diff --git a/README.md b/README.md index f3aa8a95ee..214f61cc0c 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/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 | +| [Documentation][(v2.2.0/v2.2.1/v2.2.2)](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 @@ -144,7 +144,8 @@ At some point in the future, you'll be able to seamlessly move from pre-training 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/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. +12. **[T5](https://github.com/google-research/text-to-text-transfer-transformer)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu. +13. 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/conf.py b/docs/source/conf.py index 2f8505ab3a..99b7b44922 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'2.2.1' +release = u'2.2.2' # -- General configuration --------------------------------------------------- diff --git a/docs/source/pretrained_models.rst b/docs/source/pretrained_models.rst index 775772e896..c6b990f213 100644 --- a/docs/source/pretrained_models.rst +++ b/docs/source/pretrained_models.rst @@ -217,6 +217,21 @@ Here is the full list of the currently provided pretrained models together with | | | | ALBERT xxlarge model with no dropout, additional training data and longer training | | | | (see `details `__) | +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| T5 | ``t5-small`` | | ~60M parameters with 6-layers, 512-hidden-state, 2048 feed-forward hidden-state, 8-heads, | +| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) | +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``t5-base`` | | ~220M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 12-heads, | +| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) | +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``t5-large`` | | ~770M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads, | +| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) | +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``t5-3B`` | | ~2.8B parameters with 24-layers, 1024-hidden-state, 16384 feed-forward hidden-state, 32-heads, | +| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) | +| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ +| | ``t5-11B`` | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads, | +| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) | ++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ .. `__ diff --git a/examples/run_squad.py b/examples/run_squad.py index 117b86e32c..a39915ee8b 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -580,10 +580,16 @@ def main(): # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory results = {} if args.do_eval and args.local_rank in [-1, 0]: - 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 model loading logs + + if args.do_train: + logger.info("Loading checkpoints saved during training for evaluation") + 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 model loading logs + else: + logger.info("Loading checkpoint %s for evaluation", args.model_name_or_path) + checkpoints = [args.model_name_or_path] logger.info("Evaluate the following checkpoints: %s", checkpoints) diff --git a/examples/summarization/README.md b/examples/summarization/README.md index 96825cfa46..b98581e8e5 100644 --- a/examples/summarization/README.md +++ b/examples/summarization/README.md @@ -29,7 +29,7 @@ And move all the stories to the same folder. We will refer as `$DATA_PATH` the p python run_summarization.py \ --documents_dir $DATA_PATH \ --summaries_output_dir $SUMMARIES_PATH \ # optional - --to_cpu false \ + --no_cuda false \ --batch_size 4 \ --min_length 50 \ --max_length 200 \ @@ -39,7 +39,7 @@ python run_summarization.py \ --compute_rouge true ``` -The scripts executes on GPU if one is available and if `to_cpu` is not set to `true`. Inference on multiple GPUs is not suported yet. The ROUGE scores will be displayed in the console at the end of evaluation and written in a `rouge_scores.txt` file. The script takes 30 hours to compute with a single Tesla V100 GPU and a batch size of 10 (300,000 texts to summarize). +The scripts executes on GPU if one is available and if `no_cuda` is not set to `true`. Inference on multiple GPUs is not suported yet. The ROUGE scores will be displayed in the console at the end of evaluation and written in a `rouge_scores.txt` file. The script takes 30 hours to compute with a single Tesla V100 GPU and a batch size of 10 (300,000 texts to summarize). ## Summarize any text @@ -49,7 +49,7 @@ Put the documents that you would like to summarize in a folder (the path to whic python run_summarization.py \ --documents_dir $DATA_PATH \ --summaries_output_dir $SUMMARIES_PATH \ # optional - --to_cpu false \ + --no_cuda false \ --batch_size 4 \ --min_length 50 \ --max_length 200 \ diff --git a/setup.py b/setup.py index c4af32df83..eacb5ecec0 100644 --- a/setup.py +++ b/setup.py @@ -44,7 +44,7 @@ extras['all'] = [package for package in extras.values()] setup( name="transformers", - version="2.2.1", + version="2.2.2", 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/templates/adding_a_new_model/configuration_xxx.py b/templates/adding_a_new_model/configuration_xxx.py index ca9e0d554b..12d69799a9 100644 --- a/templates/adding_a_new_model/configuration_xxx.py +++ b/templates/adding_a_new_model/configuration_xxx.py @@ -75,8 +75,6 @@ class XxxConfig(PretrainedConfig): attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, - - num_labels=1, summary_type='cls_index', summary_use_proj=True, summary_activation=None, @@ -84,7 +82,7 @@ class XxxConfig(PretrainedConfig): summary_first_dropout=0.1, **kwargs): super(XxxConfig, self).__init__(**kwargs) - self.vocab_size = vocab_size if isinstance(vocab_size, six.string_types) else -1 + self.vocab_size = vocab_size self.n_ctx = n_ctx self.n_positions = n_positions self.n_embd = n_embd @@ -95,23 +93,11 @@ class XxxConfig(PretrainedConfig): self.attn_pdrop = attn_pdrop self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range - - self.num_labels = num_labels self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_first_dropout = summary_first_dropout self.summary_proj_to_labels = summary_proj_to_labels - if isinstance(vocab_size, six.string_types): - with open(vocab_size, "r", encoding="utf-8") as reader: - json_config = json.loads(reader.read()) - for key, value in json_config.items(): - self.__dict__[key] = value - elif not isinstance(vocab_size, int): - raise ValueError( - "First argument must be either a vocabulary size (int)" - "or the path to a pretrained model config file (str)" - ) @property def max_position_embeddings(self): diff --git a/templates/adding_a_new_model/convert_xxx_original_tf_checkpoint_to_pytorch.py b/templates/adding_a_new_model/convert_xxx_original_tf_checkpoint_to_pytorch.py index d50d129cba..9d389deaad 100755 --- a/templates/adding_a_new_model/convert_xxx_original_tf_checkpoint_to_pytorch.py +++ b/templates/adding_a_new_model/convert_xxx_original_tf_checkpoint_to_pytorch.py @@ -26,9 +26,9 @@ from transformers import XxxConfig, XxxForPreTraining, load_tf_weights_in_xxx import logging logging.basicConfig(level=logging.INFO) -def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, xxx_config_file, pytorch_dump_path): +def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path): # Initialise PyTorch model - config = XxxConfig.from_json_file(xxx_config_file) + config = XxxConfig.from_json_file(config_file) print("Building PyTorch model from configuration: {}".format(str(config))) model = XxxForPreTraining(config) @@ -48,11 +48,11 @@ if __name__ == "__main__": type = str, required = True, help = "Path to the TensorFlow checkpoint path.") - parser.add_argument("--xxx_config_file", + parser.add_argument("--config_file", default = None, type = str, required = True, - help = "The config json file corresponding to the pre-trained XXX model. \n" + help = "The config json file corresponding to the pre-trained model. \n" "This specifies the model architecture.") parser.add_argument("--pytorch_dump_path", default = None, @@ -61,5 +61,5 @@ if __name__ == "__main__": help = "Path to the output PyTorch model.") args = parser.parse_args() convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, - args.xxx_config_file, + args.config_file, args.pytorch_dump_path) diff --git a/templates/adding_a_new_model/modeling_tf_xxx.py b/templates/adding_a_new_model/modeling_tf_xxx.py index 59f798bdbf..1783620998 100644 --- a/templates/adding_a_new_model/modeling_tf_xxx.py +++ b/templates/adding_a_new_model/modeling_tf_xxx.py @@ -26,6 +26,8 @@ import logging import math import os import sys +import copy +import itertools from io import open import numpy as np diff --git a/templates/adding_a_new_model/modeling_xxx.py b/templates/adding_a_new_model/modeling_xxx.py index 94c4b0db9a..c8be58e288 100644 --- a/templates/adding_a_new_model/modeling_xxx.py +++ b/templates/adding_a_new_model/modeling_xxx.py @@ -25,6 +25,8 @@ import logging import math import os import sys +import copy +import itertools from io import open import torch diff --git a/transformers/__init__.py b/transformers/__init__.py index 5d7b0b772c..740d2440c2 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -1,4 +1,4 @@ -__version__ = "2.2.1" +__version__ = "2.2.2" # Work around to update TensorFlow's absl.logging threshold which alters the # default Python logging output behavior when present. @@ -48,6 +48,7 @@ from .tokenization_roberta import RobertaTokenizer from .tokenization_distilbert import DistilBertTokenizer from .tokenization_albert import AlbertTokenizer from .tokenization_camembert import CamembertTokenizer +from .tokenization_t5 import T5Tokenizer # Configurations from .configuration_utils import PretrainedConfig @@ -58,12 +59,12 @@ from .configuration_transfo_xl import TransfoXLConfig, TRANSFO_XL_PRETRAINED_CON from .configuration_gpt2 import GPT2Config, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP from .configuration_xlnet import XLNetConfig, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP -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_PRETRAINED_CONFIG_ARCHIVE_MAP from .configuration_camembert import CamembertConfig, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP +from .configuration_t5 import T5Config, T5_PRETRAINED_CONFIG_ARCHIVE_MAP # Modeling if is_torch_available(): @@ -77,8 +78,8 @@ if is_torch_available(): BertForTokenClassification, BertForQuestionAnswering, load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel, - OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, - load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP) + OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, + load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_transfo_xl import (TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel, AdaptiveEmbedding, load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP) @@ -110,6 +111,9 @@ if is_torch_available(): CamembertForTokenClassification, CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model + from .modeling_t5 import (T5PreTrainedModel, T5Model, T5WithLMHeadModel, + load_tf_weights_in_t5, + T5_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_albert import (AlbertPreTrainedModel, AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, AlbertForQuestionAnswering, @@ -178,6 +182,10 @@ if is_tf_available(): from .modeling_tf_albert import (TFAlbertPreTrainedModel, TFAlbertModel, TFAlbertForMaskedLM, TFAlbertForSequenceClassification, TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) + + from .modeling_tf_t5 import (TFT5PreTrainedModel, TFT5Model, TFT5WithLMHeadModel, + TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP) + # Optimization from .optimization_tf import (WarmUp, create_optimizer, AdamWeightDecay, GradientAccumulator) diff --git a/transformers/__main__.py b/transformers/__main__.py index 31dbd24908..6136d768f6 100644 --- a/transformers/__main__.py +++ b/transformers/__main__.py @@ -6,6 +6,7 @@ def main(): "This command line utility let you convert original (author released) model checkpoint to pytorch.\n" "It should be used as one of: \n" ">> transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT, \n" + ">> transformers t5 TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT, \n" ">> transformers gpt OPENAI_GPT_CHECKPOINT_FOLDER_PATH PYTORCH_DUMP_OUTPUT [OPENAI_GPT_CONFIG], \n" ">> transformers transfo_xl TF_CHECKPOINT_OR_DATASET PYTORCH_DUMP_OUTPUT [TF_CONFIG] or \n" ">> transformers gpt2 TF_CHECKPOINT PYTORCH_DUMP_OUTPUT [GPT2_CONFIG] or \n" @@ -21,6 +22,23 @@ def main(): "https://www.tensorflow.org/install/ for installation instructions.") raise + if len(sys.argv) != 5: + # pylint: disable=line-too-long + print("Should be used as `transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT`") + else: + PYTORCH_DUMP_OUTPUT = sys.argv.pop() + TF_CONFIG = sys.argv.pop() + TF_CHECKPOINT = sys.argv.pop() + convert_tf_checkpoint_to_pytorch(TF_CHECKPOINT, TF_CONFIG, PYTORCH_DUMP_OUTPUT) + elif sys.argv[1] == "t5": + try: + from .convert_t5_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch + except ImportError: + print("transformers can only be used from the commandline to convert TensorFlow models in PyTorch, " + "In that case, it requires TensorFlow to be installed. Please see " + "https://www.tensorflow.org/install/ for installation instructions.") + raise + if len(sys.argv) != 5: # pylint: disable=line-too-long print("Should be used as `transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT`") diff --git a/transformers/commands/user.py b/transformers/commands/user.py index d79922ed8a..8e0e563422 100644 --- a/transformers/commands/user.py +++ b/transformers/commands/user.py @@ -19,8 +19,8 @@ class UserCommands(BaseTransformersCLICommand): 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.add_argument('path', type=str, help='Local path of the folder or individual file to upload.') + upload_parser.add_argument('--filename', type=str, default=None, help='Optional: override individual object filename on S3.') upload_parser.set_defaults(func=lambda args: UploadCommand(args)) @@ -138,28 +138,57 @@ class ListObjsCommand(BaseUserCommand): class UploadCommand(BaseUserCommand): + def walk_dir(self, rel_path): + """ + Recursively list all files in a folder. + """ + entries: List[os.DirEntry] = list(os.scandir(rel_path)) + files = [ + ( + os.path.join(os.getcwd(), f.path), # filepath + f.path # filename + ) + for f in entries if f.is_file() + ] + for f in entries: + if f.is_dir(): + files += self.walk_dir(f.path) + return files + 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( - ANSI.bold(filepath), ANSI.bold(filename) + local_path = os.path.abspath(self.args.path) + if os.path.isdir(local_path): + if self.args.filename is not None: + raise ValueError("Cannot specify a filename override when uploading a folder.") + rel_path = os.path.basename(local_path) + files = self.walk_dir(rel_path) + elif os.path.isfile(local_path): + filename = self.args.filename if self.args.filename is not None else os.path.basename(local_path) + files = [(local_path, filename)] + else: + raise ValueError("Not a valid file or directory: {}".format(local_path)) + + for filepath, filename in files: + 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( - ANSI.bold("Uploading... This might take a while if file is large") + ANSI.bold("Uploading... This might take a while if files are large") ) - access_url = self._api.presign_and_upload( - token=token, filename=filename, filepath=filepath - ) - print("Your file now lives at:") - print(access_url) + for filepath, filename in files: + 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/configuration_auto.py b/transformers/configuration_auto.py index fbc5c59199..680c55fa54 100644 --- a/transformers/configuration_auto.py +++ b/transformers/configuration_auto.py @@ -29,6 +29,7 @@ from .configuration_distilbert import DistilBertConfig from .configuration_ctrl import CTRLConfig from .configuration_camembert import CamembertConfig from .configuration_albert import AlbertConfig +from .configuration_t5 import T5Config logger = logging.getLogger(__name__) @@ -68,6 +69,7 @@ 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 `t5`: T5Config (T5 model) - contains `distilbert`: DistilBertConfig (DistilBERT model) - contains `albert`: AlbertConfig (ALBERT model) - contains `camembert`: CamembertConfig (CamemBERT model) @@ -124,7 +126,9 @@ class AutoConfig(object): assert unused_kwargs == {'foo': False} """ - if 'distilbert' in pretrained_model_name_or_path: + if 't5' in pretrained_model_name_or_path: + return T5Config.from_pretrained(pretrained_model_name_or_path, **kwargs) + elif '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) diff --git a/transformers/configuration_t5.py b/transformers/configuration_t5.py new file mode 100644 index 0000000000..6391cb4180 --- /dev/null +++ b/transformers/configuration_t5.py @@ -0,0 +1,119 @@ +# coding=utf-8 +# Copyright 2010, The T5 Authors and HuggingFace 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. +""" T5 model configuration """ + +from __future__ import absolute_import, division, print_function, unicode_literals + +import json +import logging +import sys +import six +from io import open + +from .configuration_utils import PretrainedConfig + +logger = logging.getLogger(__name__) + +T5_PRETRAINED_CONFIG_ARCHIVE_MAP = { + 't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-config.json", + 't5-base': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-config.json", + 't5-large': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-config.json", + 't5-3b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-3b-config.json", + 't5-11b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-11b-config.json", +} + + +class T5Config(PretrainedConfig): + r""" + :class:`~transformers.T5Config` is the configuration class to store the configuration of a + `T5Model`. + + + Arguments: + vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `T5Model`. + hidden_size: Size of the encoder layers and the pooler layer. + num_hidden_layers: Number of hidden layers in the Transformer encoder. + 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. + hidden_act: The non-linear activation function (function or string) in the + encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported. + hidden_dropout_prob: The dropout probabilitiy 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 + `T5Model`. + initializer_factor: A factor for initializing all weight matrices (should be kept to 1.0, used for initialization testing). + layer_norm_eps: The epsilon used by LayerNorm. + """ + pretrained_config_archive_map = T5_PRETRAINED_CONFIG_ARCHIVE_MAP + + def __init__(self, + vocab_size_or_config_json_file=32128, + n_positions=512, + d_model=512, + d_kv=64, + d_ff=2048, + num_layers=6, + num_heads=8, + relative_attention_num_buckets=32, + dropout_rate=0.1, + layer_norm_epsilon=1e-6, + initializer_factor=1.0, + **kwargs): + super(T5Config, self).__init__(**kwargs) + self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1 + self.n_positions = n_positions + self.d_model = d_model + self.d_kv = d_kv + self.d_ff = d_ff + self.num_layers = num_layers + self.num_heads = num_heads + self.relative_attention_num_buckets = relative_attention_num_buckets + self.dropout_rate = dropout_rate + self.layer_norm_epsilon = layer_norm_epsilon + self.initializer_factor = initializer_factor + + if isinstance(vocab_size_or_config_json_file, six.string_types): + with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader: + json_config = json.loads(reader.read()) + for key, value in json_config.items(): + self.__dict__[key] = value + elif not isinstance(vocab_size_or_config_json_file, int): + raise ValueError( + "First argument must be either a vocabulary size (int)" + "or the path to a pretrained model config file (str)" + ) + + @property + def max_position_embeddings(self): + return self.n_positions + + @property + def hidden_size(self): + return self.d_model + + @property + def num_attention_heads(self): + return self.num_heads + + @property + def num_hidden_layers(self): + return self.num_layers diff --git a/transformers/convert_pytorch_checkpoint_to_tf2.py b/transformers/convert_pytorch_checkpoint_to_tf2.py index 2c419888e8..4a9832f123 100644 --- a/transformers/convert_pytorch_checkpoint_to_tf2.py +++ b/transformers/convert_pytorch_checkpoint_to_tf2.py @@ -34,7 +34,8 @@ from transformers import (load_pytorch_checkpoint_in_tf2_model, RobertaConfig, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig, TFCTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, - AlbertConfig, TFAlbertForMaskedLM, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP) + AlbertConfig, TFAlbertForMaskedLM, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, + T5Config, TFT5WithLMHeadModel, T5_PRETRAINED_CONFIG_ARCHIVE_MAP) if is_torch_available(): import torch @@ -48,7 +49,8 @@ if is_torch_available(): RobertaForMaskedLM, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP, DistilBertForMaskedLM, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, - AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) + AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP, + T5WithLMHeadModel, T5_PRETRAINED_MODEL_ARCHIVE_MAP) else: (BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, @@ -59,7 +61,8 @@ else: RobertaForMaskedLM, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP, DistilBertForMaskedLM, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, - AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) = ( + AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP, + T5WithLMHeadModel, T5_PRETRAINED_MODEL_ARCHIVE_MAP) = ( None, None, None, None, None, None, None, None, @@ -69,6 +72,7 @@ else: None, None, None, None, None, None, None, None, + None, None, None, None) @@ -90,7 +94,8 @@ MODEL_CLASSES = { '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), - 'albert': (AlbertConfig, TFAlbertForMaskedLM, AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP) + 'albert': (AlbertConfig, TFAlbertForMaskedLM, AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP), + 't5': (T5Config, TFT5WithLMHeadModel, T5WithLMHeadModel, T5_PRETRAINED_MODEL_ARCHIVE_MAP, T5_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): @@ -115,24 +120,21 @@ def convert_pt_checkpoint_to_tf(model_type, pytorch_checkpoint_path, config_file tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path) if compare_with_pt_model: - inputs_list = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]] - tf_inputs = tf.constant(inputs_list) - tfo = tf_model(tf_inputs, training=False) # build the network + tfo = tf_model(tf_model.dummy_inputs, training=False) # build the network state_dict = torch.load(pytorch_checkpoint_path, map_location='cpu') pt_model = pt_model_class.from_pretrained(pretrained_model_name_or_path=None, config=config, state_dict=state_dict) - pt_inputs = torch.tensor(inputs_list) with torch.no_grad(): - pto = pt_model(pt_inputs) + pto = pt_model(**pt_model.dummy_inputs) - np_pt = pto[0].detach().numpy() + np_pt = pto[0].numpy() np_tf = tfo[0].numpy() diff = np.amax(np.abs(np_pt - np_tf)) print("Max absolute difference between models outputs {}".format(diff)) - assert diff <= 2e-2, "Error, model absolute difference is >2e-2" + assert diff <= 2e-2, "Error, model absolute difference is >2e-2: {}".format(diff) # Save pytorch-model print("Save TensorFlow model to {}".format(tf_dump_path)) diff --git a/transformers/convert_t5_original_tf_checkpoint_to_pytorch.py b/transformers/convert_t5_original_tf_checkpoint_to_pytorch.py new file mode 100755 index 0000000000..2b74d2dd93 --- /dev/null +++ b/transformers/convert_t5_original_tf_checkpoint_to_pytorch.py @@ -0,0 +1,65 @@ +# coding=utf-8 +# Copyright 2018 The T5 authors and 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 T5 checkpoint.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import torch + +from transformers import T5Config, T5Model, load_tf_weights_in_t5 + +import logging +logging.basicConfig(level=logging.INFO) + +def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path): + # Initialise PyTorch model + config = T5Config.from_json_file(config_file) + print("Building PyTorch model from configuration: {}".format(str(config))) + model = T5Model(config) + + # Load weights from tf checkpoint + load_tf_weights_in_t5(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("--config_file", + default = None, + type = str, + required = True, + help = "The config json file corresponding to the pre-trained T5 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.config_file, + args.pytorch_dump_path) diff --git a/transformers/data/processors/glue.py b/transformers/data/processors/glue.py index 518251b050..11ebd949de 100644 --- a/transformers/data/processors/glue.py +++ b/transformers/data/processors/glue.py @@ -133,7 +133,7 @@ def glue_convert_examples_to_features(examples, tokenizer, if is_tf_available() and is_tf_dataset: def gen(): for ex in features: - yield ({'input_ids': ex.input_ids, + yield ({'input_ids': ex.input_ids, 'attention_mask': ex.attention_mask, 'token_type_ids': ex.token_type_ids}, ex.label) diff --git a/transformers/data/processors/squad.py b/transformers/data/processors/squad.py index 9bc4375684..84aa429e26 100644 --- a/transformers/data/processors/squad.py +++ b/transformers/data/processors/squad.py @@ -18,19 +18,20 @@ if is_tf_available(): logger = logging.getLogger(__name__) -def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, - orig_answer_text): + +def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text): """Returns tokenized answer spans that better match the annotated answer.""" tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text)) for new_start in range(input_start, input_end + 1): for new_end in range(input_end, new_start - 1, -1): - text_span = " ".join(doc_tokens[new_start:(new_end + 1)]) + text_span = " ".join(doc_tokens[new_start : (new_end + 1)]) if text_span == tok_answer_text: return (new_start, new_end) return (input_start, input_end) + def _check_is_max_context(doc_spans, cur_span_index, position): """Check if this is the 'max context' doc span for the token.""" best_score = None @@ -50,10 +51,11 @@ def _check_is_max_context(doc_spans, cur_span_index, position): return cur_span_index == best_span_index + def _new_check_is_max_context(doc_spans, cur_span_index, position): """Check if this is the 'max context' doc span for the token.""" # if len(doc_spans) == 1: - # return True + # return True best_score = None best_span_index = None for (span_index, doc_span) in enumerate(doc_spans): @@ -71,14 +73,16 @@ def _new_check_is_max_context(doc_spans, cur_span_index, position): return cur_span_index == best_span_index + def _is_whitespace(c): if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F: return True return False -def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, - doc_stride, max_query_length, is_training, - return_dataset=False): + +def squad_convert_examples_to_features( + examples, tokenizer, max_seq_length, doc_stride, max_query_length, is_training, return_dataset=False +): """ Converts a list of examples into a list of features that can be directly given as input to a model. It is model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs. @@ -112,24 +116,23 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, ) """ - # Defining helper methods + # Defining helper methods unique_id = 1000000000 features = [] - for (example_index, example) in enumerate(tqdm(examples)): + for (example_index, example) in enumerate(tqdm(examples, desc="Converting examples to features")): if is_training and not example.is_impossible: # Get start and end position start_position = example.start_position end_position = example.end_position # If the answer cannot be found in the text, then skip this example. - actual_text = " ".join(example.doc_tokens[start_position:(end_position + 1)]) + actual_text = " ".join(example.doc_tokens[start_position : (end_position + 1)]) cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text)) if actual_text.find(cleaned_answer_text) == -1: logger.warning("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text) continue - tok_to_orig_index = [] orig_to_tok_index = [] all_doc_tokens = [] @@ -140,7 +143,6 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, tok_to_orig_index.append(i) all_doc_tokens.append(sub_token) - if is_training and not example.is_impossible: tok_start_position = orig_to_tok_index[example.start_position] if example.end_position < len(example.doc_tokens) - 1: @@ -153,36 +155,41 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, ) spans = [] - - truncated_query = tokenizer.encode(example.question_text, add_special_tokens=False, max_length=max_query_length) - sequence_added_tokens = tokenizer.max_len - tokenizer.max_len_single_sentence - sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair + + truncated_query = tokenizer.encode( + example.question_text, add_special_tokens=False, max_length=max_query_length + ) + sequence_added_tokens = tokenizer.max_len - tokenizer.max_len_single_sentence + sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair span_doc_tokens = all_doc_tokens while len(spans) * doc_stride < len(all_doc_tokens): - + encoded_dict = tokenizer.encode_plus( - truncated_query if tokenizer.padding_side == "right" else span_doc_tokens, - span_doc_tokens if tokenizer.padding_side == "right" else truncated_query, - max_length=max_seq_length, - return_overflowing_tokens=True, + truncated_query if tokenizer.padding_side == "right" else span_doc_tokens, + span_doc_tokens if tokenizer.padding_side == "right" else truncated_query, + max_length=max_seq_length, + return_overflowing_tokens=True, pad_to_max_length=True, stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens, - truncation_strategy='only_second' if tokenizer.padding_side == "right" else 'only_first' + truncation_strategy="only_second" if tokenizer.padding_side == "right" else "only_first", ) - paragraph_len = min(len(all_doc_tokens) - len(spans) * doc_stride, max_seq_length - len(truncated_query) - sequence_pair_added_tokens) + paragraph_len = min( + len(all_doc_tokens) - len(spans) * doc_stride, + max_seq_length - len(truncated_query) - sequence_pair_added_tokens, + ) - if tokenizer.pad_token_id in encoded_dict['input_ids']: - non_padded_ids = encoded_dict['input_ids'][:encoded_dict['input_ids'].index(tokenizer.pad_token_id)] + if tokenizer.pad_token_id in encoded_dict["input_ids"]: + non_padded_ids = encoded_dict["input_ids"][: encoded_dict["input_ids"].index(tokenizer.pad_token_id)] else: - non_padded_ids = encoded_dict['input_ids'] + non_padded_ids = encoded_dict["input_ids"] tokens = tokenizer.convert_ids_to_tokens(non_padded_ids) token_to_orig_map = {} for i in range(paragraph_len): - index = len(truncated_query) + sequence_added_tokens + i if tokenizer.padding_side == "right" else i + index = len(truncated_query) + sequence_added_tokens + i if tokenizer.padding_side == "right" else i token_to_orig_map[index] = tok_to_orig_index[len(spans) * doc_stride + i] encoded_dict["paragraph_len"] = paragraph_len @@ -202,16 +209,20 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, for doc_span_index in range(len(spans)): for j in range(spans[doc_span_index]["paragraph_len"]): is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j) - index = j if tokenizer.padding_side == "left" else spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j + index = ( + j + if tokenizer.padding_side == "left" + else spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j + ) spans[doc_span_index]["token_is_max_context"][index] = is_max_context for span in spans: # Identify the position of the CLS token - cls_index = span['input_ids'].index(tokenizer.cls_token_id) + cls_index = span["input_ids"].index(tokenizer.cls_token_id) # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer) # Original TF implem also keep the classification token (set to 0) (not sure why...) - p_mask = np.array(span['token_type_ids']) + p_mask = np.array(span["token_type_ids"]) p_mask = np.minimum(p_mask, 1) @@ -224,7 +235,6 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, # Set the CLS index to '0' p_mask[cls_index] = 0 - span_is_impossible = example.is_impossible start_position = 0 end_position = 0 @@ -247,55 +257,99 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, doc_offset = 0 else: doc_offset = len(truncated_query) + sequence_added_tokens - + start_position = tok_start_position - doc_start + doc_offset end_position = tok_end_position - doc_start + doc_offset - - features.append(SquadFeatures( - span['input_ids'], - span['attention_mask'], - span['token_type_ids'], - cls_index, - p_mask.tolist(), - - example_index=example_index, - unique_id=unique_id, - paragraph_len=span['paragraph_len'], - token_is_max_context=span["token_is_max_context"], - tokens=span["tokens"], - token_to_orig_map=span["token_to_orig_map"], - - start_position=start_position, - end_position=end_position - )) + features.append( + SquadFeatures( + span["input_ids"], + span["attention_mask"], + span["token_type_ids"], + cls_index, + p_mask.tolist(), + example_index=example_index, + unique_id=unique_id, + paragraph_len=span["paragraph_len"], + token_is_max_context=span["token_is_max_context"], + tokens=span["tokens"], + token_to_orig_map=span["token_to_orig_map"], + start_position=start_position, + end_position=end_position, + ) + ) unique_id += 1 - if return_dataset == 'pt': + if return_dataset == "pt": if not is_torch_available(): raise ImportError("Pytorch must be installed to return a pytorch dataset.") # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) - all_input_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long) - all_segment_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long) + all_attention_masks = 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) all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long) all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float) if not is_training: all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) - dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, - all_example_index, all_cls_index, all_p_mask) + dataset = TensorDataset( + all_input_ids, all_attention_masks, all_token_type_ids, all_example_index, all_cls_index, all_p_mask + ) else: all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long) all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long) - dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, - all_start_positions, all_end_positions, - all_cls_index, all_p_mask) + dataset = TensorDataset( + all_input_ids, + all_attention_masks, + all_token_type_ids, + all_start_positions, + all_end_positions, + all_cls_index, + all_p_mask, + ) return features, dataset - + elif return_dataset == "tf": + if not is_tf_available(): + raise ImportError("TensorFlow must be installed to return a TensorFlow dataset.") + + def gen(): + for ex in features: + yield ( + { + "input_ids": ex.input_ids, + "attention_mask": ex.attention_mask, + "token_type_ids": ex.token_type_ids, + }, { + "start_position": ex.start_position, + "end_position": ex.end_position, + "cls_index": ex.cls_index, + "p_mask": ex.p_mask, + } + ) + + return tf.data.Dataset.from_generator( + gen, + ( + {"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32}, + {"start_position": tf.int64, "end_position": tf.int64, "cls_index": tf.int64, "p_mask": tf.int32}, + ), + ( + { + "input_ids": tf.TensorShape([None]), + "attention_mask": tf.TensorShape([None]), + "token_type_ids": tf.TensorShape([None]), + }, + { + "start_position": tf.TensorShape([]), + "end_position": tf.TensorShape([]), + "cls_index": tf.TensorShape([]), + "p_mask": tf.TensorShape([None]), + }, + ), + ) return features @@ -305,31 +359,32 @@ class SquadProcessor(DataProcessor): Processor for the SQuAD data set. Overriden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and version 2.0 of SQuAD, respectively. """ + train_file = None dev_file = None def _get_example_from_tensor_dict(self, tensor_dict, evaluate=False): if not evaluate: - answer = tensor_dict['answers']['text'][0].numpy().decode('utf-8') - answer_start = tensor_dict['answers']['answer_start'][0].numpy() + answer = tensor_dict["answers"]["text"][0].numpy().decode("utf-8") + answer_start = tensor_dict["answers"]["answer_start"][0].numpy() answers = [] else: - answers = [{ - "answer_start": start.numpy(), - "text": text.numpy().decode('utf-8') - } for start, text in zip(tensor_dict['answers']["answer_start"], tensor_dict['answers']["text"])] + answers = [ + {"answer_start": start.numpy(), "text": text.numpy().decode("utf-8")} + for start, text in zip(tensor_dict["answers"]["answer_start"], tensor_dict["answers"]["text"]) + ] answer = None answer_start = None return SquadExample( - qas_id=tensor_dict['id'].numpy().decode("utf-8"), - question_text=tensor_dict['question'].numpy().decode('utf-8'), - context_text=tensor_dict['context'].numpy().decode('utf-8'), + qas_id=tensor_dict["id"].numpy().decode("utf-8"), + question_text=tensor_dict["question"].numpy().decode("utf-8"), + context_text=tensor_dict["context"].numpy().decode("utf-8"), answer_text=answer, start_position_character=answer_start, - title=tensor_dict['title'].numpy().decode('utf-8'), - answers=answers + title=tensor_dict["title"].numpy().decode("utf-8"), + answers=answers, ) def get_examples_from_dataset(self, dataset, evaluate=False): @@ -359,7 +414,7 @@ class SquadProcessor(DataProcessor): examples = [] for tensor_dict in tqdm(dataset): - examples.append(self._get_example_from_tensor_dict(tensor_dict, evaluate=evaluate)) + examples.append(self._get_example_from_tensor_dict(tensor_dict, evaluate=evaluate)) return examples @@ -379,7 +434,9 @@ class SquadProcessor(DataProcessor): if self.train_file is None: raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor") - with open(os.path.join(data_dir, self.train_file if filename is None else filename), "r", encoding='utf-8') as reader: + with open( + os.path.join(data_dir, self.train_file if filename is None else filename), "r", encoding="utf-8" + ) as reader: input_data = json.load(reader)["data"] return self._create_examples(input_data, "train") @@ -397,8 +454,10 @@ class SquadProcessor(DataProcessor): if self.dev_file is None: raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor") - - with open(os.path.join(data_dir, self.dev_file if filename is None else filename), "r", encoding='utf-8') as reader: + + with open( + os.path.join(data_dir, self.dev_file if filename is None else filename), "r", encoding="utf-8" + ) as reader: input_data = json.load(reader)["data"] return self._create_examples(input_data, "dev") @@ -406,7 +465,7 @@ class SquadProcessor(DataProcessor): is_training = set_type == "train" examples = [] for entry in tqdm(input_data): - title = entry['title'] + title = entry["title"] for paragraph in entry["paragraphs"]: context_text = paragraph["context"] for qa in paragraph["qas"]: @@ -415,7 +474,7 @@ class SquadProcessor(DataProcessor): start_position_character = None answer_text = None answers = [] - + if "is_impossible" in qa: is_impossible = qa["is_impossible"] else: @@ -424,8 +483,8 @@ class SquadProcessor(DataProcessor): if not is_impossible: if is_training: answer = qa["answers"][0] - answer_text = answer['text'] - start_position_character = answer['answer_start'] + answer_text = answer["text"] + start_position_character = answer["answer_start"] else: answers = qa["answers"] @@ -437,12 +496,13 @@ class SquadProcessor(DataProcessor): start_position_character=start_position_character, title=title, is_impossible=is_impossible, - answers=answers + answers=answers, ) examples.append(example) return examples + class SquadV1Processor(SquadProcessor): train_file = "train-v1.1.json" dev_file = "dev-v1.1.json" @@ -451,7 +511,7 @@ class SquadV1Processor(SquadProcessor): class SquadV2Processor(SquadProcessor): train_file = "train-v2.0.json" dev_file = "dev-v2.0.json" - + class SquadExample(object): """ @@ -468,21 +528,23 @@ class SquadExample(object): is_impossible: False by default, set to True if the example has no possible answer. """ - def __init__(self, - qas_id, - question_text, - context_text, - answer_text, - start_position_character, - title, - answers=[], - is_impossible=False): + def __init__( + self, + qas_id, + question_text, + context_text, + answer_text, + start_position_character, + title, + answers=[], + is_impossible=False, + ): self.qas_id = qas_id self.question_text = question_text self.context_text = context_text self.answer_text = answer_text self.title = title - self.is_impossible = is_impossible + self.is_impossible = is_impossible self.answers = answers self.start_position, self.end_position = 0, 0 @@ -537,24 +599,23 @@ class SquadFeatures(object): end_position: end of the answer token index """ - def __init__(self, - input_ids, - attention_mask, - token_type_ids, - cls_index, - p_mask, - - example_index, - unique_id, - paragraph_len, - token_is_max_context, - tokens, - token_to_orig_map, - - start_position, - end_position - ): - self.input_ids = input_ids + def __init__( + self, + input_ids, + attention_mask, + token_type_ids, + cls_index, + p_mask, + example_index, + unique_id, + paragraph_len, + token_is_max_context, + tokens, + token_to_orig_map, + start_position, + end_position, + ): + self.input_ids = input_ids self.attention_mask = attention_mask self.token_type_ids = token_type_ids self.cls_index = cls_index @@ -580,12 +641,13 @@ class SquadResult(object): start_logits: The logits corresponding to the start of the answer end_logits: The logits corresponding to the end of the answer """ + def __init__(self, unique_id, start_logits, end_logits, start_top_index=None, end_top_index=None, cls_logits=None): self.start_logits = start_logits self.end_logits = end_logits self.unique_id = unique_id - + if start_top_index: self.start_top_index = start_top_index self.end_top_index = end_top_index - self.cls_logits = cls_logits \ No newline at end of file + self.cls_logits = cls_logits diff --git a/transformers/file_utils.py b/transformers/file_utils.py index 5fd5e2ee39..03b2fdb9f4 100644 --- a/transformers/file_utils.py +++ b/transformers/file_utils.py @@ -73,8 +73,13 @@ TF2_WEIGHTS_NAME = 'tf_model.h5' TF_WEIGHTS_NAME = 'model.ckpt' CONFIG_NAME = "config.json" + +DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]] +DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]] + S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert" + def is_torch_available(): return _torch_available diff --git a/transformers/hf_api.py b/transformers/hf_api.py index 3bbb6c567a..170732339a 100644 --- a/transformers/hf_api.py +++ b/transformers/hf_api.py @@ -131,8 +131,9 @@ class HfApi: # the client still has to specify it when uploading the file. with open(filepath, "rb") as f: pf = TqdmProgressFileReader(f) + data = f if pf.total_size > 0 else "" - r = requests.put(urls.write, data=f, headers={ + r = requests.put(urls.write, data=data, headers={ "content-type": urls.type, }) r.raise_for_status() diff --git a/transformers/modeling_auto.py b/transformers/modeling_auto.py index 96f45d8ec4..19a54cca86 100644 --- a/transformers/modeling_auto.py +++ b/transformers/modeling_auto.py @@ -29,6 +29,7 @@ from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequen from .modeling_distilbert import DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification from .modeling_camembert import CamembertModel, CamembertForMaskedLM, CamembertForSequenceClassification, CamembertForMultipleChoice from .modeling_albert import AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, AlbertForQuestionAnswering +from .modeling_t5 import T5Model, T5WithLMHeadModel from .modeling_utils import PreTrainedModel, SequenceSummary @@ -49,6 +50,7 @@ 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 `t5`: T5Model (T5 model) - contains `distilbert`: DistilBertModel (DistilBERT model) - contains `albert`: AlbertModel (ALBERT model) - contains `camembert`: CamembertModel (CamemBERT model) @@ -74,6 +76,7 @@ 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 `t5`: T5Model (T5 model) - contains `distilbert`: DistilBertModel (DistilBERT model) - contains `albert`: AlbertModel (ALBERT model) - contains `camembert`: CamembertModel (CamemBERT model) @@ -146,7 +149,9 @@ class AutoModel(object): model = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ - if 'distilbert' in pretrained_model_name_or_path: + if 't5' in pretrained_model_name_or_path: + return T5Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif '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) @@ -185,6 +190,7 @@ 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 `t5`: T5ModelWithLMHead (T5 model) - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model) - contains `albert`: AlbertForMaskedLM (ALBERT model) - contains `camembert`: CamembertForMaskedLM (CamemBERT model) @@ -213,6 +219,7 @@ 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 `t5`: T5ModelWithLMHead (T5 model) - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model) - contains `albert`: AlbertForMaskedLM (ALBERT model) - contains `camembert`: CamembertForMaskedLM (CamemBERT model) @@ -284,7 +291,9 @@ class AutoModelWithLMHead(object): model = AutoModelWithLMHead.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ - if 'distilbert' in pretrained_model_name_or_path: + if 't5' in pretrained_model_name_or_path: + return T5WithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif '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) diff --git a/transformers/modeling_encoder_decoder.py b/transformers/modeling_encoder_decoder.py index 70f765b849..a91c046d8f 100644 --- a/transformers/modeling_encoder_decoder.py +++ b/transformers/modeling_encoder_decoder.py @@ -219,9 +219,7 @@ class PreTrainedEncoderDecoder(nn.Module): encoder_hidden_states = kwargs_encoder.pop("hidden_states", None) if encoder_hidden_states is None: encoder_outputs = self.encoder(encoder_input_ids, **kwargs_encoder) - encoder_hidden_states = encoder_outputs[ - 0 - ] # output the last layer hidden state + encoder_hidden_states = encoder_outputs[0] else: encoder_outputs = () diff --git a/transformers/modeling_t5.py b/transformers/modeling_t5.py new file mode 100644 index 0000000000..263dc33b70 --- /dev/null +++ b/transformers/modeling_t5.py @@ -0,0 +1,886 @@ +# coding=utf-8 +# Copyright 2018 Mesh TensorFlow authors, T5 Authors and 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 T5 model. """ + +from __future__ import absolute_import, division, print_function, unicode_literals + +import json +import logging +import math +import os +import sys +import copy +import itertools +from io import open + +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn import CrossEntropyLoss, MSELoss + +from .modeling_utils import PreTrainedModel, prune_linear_layer +from .configuration_t5 import T5Config +from .file_utils import add_start_docstrings, DUMMY_INPUTS, DUMMY_MASK + +logger = logging.getLogger(__name__) + +#################################################### +# This dict contrains shortcut names and associated url +# for the pretrained weights provided with the models +#################################################### +T5_PRETRAINED_MODEL_ARCHIVE_MAP = { + 't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-pytorch_model.bin", + 't5-base': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-pytorch_model.bin", + 't5-large': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-pytorch_model.bin", + 't5-3b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-3b-pytorch_model.bin", + 't5-11b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-11b-pytorch_model.bin", +} + +#################################################### +# This is a conversion method from TF 1.0 to PyTorch +# More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28 +#################################################### +def load_tf_weights_in_t5(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 = [] + tf_weights = {} + 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) + tf_weights[name] = array + + for txt_name in names: + name = txt_name.split('/') + # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v + # which are not required for using pretrained model + if any(n in ["adam_v", "adam_m", "global_step"] for n in name): + logger.info("Skipping {}".format("/".join(name))) + tf_weights.pop(txt_name, None) + continue + if '_slot_' in name[-1]: + logger.info("Skipping {}".format("/".join(name))) + tf_weights.pop(txt_name, None) + continue + pointer = model + array = tf_weights[txt_name] + 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] in ['kernel', 'scale', 'embedding']: + pointer = getattr(pointer, 'weight') + # elif l[0] == 'scale': + # pointer = getattr(pointer, 'weight') + # elif l[0] == 'output_bias' or l[0] == 'beta': + # pointer = getattr(pointer, 'bias') + # 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 l[0] not in ['kernel', 'scale', 'embedding']: + pointer = getattr(pointer, 'weight') + if l[0] != 'embedding': + logger.info("Transposing numpy weight of shape {} for {}".format(array.shape, name)) + array = np.transpose(array) + try: + assert pointer.shape == array.shape + except AssertionError as e: + e.args += (pointer.shape, array.shape) + raise + logger.info("Initialize PyTorch weight {}".format(name)) + pointer.data = torch.from_numpy(array.astype(np.float32)) + tf_weights.pop(txt_name, None) + + logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys()))) + # logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys()))) + return model + + +#################################################### +# PyTorch Models are constructed by sub-classing +# - torch.nn.Module for the layers and +# - PreTrainedModel for the models (it-self a sub-class of torch.nn.Module) +#################################################### + +class T5LayerNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ Construct a layernorm module in the T5 style + No bias and no substraction of mean. + """ + super(T5LayerNorm, self).__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, x): + variance = x.pow(2).mean(-1, keepdim=True) + x = x / torch.sqrt(variance + self.variance_epsilon) + return self.weight * x + + +class T5DenseReluDense(nn.Module): + def __init__(self, config): + super(T5DenseReluDense, self).__init__() + self.wi = nn.Linear(config.d_model, config.d_ff, bias=False) + self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, hidden_states): + h = self.wi(hidden_states) + h = F.relu(h) + h = self.dropout(h) + h = self.wo(h) + return h + + +class T5LayerFF(nn.Module): + def __init__(self, config): + super(T5LayerFF, self).__init__() + self.DenseReluDense = T5DenseReluDense(config) + self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, hidden_states): + norm_x = self.layer_norm(hidden_states) + y = self.DenseReluDense(norm_x) + layer_output = hidden_states + self.dropout(y) + return layer_output + + +class T5Attention(nn.Module): + NEW_ID = itertools.count() + + def __init__(self, config, has_relative_attention_bias=False): + super(T5Attention, self).__init__() + self.layer_id = next(T5Attention.NEW_ID) + self.is_decoder = config.is_decoder + self.has_relative_attention_bias = has_relative_attention_bias + + self.output_attentions = config.output_attentions + self.relative_attention_num_buckets = config.relative_attention_num_buckets + self.d_model = config.d_model + self.d_kv = config.d_kv + self.n_heads = config.num_heads + self.dropout = config.dropout_rate + self.inner_dim = self.n_heads * self.d_kv + + # Mesh TensorFlow initialization to avoid scaling before softmax + self.q = nn.Linear(self.d_model, self.inner_dim, bias=False) + self.k = nn.Linear(self.d_model, self.inner_dim, bias=False) + self.v = nn.Linear(self.d_model, self.inner_dim, bias=False) + self.o = nn.Linear(self.inner_dim, self.d_model, bias=False) + + if self.has_relative_attention_bias: + self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + mask = torch.ones(self.n_heads, self.d_kv) + heads = set(heads) - self.pruned_heads + for head in heads: + 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.q = prune_linear_layer(self.q, index) + self.k = prune_linear_layer(self.k, index) + self.v = prune_linear_layer(self.v, index) + self.o = prune_linear_layer(self.o, index, dim=1) + # Update hyper params + self.n_heads = self.n_heads - len(heads) + self.inner_dim = self.d_kv * self.n_heads + self.pruned_heads = self.pruned_heads.union(heads) + + @staticmethod + def _relative_position_bucket(relative_position, + bidirectional=True, + num_buckets=32, + max_distance=128): + """ + Adapted from Mesh Tensorflow: + https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 + + Translate relative position to a bucket number for relative attention. + The relative position is defined as memory_position - query_position, i.e. + the distance in tokens from the attending position to the attended-to + position. If bidirectional=False, then positive relative positions are + invalid. + We use smaller buckets for small absolute relative_position and larger buckets + for larger absolute relative_positions. All relative positions >=max_distance + map to the same bucket. All relative positions <=-max_distance map to the + same bucket. This should allow for more graceful generalization to longer + sequences than the model has been trained on. + Args: + relative_position: an int32 Tensor + bidirectional: a boolean - whether the attention is bidirectional + num_buckets: an integer + max_distance: an integer + Returns: + a Tensor with the same shape as relative_position, containing int32 + values in the range [0, num_buckets) + """ + ret = 0 + n = -relative_position + if bidirectional: + num_buckets //= 2 + ret += (n < 0).to(torch.long) * num_buckets # mtf.to_int32(mtf.less(n, 0)) * num_buckets + n = torch.abs(n) + else: + n = torch.max(n, torch.zeros_like(n)) + # now n is in the range [0, inf) + + # half of the buckets are for exact increments in positions + max_exact = num_buckets // 2 + is_small = (n < max_exact) + + # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance + val_if_large = max_exact + ( + torch.log(n.float() / max_exact) + / math.log(max_distance / max_exact) * (num_buckets - max_exact)).to(torch.long) + val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1)) + + ret += torch.where(is_small, n, val_if_large) + return ret + + def compute_bias(self, qlen, klen): + """ Compute binned relative position bias """ + context_position = torch.arange(qlen, dtype=torch.long)[:, None] + memory_position = torch.arange(klen, dtype=torch.long)[None, :] + relative_position = memory_position - context_position # shape (qlen, klen) + rp_bucket = self._relative_position_bucket(relative_position, # shape (qlen, klen) + bidirectional=not self.is_decoder, + num_buckets=self.relative_attention_num_buckets) + values = self.relative_attention_bias(rp_bucket) # shape (qlen, klen, num_heads) + values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, qlen, klen) + return values + + def forward(self, input, mask=None, kv=None, position_bias=None, cache=None, head_mask=None): + """ + Self-attention (if kv is None) or attention over source sentence (provided by kv). + """ + # Input is (bs, qlen, dim) + # Mask is (bs, klen) (non-causal) or (bs, klen, klen) + bs, qlen, dim = input.size() + if kv is None: + klen = qlen if cache is None else cache['slen'] + qlen + else: + klen = kv.size(1) + + def shape(x): + """ projection """ + return x.view(bs, -1, self.n_heads, self.d_kv).transpose(1, 2) + + def unshape(x): + """ compute context """ + return x.transpose(1, 2).contiguous().view(bs, -1, self.inner_dim) + + q = shape(self.q(input)) # (bs, n_heads, qlen, dim_per_head) + if kv is None: + k = shape(self.k(input)) # (bs, n_heads, qlen, dim_per_head) + v = shape(self.v(input)) # (bs, n_heads, qlen, dim_per_head) + elif cache is None or self.layer_id not in cache: + k = v = kv + k = shape(self.k(k)) # (bs, n_heads, qlen, dim_per_head) + v = shape(self.v(v)) # (bs, n_heads, qlen, dim_per_head) + + if cache is not None: + if self.layer_id in cache: + if kv is None: + k_, v_ = cache[self.layer_id] + k = torch.cat([k_, k], dim=2) # (bs, n_heads, klen, dim_per_head) + v = torch.cat([v_, v], dim=2) # (bs, n_heads, klen, dim_per_head) + else: + k, v = cache[self.layer_id] + cache[self.layer_id] = (k, v) + + # q = q / math.sqrt(dim_per_head) # No scaling in T5 + scores = torch.einsum('bnqd,bnkd->bnqk', q, k) # (bs, n_heads, qlen, klen) + + if position_bias is None: + if not self.has_relative_attention_bias: + raise ValueError("No position_bias provided and no weights to compute position_bias") + position_bias = self.compute_bias(qlen, klen) + if mask is not None: + position_bias = position_bias + mask # (bs, n_heads, qlen, klen) + + scores += position_bias + weights = F.softmax(scores.float(), dim=-1).type_as(scores) # (bs, n_heads, qlen, klen) + weights = F.dropout(weights, p=self.dropout, training=self.training) # (bs, n_heads, qlen, klen) + + # Mask heads if we want to + if head_mask is not None: + weights = weights * head_mask + + context = torch.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head) + context = unshape(context) # (bs, qlen, dim) + + context = self.o(context) + + outputs = (context,) + if self.output_attentions: + outputs = outputs + (weights,) + if self.has_relative_attention_bias: + outputs = outputs + (position_bias,) + return outputs + + +class T5LayerSelfAttention(nn.Module): + def __init__(self, config, has_relative_attention_bias=False): + super(T5LayerSelfAttention, self).__init__() + self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias) + self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, hidden_states, attention_mask=None, position_bias=None, head_mask=None): + norm_x = self.layer_norm(hidden_states) + attention_output = self.SelfAttention(norm_x, + mask=attention_mask, + position_bias=position_bias, + head_mask=head_mask) + y = attention_output[0] + layer_output = hidden_states + self.dropout(y) + outputs = (layer_output,) + attention_output[1:] # add attentions if we output them + return outputs + + +class T5LayerCrossAttention(nn.Module): + def __init__(self, config, has_relative_attention_bias=False): + super(T5LayerCrossAttention, self).__init__() + self.EncDecAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias) + self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, hidden_states, kv, attention_mask=None, position_bias=None, head_mask=None): + norm_x = self.layer_norm(hidden_states) + attention_output = self.EncDecAttention(norm_x, + mask=attention_mask, + kv=kv, + position_bias=position_bias, + head_mask=head_mask) + y = attention_output[0] + layer_output = hidden_states + self.dropout(y) + outputs = (layer_output,) + attention_output[1:] # add attentions if we output them + return outputs + + +class T5Block(nn.Module): + def __init__(self, config, has_relative_attention_bias=False): + super(T5Block, self).__init__() + self.is_decoder = config.is_decoder + self.layer = nn.ModuleList() + self.layer.append(T5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias)) + if self.is_decoder: + self.layer.append(T5LayerCrossAttention(config, has_relative_attention_bias=has_relative_attention_bias)) + self.layer.append(T5LayerFF(config)) + else: + self.layer.append(T5LayerFF(config)) + + def forward(self, hidden_states, attention_mask=None, position_bias=None, + encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None, + head_mask=None): + self_attention_outputs = self.layer[0](hidden_states, + attention_mask=attention_mask, + position_bias=position_bias, + head_mask=head_mask) + hidden_states = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # Keep self-attention outputs and relative position weights + + if not self.is_decoder: + hidden_states = self.layer[1](hidden_states) + else: + cross_attention_outputs = self.layer[1](hidden_states, + kv=encoder_hidden_states, + attention_mask=encoder_attention_mask, + position_bias=encoder_decoder_position_bias, + head_mask=head_mask) + hidden_states = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:] # Keep cross-attention outputs and relative position weights + hidden_states = self.layer[2](hidden_states) + + outputs = (hidden_states,) + outputs # add attentions if we output them + return outputs # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias) + + +class T5PreTrainedModel(PreTrainedModel): + """ An abstract class to handle weights initialization and + a simple interface for dowloading and loading pretrained models. + """ + config_class = T5Config + pretrained_model_archive_map = T5_PRETRAINED_MODEL_ARCHIVE_MAP + load_tf_weights = load_tf_weights_in_t5 + base_model_prefix = "transformer" + + @property + def dummy_inputs(self): + input_ids = torch.tensor(DUMMY_INPUTS) + input_mask = torch.tensor(DUMMY_MASK) + dummy_inputs = {'decoder_input_ids': input_ids, + 'encoder_input_ids': input_ids, + 'decoder_attention_mask': input_mask} + return dummy_inputs + + def _init_weights(self, module): + """ Initialize the weights """ + factor = self.config.initializer_factor # Used for testing weights initialization + if isinstance(module, T5LayerNorm): + module.weight.data.fill_(factor*1.0) + elif isinstance(module, (T5Model, T5WithLMHeadModel)): + # Mesh TensorFlow embeddings initialization + # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624 + module.shared.weight.data.normal_(mean=0.0, std=factor*1.0) + elif isinstance(module, T5DenseReluDense): + # Mesh TensorFlow FF initialization + # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56 + # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89 + module.wi.weight.data.normal_(mean=0.0, std=factor*((self.config.d_model) ** -0.5)) + if hasattr(module.wi, 'bias') and module.wi.bias is not None: + module.wi.bias.data.zero_() + module.wo.weight.data.normal_(mean=0.0, std=factor*((self.config.d_ff) ** -0.5)) + if hasattr(module.wo, 'bias') and module.wo.bias is not None: + module.wo.bias.data.zero_() + elif isinstance(module, T5Attention): + # Mesh TensorFlow attention initialization to avoid scaling before softmax + # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136 + d_model = self.config.d_model + d_kv = self.config.d_kv + n_heads = self.config.num_heads + module.q.weight.data.normal_(mean=0.0, std=factor*((d_model * d_kv) ** -0.5)) + module.k.weight.data.normal_(mean=0.0, std=factor*(d_model ** -0.5)) + module.v.weight.data.normal_(mean=0.0, std=factor*(d_model ** -0.5)) + module.o.weight.data.normal_(mean=0.0, std=factor*((n_heads * d_kv) ** -0.5)) + if module.has_relative_attention_bias: + module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor*((d_model) ** -0.5)) + + +class T5Stack(T5PreTrainedModel): + def __init__(self, config): + super(T5Stack, self).__init__(config) + self.output_attentions = config.output_attentions + self.output_hidden_states = config.output_hidden_states + self.is_decoder = config.is_decoder + + self.block = nn.ModuleList([T5Block(config, has_relative_attention_bias=bool(i == 0)) + for i in range(config.num_layers)]) + self.final_layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + self.init_weights() + + def forward(self, + hidden_states, + attention_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + head_mask=None): + + batch_size, seq_length = hidden_states.shape[0], hidden_states.shape[1] + if attention_mask is None: + attention_mask = torch.ones(batch_size, seq_length).to(hidden_states.device) + if self.is_decoder and encoder_attention_mask is None: + encoder_seq_length = encoder_hidden_states.shape[1] + encoder_attention_mask = torch.ones(batch_size, encoder_seq_length).to(hidden_states.device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # 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, :, :] + 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: + seq_ids = torch.arange(seq_length, device=hidden_states.device) + causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] + causal_mask = causal_mask.to(attention_mask) + extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] + else: + extended_attention_mask = attention_mask[:, None, None, :] + + # 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 -1e9 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + + # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition + # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 + # extended_attention_mask = (extended_attention_mask == extended_attention_mask.transpose(-1, -2)) + + extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -1e9 + + if self.is_decoder: + # 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, :] + + # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition + # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 + # encoder_extended_attention_mask = (encoder_extended_attention_mask == encoder_extended_attention_mask.transpose(-1, -2)) + + 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) * -1e9 + else: + encoder_extended_attention_mask = None + + # 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 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_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_layers + + all_hidden_states = () + all_attentions = () + position_bias = None + encoder_decoder_position_bias = None + + hidden_states = self.dropout(hidden_states) + for i, layer_module in enumerate(self.block): + if self.output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module(hidden_states, + attention_mask=extended_attention_mask, + position_bias=position_bias, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + encoder_decoder_position_bias=encoder_decoder_position_bias, + head_mask=head_mask[i]) + # layer_outputs is a tuple with: + # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias) + hidden_states = layer_outputs[0] + if i == 0: + # We share the position biases between the layers - the first layer store them + # layer_outputs = hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias) + position_bias = layer_outputs[2 if self.output_attentions else 1] + if self.is_decoder: + encoder_decoder_position_bias = layer_outputs[4 if self.output_attentions else 2] + + if self.output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) # We keep only self-attention weights for now + + hidden_states = self.final_layer_norm(hidden_states) + layer_output = self.dropout(hidden_states) + + # Add last layer + 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) + + +T5_START_DOCSTRING = r""" The T5 model was proposed in + `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`_ + by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. + It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting. + + 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. + + .. _`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`: + https://arxiv.org/abs/1910.10683 + + .. _`torch.nn.Module`: + https://pytorch.org/docs/stable/nn.html#module + + Parameters: + config (:class:`~transformers.T5Config`): 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. +""" + +T5_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, T5 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]`` + + (b) For single sequences: + + ``tokens: [CLS] the dog is hairy . [SEP]`` + + T5 is a model with relative position embeddings so you should be able to pad the inputs on + the right or the left. + + Indices can be obtained using :class:`transformers.T5Tokenizer`. + 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. + **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 T5 Model transformer outputting raw hidden-states" + "without any specific head on top.", + T5_START_DOCSTRING, T5_INPUTS_DOCSTRING) +class T5Model(T5PreTrainedModel): + 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. + **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 = T5Tokenizer.from_pretrained('t5-small') + model = T5Model.from_pretrained('t5-small') + input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).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 + + """ + def __init__(self, config): + super(T5Model, self).__init__(config) + self.shared = nn.Embedding(config.vocab_size, config.d_model) + + encoder_config = copy.deepcopy(config) + self.encoder = T5Stack(encoder_config) + + decoder_config = copy.deepcopy(config) + decoder_config.is_decoder = True + self.decoder = T5Stack(decoder_config) + + self.init_weights() + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, new_embeddings): + self.shared = new_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} + See base class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + def forward(self, **kwargs): + # keyword arguments come in 3 flavors: encoder-specific (prefixed by + # `encoder_`), decoder-specific (prefixed by `decoder_`) and those + # that apply to the model as whole. + # We let the specific kwargs override the common ones in case of conflict. + kwargs_common = dict((k, v) for k, v in kwargs.items() + if not k.startswith("encoder_") and not k.startswith("decoder_")) + kwargs_encoder = kwargs_common.copy() + kwargs_decoder = kwargs_common.copy() + kwargs_encoder.update(dict((k[len("encoder_"):], v) for k, v in kwargs.items() if k.startswith("encoder_"))) + kwargs_decoder.update(dict((k[len("decoder_"):], v) for k, v in kwargs.items() if k.startswith("decoder_"))) + + # Encode if needed (training, first prediction pass) + encoder_hidden_states = kwargs_encoder.pop("hidden_states", None) + encoder_attention_mask = kwargs_encoder.get("attention_mask", None) + if encoder_hidden_states is None: + # Convert encoder inputs in embeddings if needed + hidden_states = kwargs_encoder.pop("inputs_embeds", None) + if hidden_states is None: + encoder_inputs_ids = kwargs_encoder.pop("input_ids") + hidden_states = self.shared(encoder_inputs_ids) # Convert inputs in embeddings + + if encoder_attention_mask is not None: + # Apply masking + encoder_attention_mask = (encoder_attention_mask != 0).to(hidden_states) + hidden_states = hidden_states * encoder_attention_mask.unsqueeze(-1) + + encoder_outputs = self.encoder(hidden_states, **kwargs_encoder) + encoder_hidden_states = encoder_outputs[0] + else: + encoder_outputs = () + + # Decode + # Convert decoder inputs in embeddings if needed + hidden_states = kwargs_decoder.pop("inputs_embeds", None) + if hidden_states is None: + decoder_inputs_ids = kwargs_decoder.pop("input_ids") + hidden_states = self.shared(decoder_inputs_ids) + + kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states + kwargs_decoder["encoder_attention_mask"] = encoder_attention_mask + decoder_outputs = self.decoder(hidden_states, **kwargs_decoder) + + return decoder_outputs + encoder_outputs + + +@add_start_docstrings("""T5 Model with a `language modeling` head on top. """, + T5_START_DOCSTRING, T5_INPUTS_DOCSTRING) +class T5WithLMHeadModel(T5PreTrainedModel): + r""" + **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 ``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. + + Examples:: + + tokenizer = T5Tokenizer.from_pretrained('t5-small') + model = T5WithLMHeadModel.from_pretrained('t5-small') + input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 + outputs = model(input_ids, lm_labels=input_ids) + loss, prediction_scores = outputs[:2] + + """ + def __init__(self, config): + super(T5WithLMHeadModel, self).__init__(config) + self.model_dim = config.d_model + + self.shared = nn.Embedding(config.vocab_size, config.d_model) + + encoder_config = copy.deepcopy(config) + self.encoder = T5Stack(encoder_config) + + decoder_config = copy.deepcopy(config) + decoder_config.is_decoder = True + self.decoder = T5Stack(decoder_config) + + self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) + + self.init_weights() + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, new_embeddings): + self.shared = new_embeddings + + def get_output_embeddings(self): + return self.lm_head + + def forward(self, **kwargs): + # keyword arguments come in 3 flavors: encoder-specific (prefixed by + # `encoder_`), decoder-specific (prefixed by `decoder_`) and those + # that apply to the model as whole. + # We let the specific kwargs override the common ones in case of conflict. + + lm_labels = kwargs.pop('decoder_lm_labels', None) + + kwargs_common = dict((k, v) for k, v in kwargs.items() + if not k.startswith("encoder_") and not k.startswith("decoder_")) + kwargs_encoder = kwargs_common.copy() + kwargs_decoder = kwargs_common.copy() + kwargs_encoder.update(dict((k[len("encoder_"):], v) for k, v in kwargs.items() if k.startswith("encoder_"))) + kwargs_decoder.update(dict((k[len("decoder_"):], v) for k, v in kwargs.items() if k.startswith("decoder_"))) + + # Encode if needed (training, first prediction pass) + encoder_hidden_states = kwargs_encoder.pop("hidden_states", None) + if encoder_hidden_states is None: + # Convert encoder inputs in embeddings if needed + hidden_states = kwargs_encoder.pop("inputs_embeds", None) + if hidden_states is None: + encoder_inputs_ids = kwargs_encoder.pop("input_ids") + hidden_states = self.shared(encoder_inputs_ids) # Convert inputs in embeddings + + encoder_outputs = self.encoder(hidden_states, **kwargs_encoder) + encoder_hidden_states = encoder_outputs[0] + else: + encoder_outputs = () + + # Decode + # Convert decoder inputs in embeddings if needed + hidden_states = kwargs_decoder.pop("inputs_embeds", None) + if hidden_states is None: + decoder_inputs_ids = kwargs_decoder.pop("input_ids") + hidden_states = self.shared(decoder_inputs_ids) + + kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states + kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None) + decoder_outputs = self.decoder(hidden_states, **kwargs_decoder) + + sequence_output = decoder_outputs[0] + # Rescale output before projecting on vocab + # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586 + sequence_output = sequence_output * (self.model_dim ** -0.5) + lm_logits = self.lm_head(sequence_output) + + decoder_outputs = (lm_logits,) + decoder_outputs[1:] # Add hidden states and attention if they are here + if lm_labels is not None: + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = lm_labels[..., 1:].contiguous() + loss_fct = CrossEntropyLoss(ignore_index=-1) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), + shift_labels.view(-1)) + decoder_outputs = (loss,) + decoder_outputs # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666 + + return decoder_outputs + encoder_outputs diff --git a/transformers/modeling_tf_auto.py b/transformers/modeling_tf_auto.py index fac92eb866..b4ff660098 100644 --- a/transformers/modeling_tf_auto.py +++ b/transformers/modeling_tf_auto.py @@ -27,6 +27,7 @@ from .modeling_tf_xlm import TFXLMModel, TFXLMWithLMHeadModel, TFXLMForSequenceC from .modeling_tf_roberta import TFRobertaModel, TFRobertaForMaskedLM, TFRobertaForSequenceClassification from .modeling_tf_distilbert import TFDistilBertModel, TFDistilBertForQuestionAnswering, TFDistilBertForMaskedLM, TFDistilBertForSequenceClassification from .modeling_tf_ctrl import TFCTRLModel, TFCTRLLMHeadModel +from .modeling_tf_t5 import TFT5Model, TFT5WithLMHeadModel from .file_utils import add_start_docstrings @@ -45,6 +46,7 @@ class TFAutoModel(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 `t5`: TFT5Model (T5 model) - contains `distilbert`: TFDistilBertModel (DistilBERT model) - contains `roberta`: TFRobertaModel (RoBERTa model) - contains `bert`: TFBertModel (Bert model) @@ -68,6 +70,7 @@ class TFAutoModel(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 `t5`: TFT5Model (T5 model) - contains `distilbert`: TFDistilBertModel (DistilBERT model) - contains `roberta`: TFRobertaModel (RoBERTa model) - contains `bert`: TFTFBertModel (Bert model) @@ -137,7 +140,9 @@ class TFAutoModel(object): model = TFAutoModel.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config) """ - if 'distilbert' in pretrained_model_name_or_path: + if 't5' in pretrained_model_name_or_path: + return TFT5Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'distilbert' in pretrained_model_name_or_path: return TFDistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return TFRobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) @@ -173,6 +178,7 @@ class TFAutoModelWithLMHead(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 `t5`: TFT5WithLMHeadModel (T5 model) - contains `distilbert`: TFDistilBertForMaskedLM (DistilBERT model) - contains `roberta`: TFRobertaForMaskedLM (RoBERTa model) - contains `bert`: TFBertForMaskedLM (Bert model) @@ -199,6 +205,7 @@ class TFAutoModelWithLMHead(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 `t5`: TFT5WithLMHeadModel (T5 model) - contains `distilbert`: TFDistilBertForMaskedLM (DistilBERT model) - contains `roberta`: TFRobertaForMaskedLM (RoBERTa model) - contains `bert`: TFBertForMaskedLM (Bert model) @@ -269,7 +276,9 @@ class TFAutoModelWithLMHead(object): model = TFAutoModelWithLMHead.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config) """ - if 'distilbert' in pretrained_model_name_or_path: + if 't5' in pretrained_model_name_or_path: + return TFT5WithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + elif 'distilbert' in pretrained_model_name_or_path: return TFDistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return TFRobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) diff --git a/transformers/modeling_tf_pytorch_utils.py b/transformers/modeling_tf_pytorch_utils.py index aa74fcc10e..9d2b663dcb 100644 --- a/transformers/modeling_tf_pytorch_utils.py +++ b/transformers/modeling_tf_pytorch_utils.py @@ -78,6 +78,7 @@ def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_i logger.info("Loading PyTorch weights from {}".format(pt_path)) pt_state_dict = torch.load(pt_path, map_location='cpu') + logger.info("PyTorch checkpoint contains {:,} parameters".format(sum(t.numel() for t in pt_state_dict.values()))) return load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys) @@ -134,7 +135,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a start_prefix_to_remove = tf_model.base_model_prefix + '.' symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights - + tf_loaded_numel = 0 weight_value_tuples = [] all_pytorch_weights = set(list(pt_state_dict.keys())) for symbolic_weight in symbolic_weights: @@ -159,7 +160,8 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a e.args += (symbolic_weight.shape, array.shape) raise e - logger.info("Initialize TF weight {}".format(symbolic_weight.name)) + tf_loaded_numel += array.size + # logger.warning("Initialize TF weight {}".format(symbolic_weight.name)) weight_value_tuples.append((symbolic_weight, array)) all_pytorch_weights.discard(name) @@ -169,6 +171,8 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a if tf_inputs is not None: tfo = tf_model(tf_inputs, training=False) # Make sure restore ops are run + logger.info("Loaded {:,} parameters in the TF 2.0 model.".format(tf_loaded_numel)) + logger.info("Weights or buffers not loaded from PyTorch model: {}".format(all_pytorch_weights)) return tf_model @@ -272,7 +276,7 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F e.args += (pt_weight.shape, array.shape) raise e - logger.info("Initialize PyTorch weight {}".format(pt_weight_name)) + # logger.warning("Initialize PyTorch weight {}".format(pt_weight_name)) new_pt_params_dict[pt_weight_name] = torch.from_numpy(array) loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array) diff --git a/transformers/modeling_tf_t5.py b/transformers/modeling_tf_t5.py new file mode 100644 index 0000000000..1336a1c30d --- /dev/null +++ b/transformers/modeling_tf_t5.py @@ -0,0 +1,775 @@ +# coding=utf-8 +# Copyright 2018 T5 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. +""" TF 2.0 T5 model. """ + +from __future__ import absolute_import, division, print_function, unicode_literals + +import logging +import math +import copy +import itertools + +import tensorflow as tf + +from .configuration_t5 import T5Config +from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list +from .file_utils import add_start_docstrings, DUMMY_INPUTS, DUMMY_MASK + +logger = logging.getLogger(__name__) + +TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP = { + 't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-tf_model.h5", + 't5-base': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-tf_model.h5", + 't5-large': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-tf_model.h5", + 't5-3b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-3b-tf_model.h5", + 't5-11b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-11b-tf_model.h5", +} + +#################################################### +# TF 2.0 Models are constructed using Keras imperative API by sub-classing +# - tf.keras.layers.Layer for the layers and +# - TFPreTrainedModel for the models (it-self a sub-class of tf.keras.Model) +#################################################### + +class TFT5LayerNorm(tf.keras.layers.Layer): + def __init__(self, epsilon=1e-6, **kwargs): + """ Construct a layernorm module in the T5 style + No bias and no substraction of mean. + """ + super(TFT5LayerNorm, self).__init__(**kwargs) + self.variance_epsilon = epsilon + + def build(self, input_shape): + """Build shared word embedding layer """ + self.weight = self.add_weight( + "weight", + shape=(input_shape[-1],), + initializer='ones') + super(TFT5LayerNorm, self).build(input_shape) + + def call(self, x): + variance = tf.math.reduce_mean(tf.math.square(x), axis=-1, keepdims=True) + x = x * tf.math.rsqrt(variance + self.variance_epsilon) + return self.weight * x + + +class TFT5DenseReluDense(tf.keras.layers.Layer): + def __init__(self, config, **kwargs): + super(TFT5DenseReluDense, self).__init__(**kwargs) + self.wi = tf.keras.layers.Dense(config.d_ff, use_bias=False, name='wi') + self.wo = tf.keras.layers.Dense(config.d_model, use_bias=False, name='wo') + self.dropout = tf.keras.layers.Dropout(config.dropout_rate) + self.act = tf.keras.activations.relu + + def call(self, hidden_states, training=False): + h = self.wi(hidden_states) + h = self.act(h) + h = self.dropout(h, training=training) + h = self.wo(h) + return h + + +class TFT5LayerFF(tf.keras.layers.Layer): + def __init__(self, config, **kwargs): + super(TFT5LayerFF, self).__init__(**kwargs) + self.DenseReluDense = TFT5DenseReluDense(config, name='DenseReluDense') + self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, + name='layer_norm') + self.dropout = tf.keras.layers.Dropout(config.dropout_rate) + + def call(self, hidden_states, training=False): + norm_x = self.layer_norm(hidden_states) + y = self.DenseReluDense(norm_x, training=training) + layer_output = hidden_states + self.dropout(y, training=training) + return layer_output + + +class TFT5Attention(tf.keras.layers.Layer): + NEW_ID = itertools.count() + + def __init__(self, config, has_relative_attention_bias=False, **kwargs): + super(TFT5Attention, self).__init__(**kwargs) + self.layer_id = next(TFT5Attention.NEW_ID) + self.is_decoder = config.is_decoder + self.has_relative_attention_bias = has_relative_attention_bias + + self.output_attentions = config.output_attentions + self.relative_attention_num_buckets = config.relative_attention_num_buckets + self.d_model = config.d_model + self.d_kv = config.d_kv + self.n_heads = config.num_heads + self.inner_dim = self.n_heads * self.d_kv + + # Mesh TensorFlow initialization to avoid scaling before softmax + self.q = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name='q') + self.k = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name='k') + self.v = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name='v') + self.o = tf.keras.layers.Dense(self.d_model, use_bias=False, name='o') + self.dropout = tf.keras.layers.Dropout(config.dropout_rate) + + if self.has_relative_attention_bias: + self.relative_attention_bias = tf.keras.layers.Embedding(self.relative_attention_num_buckets, + self.n_heads, + name='relative_attention_bias') + self.pruned_heads = set() + + def prune_heads(self, heads): + raise NotImplementedError + + @staticmethod + def _relative_position_bucket(relative_position, + bidirectional=True, + num_buckets=32, + max_distance=128): + """ + Adapted from Mesh Tensorflow: + https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 + + Translate relative position to a bucket number for relative attention. + The relative position is defined as memory_position - query_position, i.e. + the distance in tokens from the attending position to the attended-to + position. If bidirectional=False, then positive relative positions are + invalid. + We use smaller buckets for small absolute relative_position and larger buckets + for larger absolute relative_positions. All relative positions >=max_distance + map to the same bucket. All relative positions <=-max_distance map to the + same bucket. This should allow for more graceful generalization to longer + sequences than the model has been trained on. + Args: + relative_position: an int32 Tensor + bidirectional: a boolean - whether the attention is bidirectional + num_buckets: an integer + max_distance: an integer + Returns: + a Tensor with the same shape as relative_position, containing int32 + values in the range [0, num_buckets) + """ + ret = 0 + n = -relative_position + if bidirectional: + num_buckets //= 2 + ret += tf.dtypes.cast(tf.math.less(n, 0), tf.int32) * num_buckets + n = tf.math.abs(n) + else: + n = tf.math.maximum(n, 0) + # now n is in the range [0, inf) + max_exact = num_buckets // 2 + is_small = tf.math.less(n, max_exact) + val_if_large = max_exact + tf.dtypes.cast( + tf.math.log(tf.dtypes.cast(n, tf.float32) / max_exact) + / math.log(max_distance / max_exact) * (num_buckets - max_exact), tf.int32) + val_if_large = tf.math.minimum(val_if_large, num_buckets - 1) + ret += tf.where(is_small, n, val_if_large) + return ret + + def compute_bias(self, qlen, klen): + """ Compute binned relative position bias """ + context_position = tf.range(qlen)[:, None] + memory_position = tf.range(klen)[None, :] + relative_position = memory_position - context_position # shape (qlen, klen) + rp_bucket = self._relative_position_bucket(relative_position, + bidirectional=not self.is_decoder, + num_buckets=self.relative_attention_num_buckets) + values = self.relative_attention_bias(rp_bucket) # shape (qlen, klen, num_heads) + values = tf.expand_dims(tf.transpose(values, [2, 0, 1]), axis=0) # shape (1, num_heads, qlen, klen) + return values + + def call(self, input, mask=None, kv=None, position_bias=None, cache=None, head_mask=None, training=False): + """ + Self-attention (if kv is None) or attention over source sentence (provided by kv). + """ + # Input is (bs, qlen, dim) + # Mask is (bs, klen) (non-causal) or (bs, klen, klen) + bs, qlen, dim = shape_list(input) + if kv is None: + klen = qlen if cache is None else cache['slen'] + qlen + else: + klen = shape_list(kv)[1] + + def shape(x): + """ projection """ + return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, self.d_kv)), perm=(0, 2, 1, 3)) + + def unshape(x): + """ compute context """ + return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.inner_dim)) + + q = shape(self.q(input)) # (bs, n_heads, qlen, dim_per_head) + if kv is None: + k = shape(self.k(input)) # (bs, n_heads, qlen, dim_per_head) + v = shape(self.v(input)) # (bs, n_heads, qlen, dim_per_head) + elif cache is None or self.layer_id not in cache: + k = v = kv + k = shape(self.k(k)) # (bs, n_heads, qlen, dim_per_head) + v = shape(self.v(v)) # (bs, n_heads, qlen, dim_per_head) + + if cache is not None: + if self.layer_id in cache: + if kv is None: + k_, v_ = cache[self.layer_id] + k = tf.concat([k_, k], axis=2) # (bs, n_heads, klen, dim_per_head) + v = tf.concat([v_, v], axis=2) # (bs, n_heads, klen, dim_per_head) + else: + k, v = cache[self.layer_id] + cache[self.layer_id] = (k, v) + + # q = q / math.sqrt(dim_per_head) # No scaling in T5 + # scores = tf.matmul(q, k, transpose_b=True) # (bs, n_heads, qlen, klen) + scores = tf.einsum('bnqd,bnkd->bnqk', q, k) # (bs, n_heads, qlen, klen) + + if position_bias is None: + if not self.has_relative_attention_bias: + raise ValueError("No position_bias provided and no weights to compute position_bias") + position_bias = self.compute_bias(qlen, klen) + if mask is not None: + position_bias = position_bias + mask + # mask = (mask == 0).expand_as(scores) # (bs, n_heads, qlen, klen) + # scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, qlen, klen) + + scores += position_bias + weights = tf.nn.softmax(scores, axis=-1) # (bs, n_heads, qlen, klen) + weights = self.dropout(weights, training=training) # (bs, n_heads, qlen, klen) + + # Mask heads if we want to + if head_mask is not None: + weights = weights * head_mask + + context = tf.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head) + context = unshape(context) # (bs, qlen, dim) + + context = self.o(context) + + outputs = (context,) + if self.output_attentions: + outputs = outputs + (weights,) + if self.has_relative_attention_bias: + outputs = outputs + (position_bias,) + return outputs + + +class TFT5LayerSelfAttention(tf.keras.layers.Layer): + def __init__(self, config, has_relative_attention_bias=False, **kwargs): + super(TFT5LayerSelfAttention, self).__init__(**kwargs) + self.SelfAttention = TFT5Attention(config, + has_relative_attention_bias=has_relative_attention_bias, + name='SelfAttention') + self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, + name='layer_norm') + self.dropout = tf.keras.layers.Dropout(config.dropout_rate) + + def call(self, hidden_states, attention_mask=None, position_bias=None, + head_mask=None, training=False): + norm_x = self.layer_norm(hidden_states) + attention_output = self.SelfAttention(norm_x, + mask=attention_mask, + position_bias=position_bias, + head_mask=head_mask, + training=training) + y = attention_output[0] + layer_output = hidden_states + self.dropout(y, training=training) + outputs = (layer_output,) + attention_output[1:] # add attentions if we output them + return outputs + + +class TFT5LayerCrossAttention(tf.keras.layers.Layer): + def __init__(self, config, has_relative_attention_bias=False, **kwargs): + super(TFT5LayerCrossAttention, self).__init__(**kwargs) + self.EncDecAttention = TFT5Attention(config, + has_relative_attention_bias=has_relative_attention_bias, + name='EncDecAttention') + self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, + name='layer_norm') + self.dropout = tf.keras.layers.Dropout(config.dropout_rate) + + def call(self, hidden_states, kv, attention_mask=None, position_bias=None, + head_mask=None, training=False): + norm_x = self.layer_norm(hidden_states) + attention_output = self.EncDecAttention(norm_x, + mask=attention_mask, + kv=kv, + position_bias=position_bias, + head_mask=head_mask, + training=training) + y = attention_output[0] + layer_output = hidden_states + self.dropout(y, training=training) + outputs = (layer_output,) + attention_output[1:] # add attentions if we output them + return outputs + + +class TFT5Block(tf.keras.layers.Layer): + def __init__(self, config, has_relative_attention_bias=False, **kwargs): + super(TFT5Block, self).__init__(**kwargs) + self.is_decoder = config.is_decoder + self.layer = [] + self.layer.append(TFT5LayerSelfAttention(config, + has_relative_attention_bias=has_relative_attention_bias, + name='layer_._0')) + if self.is_decoder: + self.layer.append(TFT5LayerCrossAttention(config, + has_relative_attention_bias=has_relative_attention_bias, + name='layer_._1')) + self.layer.append(TFT5LayerFF(config, name='layer_._2')) + else: + self.layer.append(TFT5LayerFF(config, name='layer_._1')) + + def call(self, hidden_states, attention_mask=None, position_bias=None, + encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None, + head_mask=None, training=False): + self_attention_outputs = self.layer[0](hidden_states, + attention_mask=attention_mask, + position_bias=position_bias, + head_mask=head_mask, + training=training) + hidden_states = self_attention_outputs[0] + outputs = self_attention_outputs[1:] + + if not self.is_decoder: + hidden_states = self.layer[1](hidden_states, training=training) + else: + cross_attention_outputs = self.layer[1](hidden_states, + kv=encoder_hidden_states, + attention_mask=encoder_attention_mask, + position_bias=encoder_decoder_position_bias, + head_mask=head_mask, + training=training) + hidden_states = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:] + hidden_states = self.layer[2](hidden_states, training=training) + + outputs = (hidden_states,) + outputs # add attentions if we output them + return outputs # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias) + + +#################################################### +# The full model without a specific pretrained or finetuning head is +# provided as a tf.keras.layers.Layer usually called "TFT5MainLayer" +#################################################### +class TFT5MainLayer(tf.keras.layers.Layer): + def __init__(self, config, **kwargs): + super(TFT5MainLayer, self).__init__(**kwargs) + self.output_attentions = config.output_attentions + self.output_hidden_states = config.output_hidden_states + self.is_decoder = config.is_decoder + self.config = config + self.num_hidden_layers = config.num_layers + + self.block = [TFT5Block(config, + has_relative_attention_bias=bool(i == 0), + name='block_._{}'.format(i)) + for i in range(config.num_layers)] + self.final_layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, + name='final_layer_norm') + self.dropout = tf.keras.layers.Dropout(config.dropout_rate) + + def _resize_token_embeddings(self, new_num_tokens): + raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models + + def _prune_heads(self, heads_to_prune): + raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models + + def call(self, hidden_states, attention_mask=None, encoder_hidden_states=None, + encoder_attention_mask=None, head_mask=None, training=False): + + batch_size, seq_length = shape_list(hidden_states)[:2] + if attention_mask is None: + attention_mask = tf.fill((batch_size, seq_length), 1) + if self.is_decoder and encoder_attention_mask is None: + encoder_seq_length = encoder_hidden_states.shape[1] + encoder_attention_mask = tf.fill((batch_size, encoder_seq_length), 1) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + attention_mask = tf.cast(attention_mask, dtype=tf.float32) + num_dims_attention_mask = len(shape_list(attention_mask)) + if num_dims_attention_mask == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif num_dims_attention_mask == 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: + seq_ids = tf.range(seq_length) + causal_mask = tf.less_equal(tf.tile(seq_ids[None, None, :], (batch_size, seq_length, 1)), + seq_ids[None, :, None]) + causal_mask = tf.cast(causal_mask, dtype=tf.float32) + extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] + else: + extended_attention_mask = attention_mask[:, None, None, :] + + # 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. + + # T5 has a mask that can compare sequence ids, we can simulate this here with this transposistion + # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 + # extended_attention_mask = tf.math.equal(extended_attention_mask, + # tf.transpose(extended_attention_mask, perm=(-1, -2))) + + extended_attention_mask = (1.0 - extended_attention_mask) * -1e9 + + if self.is_decoder: + # 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] + encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=tf.float32) + num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask)) + if num_dims_encoder_attention_mask == 3: + encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] + if num_dims_encoder_attention_mask == 2: + encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] + + # T5 has a mask that can compare sequence ids, we can simulate this here with this transposistion + # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 + # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask, + # tf.transpose(encoder_extended_attention_mask, perm=(-1, -2))) + + encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9 + else: + encoder_extended_attention_mask = None + + # 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) + + all_hidden_states = () + all_attentions = () + position_bias = None + encoder_decoder_position_bias = None + for i, layer_module in enumerate(self.block): + if self.output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module(hidden_states, + attention_mask=extended_attention_mask, + position_bias=position_bias, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + encoder_decoder_position_bias=encoder_decoder_position_bias, + head_mask=head_mask[i], + training=training) + hidden_states = layer_outputs[0] + if i == 0: + # We share the position biases between the layers - the first layer store them + # layer_outputs = hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias) + position_bias = layer_outputs[2 if self.output_attentions else 1] + if self.is_decoder: + encoder_decoder_position_bias = layer_outputs[4 if self.output_attentions else 2] + + if self.output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + hidden_states = self.final_layer_norm(hidden_states) + layer_output = self.dropout(hidden_states, training=training) + + # Add last layer + 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) + + +#################################################### +# TFT5PreTrainedModel is a sub-class of tf.keras.Model +# which take care of loading and saving pretrained weights +# and various common utilities. +# Here you just need to specify a few (self-explanatory) +# pointers for your model. +#################################################### +class TFT5PreTrainedModel(TFPreTrainedModel): + """ An abstract class to handle weights initialization and + a simple interface for dowloading and loading pretrained models. + """ + config_class = T5Config + pretrained_model_archive_map = TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP + base_model_prefix = "transformer" + + @property + def dummy_inputs(self): + input_ids = tf.constant(DUMMY_INPUTS) + input_mask = tf.constant(DUMMY_MASK) + dummy_inputs = {'decoder_input_ids': input_ids, + 'encoder_input_ids': input_ids, + 'decoder_attention_mask': input_mask} + return dummy_inputs + + +T5_START_DOCSTRING = r""" The T5 model was proposed in + `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`_ + by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. + It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting. + + 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. + + .. _`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`: + https://arxiv.org/abs/1910.10683 + + .. _`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.T5Config`): 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. +""" + +T5_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, T5 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]`` + + (b) For single sequences: + + ``tokens: [CLS] the dog is hairy . [SEP]`` + + + T5 is a model with relative position embeddings so you should be able to pad the inputs on + the right or the left. + + Indices can be obtained using :class:`transformers.T5Tokenizer`. + 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. + **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 T5 Model transformer outputting raw hidden-states" + "without any specific head on top.", + T5_START_DOCSTRING, T5_INPUTS_DOCSTRING) +class TFT5Model(TFT5PreTrainedModel): + 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. + **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 T5Tokenizer, TFT5Model + + tokenizer = T5Tokenizer.from_pretrained('t5-small') + model = TFT5Model.from_pretrained('t5-small') + 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, *inputs, **kwargs): + super(TFT5Model, self).__init__(config, *inputs, **kwargs) + self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, + name='shared') + + encoder_config = copy.deepcopy(config) + self.encoder = TFT5MainLayer(encoder_config, name='encoder') + + decoder_config = copy.deepcopy(config) + decoder_config.is_decoder = True + self.decoder = TFT5MainLayer(decoder_config, name='decoder') + + def get_input_embeddings(self): + return self.shared + + def get_output_embeddings(self): + return self.shared + + def call(self, decoder_input_ids, **kwargs): + # We allow two types of multi-inputs: + # - traditional keyword arguments in the call method + # - all the arguments provided as a dict in the first positional argument of call + # The last option is useful to use the tf.keras fit() method. + + if isinstance(decoder_input_ids, dict): + kwargs.update(decoder_input_ids) + else: + kwargs['decoder_input_ids'] = decoder_input_ids + + kwargs_common = dict((k, v) for k, v in kwargs.items() + if not k.startswith("encoder_") and not k.startswith("decoder_")) + kwargs_encoder = kwargs_common.copy() + kwargs_decoder = kwargs_common.copy() + kwargs_encoder.update(dict((k[len("encoder_"):], v) for k, v in kwargs.items() if k.startswith("encoder_"))) + kwargs_decoder.update(dict((k[len("decoder_"):], v) for k, v in kwargs.items() if k.startswith("decoder_"))) + + # Encode if needed (training, first prediction pass) + encoder_hidden_states = kwargs_encoder.pop("hidden_states", None) + if encoder_hidden_states is None: + # Convert encoder inputs in embeddings if needed + hidden_states = kwargs_encoder.pop("inputs_embeds", None) + if hidden_states is None: + encoder_inputs_ids = kwargs_encoder.pop("input_ids") + hidden_states = self.shared(encoder_inputs_ids) # Convert inputs in embeddings + + encoder_outputs = self.encoder(hidden_states, **kwargs_encoder) + encoder_hidden_states = encoder_outputs[0] + else: + encoder_outputs = () + + # Decode + # Convert decoder inputs in embeddings if needed + hidden_states = kwargs_decoder.pop("inputs_embeds", None) + if hidden_states is None: + decoder_inputs_ids = kwargs_decoder.pop("input_ids") + hidden_states = self.shared(decoder_inputs_ids) + + kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states + kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None) + decoder_outputs = self.decoder(hidden_states, **kwargs_decoder) + + return decoder_outputs + encoder_outputs + + +@add_start_docstrings("""T5 Model with a `language modeling` head on top. """, + T5_START_DOCSTRING, T5_INPUTS_DOCSTRING) +class TFT5WithLMHeadModel(TFT5PreTrainedModel): + 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 T5Tokenizer, TFT5WithLMHeadModel + + tokenizer = T5Tokenizer.from_pretrained('t5-small') + model = TFT5WithLMHeadModel.from_pretrained('t5-small') + 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(TFT5WithLMHeadModel, self).__init__(config, *inputs, **kwargs) + self.model_dim = config.d_model + + self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, + name='shared') + + encoder_config = copy.deepcopy(config) + self.encoder = TFT5MainLayer(encoder_config, name='encoder') + + decoder_config = copy.deepcopy(config) + decoder_config.is_decoder = True + self.decoder = TFT5MainLayer(decoder_config, name='decoder') + + def get_input_embeddings(self): + return self.shared + + def get_output_embeddings(self): + return self.shared + + def call(self, decoder_input_ids, **kwargs): + # We allow two types of multi-inputs: + # - traditional keyword arguments in the call method + # - all the arguments provided as a dict in the first positional argument of call + # The last option is useful to use the tf.keras fit() method. + + if isinstance(decoder_input_ids, dict): + kwargs.update(decoder_input_ids) + else: + kwargs['decoder_input_ids'] = decoder_input_ids + + kwargs_common = dict((k, v) for k, v in kwargs.items() + if not k.startswith("encoder_") and not k.startswith("decoder_")) + kwargs_encoder = kwargs_common.copy() + kwargs_decoder = kwargs_common.copy() + kwargs_encoder.update(dict((k[len("encoder_"):], v) for k, v in kwargs.items() if k.startswith("encoder_"))) + kwargs_decoder.update(dict((k[len("decoder_"):], v) for k, v in kwargs.items() if k.startswith("decoder_"))) + + # Encode if needed (training, first prediction pass) + encoder_hidden_states = kwargs_encoder.pop("hidden_states", None) + if encoder_hidden_states is None: + # Convert encoder inputs in embeddings if needed + hidden_states = kwargs_encoder.pop("inputs_embeds", None) + if hidden_states is None: + encoder_inputs_ids = kwargs_encoder.pop("input_ids") + hidden_states = self.shared(encoder_inputs_ids) # Convert inputs in embeddings + + encoder_outputs = self.encoder(hidden_states, **kwargs_encoder) + encoder_hidden_states = encoder_outputs[0] + else: + encoder_outputs = () + + # Decode + # Convert decoder inputs in embeddings if needed + hidden_states = kwargs_decoder.pop("inputs_embeds", None) + if hidden_states is None: + decoder_inputs_ids = kwargs_decoder.pop("input_ids") + hidden_states = self.shared(decoder_inputs_ids) + + kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states + kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None) + decoder_outputs = self.decoder(hidden_states, **kwargs_decoder) + + sequence_output = decoder_outputs[0] * (self.model_dim ** -0.5) + lm_logits = self.shared(sequence_output, mode="linear") + decoder_outputs = (lm_logits,) + decoder_outputs[1:] + + return decoder_outputs + encoder_outputs diff --git a/transformers/modeling_tf_utils.py b/transformers/modeling_tf_utils.py index d9a93af21b..6fb4850b05 100644 --- a/transformers/modeling_tf_utils.py +++ b/transformers/modeling_tf_utils.py @@ -24,14 +24,12 @@ import os import tensorflow as tf from .configuration_utils import PretrainedConfig -from .file_utils import (TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, WEIGHTS_NAME, +from .file_utils import (TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, WEIGHTS_NAME, DUMMY_INPUTS, cached_path, hf_bucket_url, is_remote_url) from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model logger = logging.getLogger(__name__) -DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]] - class TFPreTrainedModel(tf.keras.Model): r""" Base class for all TF models. @@ -60,7 +58,7 @@ class TFPreTrainedModel(tf.keras.Model): Returns: tf.Tensor with dummy inputs """ - return tf.constant(DUMMY_INPUTS) + return {'input_ids': tf.constant(DUMMY_INPUTS)} def __init__(self, config, *inputs, **kwargs): super(TFPreTrainedModel, self).__init__(*inputs, **kwargs) diff --git a/transformers/modeling_tf_xlm.py b/transformers/modeling_tf_xlm.py index 6f11b0537d..903a8596c3 100644 --- a/transformers/modeling_tf_xlm.py +++ b/transformers/modeling_tf_xlm.py @@ -460,7 +460,7 @@ class TFXLMPreTrainedModel(TFPreTrainedModel): langs_list = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]]) else: langs_list = None - return [inputs_list, attns_list, langs_list] + return {'input_ids': inputs_list, 'attention_mask': attns_list, 'langs': langs_list} XLM_START_DOCSTRING = r""" The XLM model was proposed in diff --git a/transformers/modeling_utils.py b/transformers/modeling_utils.py index 676f355986..9bd99b25dc 100644 --- a/transformers/modeling_utils.py +++ b/transformers/modeling_utils.py @@ -31,12 +31,11 @@ from torch.nn import CrossEntropyLoss from torch.nn import functional as F from .configuration_utils import PretrainedConfig -from .file_utils import (TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, WEIGHTS_NAME, +from .file_utils import (TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, WEIGHTS_NAME, DUMMY_INPUTS, cached_path, hf_bucket_url, is_remote_url) logger = logging.getLogger(__name__) - try: from torch.nn import Identity except ImportError: @@ -72,6 +71,15 @@ class PreTrainedModel(nn.Module): load_tf_weights = lambda model, config, path: None base_model_prefix = "" + @property + def dummy_inputs(self): + """ Dummy inputs to do a forward pass in the network. + + Returns: + torch.Tensor with dummy inputs + """ + return {'input_ids': torch.tensor(DUMMY_INPUTS)} + def __init__(self, config, *inputs, **kwargs): super(PreTrainedModel, self).__init__() if not isinstance(config, PretrainedConfig): @@ -161,8 +169,7 @@ class PreTrainedModel(nn.Module): base_model.vocab_size = new_num_tokens # Tie weights again if needed - if hasattr(self, 'tie_weights'): - self.tie_weights() + self.tie_weights() return model_embeds @@ -478,8 +485,7 @@ class PreTrainedModel(nn.Module): raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( model.__class__.__name__, "\n\t".join(error_msgs))) - if hasattr(model, 'tie_weights'): - model.tie_weights() # make sure word embedding weights are still tied + model.tie_weights() # make sure word embedding weights are still tied if needed # Set model in evaluation mode to desactivate DropOut modules by default model.eval() diff --git a/transformers/modeling_xlm.py b/transformers/modeling_xlm.py index 257f0da394..b604ae669d 100644 --- a/transformers/modeling_xlm.py +++ b/transformers/modeling_xlm.py @@ -227,6 +227,16 @@ class XLMPreTrainedModel(PreTrainedModel): def __init__(self, *inputs, **kwargs): super(XLMPreTrainedModel, self).__init__(*inputs, **kwargs) + @property + def dummy_inputs(self): + inputs_list = torch.tensor([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]) + attns_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]]) + if self.config.use_lang_emb and self.config.n_langs > 1: + langs_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]]) + else: + langs_list = None + return {'input_ids': inputs_list, 'attention_mask': attns_list, 'langs': langs_list} + def _init_weights(self, module): """ Initialize the weights. """ if isinstance(module, nn.Embedding): @@ -646,7 +656,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel): langs=langs, token_type_ids=token_type_ids, position_ids=position_ids, - lengths=lengths, + lengths=lengths, cache=cache, head_mask=head_mask, inputs_embeds=inputs_embeds) diff --git a/transformers/tests/fixtures/empty.txt b/transformers/tests/fixtures/empty.txt new file mode 100644 index 0000000000..e69de29bb2 diff --git a/transformers/tests/hf_api_test.py b/transformers/tests/hf_api_test.py index 92d41b6dff..b45f5aceed 100644 --- a/transformers/tests/hf_api_test.py +++ b/transformers/tests/hf_api_test.py @@ -15,18 +15,30 @@ from __future__ import absolute_import, division, print_function import os -import six import time import unittest -from transformers.hf_api import HfApi, S3Obj, PresignedUrl, HfFolder, HTTPError +import requests +import six + +from transformers.hf_api import HfApi, HfFolder, HTTPError, PresignedUrl, S3Obj 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" -) +FILES = [ + ( + "Test-{}.txt".format(int(time.time())), + os.path.join( + os.path.dirname(os.path.abspath(__file__)), "fixtures/input.txt" + ) + ), + ( + "yoyo {}.txt".format(int(time.time())), # space is intentional + os.path.join( + os.path.dirname(os.path.abspath(__file__)), "fixtures/empty.txt" + ) + ), +] @@ -57,15 +69,21 @@ class HfApiEndpointsTest(HfApiCommonTest): self.assertEqual(user, USER) def test_presign(self): - urls = self._api.presign(token=self._token, filename=FILE_KEY) - self.assertIsInstance(urls, PresignedUrl) - self.assertEqual(urls.type, "text/plain") + for FILE_KEY, FILE_PATH in FILES: + 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, six.string_types) + for FILE_KEY, FILE_PATH in FILES: + access_url = self._api.presign_and_upload( + token=self._token, filename=FILE_KEY, filepath=FILE_PATH + ) + self.assertIsInstance(access_url, six.string_types) + with open(FILE_PATH, 'r') as f: + body = f.read() + r = requests.get(access_url) + self.assertEqual(r.text, body) def test_list_objs(self): objs = self._api.list_objs(token=self._token) diff --git a/transformers/tests/modeling_common_test.py b/transformers/tests/modeling_common_test.py index f86eb7a3d0..c84162117a 100644 --- a/transformers/tests/modeling_common_test.py +++ b/transformers/tests/modeling_common_test.py @@ -58,7 +58,7 @@ else: def _config_zero_init(config): configs_no_init = copy.deepcopy(config) for key in configs_no_init.__dict__.keys(): - if '_range' in key or '_std' in key: + if '_range' in key or '_std' in key or 'initializer_factor' in key: setattr(configs_no_init, key, 0.0) return configs_no_init @@ -73,6 +73,7 @@ class CommonTestCases: test_pruning = True test_resize_embeddings = True test_head_masking = True + is_encoder_decoder = False def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() @@ -83,6 +84,8 @@ class CommonTestCases: model.eval() with torch.no_grad(): outputs = model(**inputs_dict) + out_2 = outputs[0].numpy() + out_2[np.isnan(out_2)] = 0 with TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) @@ -93,9 +96,7 @@ class CommonTestCases: # Make sure we don't have nans 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)] + out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) @@ -117,20 +118,32 @@ class CommonTestCases: 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) - + with torch.no_grad(): + first = model(**inputs_dict)[0] + second = model(**inputs_dict)[0] + out_1 = first.cpu().numpy() + out_2 = second.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)) + self.assertLessEqual(max_diff, 1e-5) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() + decoder_seq_length = self.model_tester.decoder_seq_length if hasattr(self.model_tester, 'decoder_seq_length') else self.model_tester.seq_length + encoder_seq_length = self.model_tester.encoder_seq_length if hasattr(self.model_tester, 'encoder_seq_length') else self.model_tester.seq_length + decoder_key_length = self.model_tester.key_length if hasattr(self.model_tester, 'key_length') else decoder_seq_length + encoder_key_length = self.model_tester.key_length if hasattr(self.model_tester, 'key_length') else encoder_seq_length + for model_class in self.all_model_classes: config.output_attentions = True config.output_hidden_states = False model = model_class(config) model.to(torch_device) model.eval() - outputs = model(**inputs_dict) + with torch.no_grad(): + outputs = model(**inputs_dict) attentions = outputs[-1] self.assertEqual(model.config.output_attentions, True) self.assertEqual(model.config.output_hidden_states, False) @@ -138,28 +151,42 @@ class CommonTestCases: self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, - self.model_tester.seq_length, - self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length]) + encoder_seq_length , + encoder_key_length]) out_len = len(outputs) + if self.is_encoder_decoder: + self.assertEqual(out_len % 2, 0) + decoder_attentions = outputs[(out_len // 2)-1] + self.assertEqual(model.config.output_attentions, True) + self.assertEqual(model.config.output_hidden_states, False) + self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) + self.assertListEqual( + list(decoder_attentions[0].shape[-3:]), + [self.model_tester.num_attention_heads, + decoder_seq_length, + decoder_key_length + ]) + # Check attention is always last and order is fine 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)) + with torch.no_grad(): + outputs = model(**inputs_dict) + self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs)) self.assertEqual(model.config.output_attentions, True) self.assertEqual(model.config.output_hidden_states, True) - attentions = outputs[-1] - self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) + self_attentions = outputs[-1] + self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( - list(attentions[0].shape[-3:]), + list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, - self.model_tester.seq_length, - self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length]) + encoder_seq_length, + encoder_key_length]) def test_torchscript(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() @@ -223,7 +250,6 @@ class CommonTestCases: self.assertTrue(models_equal) - def test_headmasking(self): if not self.test_head_masking: return @@ -278,7 +304,6 @@ class CommonTestCases: self.assertNotEqual( attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0) - def test_head_pruning(self): if not self.test_pruning: return @@ -297,7 +322,8 @@ class CommonTestCases: 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) + with torch.no_grad(): + outputs = model(**inputs_dict) attentions = outputs[-1] @@ -333,7 +359,8 @@ class CommonTestCases: model = model_class.from_pretrained(directory) model.to(torch_device) - outputs = model(**inputs_dict) + with torch.no_grad(): + 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) @@ -362,7 +389,8 @@ class CommonTestCases: model.to(torch_device) model.eval() - outputs = model(**inputs_dict) + with torch.no_grad(): + outputs = model(**inputs_dict) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], 1) @@ -389,7 +417,8 @@ class CommonTestCases: model.to(torch_device) model.eval() - outputs = model(**inputs_dict) + with torch.no_grad(): + outputs = model(**inputs_dict) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) @@ -406,7 +435,8 @@ class CommonTestCases: model.to(torch_device) shutil.rmtree(directory) - outputs = model(**inputs_dict) + with torch.no_grad(): + outputs = model(**inputs_dict) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1) @@ -417,7 +447,8 @@ class CommonTestCases: heads_to_prune = {0: [0], 2: [1, 2]} model.prune_heads(heads_to_prune) - outputs = model(**inputs_dict) + with torch.no_grad(): + outputs = model(**inputs_dict) attentions = outputs[-1] self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads -1) @@ -427,7 +458,6 @@ class CommonTestCases: self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]}) - def test_hidden_states_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() @@ -437,14 +467,16 @@ class CommonTestCases: model = model_class(config) model.to(torch_device) model.eval() - outputs = model(**inputs_dict) + with torch.no_grad(): + outputs = model(**inputs_dict) hidden_states = outputs[-1] self.assertEqual(model.config.output_attentions, False) self.assertEqual(model.config.output_hidden_states, True) self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1) self.assertListEqual( list(hidden_states[0].shape[-2:]), - [self.model_tester.seq_length, self.model_tester.hidden_size]) + [self.model_tester.encoder_seq_length if hasattr(self.model_tester, 'encoder_seq_length') else self.model_tester.seq_length, + self.model_tester.hidden_size]) def test_resize_tokens_embeddings(self): original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() @@ -550,8 +582,14 @@ class CommonTestCases: def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() - input_ids = inputs_dict["input_ids"] - del inputs_dict["input_ids"] + if not self.is_encoder_decoder: + input_ids = inputs_dict["input_ids"] + del inputs_dict["input_ids"] + else: + encoder_input_ids = inputs_dict["encoder_input_ids"] + decoder_input_ids = inputs_dict["decoder_input_ids"] + del inputs_dict["encoder_input_ids"] + del inputs_dict["decoder_input_ids"] for model_class in self.all_model_classes: model = model_class(config) @@ -559,9 +597,14 @@ class CommonTestCases: model.eval() wte = model.get_input_embeddings() - inputs_dict["inputs_embeds"] = wte(input_ids) - outputs = model(**inputs_dict) + if not self.is_encoder_decoder: + inputs_dict["inputs_embeds"] = wte(input_ids) + else: + inputs_dict["encoder_inputs_embeds"] = wte(encoder_input_ids) + inputs_dict["decoder_inputs_embeds"] = wte(decoder_input_ids) + with torch.no_grad(): + outputs = model(**inputs_dict) class GPTModelTester(CommonModelTester): @@ -649,9 +692,10 @@ class CommonTestCases: model.to(torch_device) model.eval() - outputs = model(input_ids, position_ids, token_type_ids) - outputs = model(input_ids, position_ids) - outputs = model(input_ids) + with torch.no_grad(): + outputs = model(input_ids, position_ids, token_type_ids) + outputs = model(input_ids, position_ids) + outputs = model(input_ids) hidden_state = outputs[0] self.parent.assertListEqual( @@ -664,7 +708,8 @@ class CommonTestCases: model = self.lm_head_model_class(config) model.to(torch_device) model.eval() - outputs = model(input_ids, position_ids, token_type_ids, lm_labels) + with torch.no_grad(): + outputs = model(input_ids, position_ids, token_type_ids, lm_labels) loss, lm_logits = outputs[:2] total_voc = self.vocab_size @@ -681,7 +726,8 @@ class CommonTestCases: model = model_class(config) model.to(torch_device) model.eval() - outputs = model(input_ids) + with torch.no_grad(): + outputs = model(input_ids) presents = outputs[-1] self.parent.assertEqual(self.num_hidden_layers, len(presents)) self.parent.assertListEqual( @@ -694,7 +740,8 @@ class CommonTestCases: 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, + with torch.no_grad(): + 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) lm_loss, mc_loss, lm_logits, mc_logits = outputs[:4] loss = [lm_loss, mc_loss] diff --git a/transformers/tests/modeling_t5_test.py b/transformers/tests/modeling_t5_test.py new file mode 100644 index 0000000000..a539cc868a --- /dev/null +++ b/transformers/tests/modeling_t5_test.py @@ -0,0 +1,185 @@ +# coding=utf-8 +# Copyright 2018 Google T5 Authors and 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 +from __future__ import division +from __future__ import print_function + +import unittest +import shutil + +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 (T5Config, T5Model, T5WithLMHeadModel) + from transformers.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_MAP + + +@require_torch +class T5ModelTest(CommonTestCases.CommonModelTester): + + all_model_classes = (T5Model, T5WithLMHeadModel) if is_torch_available() else () + test_pruning = False + test_torchscript = False + test_resize_embeddings = False + is_encoder_decoder = True + + class T5ModelTester(object): + + def __init__(self, + parent, + batch_size=13, + encoder_seq_length=7, + decoder_seq_length=9, + is_training=True, + use_attention_mask=True, + use_labels=True, + vocab_size=99, + n_positions=14, + hidden_size=32, + num_hidden_layers=5, + num_attention_heads=4, + d_ff=37, + relative_attention_num_buckets=8, + dropout_rate=0.1, + initializer_factor=0.002, + scope=None, + ): + self.parent = parent + self.batch_size = batch_size + self.encoder_seq_length = encoder_seq_length + self.decoder_seq_length = decoder_seq_length + self.is_training = is_training + self.use_attention_mask = use_attention_mask + self.use_labels = use_labels + self.vocab_size = vocab_size + self.n_positions = n_positions + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.d_ff = d_ff + self.relative_attention_num_buckets = relative_attention_num_buckets + self.dropout_rate = dropout_rate + self.initializer_factor = initializer_factor + self.scope = scope + + def prepare_config_and_inputs(self): + encoder_input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) + decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) + + encoder_attention_mask = None + decoder_attention_mask = None + if self.use_attention_mask: + encoder_attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) + decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) + + decoder_lm_labels = None + if self.use_labels: + decoder_lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) + + config = T5Config( + vocab_size_or_config_json_file=self.vocab_size, + n_positions=self.n_positions, + d_model=self.hidden_size, + d_ff=self.d_ff, + d_kv=self.hidden_size // self.num_attention_heads, + num_layers=self.num_hidden_layers, + num_heads=self.num_attention_heads, + relative_attention_num_buckets=self.relative_attention_num_buckets, + dropout_rate=self.dropout_rate, + initializer_factor=self.initializer_factor) + + return (config, encoder_input_ids, decoder_input_ids, encoder_attention_mask, decoder_attention_mask, decoder_lm_labels) + + def check_loss_output(self, result): + self.parent.assertListEqual( + list(result["loss"].size()), + []) + + def create_and_check_t5_model(self, config, encoder_input_ids, decoder_input_ids, encoder_attention_mask, decoder_attention_mask, decoder_lm_labels): + model = T5Model(config=config) + model.eval() + decoder_output, encoder_output = model(encoder_input_ids=encoder_input_ids, + decoder_input_ids=decoder_input_ids, + encoder_attention_mask=encoder_attention_mask, + decoder_attention_mask=decoder_attention_mask) + decoder_output, encoder_output = model(encoder_input_ids=encoder_input_ids, + decoder_input_ids=decoder_input_ids) + + result = { + "encoder_output": encoder_output, + "decoder_output": decoder_output, + } + self.parent.assertListEqual( + list(result["encoder_output"].size()), + [self.batch_size, self.encoder_seq_length, self.hidden_size]) + self.parent.assertListEqual( + list(result["decoder_output"].size()), + [self.batch_size, self.decoder_seq_length, self.hidden_size]) + + + def create_and_check_t5_with_lm_head(self, config, encoder_input_ids, decoder_input_ids, encoder_attention_mask, decoder_attention_mask, decoder_lm_labels): + model = T5WithLMHeadModel(config=config) + model.eval() + outputs = model(encoder_input_ids=encoder_input_ids, decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, decoder_lm_labels=decoder_lm_labels) + loss, prediction_scores = outputs[0], outputs[1] + result = { + "loss": loss, + "prediction_scores": prediction_scores, + } + self.parent.assertListEqual( + list(result["prediction_scores"].size()), + [self.batch_size, self.decoder_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, encoder_input_ids, decoder_input_ids, encoder_attention_mask, + decoder_attention_mask, decoder_lm_labels) = config_and_inputs + inputs_dict = {'encoder_input_ids': encoder_input_ids, + 'decoder_input_ids': decoder_input_ids, + 'decoder_attention_mask': decoder_attention_mask, + 'encoder_attention_mask': encoder_attention_mask} + return config, inputs_dict + + def setUp(self): + self.model_tester = T5ModelTest.T5ModelTester(self) + self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37) + + def test_config(self): + self.config_tester.run_common_tests() + + def test_t5_model(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_t5_model(*config_and_inputs) + + def test_with_lm_head(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_t5_with_lm_head(*config_and_inputs) + + @slow + def test_model_from_pretrained(self): + cache_dir = "/tmp/transformers_test/" + for model_name in list(T5_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: + model = T5Model.from_pretrained(model_name, cache_dir=cache_dir) + shutil.rmtree(cache_dir) + self.assertIsNotNone(model) + +if __name__ == "__main__": + unittest.main() diff --git a/transformers/tests/modeling_tf_common_test.py b/transformers/tests/modeling_tf_common_test.py index 439360ba35..5a5873e81b 100644 --- a/transformers/tests/modeling_tf_common_test.py +++ b/transformers/tests/modeling_tf_common_test.py @@ -69,6 +69,7 @@ class TFCommonTestCases: test_torchscript = True test_pruning = True test_resize_embeddings = True + is_encoder_decoder = False def test_initialization(self): pass @@ -129,8 +130,12 @@ class TFCommonTestCases: for name, key in inputs_dict.items()) with torch.no_grad(): pto = pt_model(**pt_inputs_dict) - tfo = tf_model(inputs_dict) - max_diff = np.amax(np.abs(tfo[0].numpy() - pto[0].numpy())) + tfo = tf_model(inputs_dict, training=False) + tf_hidden_states = tfo[0].numpy() + pt_hidden_states = pto[0].numpy() + tf_hidden_states[np.isnan(tf_hidden_states)] = 0 + pt_hidden_states[np.isnan(pt_hidden_states)] = 0 + max_diff = np.amax(np.abs(tf_hidden_states - pt_hidden_states)) self.assertLessEqual(max_diff, 2e-2) # Check we can load pt model in tf and vice-versa with checkpoint => model functions @@ -150,13 +155,21 @@ class TFCommonTestCases: with torch.no_grad(): pto = pt_model(**pt_inputs_dict) tfo = tf_model(inputs_dict) - max_diff = np.amax(np.abs(tfo[0].numpy() - pto[0].numpy())) + tfo = tfo[0].numpy() + pto = pto[0].numpy() + tfo[np.isnan(tfo)] = 0 + pto[np.isnan(pto)] = 0 + max_diff = np.amax(np.abs(tfo - pto)) self.assertLessEqual(max_diff, 2e-2) def test_compile_tf_model(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() - input_ids = tf.keras.Input(batch_shape=(2, 2000), name='input_ids', dtype='int32') + if self.is_encoder_decoder: + input_ids = {'decoder_input_ids': tf.keras.Input(batch_shape=(2, 2000), name='decoder_input_ids', dtype='int32'), + 'encoder_input_ids': tf.keras.Input(batch_shape=(2, 2000), name='encoder_input_ids', dtype='int32')} + else: + input_ids = tf.keras.Input(batch_shape=(2, 2000), name='input_ids', dtype='int32') optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy') @@ -189,7 +202,7 @@ class TFCommonTestCases: outputs_dict = model(inputs_dict) inputs_keywords = copy.deepcopy(inputs_dict) - input_ids = inputs_keywords.pop('input_ids') + input_ids = inputs_keywords.pop('input_ids' if not self.is_encoder_decoder else 'decoder_input_ids', None) outputs_keywords = model(input_ids, **inputs_keywords) output_dict = outputs_dict[0].numpy() @@ -200,6 +213,11 @@ class TFCommonTestCases: def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() + decoder_seq_length = self.model_tester.decoder_seq_length if hasattr(self.model_tester, 'decoder_seq_length') else self.model_tester.seq_length + encoder_seq_length = self.model_tester.encoder_seq_length if hasattr(self.model_tester, 'encoder_seq_length') else self.model_tester.seq_length + decoder_key_length = self.model_tester.key_length if hasattr(self.model_tester, 'key_length') else decoder_seq_length + encoder_key_length = self.model_tester.key_length if hasattr(self.model_tester, 'key_length') else encoder_seq_length + for model_class in self.all_model_classes: config.output_attentions = True config.output_hidden_states = False @@ -212,16 +230,28 @@ class TFCommonTestCases: self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, - self.model_tester.seq_length, - self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length]) + encoder_seq_length, + encoder_key_length]) out_len = len(outputs) + if self.is_encoder_decoder: + self.assertEqual(out_len % 2, 0) + decoder_attentions = outputs[(out_len // 2)-1] + self.assertEqual(model.config.output_attentions, True) + self.assertEqual(model.config.output_hidden_states, False) + self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) + self.assertListEqual( + list(decoder_attentions[0].shape[-3:]), + [self.model_tester.num_attention_heads, + decoder_seq_length, + decoder_key_length]) + # Check attention is always last and order is fine config.output_attentions = True config.output_hidden_states = True model = model_class(config) outputs = model(inputs_dict) - self.assertEqual(out_len+1, len(outputs)) + self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs)) self.assertEqual(model.config.output_attentions, True) self.assertEqual(model.config.output_hidden_states, True) @@ -230,8 +260,8 @@ class TFCommonTestCases: self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, - self.model_tester.seq_length, - self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length]) + encoder_seq_length, + encoder_key_length]) def test_hidden_states_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() @@ -264,35 +294,53 @@ class TFCommonTestCases: for model_class in self.all_model_classes: model = model_class(config) first, second = model(inputs_dict, training=False)[0], model(inputs_dict, training=False)[0] - self.assertTrue(tf.math.equal(first, second).numpy().all()) + out_1 = first.numpy() + out_2 = second.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)) + self.assertLessEqual(max_diff, 1e-5) + + def _get_embeds(self, wte, input_ids): + # ^^ 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: + try: + x = wte(input_ids, mode="embedding") + except: + try: + x = wte([input_ids], mode="embedding") + except: + 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) + return x def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() - input_ids = inputs_dict["input_ids"] - del inputs_dict["input_ids"] + if not self.is_encoder_decoder: + input_ids = inputs_dict["input_ids"] + del inputs_dict["input_ids"] + else: + encoder_input_ids = inputs_dict["encoder_input_ids"] + decoder_input_ids = inputs_dict["decoder_input_ids"] + del inputs_dict["encoder_input_ids"] + del inputs_dict["decoder_input_ids"] for model_class in self.all_model_classes: model = model_class(config) wte = model.get_input_embeddings() - try: - x = wte(input_ids, mode="embedding") - except: - try: - x = wte([input_ids], mode="embedding") - except: - 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: - # - inputs_dict["inputs_embeds"] = x + if not self.is_encoder_decoder: + inputs_dict["inputs_embeds"] = self._get_embeds(wte, input_ids) + else: + inputs_dict["encoder_inputs_embeds"] = self._get_embeds(wte, encoder_input_ids) + inputs_dict["decoder_inputs_embeds"] = self._get_embeds(wte, decoder_input_ids) + outputs = model(inputs_dict) diff --git a/transformers/tests/modeling_tf_t5_test.py b/transformers/tests/modeling_tf_t5_test.py new file mode 100644 index 0000000000..99eec313f9 --- /dev/null +++ b/transformers/tests/modeling_tf_t5_test.py @@ -0,0 +1,172 @@ +# coding=utf-8 +# Copyright 2018 Google T5 Authors and 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 +from __future__ import division +from __future__ import print_function + +import unittest +import shutil +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 T5Config, is_tf_available + +if is_tf_available(): + import tensorflow as tf + from transformers.modeling_tf_t5 import (TFT5Model, TFT5WithLMHeadModel, + TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP) + + +@require_tf +class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester): + + is_encoder_decoder = True + all_model_classes = (TFT5Model, TFT5WithLMHeadModel) if is_tf_available() else () + + class TFT5ModelTester(object): + + def __init__(self, + parent, + batch_size=13, + seq_length=7, + is_training=True, + use_input_mask=True, + use_labels=True, + vocab_size=99, + n_positions=14, + hidden_size=32, + num_hidden_layers=5, + num_attention_heads=4, + d_ff=37, + relative_attention_num_buckets=8, + dropout_rate=0.1, + initializer_factor=0.002, + 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_labels = use_labels + self.vocab_size = vocab_size + self.n_positions = n_positions + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.d_ff = d_ff + self.relative_attention_num_buckets = relative_attention_num_buckets + self.dropout_rate = dropout_rate + self.initializer_factor = initializer_factor + 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_labels = None + if self.use_labels: + token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) + + config = T5Config( + vocab_size_or_config_json_file=self.vocab_size, + n_positions=self.n_positions, + d_model=self.hidden_size, + d_ff=self.d_ff, + d_kv=self.hidden_size // self.num_attention_heads, + num_layers=self.num_hidden_layers, + num_heads=self.num_attention_heads, + relative_attention_num_buckets=self.relative_attention_num_buckets, + dropout_rate=self.dropout_rate, + initializer_factor=self.initializer_factor) + + return (config, input_ids, input_mask, token_labels) + + def create_and_check_t5_model(self, config, input_ids, input_mask, token_labels): + model = TFT5Model(config=config) + inputs = {'encoder_input_ids': input_ids, + 'decoder_input_ids': input_ids, + 'decoder_attention_mask': input_mask} + encoder_output, decoder_output = model(inputs) + + encoder_output, decoder_output = model(input_ids, + decoder_attention_mask=input_mask, + encoder_input_ids=input_ids) + + result = { + "encoder_output": encoder_output.numpy(), + "decoder_output": decoder_output.numpy(), + } + self.parent.assertListEqual( + list(result["encoder_output"].shape), + [self.batch_size, self.seq_length, self.hidden_size]) + self.parent.assertListEqual( + list(result["decoder_output"].shape), + [self.batch_size, self.seq_length, self.hidden_size]) + + + def create_and_check_t5_with_lm_head(self, config, input_ids, input_mask, token_labels): + model = TFT5WithLMHeadModel(config=config) + inputs = {'encoder_input_ids': input_ids, + 'decoder_input_ids': input_ids, + 'decoder_attention_mask': input_mask} + prediction_scores, decoder_output = 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 prepare_config_and_inputs_for_common(self): + config_and_inputs = self.prepare_config_and_inputs() + (config, input_ids, input_mask, token_labels) = config_and_inputs + inputs_dict = {'encoder_input_ids': input_ids, + 'decoder_input_ids': input_ids, + 'decoder_attention_mask': input_mask} + return config, inputs_dict + + def setUp(self): + self.model_tester = TFT5ModelTest.TFT5ModelTester(self) + self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37) + + def test_config(self): + self.config_tester.run_common_tests() + + def test_t5_model(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_t5_model(*config_and_inputs) + + def test_with_lm_head(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_t5_with_lm_head(*config_and_inputs) + + @slow + def test_model_from_pretrained(self): + cache_dir = "/tmp/transformers_test/" + for model_name in ['t5-small']: + model = TFT5Model.from_pretrained(model_name, cache_dir=cache_dir) + shutil.rmtree(cache_dir) + self.assertIsNotNone(model) + +if __name__ == "__main__": + unittest.main() diff --git a/transformers/tests/modeling_tf_transfo_xl_test.py b/transformers/tests/modeling_tf_transfo_xl_test.py index 553263250a..746a6a1321 100644 --- a/transformers/tests/modeling_tf_transfo_xl_test.py +++ b/transformers/tests/modeling_tf_transfo_xl_test.py @@ -67,7 +67,7 @@ class TFTransfoXLModelTest(TFCommonTestCases.TFCommonModelTester): self.batch_size = batch_size self.seq_length = seq_length self.mem_len = mem_len - self.key_len = seq_length + mem_len + self.key_length = seq_length + mem_len self.clamp_len = clamp_len self.is_training = is_training self.use_labels = use_labels diff --git a/transformers/tests/modeling_transfo_xl_test.py b/transformers/tests/modeling_transfo_xl_test.py index dca46444ba..f41d50a3a0 100644 --- a/transformers/tests/modeling_transfo_xl_test.py +++ b/transformers/tests/modeling_transfo_xl_test.py @@ -66,7 +66,7 @@ class TransfoXLModelTest(CommonTestCases.CommonModelTester): self.batch_size = batch_size self.seq_length = seq_length self.mem_len = mem_len - self.key_len = seq_length + mem_len + self.key_length = seq_length + mem_len self.clamp_len = clamp_len self.is_training = is_training self.use_labels = use_labels diff --git a/transformers/tests/tokenization_bert_test.py b/transformers/tests/tokenization_bert_test.py index f390248956..c503ea5e1e 100644 --- a/transformers/tests/tokenization_bert_test.py +++ b/transformers/tests/tokenization_bert_test.py @@ -139,5 +139,6 @@ class BertTokenizationTest(CommonTestCases.CommonTokenizerTester): assert encoded_sentence == [101] + text + [102] assert encoded_pair == [101] + text + [102] + text_2 + [102] + if __name__ == '__main__': unittest.main() diff --git a/transformers/tests/tokenization_gpt2_test.py b/transformers/tests/tokenization_gpt2_test.py index a77cc75ec2..5eae767bdf 100644 --- a/transformers/tests/tokenization_gpt2_test.py +++ b/transformers/tests/tokenization_gpt2_test.py @@ -67,6 +67,5 @@ class GPT2TokenizationTest(CommonTestCases.CommonTokenizerTester): self.assertListEqual( tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) - if __name__ == '__main__': unittest.main() diff --git a/transformers/tests/tokenization_t5_test.py b/transformers/tests/tokenization_t5_test.py new file mode 100644 index 0000000000..0b4f960e32 --- /dev/null +++ b/transformers/tests/tokenization_t5_test.py @@ -0,0 +1,77 @@ +# coding=utf-8 +# Copyright 2018 Google T5 Authors and 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, unicode_literals + +import os +import unittest + +from transformers.tokenization_t5 import (T5Tokenizer) +from transformers.tokenization_xlnet import SPIECE_UNDERLINE + +from .tokenization_tests_commons import CommonTestCases + +SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), + 'fixtures/test_sentencepiece.model') + +class T5TokenizationTest(CommonTestCases.CommonTokenizerTester): + + tokenizer_class = T5Tokenizer + + def setUp(self): + super(T5TokenizationTest, self).setUp() + + # We have a SentencePiece fixture for testing + tokenizer = T5Tokenizer(SAMPLE_VOCAB) + tokenizer.save_pretrained(self.tmpdirname) + + def get_tokenizer(self, **kwargs): + return T5Tokenizer.from_pretrained(self.tmpdirname, **kwargs) + + def get_input_output_texts(self): + input_text = u"This is a test" + output_text = u"This is a test" + return input_text, output_text + + def test_full_tokenizer(self): + tokenizer = T5Tokenizer(SAMPLE_VOCAB) + + tokens = tokenizer.tokenize(u'This is a test') + self.assertListEqual(tokens, [u'▁This', u'▁is', u'▁a', u'▁t', u'est']) + + self.assertListEqual( + tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382]) + + tokens = tokenizer.tokenize(u"I was born in 92000, and this is falsé.") + self.assertListEqual(tokens, [SPIECE_UNDERLINE + u'I', SPIECE_UNDERLINE + u'was', SPIECE_UNDERLINE + u'b', + u'or', u'n', SPIECE_UNDERLINE + u'in', SPIECE_UNDERLINE + u'', + 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's', u'é', u'.']) + ids = tokenizer.convert_tokens_to_ids(tokens) + self.assertListEqual( + ids, [8, 21, 84, 55, 24, 19, 7, 0, + 602, 347, 347, 347, 3, 12, 66, + 46, 72, 80, 6, 0, 4]) + + back_tokens = tokenizer.convert_ids_to_tokens(ids) + self.assertListEqual(back_tokens, [SPIECE_UNDERLINE + u'I', SPIECE_UNDERLINE + u'was', SPIECE_UNDERLINE + u'b', + u'or', u'n', SPIECE_UNDERLINE + u'in', + SPIECE_UNDERLINE + u'', u'', 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's', + u'', u'.']) + + +if __name__ == '__main__': + unittest.main() diff --git a/transformers/tests/tokenization_tests_commons.py b/transformers/tests/tokenization_tests_commons.py index c009958135..13e7ae746a 100644 --- a/transformers/tests/tokenization_tests_commons.py +++ b/transformers/tests/tokenization_tests_commons.py @@ -232,6 +232,15 @@ class CommonTestCases: self.assertNotEqual(len(tokens_2), 0) self.assertIsInstance(text_2, (str, unicode)) + def test_encode_decode_with_spaces(self): + tokenizer = self.get_tokenizer() + + new_toks = ['[ABC]', '[DEF]', 'GHI IHG'] + tokenizer.add_tokens(new_toks) + input = "[ABC] [DEF] [ABC] GHI IHG [DEF]" + encoded = tokenizer.encode(input, add_special_tokens=False) + decoded = tokenizer.decode(encoded) + self.assertEqual(decoded, input) def test_pretrained_model_lists(self): weights_list = list(self.tokenizer_class.max_model_input_sizes.keys()) diff --git a/transformers/tokenization_auto.py b/transformers/tokenization_auto.py index 1f0599ef7f..173dee0e2b 100644 --- a/transformers/tokenization_auto.py +++ b/transformers/tokenization_auto.py @@ -30,6 +30,7 @@ from .tokenization_roberta import RobertaTokenizer from .tokenization_distilbert import DistilBertTokenizer from .tokenization_camembert import CamembertTokenizer from .tokenization_albert import AlbertTokenizer +from .tokenization_t5 import T5Tokenizer logger = logging.getLogger(__name__) @@ -44,6 +45,7 @@ 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 `t5`: T5Tokenizer (T5 model) - contains `distilbert`: DistilBertTokenizer (DistilBert model) - contains `albert`: AlbertTokenizer (ALBERT model) - contains `camembert`: CamembertTokenizer (CamemBERT model) @@ -69,6 +71,7 @@ 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 `t5`: T5Tokenizer (T5 model) - contains `distilbert`: DistilBertTokenizer (DistilBert model) - contains `albert`: AlbertTokenizer (ALBERT model) - contains `camembert`: CamembertTokenizer (CamemBERT model) @@ -119,7 +122,9 @@ class AutoTokenizer(object): tokenizer = AutoTokenizer.from_pretrained('./test/bert_saved_model/') """ - if 'distilbert' in pretrained_model_name_or_path: + if 't5' in pretrained_model_name_or_path: + return T5Tokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) + elif '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) diff --git a/transformers/tokenization_t5.py b/transformers/tokenization_t5.py new file mode 100644 index 0000000000..9fd37b67c0 --- /dev/null +++ b/transformers/tokenization_t5.py @@ -0,0 +1,176 @@ +# coding=utf-8 +# Copyright 2018 T5 Authors and 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 class for model T5.""" + +from __future__ import absolute_import, division, print_function, unicode_literals + +import logging +import os +import re +import six +from shutil import copyfile + +from .tokenization_utils import PreTrainedTokenizer + +logger = logging.getLogger(__name__) + +SPIECE_UNDERLINE = u'▁' + +#################################################### +# Mapping from the keyword arguments names of Tokenizer `__init__` +# to file names for serializing Tokenizer instances +#################################################### +VOCAB_FILES_NAMES = {'vocab_file': 'spiece.model'} + +#################################################### +# Mapping from the keyword arguments names of Tokenizer `__init__` +# to pretrained vocabulary URL for all the model shortcut names. +#################################################### +PRETRAINED_VOCAB_FILES_MAP = { + 'vocab_file': + { + 't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-spiece.model", + 't5-base': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-spiece.model", + 't5-large': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-spiece.model", + 't5-3b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-spiece.model", + 't5-11b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-spiece.model", + } +} + +#################################################### +# Mapping from model shortcut names to max length of inputs +#################################################### +PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { + 't5-small': 512, + 't5-base': 512, + 't5-large': 512, + 't5-3b': 512, + 't5-11b': 512, +} + +class T5Tokenizer(PreTrainedTokenizer): + """ + SentencePiece based tokenizer. Peculiarities: + + - requires `SentencePiece `_ + - `extra_ids` add a number of extra ids added to the end of the vocabulary for use as sentinels. + These tokens are accessible as `` where `{%d}` is a number between 0 and extra_ids-1. + Extra tokens are indexed from the end of the vocabulary up to beginnning ( is the last token in the vocabulary) + (like in T5 preprocessing + see: https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117) + """ + 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, eos_token="", unk_token="", + pad_token="", extra_ids=100, additional_special_tokens=None, **kwargs): + # Add extra_ids to the special token list + if extra_ids > 0: + if additional_special_tokens is None: + additional_special_tokens = [] + additional_special_tokens.extend([u"".format(i) for i in range(extra_ids)]) + + super(T5Tokenizer, self).__init__(eos_token=eos_token, unk_token=unk_token, + pad_token=pad_token, additional_special_tokens=additional_special_tokens, + **kwargs) + + try: + import sentencepiece as spm + except ImportError: + logger.warning("You need to install SentencePiece to use T5Tokenizer:" + "https://github.com/google/sentencepiece" + "pip install sentencepiece") + + self.vocab_file = vocab_file + self._extra_ids = extra_ids + + self.sp_model = spm.SentencePieceProcessor() + self.sp_model.Load(vocab_file) + + @property + def vocab_size(self): + return self.sp_model.get_piece_size() + self._extra_ids + + 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 XLNetTokenizer: https://github.com/google/sentencepiece" + "pip install sentencepiece") + self.sp_model = spm.SentencePieceProcessor() + self.sp_model.Load(self.vocab_file) + + def _tokenize(self, text, return_unicode=True, sample=False): + """ Take as input a string and return a list of strings (tokens) for words/sub-words + """ + if not sample: + pieces = self.sp_model.EncodeAsPieces(text) + else: + pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1) + + # convert back to unicode for py2 + if six.PY2 and return_unicode: + ret_pieces = [] + for piece in pieces: + if isinstance(piece, str): + piece = piece.decode('utf-8') + ret_pieces.append(piece) + pieces = ret_pieces + + return pieces + + def _convert_token_to_id(self, token): + """ Converts a token (str/unicode) in an id using the vocab. """ + if token.startswith(u"', token) + num = int(l.group(1)) + return self.vocab_size - num - 1 + return self.sp_model.piece_to_id(token) + + def _convert_id_to_token(self, index, return_unicode=True): + """Converts an index (integer) in a token (string/unicode) using the vocab.""" + if index < self.sp_model.get_piece_size(): + token = self.sp_model.IdToPiece(index) + else: + token = u"".format(self.vocab_size - 1 - 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 (string) in a single string. """ + out_string = self.sp_model.decode_pieces(tokens) + return out_string + + 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,) diff --git a/transformers/tokenization_utils.py b/transformers/tokenization_utils.py index 317ecd167b..8aef80fec8 100644 --- a/transformers/tokenization_utils.py +++ b/transformers/tokenization_utils.py @@ -637,9 +637,11 @@ class PreTrainedTokenizer(object): text: The sequence to be encoded. **kwargs: passed to the child `self.tokenize()` method """ + all_special_tokens = self.all_special_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] + escaped_special_toks = [re.escape(s_tok) for s_tok in all_special_tokens] pattern = r'(^' + r'|'.join(escaped_special_toks) + r')|' + \ r'(.+?)' return re.sub( @@ -680,17 +682,17 @@ class PreTrainedTokenizer(object): tokenized_text = [] for sub_text in text_list: if sub_text not in self.added_tokens_encoder \ - and sub_text not in self.all_special_tokens: + and sub_text not in all_special_tokens: tokenized_text += split_on_token(tok, sub_text) else: tokenized_text += [sub_text] text_list = tokenized_text return list(itertools.chain.from_iterable((self._tokenize(token, **kwargs) if token not \ - in self.added_tokens_encoder and token not in self.all_special_tokens \ + in self.added_tokens_encoder and token not in all_special_tokens \ else [token] for token in tokenized_text))) - added_tokens = list(self.added_tokens_encoder.keys()) + self.all_special_tokens + added_tokens = list(self.added_tokens_encoder.keys()) + all_special_tokens tokenized_text = split_on_tokens(added_tokens, text) return tokenized_text @@ -1178,12 +1180,12 @@ class PreTrainedTokenizer(object): if current_sub_text: sub_texts.append(self.convert_tokens_to_string(current_sub_text)) current_sub_text = [] - sub_texts.append(" " + token) + sub_texts.append(token) else: current_sub_text.append(token) if current_sub_text: sub_texts.append(self.convert_tokens_to_string(current_sub_text)) - text = ''.join(sub_texts) + text = ' '.join(sub_texts) if clean_up_tokenization_spaces: clean_text = self.clean_up_tokenization(text)