU. + torch_device = "cuda" if os.environ.get("USE_CUDA") else "cpu" +else: + torch_device = None diff --git a/transformers/tokenization_albert.py b/transformers/tokenization_albert.py index 40a4b29206..6b92d07218 100644 --- a/transformers/tokenization_albert.py +++ b/transformers/tokenization_albert.py @@ -141,7 +141,7 @@ class AlbertTokenizer(PreTrainedTokenizer): pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1) new_pieces = [] for piece in pieces: - if len(piece) > 1 and piece[-1] == ',' and piece[-2].isdigit(): + if len(piece) > 1 and piece[-1] == str(',') and piece[-2].isdigit(): cur_pieces = self.sp_model.EncodeAsPieces( piece[:-1].replace(SPIECE_UNDERLINE, '')) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: @@ -225,9 +225,9 @@ class AlbertTokenizer(PreTrainedTokenizer): """ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT sequence pair mask has the following format: - 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 - | first sequence | second sequence - + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence + if token_ids_1 is None, only returns the first portion of the mask (0's). """ sep = [self.sep_token_id] diff --git a/transformers/tokenization_ctrl.py b/transformers/tokenization_ctrl.py index 9454cbbaf3..219f17c404 100644 --- a/transformers/tokenization_ctrl.py +++ b/transformers/tokenization_ctrl.py @@ -133,9 +133,11 @@ class CTRLTokenizer(PreTrainedTokenizer): self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens - self.encoder = json.load(open(vocab_file, encoding="utf-8")) + with open(vocab_file, encoding="utf-8") as vocab_handle: + self.encoder = json.load(vocab_handle) self.decoder = {v:k for k,v in self.encoder.items()} - merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1] + with open(merges_file, encoding='utf-8') as merges_handle: + merges = merges_handle.read().split('\n')[1:-1] merges = [tuple(merge.split()) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} diff --git a/transformers/tokenization_gpt2.py b/transformers/tokenization_gpt2.py index 5fda709448..68c6101860 100644 --- a/transformers/tokenization_gpt2.py +++ b/transformers/tokenization_gpt2.py @@ -72,7 +72,7 @@ def bytes_to_unicode(): """ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. - + The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. @@ -122,13 +122,15 @@ class GPT2Tokenizer(PreTrainedTokenizer): self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens - self.encoder = json.load(open(vocab_file, encoding="utf-8")) + with open(vocab_file, encoding="utf-8") as vocab_handle: + self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} - bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1] - bpe_merges = [tuple(merge.split()) for merge in bpe_data] + with open(merges_file, encoding='utf-8') as merges_handle: + bpe_merges = merges_handle.read().split('\n')[1:-1] + bpe_merges = [tuple(merge.split()) for merge in bpe_merges] self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} @@ -234,4 +236,4 @@ class GPT2Tokenizer(PreTrainedTokenizer): writer.write(' '.join(bpe_tokens) + u'\n') index += 1 - return vocab_file, merge_file \ No newline at end of file + return vocab_file, merge_file diff --git a/transformers/tokenization_openai.py b/transformers/tokenization_openai.py index 0efbdb37c0..a4c64b7020 100644 --- a/transformers/tokenization_openai.py +++ b/transformers/tokenization_openai.py @@ -101,9 +101,11 @@ class OpenAIGPTTokenizer(PreTrainedTokenizer): self.nlp = BasicTokenizer(do_lower_case=True) self.fix_text = None - self.encoder = json.load(open(vocab_file, encoding="utf-8")) + with open(vocab_file, encoding="utf-8") as vocab_handle: + self.encoder = json.load(vocab_handle) self.decoder = {v:k for k,v in self.encoder.items()} - merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1] + with open(merges_file, encoding='utf-8') as merges_handle: + merges = merges_handle.read().split('\n')[1:-1] merges = [tuple(merge.split()) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} diff --git a/transformers/tokenization_utils.py b/transformers/tokenization_utils.py index 5d683629f0..4c6cbd8986 100644 --- a/transformers/tokenization_utils.py +++ b/transformers/tokenization_utils.py @@ -347,7 +347,7 @@ class PreTrainedTokenizer(object): "We assumed '{}' was a path or url to a directory containing vocabulary files " "named {} but couldn't find such vocabulary files at this path or url.".format( pretrained_model_name_or_path, ', '.join(s3_models), - pretrained_model_name_or_path, + pretrained_model_name_or_path, list(cls.vocab_files_names.values()))) # Get files from url, cache, or disk depending on the case @@ -382,7 +382,8 @@ class PreTrainedTokenizer(object): # Did we saved some inputs and kwargs to reload ? tokenizer_config_file = resolved_vocab_files.pop('tokenizer_config_file', None) if tokenizer_config_file is not None: - init_kwargs = json.load(open(tokenizer_config_file, encoding="utf-8")) + with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle: + init_kwargs = json.load(tokenizer_config_handle) saved_init_inputs = init_kwargs.pop('init_inputs', ()) if not init_inputs: init_inputs = saved_init_inputs @@ -407,7 +408,8 @@ class PreTrainedTokenizer(object): if args_name not in init_kwargs: init_kwargs[args_name] = file_path if special_tokens_map_file is not None: - special_tokens_map = json.load(open(special_tokens_map_file, encoding="utf-8")) + with open(special_tokens_map_file, encoding="utf-8") as special_tokens_map_handle: + special_tokens_map = json.load(special_tokens_map_handle) for key, value in special_tokens_map.items(): if key not in init_kwargs: init_kwargs[key] = value @@ -421,7 +423,8 @@ class PreTrainedTokenizer(object): # Add supplementary tokens. if added_tokens_file is not None: - added_tok_encoder = json.load(open(added_tokens_file, encoding="utf-8")) + with open(added_tokens_file, encoding="utf-8") as added_tokens_handle: + added_tok_encoder = json.load(added_tokens_handle) added_tok_decoder = {v:k for k, v in added_tok_encoder.items()} tokenizer.added_tokens_encoder.update(added_tok_encoder) tokenizer.added_tokens_decoder.update(added_tok_decoder) @@ -937,7 +940,7 @@ class PreTrainedTokenizer(object): logger.warning("Token indices sequence length is longer than the specified maximum sequence length " "for this model ({} > {}). Running this sequence through the model will result in " "indexing errors".format(len(ids), self.max_len)) - + return encoded_inputs def truncate_sequences(self, ids, pair_ids=None, num_tokens_to_remove=0, truncation_strategy='longest_first', stride=0): diff --git a/transformers/tokenization_xlm.py b/transformers/tokenization_xlm.py index ba994dc356..6c9f8e5e5c 100644 --- a/transformers/tokenization_xlm.py +++ b/transformers/tokenization_xlm.py @@ -524,7 +524,7 @@ class XLMTokenizer(PreTrainedTokenizer): - argument ``special_tokens`` and function ``set_special_tokens``, can be used to add additional symbols \ (ex: "__classify__") to a vocabulary - + - `lang2id` attribute maps the languages supported by the model with their ids if provided (automatically set for pretrained vocabularies) - `id2lang` attributes does reverse mapping if provided (automatically set for pretrained vocabularies) @@ -564,9 +564,11 @@ class XLMTokenizer(PreTrainedTokenizer): self.ja_word_tokenizer = None self.zh_word_tokenizer = None - self.encoder = json.load(open(vocab_file, encoding="utf-8")) + with open(vocab_file, encoding="utf-8") as vocab_handle: + self.encoder = json.load(vocab_handle) self.decoder = {v:k for k,v in self.encoder.items()} - merges = open(merges_file, encoding='utf-8').read().split('\n')[:-1] + with open(merges_file, encoding='utf-8') as merges_handle: + merges = merges_handle.read().split('\n')[:-1] merges = [tuple(merge.split()[:2]) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} diff --git a/transformers/tokenization_xlnet.py b/transformers/tokenization_xlnet.py index c01fbbbeeb..8c86a5bd60 100644 --- a/transformers/tokenization_xlnet.py +++ b/transformers/tokenization_xlnet.py @@ -141,7 +141,7 @@ class XLNetTokenizer(PreTrainedTokenizer): pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1) new_pieces = [] for piece in pieces: - if len(piece) > 1 and piece[-1] == ',' and piece[-2].isdigit(): + if len(piece) > 1 and piece[-1] == str(',') and piece[-2].isdigit(): cur_pieces = self.sp_model.EncodeAsPieces( piece[:-1].replace(SPIECE_UNDERLINE, '')) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: @@ -227,7 +227,7 @@ class XLNetTokenizer(PreTrainedTokenizer): An XLNet sequence pair mask has the following format: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2 | first sequence | second sequence | CLS segment ID - + if token_ids_1 is None, only returns the first portion of the mask (0's). """ sep = [self.sep_token_id] From 2670b0d682746e1fe94ab9c7b4d2fd7f4af03193 Mon Sep 17 00:00:00 2001 From: Michael Watkins Date: Wed, 4 Dec 2019 17:53:25 +0200 Subject: [PATCH 178/179] Fix bug which lowercases special tokens --- transformers/tests/tokenization_tests_commons.py | 8 +++++--- transformers/tokenization_utils.py | 15 +++++++++++++-- 2 files changed, 18 insertions(+), 5 deletions(-) diff --git a/transformers/tests/tokenization_tests_commons.py b/transformers/tests/tokenization_tests_commons.py index faff003f4b..d904f0067e 100644 --- a/transformers/tests/tokenization_tests_commons.py +++ b/transformers/tests/tokenization_tests_commons.py @@ -115,8 +115,10 @@ class CommonTestCases: def test_added_tokens_do_lower_case(self): tokenizer = self.get_tokenizer(do_lower_case=True) - text = "aaaaa bbbbbb low cccccccccdddddddd l" - text2 = "AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l" + special_token = tokenizer.all_special_tokens[0] + + text = special_token + " aaaaa bbbbbb low cccccccccdddddddd l " + special_token + text2 = special_token + " AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l " + special_token toks0 = tokenizer.tokenize(text) # toks before adding new_toks @@ -141,7 +143,7 @@ class CommonTestCases: self.assertEqual(len(toks), len(toks2)) # Length should still be the same self.assertNotEqual(len(toks), len(toks0)) - self.assertNotEqual(toks[0], toks2[0]) # But at least the first tokens should differ + self.assertNotEqual(toks[1], toks2[1]) # But at least the first non-special tokens should differ def test_add_tokens_tokenizer(self): tokenizer = self.get_tokenizer() diff --git a/transformers/tokenization_utils.py b/transformers/tokenization_utils.py index 4c6cbd8986..eb22c50ebd 100644 --- a/transformers/tokenization_utils.py +++ b/transformers/tokenization_utils.py @@ -22,6 +22,7 @@ import json import six import copy import itertools +import re from io import open from .file_utils import cached_path, is_tf_available, is_torch_available @@ -520,7 +521,7 @@ class PreTrainedTokenizer(object): to_add_tokens = [] for token in new_tokens: assert isinstance(token, str) or (six.PY2 and isinstance(token, unicode)) - if self.init_kwargs.get('do_lower_case', False): + if self.init_kwargs.get('do_lower_case', False) and token not in self.all_special_tokens: token = token.lower() if token != self.unk_token and \ self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token) and \ @@ -615,8 +616,18 @@ class PreTrainedTokenizer(object): Take care of added tokens. """ + def lowercase_text(t): + # convert non-special tokens to lowercase + escaped_special_toks = [re.escape(s_tok) for s_tok in self.all_special_tokens] + pattern = r'(^' + r'|'.join(escaped_special_toks) + r')|' + \ + r'(.+?)' + return re.sub( + pattern, + lambda m: m.groups()[0] or m.groups()[1].lower(), + t) + if self.init_kwargs.get('do_lower_case', False): - text = text.lower() + text = lowercase_text(text) def split_on_token(tok, text): result = [] From 0cb163865a4c761c226b151283309eedb2b1ca4d Mon Sep 17 00:00:00 2001 From: Aymeric Augustin Date: Sat, 7 Dec 2019 13:46:14 +0100 Subject: [PATCH 179/179] Remove pytest dependency. (#2093) --- transformers/tests/optimization_tf_test.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/transformers/tests/optimization_tf_test.py b/transformers/tests/optimization_tf_test.py index ac5109cb56..515d12a158 100644 --- a/transformers/tests/optimization_tf_test.py +++ b/transformers/tests/optimization_tf_test.py @@ -3,18 +3,19 @@ from __future__ import division from __future__ import print_function import unittest -import pytest from transformers import is_tf_available +from .utils import require_tf + if is_tf_available(): import tensorflow as tf from tensorflow.python.eager import context from tensorflow.python.framework import ops from transformers import (create_optimizer, GradientAccumulator) -else: - pytestmark = pytest.mark.skip("Require TensorFlow") + +@require_tf class OptimizationFTest(unittest.TestCase): def assertListAlmostEqual(self, list1, list2, tol): self.assertEqual(len(list1), len(list2))

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