initial commit

.circleci/config.yml

@@ -79,7 +79,6 @@ jobs:
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
             - run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
@@ -106,7 +105,6 @@ jobs:
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
             - run: pip install .[sklearn,torch,testing,sentencepiece]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
@@ -185,7 +183,6 @@ jobs:
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
             - run: pip install .[sklearn,torch,testing,sentencepiece]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:

.circleci/deploy.sh

@@ -53,5 +53,4 @@ deploy_doc "3ebb1b3" v3.2.0
 deploy_doc "0613f05" v3.3.1
 deploy_doc "eb0e0ce" v3.4.0
 deploy_doc "818878d" v3.5.1
-deploy_doc "c781171" v4.0.0
-deploy_doc "f5438ab" # v4.1.0 Latest stable release
+deploy_doc "c781171" # v4.0.0 Latest stable release

.github/workflows/self-push.yml

@@ -50,7 +50,6 @@ jobs:
           pip install --upgrade pip
           pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
           pip install git+https://github.com/huggingface/datasets
-          pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
@@ -188,7 +187,6 @@ jobs:
           pip install --upgrade pip
           pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
           pip install git+https://github.com/huggingface/datasets
-          pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
 
       - name: Are GPUs recognized by our DL frameworks
         run: |

README.md

@@ -222,7 +222,6 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
 1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (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.
-1. **[TAPAS](https://huggingface.co/transformers/master/model_doc/tapas.html)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
 1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.

docs/source/_static/js/custom.js

@@ -1,11 +1,10 @@
 // These two things need to be updated at each release for the version selector.
 // Last stable version
-const stableVersion = "v4.1.0"
+const stableVersion = "v4.0.0"
 // Dictionary doc folder to label. The last stable version should have an empty key.
 const versionMapping = {
     "master": "master",
-    "": "v4.1.0 (stable)",
-    "v4.0.1": "v4.0.0/v4.0.1",
+    "": "v4.0.0 (stable)",
     "v3.5.1": "v3.5.0/v3.5.1",
     "v3.4.0": "v3.4.0",
     "v3.3.1": "v3.3.0/v3.3.1",

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'4.1.1'
+release = u'4.0.0'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/index.rst

@@ -176,22 +176,19 @@ and conversion utilities for the following models:
 30. :doc:`T5 <model_doc/t5>` (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.
-31. `TAPAS <https://huggingface.co/transformers/master/model_doc/tapas.html>`__ (from Google AI) released with the
-    paper `TAPAS: Weakly Supervised Table Parsing via Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan
-    Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
-32. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+31. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
     Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
     Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-33. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+32. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
     Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
-34. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+33. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
     Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
     Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-35. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+34. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
     Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
     Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
     Zettlemoyer and Veselin Stoyanov.
-36. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
+35. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
     Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
     Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
 
@@ -272,8 +269,6 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            TAPAS            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
@@ -387,7 +382,6 @@ TensorFlow and/or Flax.
     model_doc/roberta
     model_doc/squeezebert
     model_doc/t5
-    model_doc/tapas
     model_doc/transformerxl
     model_doc/xlm
     model_doc/xlmprophetnet

docs/source/main_classes/pipelines.rst

@@ -34,7 +34,6 @@ There are two categories of pipeline abstractions to be aware about:
     - :class:`~transformers.TranslationPipeline`
     - :class:`~transformers.ZeroShotClassificationPipeline`
     - :class:`~transformers.Text2TextGenerationPipeline`
-    - :class:`~transformers.TableQuestionAnsweringPipeline`
 
 The pipeline abstraction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -92,13 +91,6 @@ SummarizationPipeline
     :special-members: __call__
     :members:
 
-TableQuestionAnsweringPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.TableQuestionAnsweringPipeline
-    :special-members: __call__
-
-
 TextClassificationPipeline
 =======================================================================================================================
 

docs/source/model_doc/auto.rst

@@ -114,13 +114,6 @@ AutoModelForQuestionAnswering
     :members:
 
 
-AutoModelForTableQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForTableQuestionAnswering
-    :members:
-
-
 TFAutoModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/gpt.rst

@@ -138,9 +138,3 @@ TFOpenAIGPTDoubleHeadsModel
 
 .. autoclass:: transformers.TFOpenAIGPTDoubleHeadsModel
     :members: call
-
-TFOpenAIGPTForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFOpenAIGPTForSequenceClassification
-    :members: call

docs/source/model_doc/longformer.rst

@@ -34,12 +34,6 @@ contrast to most prior work, we also pretrain Longformer and finetune it on a va
 pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on
 WikiHop and TriviaQA.*
 
-Tips:
-
-- Since the Longformer is based on RoBERTa, it doesn't have :obj:`token_type_ids`. You don't need to indicate which
-  token belongs to which segment. Just separate your segments with the separation token :obj:`tokenizer.sep_token` (or
-  :obj:`</s>`).
-
 The Authors' code can be found `here <https://github.com/allenai/longformer>`__.
 
 Longformer Self Attention

docs/source/model_doc/tapas.rst

@@ -1,434 +0,0 @@
-TAPAS
------------------------------------------------------------------------------------------------------------------------
-
-.. note::
-
-    This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
-    breaking changes to fix them in the future.
-
-
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The TAPAS model was proposed in `TAPAS: Weakly Supervised Table Parsing via Pre-training
-<https://www.aclweb.org/anthology/2020.acl-main.398>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
-Francesco Piccinno and Julian Martin Eisenschlos. It's a BERT-based model specifically designed (and pre-trained) for
-answering questions about tabular data. Compared to BERT, TAPAS uses relative position embeddings and has 7 token types
-that encode tabular structure. TAPAS is pre-trained on the masked language modeling (MLM) objective on a large dataset
-comprising millions of tables from English Wikipedia and corresponding texts. For question answering, TAPAS has 2 heads
-on top: a cell selection head and an aggregation head, for (optionally) performing aggregations (such as counting or
-summing) among selected cells. TAPAS has been fine-tuned on several datasets: `SQA
-<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`__ (Sequential Question Answering by Microsoft), `WTQ
-<https://github.com/ppasupat/WikiTableQuestions>`__ (Wiki Table Questions by Stanford University) and `WikiSQL
-<https://github.com/salesforce/WikiSQL>`__ (by Salesforce). It achieves state-of-the-art on both SQA and WTQ, while
-having comparable performance to SOTA on WikiSQL, with a much simpler architecture.
-
-The abstract from the paper is the following:
-
-*Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the
-collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations
-instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition,
-the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we
-present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak
-supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation
-operator to such selection. TAPAS extends BERT's architecture to encode tables as input, initializes from an effective
-joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with
-three different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by
-improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WIKISQL
-and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our
-setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.*
-
-In addition, the authors have further pre-trained TAPAS to recognize **table entailment**, by creating a balanced
-dataset of millions of automatically created training examples which are learned in an intermediate step prior to
-fine-tuning. The authors of TAPAS call this further pre-training intermediate pre-training (since TAPAS is first
-pre-trained on MLM, and then on another dataset). They found that intermediate pre-training further improves
-performance on SQA, achieving a new state-of-the-art as well as state-of-the-art on `TabFact
-<https://github.com/wenhuchen/Table-Fact-Checking>`__, a large-scale dataset with 16k Wikipedia tables for table
-entailment (a binary classification task). For more details, see their follow-up paper: `Understanding tables with
-intermediate pre-training <https://www.aclweb.org/anthology/2020.findings-emnlp.27/>`__ by Julian Martin Eisenschlos,
-Syrine Krichene and Thomas Müller.
-
-The original code can be found `here <https://github.com/google-research/tapas>`__.
-
-Tips:
-
-- TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell
-  of the table). Note that this is something that was added after the publication of the original TAPAS paper.
-  According to the authors, this usually results in a slightly better performance, and allows you to encode longer
-  sequences without running out of embeddings. This is reflected in the ``reset_position_index_per_cell`` parameter of
-  :class:`~transformers.TapasConfig`, which is set to ``True`` by default. The default versions of the models available
-  in the `model hub <https://huggingface.co/models?search=tapas>`_ all use relative position embeddings. You can still
-  use the ones with absolute position embeddings by passing in an additional argument ``revision="no_reset"`` when
-  calling the ``.from_pretrained()`` method. Note that it's usually advised to pad the inputs on the right rather than
-  the left.
-- TAPAS is based on BERT, so ``TAPAS-base`` for example corresponds to a ``BERT-base`` architecture. Of course,
-  TAPAS-large will result in the best performance (the results reported in the paper are from TAPAS-large). Results of
-  the various sized models are shown on the `original Github repository <https://github.com/google-research/tapas>`_.
-- TAPAS has checkpoints fine-tuned on SQA, which are capable of answering questions related to a table in a
-  conversational set-up. This means that you can ask follow-up questions such as "what is his age?" related to the
-  previous question. Note that the forward pass of TAPAS is a bit different in case of a conversational set-up: in that
-  case, you have to feed every table-question pair one by one to the model, such that the `prev_labels` token type ids
-  can be overwritten by the predicted `labels` of the model to the previous question. See "Usage" section for more
-  info.
-- TAPAS is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
-  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
-  with a causal language modeling (CLM) objective are better in that regard.
-
-
-Usage: fine-tuning
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Here we explain how you can fine-tune :class:`~transformers.TapasForQuestionAnswering` on your own dataset.
-
-**STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment**
-
-Basically, there are 3 different ways in which one can fine-tune :class:`~transformers.TapasForQuestionAnswering`,
-corresponding to the different datasets on which Tapas was fine-tuned:
-
-1. SQA: if you're interested in asking follow-up questions related to a table, in a conversational set-up. For example
-   if you first ask "what's the name of the first actor?" then you can ask a follow-up question such as "how old is
-   he?". Here, questions do not involve any aggregation (all questions are cell selection questions).
-2. WTQ: if you're not interested in asking questions in a conversational set-up, but rather just asking questions
-   related to a table, which might involve aggregation, such as counting a number of rows, summing up cell values or
-   averaging cell values. You can then for example ask "what's the total number of goals Cristiano Ronaldo made in his
-   career?". This case is also called **weak supervision**, since the model itself must learn the appropriate
-   aggregation operator (SUM/COUNT/AVERAGE/NONE) given only the answer to the question as supervision.
-3. WikiSQL-supervised: this dataset is based on WikiSQL with the model being given the ground truth aggregation
-   operator during training. This is also called **strong supervision**. Here, learning the appropriate aggregation
-   operator is much easier.
-
-To summarize:
-
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| **Task**                           | **Example dataset**  | **Description**                                                                                                   |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| Conversational                     | SQA                  | Conversational, only cell selection questions                                                                     |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| Weak supervision for aggregation   | WTQ                  | Questions might involve aggregation, and the model must learn this given only the answer as supervision           |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| Strong supervision for aggregation | WikiSQL-supervised   | Questions might involve aggregation, and the model must learn this given the gold aggregation operator            |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-
-Initializing a model with a pre-trained base and randomly initialized classification heads from the model hub can be
-done as follows (be sure to have installed the `torch-scatter dependency <https://github.com/rusty1s/pytorch_scatter>`_
-for your environment):
-
-.. code-block::
-
-        >>> from transformers import TapasConfig, TapasForQuestionAnswering
-
-        >>> # for example, the base sized model with default SQA configuration
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base')
-
-        >>> # or, the base sized model with WTQ configuration
-        >>> config = TapasConfig.from_pretrained('google/tapas-base-finetuned-wtq')
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
-
-        >>> # or, the base sized model with WikiSQL configuration
-        >>> config = TapasConfig('google-base-finetuned-wikisql-supervised')
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
-
-
-Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also
-experiment by defining any hyperparameters you want when initializing :class:`~transformers.TapasConfig`, and then
-create a :class:`~transformers.TapasForQuestionAnswering` based on that configuration. For example, if you have a
-dataset that has both conversational questions and questions that might involve aggregation, then you can do it this
-way. Here's an example:
-
-.. code-block::
-
-        >>> from transformers import TapasConfig, TapasForQuestionAnswering
-
-        >>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
-        >>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True, select_one_column=False)
-        >>> # initializing the pre-trained base sized model with our custom classification heads
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
-
-What you can also do is start from an already fine-tuned checkpoint. A note here is that the already fine-tuned
-checkpoint on WTQ has some issues due to the L2-loss which is somewhat brittle. See `here
-<https://github.com/google-research/tapas/issues/91#issuecomment-735719340>`__ for more info.
-
-For a list of all pre-trained and fine-tuned TAPAS checkpoints available in the HuggingFace model hub, see `here
-<https://huggingface.co/models?search=tapas>`__.
-
-**STEP 2: Prepare your data in the SQA format**
-
-Second, no matter what you picked above, you should prepare your dataset in the `SQA format
-<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`__. This format is a TSV/CSV file with the following
-columns:
-
-- ``id``: optional, id of the table-question pair, for bookkeeping purposes.
-- ``annotator``: optional, id of the person who annotated the table-question pair, for bookkeeping purposes.
-- ``position``: integer indicating if the question is the first, second, third,... related to the table. Only required
-  in case of conversational setup (SQA). You don't need this column in case you're going for WTQ/WikiSQL-supervised.
-- ``question``: string
-- ``table_file``: string, name of a csv file containing the tabular data
-- ``answer_coordinates``: list of one or more tuples (each tuple being a cell coordinate, i.e. row, column pair that is
-  part of the answer)
-- ``answer_text``: list of one or more strings (each string being a cell value that is part of the answer)
-- ``aggregation_label``: index of the aggregation operator. Only required in case of strong supervision for aggregation
-  (the WikiSQL-supervised case)
-- ``float_answer``: the float answer to the question, if there is one (np.nan if there isn't). Only required in case of
-  weak supervision for aggregation (such as WTQ and WikiSQL)
-
-The tables themselves should be present in a folder, each table being a separate csv file. Note that the authors of the
-TAPAS algorithm used conversion scripts with some automated logic to convert the other datasets (WTQ, WikiSQL) into the
-SQA format. The author explains this `here
-<https://github.com/google-research/tapas/issues/50#issuecomment-705465960>`__. Interestingly, these conversion scripts
-are not perfect (the ``answer_coordinates`` and ``float_answer`` fields are populated based on the ``answer_text``),
-meaning that WTQ and WikiSQL results could actually be improved.
-
-**STEP 3: Convert your data into PyTorch tensors using TapasTokenizer**
-
-Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular
-data), you can then use :class:`~transformers.TapasTokenizer` to convert table-question pairs into :obj:`input_ids`,
-:obj:`attention_mask`, :obj:`token_type_ids` and so on. Again, based on which of the three cases you picked above,
-:class:`~transformers.TapasForQuestionAnswering` requires different inputs to be fine-tuned:
-
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| **Task**                           | **Required inputs**                                                                          |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| Conversational                     | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels``                            |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| Weak supervision for aggregation   | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels``, ``numeric_values``,       |
-|                                    | ``numeric_values_scale``, ``float_answer``                                                   |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| Strong supervision for aggregation | ``input ids``, ``attention mask``, ``token type ids``, ``labels``, ``aggregation_labels``    |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-
-:class:`~transformers.TapasTokenizer` creates the ``labels``, ``numeric_values`` and ``numeric_values_scale`` based on
-the ``answer_coordinates`` and ``answer_text`` columns of the TSV file. The ``float_answer`` and ``aggregation_labels``
-are already in the TSV file of step 2. Here's an example:
-
-.. code-block::
-
-        >>> from transformers import TapasTokenizer
-        >>> import pandas as pd
-
-        >>> model_name = 'google/tapas-base'
-        >>> tokenizer = TapasTokenizer.from_pretrained(model_name)
-
-        >>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
-        >>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
-        >>> answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
-        >>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
-        >>> table = pd.DataFrame.from_dict(data)
-        >>> inputs = tokenizer(table=table, queries=queries, answer_coordinates=answer_coordinates, answer_text=answer_text, padding='max_length', return_tensors='pt')
-        >>> inputs
-        {'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
-        'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}
-
-Note that :class:`~transformers.TapasTokenizer` expects the data of the table to be **text-only**. You can use
-``.astype(str)`` on a dataframe to turn it into text-only data. Of course, this only shows how to encode a single
-training example. It is advised to create a PyTorch dataset and a corresponding dataloader:
-
-.. code-block::
-
-        >>> import torch
-        >>> import pandas as pd
-
-        >>> tsv_path = "your_path_to_the_tsv_file"
-        >>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
-
-        >>> class TableDataset(torch.utils.data.Dataset):
-        ...     def __init__(self, data, tokenizer):
-        ...         self.data = data
-        ...         self.tokenizer = tokenizer
-        ...
-        ...     def __getitem__(self, idx):
-        ...         item = data.iloc[idx]
-        ...         table = pd.read_csv(table_csv_path + item.table_file).astype(str) # be sure to make your table data text only
-        ...         encoding = self.tokenizer(table=table, 
-        ...                                   queries=item.question, 
-        ...                                   answer_coordinates=item.answer_coordinates, 
-        ...                                   answer_text=item.answer_text,
-        ...                                   truncation=True,
-        ...                                   padding="max_length",
-        ...                                   return_tensors="pt"
-        ...         )
-        ...         # remove the batch dimension which the tokenizer adds by default
-        ...         encoding = {key: val.squeeze(0) for key, val in encoding.items()}
-        ...         # add the float_answer which is also required (weak supervision for aggregation case)
-        ...         encoding["float_answer"] = torch.tensor(item.float_answer) 
-        ...         return encoding
-        ...
-        ...     def __len__(self):
-        ...        return len(self.data)
-
-        >>> data = pd.read_csv(tsv_path, sep='\t')
-        >>> train_dataset = TableDataset(data, tokenizer)
-        >>> train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)
-
-Note that here, we encode each table-question pair independently. This is fine as long as your dataset is **not
-conversational**. In case your dataset involves conversational questions (such as in SQA), then you should first group
-together the ``queries``, ``answer_coordinates`` and ``answer_text`` per table (in the order of their ``position``
-index) and batch encode each table with its questions. This will make sure that the ``prev_labels`` token types (see
-docs of :class:`~transformers.TapasTokenizer`) are set correctly. See `this notebook
-<https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__
-for more info.
-
-**STEP 4: Train (fine-tune) TapasForQuestionAnswering**
-
-You can then fine-tune :class:`~transformers.TapasForQuestionAnswering` using native PyTorch as follows (shown here for
-the weak supervision for aggregation case):
-
-.. code-block::
-
-        >>> from transformers import TapasConfig, TapasForQuestionAnswering, AdamW
-
-        >>> # this is the default WTQ configuration
-        >>> config = TapasConfig(
-        ...            num_aggregation_labels = 4,
-        ...            use_answer_as_supervision = True,
-        ...            answer_loss_cutoff = 0.664694,
-        ...            cell_selection_preference = 0.207951,
-        ...            huber_loss_delta = 0.121194,
-        ...            init_cell_selection_weights_to_zero = True,
-        ...            select_one_column = True,
-        ...            allow_empty_column_selection = False,
-        ...            temperature = 0.0352513,
-        ... )
-        >>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
-
-        >>> optimizer = AdamW(model.parameters(), lr=5e-5)
-
-        >>> for epoch in range(2):  # loop over the dataset multiple times
-        ...    for idx, batch in enumerate(train_dataloader):
-        ...         # get the inputs; 
-        ...         input_ids = batch["input_ids"]
-        ...         attention_mask = batch["attention_mask"]
-        ...         token_type_ids = batch["token_type_ids"]
-        ...         labels = batch["labels"]
-        ...         numeric_values = batch["numeric_values"]
-        ...         numeric_values_scale = batch["numeric_values_scale"]
-        ...         float_answer = batch["float_answer"]
-
-        ...         # zero the parameter gradients
-        ...         optimizer.zero_grad()
-
-        ...         # forward + backward + optimize
-        ...         outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, 
-        ...                        labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale, 
-        ...                        float_answer=float_answer)
-        ...         loss = outputs.loss
-        ...         loss.backward()
-        ...         optimizer.step()
-
-Usage: inference
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Here we explain how you can use :class:`~transformers.TapasForQuestionAnswering` for inference (i.e. making predictions
-on new data). For inference, only ``input_ids``, ``attention_mask`` and ``token_type_ids`` (which you can obtain using
-:class:`~transformers.TapasTokenizer`) have to be provided to the model to obtain the logits. Next, you can use the
-handy ``convert_logits_to_predictions`` method of :class:`~transformers.TapasTokenizer` to convert these into predicted
-coordinates and optional aggregation indices.
-
-However, note that inference is **different** depending on whether or not the setup is conversational. In a
-non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here's an example
-of that:
-
-.. code-block::
-
-        >>> from transformers import TapasTokenizer, TapasForQuestionAnswering
-        >>> import pandas as pd 
-
-        >>> model_name = 'google/tapas-base-finetuned-wtq'
-        >>> model = TapasForQuestionAnswering.from_pretrained(model_name)
-        >>> tokenizer = TapasTokenizer.from_pretrained(model_name)
-
-        >>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
-        >>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
-        >>> table = pd.DataFrame.from_dict(data)
-        >>> inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="pt") 
-        >>> outputs = model(**inputs)
-        >>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
-        ...         inputs, 
-        ...         outputs.logits.detach(), 
-        ...         outputs.logits_aggregation.detach()
-        ... )
-
-        >>> # let's print out the results:
-        >>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
-        >>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
-
-        >>> answers = []
-        >>> for coordinates in predicted_answer_coordinates:
-        ...   if len(coordinates) == 1:
-        ...     # only a single cell:
-        ...     answers.append(table.iat[coordinates[0]])
-        ...   else:
-        ...     # multiple cells
-        ...     cell_values = []
-        ...     for coordinate in coordinates:
-        ...        cell_values.append(table.iat[coordinate])
-        ...     answers.append(", ".join(cell_values))
-
-        >>> display(table)
-        >>> print("")
-        >>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
-        ...   print(query)
-        ...   if predicted_agg == "NONE":
-        ...     print("Predicted answer: " + answer)
-        ...   else:
-        ...     print("Predicted answer: " + predicted_agg + " > " + answer)    
-        What is the name of the first actor?
-        Predicted answer: Brad Pitt
-        How many movies has George Clooney played in?
-        Predicted answer: COUNT > 69
-        What is the total number of movies?
-        Predicted answer: SUM > 87, 53, 69
-
-In case of a conversational set-up, then each table-question pair must be provided **sequentially** to the model, such
-that the ``prev_labels`` token types can be overwritten by the predicted ``labels`` of the previous table-question
-pair. Again, more info can be found in `this notebook
-<https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__.
-
-
-Tapas specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.models.tapas.modeling_tapas.TableQuestionAnsweringOutput
-    :members:
-
-
-TapasConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasConfig
-    :members:
-
-
-TapasTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasTokenizer
-    :members: __call__, convert_logits_to_predictions, save_vocabulary
-
-
-TapasModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasModel
-    :members: forward
-
-
-TapasForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForMaskedLM
-    :members: forward
-
-
-TapasForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForSequenceClassification
-    :members: forward
-
-
-TapasForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForQuestionAnswering
-    :members: forward

examples/README.md

@@ -55,11 +55,11 @@ Coming soon!
 |---|---|:---:|:---:|:---:|:---:|
 | [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling)       | Raw text        | ✅ | -  | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
 | [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice)           | SWAG, RACE, ARC | ✅ | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
-| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/notebooks/blob/master/examples/question_answering.ipynb)
+| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | ✅ | ✅ | ✅ | -
 | [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                     | CNN/Daily Mail  | ✅  | - | - | -
 | [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification)   | GLUE, XNLI      | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb)
 | [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
-| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/notebooks/blob/master/examples/token_classification.ipynb)
+| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | -
 | [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                       | WMT             | ✅  | - | - | -
 
 

examples/language-modeling/run_clm.py

@@ -113,12 +113,6 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
-    validation_split_percentage: Optional[int] = field(
-        default=5,
-        metadata={
-            "help": "The percentage of the train set used as validation set in case there's no validation split"
-        },
-    )
     preprocessing_num_workers: Optional[int] = field(
         default=None,
         metadata={"help": "The number of processes to use for the preprocessing."},
@@ -194,17 +188,6 @@ def main():
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
-        if "validation" not in datasets.keys():
-            datasets["validation"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[:{data_args.validation_split_percentage}%]",
-            )
-            datasets["train"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[{data_args.validation_split_percentage}%:]",
-            )
     else:
         data_files = {}
         if data_args.train_file is not None:

examples/language-modeling/run_mlm.py

@@ -103,12 +103,6 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
-    validation_split_percentage: Optional[int] = field(
-        default=5,
-        metadata={
-            "help": "The percentage of the train set used as validation set in case there's no validation split"
-        },
-    )
     max_seq_length: Optional[int] = field(
         default=None,
         metadata={
@@ -205,17 +199,6 @@ def main():
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
-        if "validation" not in datasets.keys():
-            datasets["validation"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[:{data_args.validation_split_percentage}%]",
-            )
-            datasets["train"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[{data_args.validation_split_percentage}%:]",
-            )
     else:
         data_files = {}
         if data_args.train_file is not None:

examples/language-modeling/run_mlm_flax.py

@@ -134,12 +134,6 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
-    validation_split_percentage: Optional[int] = field(
-        default=5,
-        metadata={
-            "help": "The percentage of the train set used as validation set in case there's no validation split"
-        },
-    )
     max_seq_length: Optional[int] = field(
         default=None,
         metadata={
@@ -479,17 +473,6 @@ if __name__ == "__main__":
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
-        if "validation" not in datasets.keys():
-            datasets["validation"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[:{data_args.validation_split_percentage}%]",
-            )
-            datasets["train"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[{data_args.validation_split_percentage}%:]",
-            )
     else:
         data_files = {}
         if data_args.train_file is not None:
@@ -542,9 +525,9 @@ if __name__ == "__main__":
 
     def tokenize_function(examples):
         # Remove empty lines
-        examples = [line for line in examples if len(line) > 0 and not line.isspace()]
+        examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
         return tokenizer(
-            examples,
+            examples["text"],
             return_special_tokens_mask=True,
             padding=padding,
             truncation=True,
@@ -553,10 +536,9 @@ if __name__ == "__main__":
 
     tokenized_datasets = datasets.map(
         tokenize_function,
-        input_columns=[text_column_name],
         batched=True,
         num_proc=data_args.preprocessing_num_workers,
-        remove_columns=column_names,
+        remove_columns=[text_column_name],
         load_from_cache_file=not data_args.overwrite_cache,
     )
 
@@ -589,9 +571,8 @@ if __name__ == "__main__":
     ).create(model.params)
 
     # Create learning rate scheduler
-    # warmup_steps = 0 causes the Flax optimizer to return NaNs; warmup_steps = 1 is functionally equivalent.
     lr_scheduler_fn = create_learning_rate_scheduler(
-        base_learning_rate=training_args.learning_rate, warmup_steps=min(training_args.warmup_steps, 1)
+        base_learning_rate=training_args.learning_rate, warmup_steps=training_args.warmup_steps
     )
 
     # Create parallel version of the training and evaluation steps
@@ -630,13 +611,13 @@ if __name__ == "__main__":
         epochs.write(f"Loss: {loss}")
 
         # ======================== Evaluating ==============================
-        nb_eval_samples = len(tokenized_datasets["validation"])
+        nb_eval_samples = len(tokenized_datasets["test"])
         eval_samples_idx = jnp.arange(nb_eval_samples)
         eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
 
         eval_metrics = []
         for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
-            samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
+            samples = [tokenized_datasets["test"][int(idx)] for idx in batch_idx]
             model_inputs = data_collator(samples, pad_to_multiple_of=16)
 
             # Model forward

examples/language-modeling/run_mlm_wwm.py

@@ -91,12 +91,6 @@ class DataTrainingArguments:
     Arguments pertaining to what data we are going to input our model for training and eval.
     """
 
-    dataset_name: Optional[str] = field(
-        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
-    )
-    dataset_config_name: Optional[str] = field(
-        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
-    )
     train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
     validation_file: Optional[str] = field(
         default=None,
@@ -113,12 +107,6 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
-    validation_split_percentage: Optional[int] = field(
-        default=5,
-        metadata={
-            "help": "The percentage of the train set used as validation set in case there's no validation split"
-        },
-    )
     max_seq_length: Optional[int] = field(
         default=None,
         metadata={
@@ -215,30 +203,15 @@ def main():
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
-    if data_args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
-        if "validation" not in datasets.keys():
-            datasets["validation"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[:{data_args.validation_split_percentage}%]",
-            )
-            datasets["train"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[{data_args.validation_split_percentage}%:]",
-            )
-    else:
-        data_files = {}
-        if data_args.train_file is not None:
-            data_files["train"] = data_args.train_file
-        if data_args.validation_file is not None:
-            data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
-        if extension == "txt":
-            extension = "text"
-        datasets = load_dataset(extension, data_files=data_files)
+    data_files = {}
+    if data_args.train_file is not None:
+        data_files["train"] = data_args.train_file
+    if data_args.validation_file is not None:
+        data_files["validation"] = data_args.validation_file
+    extension = data_args.train_file.split(".")[-1]
+    if extension == "txt":
+        extension = "text"
+    datasets = load_dataset(extension, data_files=data_files)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.
 

examples/language-modeling/run_plm.py

@@ -93,12 +93,6 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
-    validation_split_percentage: Optional[int] = field(
-        default=5,
-        metadata={
-            "help": "The percentage of the train set used as validation set in case there's no validation split"
-        },
-    )
     max_seq_length: int = field(
         default=512,
         metadata={
@@ -202,17 +196,6 @@ def main():
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
-        if "validation" not in datasets.keys():
-            datasets["validation"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[:{data_args.validation_split_percentage}%]",
-            )
-            datasets["train"] = load_dataset(
-                data_args.dataset_name,
-                data_args.dataset_config_name,
-                split=f"train[{data_args.validation_split_percentage}%:]",
-            )
     else:
         data_files = {}
         if data_args.train_file is not None:

examples/research_projects/performer/run_mlm_performer.py

@@ -0,0 +1,659 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Team 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.
+"""
+Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a
+text file or a dataset.
+
+Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
+https://huggingface.co/models?filter=masked-lm
+"""
+import logging
+import os
+import sys
+from dataclasses import dataclass, field
+
+# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+from datasets import load_dataset
+from tqdm import tqdm
+
+import jax
+import jax.numpy as jnp
+from flax import jax_utils
+from flax.optim import Adam
+from flax.training import common_utils
+from flax.training.common_utils import get_metrics
+from jax.nn import log_softmax
+from transformers import (
+    CONFIG_MAPPING,
+    MODEL_FOR_MASKED_LM_MAPPING,
+    AutoConfig,
+    AutoTokenizer,
+    FlaxBertForMaskedLM,
+    HfArgumentParser,
+    PreTrainedTokenizerBase,
+    TensorType,
+    TrainingArguments,
+    is_tensorboard_available,
+    set_seed,
+)
+
+
+# Cache the result
+has_tensorboard = is_tensorboard_available()
+if has_tensorboard:
+    try:
+        from flax.metrics.tensorboard import SummaryWriter
+    except ImportError as ie:
+        has_tensorboard = False
+        print(f"Unable to display metrics through TensorBoard because some package are not installed: {ie}")
+
+else:
+    print(
+        "Unable to display metrics through TensorBoard because the package is not installed: "
+        "Please run pip install tensorboard to enable."
+    )
+
+
+MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+    """
+
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The model checkpoint for weights initialization."
+            "Don't set if you want to train a model from scratch."
+        },
+    )
+    model_type: Optional[str] = field(
+        default=None,
+        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
+    )
+    train_ref_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
+    )
+    validation_ref_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    validation_split_percentage: Optional[int] = field(
+        default=5,
+        metadata={
+            "help": "The percentage of the train set used as validation set in case there's no validation split"
+        },
+    )
+    max_seq_length: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated. Default to the max input length of the model."
+        },
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    mlm_probability: float = field(
+        default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
+    )
+    pad_to_max_length: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to pad all samples to `max_seq_length`. "
+            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
+        },
+    )
+
+    def __post_init__(self):
+        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
+            raise ValueError("Need either a dataset name or a training/validation file.")
+        else:
+            if self.train_file is not None:
+                extension = self.train_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
+            if self.validation_file is not None:
+                extension = self.validation_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
+
+
+# Adapted from transformers/data/data_collator.py
+# Letting here for now, let's discuss where it should live
+@dataclass
+class FlaxDataCollatorForLanguageModeling:
+    """
+    Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
+    are not all of the same length.
+
+    Args:
+        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
+            The tokenizer used for encoding the data.
+        mlm (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to use masked language modeling. If set to :obj:`False`, the labels are the same as the
+            inputs with the padding tokens ignored (by setting them to -100). Otherwise, the labels are -100 for
+            non-masked tokens and the value to predict for the masked token.
+        mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
+            The probability with which to (randomly) mask tokens in the input, when :obj:`mlm` is set to :obj:`True`.
+
+    .. note::
+
+        For best performance, this data collator should be used with a dataset having items that are dictionaries or
+        BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
+        :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
+        argument :obj:`return_special_tokens_mask=True`.
+    """
+
+    tokenizer: PreTrainedTokenizerBase
+    mlm: bool = True
+    mlm_probability: float = 0.15
+
+    def __post_init__(self):
+        if self.mlm and self.tokenizer.mask_token is None:
+            raise ValueError(
+                "This tokenizer does not have a mask token which is necessary for masked language modeling. "
+                "You should pass `mlm=False` to train on causal language modeling instead."
+            )
+
+    def __call__(self, examples: List[Dict[str, np.ndarray]], pad_to_multiple_of: int) -> Dict[str, np.ndarray]:
+        # Handle dict or lists with proper padding and conversion to tensor.
+        batch = self.tokenizer.pad(examples, pad_to_multiple_of=pad_to_multiple_of, return_tensors=TensorType.NUMPY)
+
+        # If special token mask has been preprocessed, pop it from the dict.
+        special_tokens_mask = batch.pop("special_tokens_mask", None)
+        if self.mlm:
+            batch["input_ids"], batch["labels"] = self.mask_tokens(
+                batch["input_ids"], special_tokens_mask=special_tokens_mask
+            )
+        else:
+            labels = batch["input_ids"].copy()
+            if self.tokenizer.pad_token_id is not None:
+                labels[labels == self.tokenizer.pad_token_id] = -100
+            batch["labels"] = labels
+        return batch
+
+    def mask_tokens(
+        self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
+    ) -> Tuple[jnp.ndarray, jnp.ndarray]:
+        """
+        Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
+        """
+        labels = inputs.copy()
+        # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
+        probability_matrix = np.full(labels.shape, self.mlm_probability)
+        special_tokens_mask = special_tokens_mask.astype("bool")
+
+        probability_matrix[special_tokens_mask] = 0.0
+        masked_indices = np.random.binomial(1, probability_matrix).astype("bool")
+        labels[~masked_indices] = -100  # We only compute loss on masked tokens
+
+        # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
+        indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices
+        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
+
+        # 10% of the time, we replace masked input tokens with random word
+        indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool")
+        indices_random &= masked_indices & ~indices_replaced
+
+        random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4")
+        inputs[indices_random] = random_words[indices_random]
+
+        # The rest of the time (10% of the time) we keep the masked input tokens unchanged
+        return inputs, labels
+
+
+def create_learning_rate_scheduler(
+    factors="constant * linear_warmup * rsqrt_decay",
+    base_learning_rate=0.5,
+    warmup_steps=1000,
+    decay_factor=0.5,
+    steps_per_decay=20000,
+    steps_per_cycle=100000,
+):
+    """Creates learning rate schedule.
+    Interprets factors in the factors string which can consist of:
+    * constant: interpreted as the constant value,
+    * linear_warmup: interpreted as linear warmup until warmup_steps,
+    * rsqrt_decay: divide by square root of max(step, warmup_steps)
+    * rsqrt_normalized_decay: divide by square root of max(step/warmup_steps, 1)
+    * decay_every: Every k steps decay the learning rate by decay_factor.
+    * cosine_decay: Cyclic cosine decay, uses steps_per_cycle parameter.
+    Args:
+      factors: string, factors separated by "*" that defines the schedule.
+      base_learning_rate: float, the starting constant for the lr schedule.
+      warmup_steps: int, how many steps to warm up for in the warmup schedule.
+      decay_factor: float, the amount to decay the learning rate by.
+      steps_per_decay: int, how often to decay the learning rate.
+      steps_per_cycle: int, steps per cycle when using cosine decay.
+    Returns:
+      a function learning_rate(step): float -> {"learning_rate": float}, the
+      step-dependent lr.
+    """
+    factors = [n.strip() for n in factors.split("*")]
+
+    def step_fn(step):
+        """Step to learning rate function."""
+        ret = 1.0
+        for name in factors:
+            if name == "constant":
+                ret *= base_learning_rate
+            elif name == "linear_warmup":
+                ret *= jnp.minimum(1.0, step / warmup_steps)
+            elif name == "rsqrt_decay":
+                ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))
+            elif name == "rsqrt_normalized_decay":
+                ret *= jnp.sqrt(warmup_steps)
+                ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))
+            elif name == "decay_every":
+                ret *= decay_factor ** (step // steps_per_decay)
+            elif name == "cosine_decay":
+                progress = jnp.maximum(0.0, (step - warmup_steps) / float(steps_per_cycle))
+                ret *= jnp.maximum(0.0, 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))
+            else:
+                raise ValueError("Unknown factor %s." % name)
+        return jnp.asarray(ret, dtype=jnp.float32)
+
+    return step_fn
+
+
+def compute_metrics(logits, labels, weights, label_smoothing=0.0):
+    """Compute summary metrics."""
+    loss, normalizer = cross_entropy(logits, labels, weights, label_smoothing)
+    acc, _ = accuracy(logits, labels, weights)
+    metrics = {"loss": loss, "accuracy": acc, "normalizer": normalizer}
+    metrics = jax.lax.psum(metrics, axis_name="batch")
+    return metrics
+
+
+def accuracy(logits, targets, weights=None):
+    """Compute weighted accuracy for log probs and targets.
+    Args:
+     logits: [batch, length, num_classes] float array.
+     targets: categorical targets [batch, length] int array.
+     weights: None or array of shape [batch, length]
+    Returns:
+      Tuple of scalar loss and batch normalizing factor.
+    """
+    if logits.ndim != targets.ndim + 1:
+        raise ValueError(
+            "Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
+        )
+
+    loss = jnp.equal(jnp.argmax(logits, axis=-1), targets)
+    loss *= weights
+
+    return loss.sum(), weights.sum()
+
+
+def cross_entropy(logits, targets, weights=None, label_smoothing=0.0):
+    """Compute cross entropy and entropy for log probs and targets.
+    Args:
+     logits: [batch, length, num_classes] float array.
+     targets: categorical targets [batch, length] int array.
+     weights: None or array of shape [batch, length]
+     label_smoothing: label smoothing constant, used to determine the on and off values.
+    Returns:
+      Tuple of scalar loss and batch normalizing factor.
+    """
+    if logits.ndim != targets.ndim + 1:
+        raise ValueError(
+            "Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
+        )
+
+    vocab_size = logits.shape[-1]
+    confidence = 1.0 - label_smoothing
+    low_confidence = (1.0 - confidence) / (vocab_size - 1)
+    normalizing_constant = -(
+        confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
+    )
+    soft_targets = common_utils.onehot(targets, vocab_size, on_value=confidence, off_value=low_confidence)
+
+    loss = -jnp.sum(soft_targets * log_softmax(logits), axis=-1)
+    loss = loss - normalizing_constant
+
+    if weights is not None:
+        loss = loss * weights
+        normalizing_factor = weights.sum()
+    else:
+        normalizing_factor = np.prod(targets.shape)
+
+    return loss.sum(), normalizing_factor
+
+
+def training_step(optimizer, batch, dropout_rng):
+    dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
+
+    def loss_fn(params):
+        targets = batch.pop("labels")
+
+        # Hide away tokens which doesn't participate in the optimization
+        token_mask = jnp.where(targets > 0, 1.0, 0.0)
+
+        logits = model(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
+        loss, weight_sum = cross_entropy(logits, targets, token_mask)
+        return loss / weight_sum
+
+    step = optimizer.state.step
+    lr = lr_scheduler_fn(step)
+    grad_fn = jax.value_and_grad(loss_fn)
+    loss, grad = grad_fn(optimizer.target)
+    grad = jax.lax.pmean(grad, "batch")
+    optimizer = optimizer.apply_gradient(grad, learning_rate=lr)
+
+    return loss, optimizer, new_dropout_rng
+
+
+def eval_step(params, batch):
+    """
+    Calculate evaluation metrics on a batch.
+    """
+    targets = batch.pop("labels")
+
+    # Hide away tokens which doesn't participate in the optimization
+    token_mask = jnp.where(targets > 0, 1.0, 0.0)
+    logits = model(**batch, params=params, train=False)[0]
+
+    return compute_metrics(logits, targets, token_mask)
+
+
+def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndarray:
+    nb_samples = len(samples_idx)
+    samples_to_remove = nb_samples % batch_size
+
+    if samples_to_remove != 0:
+        samples_idx = samples_idx[:-samples_to_remove]
+    sections_split = nb_samples // batch_size
+    batch_idx = np.split(samples_idx, sections_split)
+    return batch_idx
+
+
+if __name__ == "__main__":
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    if (
+        os.path.exists(training_args.output_dir)
+        and os.listdir(training_args.output_dir)
+        and training_args.do_train
+        and not training_args.overwrite_output_dir
+    ):
+        raise ValueError(
+            f"Output directory ({training_args.output_dir}) already exists and is not empty."
+            "Use --overwrite_output_dir to overcome."
+        )
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        level="NOTSET",
+        datefmt="[%X]",
+    )
+
+    # Log on each process the small summary:
+    logger = logging.getLogger(__name__)
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
+    # 'text' is found. You can easily tweak this behavior (see below).
+    #
+    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
+    # download the dataset.
+    if data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        if "validation" not in datasets.keys():
+            datasets["validation"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[:{data_args.validation_split_percentage}%]",
+            )
+            datasets["train"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[{data_args.validation_split_percentage}%:]",
+            )
+    else:
+        data_files = {}
+        if data_args.train_file is not None:
+            data_files["train"] = data_args.train_file
+        if data_args.validation_file is not None:
+            data_files["validation"] = data_args.validation_file
+        extension = data_args.train_file.split(".")[-1]
+        if extension == "txt":
+            extension = "text"
+        datasets = load_dataset(extension, data_files=data_files)
+    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+    # https://huggingface.co/docs/datasets/loading_datasets.html.
+
+    # Load pretrained model and tokenizer
+
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    if model_args.config_name:
+        config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
+    elif model_args.model_name_or_path:
+        config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
+    else:
+        config = CONFIG_MAPPING[model_args.model_type]()
+        logger.warning("You are instantiating a new config instance from scratch.")
+
+    if model_args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
+        )
+    elif model_args.model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
+        )
+    else:
+        raise ValueError(
+            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
+            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+        )
+
+    # Preprocessing the datasets.
+    # First we tokenize all the texts.
+    if training_args.do_train:
+        column_names = datasets["train"].column_names
+    else:
+        column_names = datasets["validation"].column_names
+    text_column_name = "text" if "text" in column_names else column_names[0]
+
+    padding = "max_length" if data_args.pad_to_max_length else False
+
+    def tokenize_function(examples):
+        # Remove empty lines
+        examples = [line for line in examples if len(line) > 0 and not line.isspace()]
+        return tokenizer(
+            examples,
+            return_special_tokens_mask=True,
+            padding=padding,
+            truncation=True,
+            max_length=data_args.max_seq_length,
+        )
+
+    tokenized_datasets = datasets.map(
+        tokenize_function,
+        input_columns=[text_column_name],
+        batched=True,
+        num_proc=data_args.preprocessing_num_workers,
+        remove_columns=column_names,
+        load_from_cache_file=not data_args.overwrite_cache,
+    )
+
+    # Enable tensorboard only on the master node
+    if has_tensorboard and jax.host_id() == 0:
+        summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir).joinpath("logs").as_posix())
+
+    # Data collator
+    # This one will take care of randomly masking the tokens.
+    data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
+
+    # Initialize our training
+    rng = jax.random.PRNGKey(training_args.seed)
+    dropout_rngs = jax.random.split(rng, jax.local_device_count())
+
+    model = FlaxBertForMaskedLM.from_pretrained(
+        "bert-base-cased",
+        dtype=jnp.float32,
+        input_shape=(training_args.train_batch_size, config.max_position_embeddings),
+        seed=training_args.seed,
+        dropout_rate=0.1,
+    )
+
+    # Setup optimizer
+    optimizer = Adam(
+        learning_rate=training_args.learning_rate,
+        weight_decay=training_args.weight_decay,
+        beta1=training_args.adam_beta1,
+        beta2=training_args.adam_beta2,
+    ).create(model.params)
+
+    # Create learning rate scheduler
+    lr_scheduler_fn = create_learning_rate_scheduler(
+        base_learning_rate=training_args.learning_rate, warmup_steps=max(training_args.warmup_steps, 1)
+    )
+
+    # Create parallel version of the training and evaluation steps
+    p_training_step = jax.pmap(training_step, "batch", donate_argnums=(0,))
+    p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
+
+    # Replicate the optimizer on each device
+    optimizer = jax_utils.replicate(optimizer)
+
+    # Store some constant
+    nb_epochs = int(training_args.num_train_epochs)
+    batch_size = int(training_args.train_batch_size)
+    eval_batch_size = int(training_args.eval_batch_size)
+
+    epochs = tqdm(range(nb_epochs), desc=f"Epoch ... (1/{nb_epochs})", position=0)
+    for epoch in epochs:
+
+        # ======================== Training ================================
+        # Create sampling rng
+        rng, training_rng, eval_rng = jax.random.split(rng, 3)
+
+        # Generate an epoch by shuffling sampling indices from the train dataset
+        nb_training_samples = len(tokenized_datasets["train"])
+        training_samples_idx = jax.random.permutation(training_rng, jnp.arange(nb_training_samples))
+        training_batch_idx = generate_batch_splits(training_samples_idx, batch_size)
+
+        # Gather the indexes for creating the batch and do a training step
+        for batch_idx in tqdm(training_batch_idx, desc="Training...", position=1):
+            samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
+            model_inputs = data_collator(samples, pad_to_multiple_of=16)
+
+            # Model forward
+            model_inputs = common_utils.shard(model_inputs.data)
+            loss, optimizer, dropout_rngs = p_training_step(optimizer, model_inputs, dropout_rngs)
+
+        epochs.write(f"Loss: {loss}")
+
+        # ======================== Evaluating ==============================
+        nb_eval_samples = len(tokenized_datasets["validation"])
+        eval_samples_idx = jnp.arange(nb_eval_samples)
+        eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
+
+        eval_metrics = []
+        for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
+            samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
+            model_inputs = data_collator(samples, pad_to_multiple_of=16)
+
+            # Model forward
+            model_inputs = common_utils.shard(model_inputs.data)
+            metrics = p_eval_step(optimizer.target, model_inputs)
+            eval_metrics.append(metrics)
+
+        eval_metrics_np = get_metrics(eval_metrics)
+        eval_metrics_np = jax.tree_map(jnp.sum, eval_metrics_np)
+        eval_normalizer = eval_metrics_np.pop("normalizer")
+        eval_summary = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics_np)
+
+        # Update progress bar
+        epochs.desc = (
+            f"Epoch... ({epoch + 1}/{nb_epochs} | Loss: {eval_summary['loss']}, Acc: {eval_summary['accuracy']})"
+        )
+
+        # Save metrics
+        if has_tensorboard and jax.host_id() == 0:
+            for name, value in eval_summary.items():
+                summary_writer.scalar(name, value, epoch)

examples/research_projects/rag/eval_rag.py

@@ -96,7 +96,7 @@ def evaluate_batch_retrieval(args, rag_model, questions):
     )["input_ids"].to(args.device)
 
     question_enc_outputs = rag_model.rag.question_encoder(retriever_input_ids)
-    question_enc_pool_output = question_enc_outputs[0]
+    question_enc_pool_output = question_enc_outputs.pooler_output
 
     result = rag_model.retriever(
         retriever_input_ids,

examples/seq2seq/README.md

@@ -97,7 +97,7 @@ The `.source` files are the input, the `.target` files are the desired output.
 
 ### Potential issues
 
-- native AMP (`--fp16` and no apex) may lead to a huge memory leak and require 10x gpu memory. This has been fixed in pytorch-nightly and the minimal official version to have this fix will be pytorch-1.7.1. Until then if you have to use mixed precision please use AMP only with pytorch-nightly or NVIDIA's apex. Reference: https://github.com/huggingface/transformers/issues/8403
+- native AMP (`--fp16` and no apex) may lead to a huge memory leak and require 10x gpu memory. This has been fixed in pytorch-nightly and the minimal official version to have this fix will be pytorch-1.8. Until then if you have to use mixed precision please use AMP only with pytorch-nightly or NVIDIA's apex. Reference: https://github.com/huggingface/transformers/issues/8403
 
 
 ### Tips and Tricks

examples/seq2seq/finetune_trainer.py

@@ -16,7 +16,6 @@
 import logging
 import os
 import sys
-import time
 from dataclasses import dataclass, field
 from typing import Optional
 
@@ -120,46 +119,6 @@ class DataTrainingArguments:
     )
 
 
-def speed_metrics(split, start_time, num_samples):
-    """
-    Measure and return speed performance metrics.
-
-    This function requires a time snapshot `start_time` before the operation to be measured starts and this
-    function should be run immediately after the operation to be measured has completed.
-
-    Args:
-    - split: one of train, val, test
-    - start_time: operation start time
-    - num_samples: number of samples processed
-
-    """
-    runtime = time.time() - start_time
-    result = {}
-
-    samples_per_second = 1 / (runtime / num_samples)
-    result[f"{split}_samples_per_second"] = round(samples_per_second, 3)
-    result[f"{split}_runtime"] = round(runtime, 4)
-
-    result[f"{split}_n_ojbs"] = num_samples
-    return result
-
-
-def handle_metrics(split, metrics, output_dir):
-    """
-    Log and save metrics
-
-    Args:
-    - split: one of train, val, test
-    - metrics: metrics dict
-    - output_dir: where to save the metrics
-    """
-
-    logger.info(f"***** {split} metrics *****")
-    for key, value in metrics.items():
-        logger.info(f"  {key} = {value}")
-    save_json(metrics, os.path.join(output_dir, f"{split}_results.json"))
-
-
 def main():
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
@@ -306,56 +265,45 @@ def main():
         data_args=data_args,
     )
 
-    all_metrics = {}
     # Training
     if training_args.do_train:
-        logger.info("*** Train ***")
-
-        start_time = time.time()
         trainer.train(
             model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
         )
-        metrics = speed_metrics("train", start_time, data_args.n_train)
-
-        trainer.save_model()  # this also saves the tokenizer
-
+        trainer.save_model()
+        # For convenience, we also re-save the tokenizer to the same directory,
+        # so that you can share your model easily on huggingface.co/models =)
         if trainer.is_world_process_zero():
-            handle_metrics("train", metrics, training_args.output_dir)
-            all_metrics.update(metrics)
-
-            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
             trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
-
-            # For convenience, we also re-save the tokenizer to the same directory,
-            # so that you can share your model easily on huggingface.co/models =)
             tokenizer.save_pretrained(training_args.output_dir)
 
     # Evaluation
+    eval_results = {}
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
-        start_time = time.time()
-        metrics = trainer.evaluate(metric_key_prefix="val")
-        metrics.update(speed_metrics("val", start_time, data_args.n_val))
-        metrics["val_loss"] = round(metrics["val_loss"], 4)
+        result = trainer.evaluate()
 
         if trainer.is_world_process_zero():
-
-            handle_metrics("val", metrics, training_args.output_dir)
-            all_metrics.update(metrics)
+            logger.info("***** Eval results *****")
+            for key, value in result.items():
+                logger.info("  %s = %s", key, value)
+            save_json(result, os.path.join(training_args.output_dir, "eval_results.json"))
+            eval_results.update(result)
 
     if training_args.do_predict:
-        logger.info("*** Predict ***")
+        logging.info("*** Test ***")
 
-        start_time = time.time()
-        test_output = trainer.predict(test_dataset=test_dataset, metric_key_prefix="test")
-        metrics = test_output.metrics
-        metrics.update(speed_metrics("test", start_time, data_args.n_test))
+        test_output = trainer.predict(test_dataset=test_dataset)
+        test_metrics = {k.replace("eval", "test"): v for k, v in test_output.metrics.items()}
 
         if trainer.is_world_process_zero():
-            metrics["test_loss"] = round(metrics["test_loss"], 4)
-            handle_metrics("test", metrics, training_args.output_dir)
-            all_metrics.update(metrics)
+            logger.info("***** Test results *****")
+            for key, value in test_metrics.items():
+                logger.info("  %s = %s", key, value)
+
+            save_json(test_metrics, os.path.join(training_args.output_dir, "test_results.json"))
+            eval_results.update(test_metrics)
 
             if training_args.predict_with_generate:
                 test_preds = tokenizer.batch_decode(
@@ -365,9 +313,8 @@ def main():
                 write_txt_file(test_preds, os.path.join(training_args.output_dir, "test_generations.txt"))
 
     if trainer.is_world_process_zero():
-        save_json(all_metrics, os.path.join(training_args.output_dir, "all_results.json"))
-
-    return all_metrics
+        save_json(eval_results, "all_results.json")
+    return eval_results
 
 
 def _mp_fn(index):

examples/seq2seq/seq2seq_trainer.py

@@ -20,7 +20,6 @@ from torch.utils.data import DistributedSampler, RandomSampler
 
 from transformers import PreTrainedModel, Trainer, logging
 from transformers.file_utils import is_torch_tpu_available
-from transformers.integrations import is_fairscale_available
 from transformers.models.fsmt.configuration_fsmt import FSMTConfig
 from transformers.optimization import (
     Adafactor,
@@ -36,10 +35,6 @@ from transformers.trainer_pt_utils import get_tpu_sampler
 from transformers.training_args import ParallelMode
 
 
-if is_fairscale_available():
-    from fairscale.optim import OSS
-
-
 logger = logging.get_logger(__name__)
 
 arg_to_scheduler = {
@@ -104,25 +99,18 @@ class Seq2SeqTrainer(Trainer):
                     "weight_decay": 0.0,
                 },
             ]
-            optimizer_cls = Adafactor if self.args.adafactor else AdamW
             if self.args.adafactor:
-                optimizer_cls = Adafactor
-                optimizer_kwargs = {"scale_parameter": False, "relative_step": False}
-            else:
-                optimizer_cls = AdamW
-                optimizer_kwargs = {
-                    "betas": (self.args.adam_beta1, self.args.adam_beta2),
-                    "eps": self.args.adam_epsilon,
-                }
-            optimizer_kwargs["lr"] = self.args.learning_rate
-            if self.sharded_dpp:
-                self.optimizer = OSS(
-                    params=optimizer_grouped_parameters,
-                    optim=optimizer_cls,
-                    **optimizer_kwargs,
+                self.optimizer = Adafactor(
+                    optimizer_grouped_parameters,
+                    lr=self.args.learning_rate,
+                    scale_parameter=False,
+                    relative_step=False,
                 )
+
             else:
-                self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
+                self.optimizer = AdamW(
+                    optimizer_grouped_parameters, lr=self.args.learning_rate, eps=self.args.adam_epsilon
+                )
 
         if self.lr_scheduler is None:
             self.lr_scheduler = self._get_lr_scheduler(num_training_steps)

examples/seq2seq/utils.py

@@ -462,7 +462,7 @@ def save_git_info(folder_path: str) -> None:
 
 def save_json(content, path, indent=4, **json_dump_kwargs):
     with open(path, "w") as f:
-        json.dump(content, f, indent=indent, sort_keys=True, **json_dump_kwargs)
+        json.dump(content, f, indent=indent, **json_dump_kwargs)
 
 
 def load_json(path):

model_cards/google/tapas-base/README.md

@@ -1,123 +0,0 @@
----
-language: en
-tags:
-- tapas
-- masked-lm
-license: apache-2.0
----
-
-# TAPAS base model 
-
-This model corresponds to the `tapas_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). 
-
-Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
-the Hugging Face team and contributors.
-
-## Model description
-
-TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. 
-This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
-can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
-was pretrained with two objectives:
-
-- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in 
-  the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words. 
-  This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, 
-  or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional 
-  representation of a table and associated text.
-- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating 
-  a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence 
-  is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
-
-This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used 
-to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
-or refuted by the contents of a table. Fine-tuning is done by adding classification heads on top of the pre-trained model, and then jointly
-train the randomly initialized classification heads with the base model on a labelled dataset. 
-
-## Intended uses & limitations
-
-You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. 
-See the [model hub](https://huggingface.co/models?filter=tapas) to look for fine-tuned versions on a task that interests you.
-
-
-Here is how to use this model to get the features of a given table-text pair in PyTorch:
-
-```python
-from transformers import TapasTokenizer, TapasModel
-import pandas as pd
-tokenizer = TapasTokenizer.from_pretrained('tapase-base')
-model = TapasModel.from_pretrained("tapas-base")
-data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
-         'Age': ["56", "45", "59"],
-         'Number of movies': ["87", "53", "69"]
-}
-table = pd.DataFrame.from_dict(data)
-queries = ["How many movies has George Clooney played in?"]
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(table=table, queries=queries, return_tensors='pt')
-output = model(**encoded_input)
-```
-
-## Training data
-
-For masked language modeling (MLM), a collection of 6.2 million tables was extracted from English Wikipedia: 3.3M of class [Infobox](https://en.wikipedia.org/wiki/Help:Infobox)
-and 2.9M of class WikiTable. The author only considered tables with at most 500 cells. As a proxy for questions that appear in the 
-downstream tasks, the authros extracted the table caption, article title, article description, segment title and text of the segment 
-the table occurs in as relevant text snippets. In this way, 21.3M snippets were created. For more info, see the original [TAPAS paper](https://www.aclweb.org/anthology/2020.acl-main.398.pdf).
-
-For intermediate pre-training, 2 tasks are introduced: one based on synthetic and the other from counterfactual statements. The first one 
-generates a sentence by sampling from a set of logical expressions that filter, combine and compare the information on the table, which is 
-required in table entailment (e.g., knowing that Gerald Ford is taller than the average president requires summing
-all presidents and dividing by the number of presidents). The second one corrupts sentences about tables appearing on Wikipedia by swapping 
-entities for plausible alternatives. Examples of the two tasks can be seen in Figure 1. The procedure is described in detail in section 3 of 
-the [TAPAS follow-up paper](https://www.aclweb.org/anthology/2020.findings-emnlp.27.pdf).
-
-## Training procedure
-
-### Preprocessing
-
-The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
-then of the form:
-
-```
-[CLS] Context [SEP] Flattened table [SEP]
-```
-
-The details of the masking procedure for each sequence are the following:
-- 15% of the tokens are masked.
-- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
-- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
-- In the 10% remaining cases, the masked tokens are left as is.
-
-The details of the creation of the synthetic and counterfactual examples can be found in the [follow-up paper](https://arxiv.org/abs/2010.00571). 
-
-### Pretraining
-
-The model was trained on 32 Cloud TPU v3 cores for one million steps with maximum sequence length 512 and batch size of 512.
-In this setup, pre-training takes around 3 days. The optimizer used is Adam with a learning rate of 5e-5, and a warmup ratio 
-of 0.10. 
-
-
-### BibTeX entry and citation info
-
-```bibtex
-@misc{herzig2020tapas,
-      title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, 
-      author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos},
-      year={2020},
-      eprint={2004.02349},
-      archivePrefix={arXiv},
-      primaryClass={cs.IR}
-}
-```
-
-```bibtex
-@misc{eisenschlos2020understanding,
-      title={Understanding tables with intermediate pre-training}, 
-      author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller},
-      year={2020},
-      eprint={2010.00571},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```

notebooks/02-transformers.ipynb

@@ -73,14 +73,12 @@
     "\n",
     "The transformers library allows you to benefits from large, pretrained language models without requiring a huge and costly computational\n",
     "infrastructure. Most of the State-of-the-Art models are provided directly by their author and made available in the library \n",
-    "in PyTorch and TensorFlow in a transparent and interchangeable way. \n",
-    "\n",
-    "If you're executing this notebook in Colab, you will need to install the transformers library. You can do so with this command:"
+    "in PyTorch and TensorFlow in a transparent and interchangeable way. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "id": "KnT3Jn6fSXai",
     "pycharm": {
@@ -91,12 +89,13 @@
    },
    "outputs": [],
    "source": [
-    "# !pip install transformers"
+    "!pip install transformers\n",
+    "!pip install --upgrade tensorflow"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -112,11 +111,13 @@
     {
      "data": {
       "text/plain": [
-       "<torch.autograd.grad_mode.set_grad_enabled at 0x7ff0cc2a2c50>"
+       "<torch.autograd.grad_mode.set_grad_enabled at 0x7f9c03e5b3c8>"
       ]
      },
-     "execution_count": 3,
-     "metadata": {},
+     "execution_count": 2,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -129,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "id": "1xMDTHQXSXai",
     "pycharm": {
@@ -158,50 +159,12 @@
    "source": [
     "With only the above two lines of code, you're ready to use a BERT pre-trained model. \n",
     "The tokenizers will allow us to map a raw textual input to a sequence of integers representing our textual input\n",
-    "in a way the model can manipulate. Since we will be using a PyTorch model, we ask the tokenizer to return to us PyTorch tensors."
+    "in a way the model can manipulate."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "input_ids:\n",
-      "\ttensor([[ 101, 1188, 1110, 1126, 7758, 1859,  102]])\n",
-      "token_type_ids:\n",
-      "\ttensor([[0, 0, 0, 0, 0, 0, 0]])\n",
-      "attention_mask:\n",
-      "\ttensor([[1, 1, 1, 1, 1, 1, 1]])\n"
-     ]
-    }
-   ],
-   "source": [
-    "tokens_pt = tokenizer(\"This is an input example\", return_tensors=\"pt\")\n",
-    "for key, value in tokens_pt.items():\n",
-    "    print(\"{}:\\n\\t{}\".format(key, value))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The tokenizer automatically converted our input to all the inputs expected by the model. It generated some additional tensors on top of the IDs: \n",
-    "\n",
-    "- token_type_ids: This tensor will map every tokens to their corresponding segment (see below).\n",
-    "- attention_mask: This tensor is used to \"mask\" padded values in a batch of sequence with different lengths (see below).\n",
-    "\n",
-    "You can check our [glossary](https://huggingface.co/transformers/glossary.html) for more information about each of those keys. \n",
-    "\n",
-    "We can just feed this directly into our model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -218,12 +181,32 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Tokens: ['This', 'is', 'an', 'input', 'example']\n",
+      "Tokens id: [1188, 1110, 1126, 7758, 1859]\n",
+      "Tokens PyTorch: tensor([[ 101, 1188, 1110, 1126, 7758, 1859,  102]])\n",
       "Token wise output: torch.Size([1, 7, 768]), Pooled output: torch.Size([1, 768])\n"
      ]
     }
    ],
    "source": [
-    "outputs = model(**tokens_pt)\n",
+    "# Tokens comes from a process that splits the input into sub-entities with interesting linguistic properties. \n",
+    "tokens = tokenizer.tokenize(\"This is an input example\")\n",
+    "print(\"Tokens: {}\".format(tokens))\n",
+    "\n",
+    "# This is not sufficient for the model, as it requires integers as input, \n",
+    "# not a problem, let's convert tokens to ids.\n",
+    "tokens_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
+    "print(\"Tokens id: {}\".format(tokens_ids))\n",
+    "\n",
+    "# Add the required special tokens\n",
+    "tokens_ids = tokenizer.build_inputs_with_special_tokens(tokens_ids)\n",
+    "\n",
+    "# We need to convert to a Deep Learning framework specific format, let's use PyTorch for now.\n",
+    "tokens_pt = torch.tensor([tokens_ids])\n",
+    "print(\"Tokens PyTorch: {}\".format(tokens_pt))\n",
+    "\n",
+    "# Now we're ready to go through BERT with out input\n",
+    "outputs = model(tokens_pt)\n",
     "last_hidden_state = outputs.last_hidden_state\n",
     "pooler_output = outputs.pooler_output\n",
     "\n",
@@ -250,9 +233,85 @@
     "require a fine-grained token-level. This is the case for Sentiment-Analysis of the sequence or Information Retrieval."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DCxuDWH2SXaj",
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "The code you saw in the previous section introduced all the steps required to do simple model invocation.\n",
+    "For more day-to-day usage, transformers provides you higher-level methods which will makes your NLP journey easier.\n",
+    "Let's improve our previous example"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "sgcNCdXUSXaj",
+    "outputId": "af2fb928-7c17-475b-cf81-89cfc4b1d9e5",
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%% code\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input_ids:\n",
+      "\ttensor([[ 101, 1188, 1110, 1126, 7758, 1859,  102]])\n",
+      "token_type_ids:\n",
+      "\ttensor([[0, 0, 0, 0, 0, 0, 0]])\n",
+      "attention_mask:\n",
+      "\ttensor([[1, 1, 1, 1, 1, 1, 1]])\n",
+      "Difference with previous code: (0.0, 0.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# tokens = tokenizer.tokenize(\"This is an input example\")\n",
+    "# tokens_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
+    "# tokens_pt = torch.tensor([tokens_ids])\n",
+    "\n",
+    "# This code can be factored into one-line as follow\n",
+    "tokens_pt2 = tokenizer(\"This is an input example\", return_tensors=\"pt\")\n",
+    "\n",
+    "for key, value in tokens_pt2.items():\n",
+    "    print(\"{}:\\n\\t{}\".format(key, value))\n",
+    "\n",
+    "outputs2 = model(**tokens_pt2)\n",
+    "last_hidden_state2 = outputs2.last_hidden_state\n",
+    "pooler_output2 = outputs2.pooler_output\n",
+    "\n",
+    "print(\"Difference with previous code: ({}, {})\".format((last_hidden_state2 - last_hidden_state).sum(), (pooler_output2 - pooler_output).sum()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gC-7xGYPSXal"
+   },
+   "source": [
+    "As you can see above, calling the tokenizer provides a convenient way to generate all the required parameters\n",
+    "that will go through the model. \n",
+    "\n",
+    "Moreover, you might have noticed it generated some additional tensors: \n",
+    "\n",
+    "- token_type_ids: This tensor will map every tokens to their corresponding segment (see below).\n",
+    "- attention_mask: This tensor is used to \"mask\" padded values in a batch of sequence with different lengths (see below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -298,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -355,47 +414,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "id": "Kubwm-wJSXan",
     "pycharm": {
      "is_executing": false
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3b971be3639d4fedb02778fb5c6898a0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=526681800.0, style=ProgressStyle(descri…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some layers from the model checkpoint at bert-base-cased were not used when initializing TFBertModel: ['nsp___cls', 'mlm___cls']\n",
-      "- This IS expected if you are initializing TFBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "All the layers of TFBertModel were initialized from the model checkpoint at bert-base-cased.\n",
-      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from transformers import TFBertModel, BertModel\n",
     "\n",
@@ -406,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -422,8 +448,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "last_hidden_state differences: 1.2933e-05\n",
-      "pooler_output differences: 2.9691e-06\n"
+      "last_hidden_state differences: 1.0094e-05\n",
+      "pooler_output differences: 7.2969e-07\n"
      ]
     }
    ],
@@ -456,7 +482,7 @@
     "\n",
     "For example, Google released a few months ago **T5** an Encoder/Decoder architecture based on Transformer and available in `transformers` with no more than 11 billions parameters. Microsoft also recently entered the game with **Turing-NLG** using 17 billions parameters. This kind of model requires tens of gigabytes to store the weights and a tremendous compute infrastructure to run such models which makes it impracticable for the common man !\n",
     "\n",
-    "![transformers-parameters](https://github.com/huggingface/notebooks/blob/master/examples/images/model_parameters.png?raw=true)\n",
+    "![transformers-parameters](https://lh5.googleusercontent.com/NRdXzEcgZV3ooykjIaTm9uvbr9QnSjDQHHAHb2kk_Lm9lIF0AhS-PJdXGzpcBDztax922XAp386hyNmWZYsZC1lUN2r4Ip5p9v-PHO19-jevRGg4iQFxgv5Olq4DWaqSA_8ptep7)\n",
     "\n",
     "With the goal of making Transformer-based NLP accessible to everyone we @huggingface developed models that take advantage of a training process called **Distillation** which allows us to drastically reduce the resources needed to run such models with almost zero drop in performances.\n",
     "\n",
@@ -647,7 +673,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.3"
   },
   "pycharm": {
    "stem_cell": {

notebooks/README.md

@@ -31,11 +31,7 @@ Pull Request so it can be included under the Community notebooks.
 | [Getting Started Tokenizers](https://github.com/huggingface/transformers/blob/master/notebooks/01-training-tokenizers.ipynb)  | How to train and use your very own tokenizer  |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/transformers/blob/master/notebooks/01-training-tokenizers.ipynb) |
 | [Getting Started Transformers](https://github.com/huggingface/transformers/blob/master/notebooks/02-transformers.ipynb)   | How to easily start using transformers  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/transformers/blob/master/notebooks/02-transformers.ipynb) |
 | [How to use Pipelines](https://github.com/huggingface/transformers/blob/master/notebooks/03-pipelines.ipynb)  | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/transformers/blob/master/notebooks/03-pipelines.ipynb) |
-| [How to fine-tune a model on text classification](https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb)|
-| [How to fine-tune a model on language modeling](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling.ipynb)|
-| [How to fine-tune a model on token classification](https://github.com/huggingface/notebooks/blob/master/examples/token_classification.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb)|
-| [How to fine-tune a model on question answering](https://github.com/huggingface/notebooks/blob/master/examples/question_answering.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb)|
-| [How to train a language model from scratch](https://github.com/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)| Highlight all the steps to effectively train Transformer model on custom data | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)|
+| [How to train a language model](https://github.com/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)| Highlight all the steps to effectively train Transformer model on custom data | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)|
 | [How to generate text](https://github.com/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)| How to use different decoding methods for language generation with transformers | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)|
 | [How to export model to ONNX](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb) | Highlight how to export and run inference workloads through ONNX |
 | [How to use Benchmarks](https://github.com/huggingface/transformers/blob/master/notebooks/05-benchmark.ipynb) | How to benchmark models with transformers | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/transformers/blob/master/notebooks/05-benchmark.ipynb)|
@@ -73,5 +69,3 @@ Pull Request so it can be included under the Community notebooks.
 |[Classify text with DistilBERT and Tensorflow](https://github.com/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb) | How to fine-tune DistilBERT for text classification in TensorFlow | [Peter Bayerle](https://github.com/peterbayerle) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb)|
 |[Leverage BERT for Encoder-Decoder Summarization on CNN/Dailymail](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb) | How to warm-start a *EncoderDecoderModel* with a *bert-base-uncased* checkpoint for summarization on CNN/Dailymail | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb)|
 |[Leverage RoBERTa for Encoder-Decoder Summarization on BBC XSum](https://github.com/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb) | How to warm-start a shared *EncoderDecoderModel* with a *roberta-base* checkpoint for summarization on BBC/XSum | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb)|
-|[Fine-tuning TAPAS on Sequential Question Answering (SQA)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) | How to fine-tune *TapasForQuestionAnswering* with a *tapas-base* checkpoint on the Sequential Question Answering (SQA) dataset | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/Fine_tuning_TapasForQuestionAnswering_on_SQAipynb)|
-|[Evaluating TAPAS on Table Fact Checking (TabFact)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb) | How to evaluate a fine-tuned *TapasForSequenceClassification* with a *tapas-base-finetuned-tabfact* checkpoint using a combination of the 🤗 datasets and 🤗 transformers libraries | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb)|

setup.py

@@ -127,8 +127,8 @@ _deps = [
     "sphinx-rtd-theme==0.4.3",  # sphinx-rtd-theme==0.5.0 introduced big changes in the style.
     "sphinx==3.2.1",
     "starlette",
-    "tensorflow-cpu>=2.0",
-    "tensorflow>=2.0",
+    "tensorflow-cpu>=2.0,<2.4",
+    "tensorflow>=2.0,<2.4",
     "timeout-decorator",
     "tokenizers==0.9.4",
     "torch>=1.0",
@@ -244,7 +244,7 @@ install_requires = [
 
 setup(
     name="transformers",
-    version="4.1.1",
+    version="4.1.0.dev0",
     author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Sam Shleifer, Patrick von Platen, Sylvain Gugger, 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",

src/transformers/__init__.py

@@ -16,7 +16,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "4.1.1"
+__version__ = "4.1.0.dev0"
 
 # Work around to update TensorFlow's absl.logging threshold which alters the
 # default Python logging output behavior when present.
@@ -164,7 +164,6 @@ from .models.retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBert
 from .models.roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaTokenizer
 from .models.squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertTokenizer
 from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
-from .models.tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig, TapasTokenizer
 from .models.transfo_xl import (
     TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TransfoXLConfig,
@@ -190,7 +189,6 @@ from .pipelines import (
     PipelineDataFormat,
     QuestionAnsweringPipeline,
     SummarizationPipeline,
-    TableQuestionAnsweringPipeline,
     Text2TextGenerationPipeline,
     TextClassificationPipeline,
     TextGenerationPipeline,
@@ -359,7 +357,6 @@ if is_torch_available():
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
-        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         MODEL_MAPPING,
         MODEL_WITH_LM_HEAD_MAPPING,
@@ -372,7 +369,6 @@ if is_torch_available():
         AutoModelForQuestionAnswering,
         AutoModelForSeq2SeqLM,
         AutoModelForSequenceClassification,
-        AutoModelForTableQuestionAnswering,
         AutoModelForTokenClassification,
         AutoModelWithLMHead,
     )
@@ -609,13 +605,6 @@ if is_torch_available():
         T5PreTrainedModel,
         load_tf_weights_in_t5,
     )
-    from .models.tapas import (
-        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
-        TapasForMaskedLM,
-        TapasForQuestionAnswering,
-        TapasForSequenceClassification,
-        TapasModel,
-    )
     from .models.transfo_xl import (
         TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
         AdaptiveEmbedding,
@@ -728,7 +717,7 @@ if is_tf_available():
         TFAutoModelForTokenClassification,
         TFAutoModelWithLMHead,
     )
-    from .models.bart import TFBartForConditionalGeneration, TFBartModel, TFBartPretrainedModel
+    from .models.bart import TFBartForConditionalGeneration, TFBartModel
     from .models.bert import (
         TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFBertEmbeddings,
@@ -870,7 +859,6 @@ if is_tf_available():
     from .models.openai import (
         TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFOpenAIGPTDoubleHeadsModel,
-        TFOpenAIGPTForSequenceClassification,
         TFOpenAIGPTLMHeadModel,
         TFOpenAIGPTMainLayer,
         TFOpenAIGPTModel,

src/transformers/configuration_utils.py

@@ -317,9 +317,6 @@ class PretrainedConfig(object):
             proxies (:obj:`Dict[str, str]`, `optional`):
                 A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
                 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
-            use_auth_token (:obj:`str` or `bool`, `optional`):
-                The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
-                generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
             revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
                 The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                 git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
@@ -335,10 +332,6 @@ class PretrainedConfig(object):
                 values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
                 by the ``return_unused_kwargs`` keyword parameter.
 
-        .. note::
-
-            Passing :obj:`use_auth_token=True` is required when you want to use a private model.
-
         Returns:
             :class:`PretrainedConfig`: The configuration object instantiated from this pretrained model.
 
@@ -380,7 +373,6 @@ class PretrainedConfig(object):
         force_download = kwargs.pop("force_download", False)
         resume_download = kwargs.pop("resume_download", False)
         proxies = kwargs.pop("proxies", None)
-        use_auth_token = kwargs.pop("use_auth_token", None)
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
 
@@ -403,7 +395,6 @@ class PretrainedConfig(object):
                 proxies=proxies,
                 resume_download=resume_download,
                 local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
             )
             # Load config dict
             config_dict = cls._dict_from_json_file(resolved_config_file)

src/transformers/dependency_versions_table.py

@@ -40,8 +40,8 @@ deps = {
     "sphinx-rtd-theme": "sphinx-rtd-theme==0.4.3",
     "sphinx": "sphinx==3.2.1",
     "starlette": "starlette",
-    "tensorflow-cpu": "tensorflow-cpu>=2.0",
-    "tensorflow": "tensorflow>=2.0",
+    "tensorflow-cpu": "tensorflow-cpu>=2.0,<2.4",
+    "tensorflow": "tensorflow>=2.0,<2.4",
     "timeout-decorator": "timeout-decorator",
     "tokenizers": "tokenizers==0.9.4",
     "torch": "torch>=1.0",

src/transformers/file_utils.py

@@ -16,7 +16,6 @@ Utilities for working with the local dataset cache. Parts of this file is adapte
 https://github.com/allenai/allennlp.
 """
 
-import copy
 import fnmatch
 import io
 import json
@@ -43,7 +42,6 @@ import requests
 from filelock import FileLock
 
 from . import __version__
-from .hf_api import HfFolder
 from .utils import logging
 
 
@@ -218,29 +216,6 @@ except ImportError:
     _tokenizers_available = False
 
 
-try:
-    import pandas  # noqa: F401
-
-    _pandas_available = True
-
-except ImportError:
-    _pandas_available = False
-
-
-try:
-    import torch_scatter
-
-    # Check we're not importing a "torch_scatter" directory somewhere
-    _scatter_available = hasattr(torch_scatter, "__version__") and hasattr(torch_scatter, "scatter")
-    if _scatter_available:
-        logger.debug(f"Succesfully imported torch-scatter version {torch_scatter.__version__}")
-    else:
-        logger.debug("Imported a torch_scatter object but this doesn't seem to be the torch-scatter library.")
-
-except ImportError:
-    _scatter_available = False
-
-
 old_default_cache_path = os.path.join(torch_cache_home, "transformers")
 # New default cache, shared with the Datasets library
 hf_cache_home = os.path.expanduser(
@@ -351,14 +326,6 @@ def is_in_notebook():
     return _in_notebook
 
 
-def is_scatter_available():
-    return _scatter_available
-
-
-def is_pandas_available():
-    return _pandas_available
-
-
 def torch_only_method(fn):
     def wrapper(*args, **kwargs):
         if not _torch_available:
@@ -461,20 +428,6 @@ installation page: https://github.com/google/flax and follow the ones that match
 """
 
 
-# docstyle-ignore
-SCATTER_IMPORT_ERROR = """
-{0} requires the torch-scatter library but it was not found in your environment. You can install it with pip as
-explained here: https://github.com/rusty1s/pytorch_scatter.
-"""
-
-
-# docstyle-ignore
-PANDAS_IMPORT_ERROR = """
-{0} requires the pandas library but it was not found in your environment. You can install it with pip as
-explained here: https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html.
-"""
-
-
 def requires_datasets(obj):
     name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
     if not is_datasets_available():
@@ -529,18 +482,6 @@ def requires_protobuf(obj):
         raise ImportError(PROTOBUF_IMPORT_ERROR.format(name))
 
 
-def requires_pandas(obj):
-    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
-    if not is_pandas_available():
-        raise ImportError(PANDAS_IMPORT_ERROR.format(name))
-
-
-def requires_scatter(obj):
-    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
-    if not is_scatter_available():
-        raise ImportError(SCATTER_IMPORT_ERROR.format(name))
-
-
 def add_start_docstrings(*docstr):
     def docstring_decorator(fn):
         fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
@@ -1039,7 +980,6 @@ def cached_path(
     user_agent: Union[Dict, str, None] = None,
     extract_compressed_file=False,
     force_extract=False,
-    use_auth_token: Union[bool, str, None] = None,
     local_files_only=False,
 ) -> Optional[str]:
     """
@@ -1052,8 +992,6 @@ def cached_path(
         force_download: if True, re-download the file even if it's already cached in the cache dir.
         resume_download: if True, resume the download if incompletely received file is found.
         user_agent: Optional string or dict that will be appended to the user-agent on remote requests.
-        use_auth_token: Optional string or boolean to use as Bearer token for remote files. If True,
-            will get token from ~/.huggingface.
         extract_compressed_file: if True and the path point to a zip or tar file, extract the compressed
             file in a folder along the archive.
         force_extract: if True when extract_compressed_file is True and the archive was already extracted,
@@ -1081,7 +1019,6 @@ def cached_path(
             proxies=proxies,
             resume_download=resume_download,
             user_agent=user_agent,
-            use_auth_token=use_auth_token,
             local_files_only=local_files_only,
         )
     elif os.path.exists(url_or_filename):
@@ -1144,11 +1081,11 @@ def http_user_agent(user_agent: Union[Dict, str, None] = None) -> str:
     return ua
 
 
-def http_get(url: str, temp_file: BinaryIO, proxies=None, resume_size=0, headers: Optional[Dict[str, str]] = None):
+def http_get(url: str, temp_file: BinaryIO, proxies=None, resume_size=0, user_agent: Union[Dict, str, None] = None):
     """
     Donwload remote file. Do not gobble up errors.
     """
-    headers = copy.deepcopy(headers)
+    headers = {"user-agent": http_user_agent(user_agent)}
     if resume_size > 0:
         headers["Range"] = "bytes=%d-" % (resume_size,)
     r = requests.get(url, stream=True, proxies=proxies, headers=headers)
@@ -1178,7 +1115,6 @@ def get_from_cache(
     etag_timeout=10,
     resume_download=False,
     user_agent: Union[Dict, str, None] = None,
-    use_auth_token: Union[bool, str, None] = None,
     local_files_only=False,
 ) -> Optional[str]:
     """
@@ -1198,19 +1134,11 @@ def get_from_cache(
 
     os.makedirs(cache_dir, exist_ok=True)
 
-    headers = {"user-agent": http_user_agent(user_agent)}
-    if isinstance(use_auth_token, str):
-        headers["authorization"] = "Bearer {}".format(use_auth_token)
-    elif use_auth_token:
-        token = HfFolder.get_token()
-        if token is None:
-            raise EnvironmentError("You specified use_auth_token=True, but a huggingface token was not found.")
-        headers["authorization"] = "Bearer {}".format(token)
-
     url_to_download = url
     etag = None
     if not local_files_only:
         try:
+            headers = {"user-agent": http_user_agent(user_agent)}
             r = requests.head(url, headers=headers, allow_redirects=False, proxies=proxies, timeout=etag_timeout)
             r.raise_for_status()
             etag = r.headers.get("X-Linked-Etag") or r.headers.get("ETag")
@@ -1300,7 +1228,7 @@ def get_from_cache(
         with temp_file_manager() as temp_file:
             logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)
 
-            http_get(url_to_download, temp_file, proxies=proxies, resume_size=resume_size, headers=headers)
+            http_get(url_to_download, temp_file, proxies=proxies, resume_size=resume_size, user_agent=user_agent)
 
         logger.info("storing %s in cache at %s", url, cache_path)
         os.replace(temp_file.name, cache_path)

src/transformers/hf_api.py

@@ -206,7 +206,7 @@ class HfApi:
 
     def model_list(self) -> List[ModelInfo]:
         """
-        Get the public list of all the models on huggingface.co
+        Get the public list of all the models on huggingface, including the community models
         """
         path = "{}/api/models".format(self.endpoint)
         r = requests.get(path)
@@ -228,13 +228,7 @@ class HfApi:
         return [RepoObj(**x) for x in d]
 
     def create_repo(
-        self,
-        token: str,
-        name: str,
-        organization: Optional[str] = None,
-        private: Optional[bool] = None,
-        exist_ok=False,
-        lfsmultipartthresh: Optional[int] = None,
+        self, token: str, name: str, organization: Optional[str] = None, lfsmultipartthresh: Optional[int] = None
     ) -> str:
         """
         HuggingFace git-based system, used for models.
@@ -242,14 +236,10 @@ class HfApi:
         Call HF API to create a whole repo.
 
         Params:
-            private: Whether the model repo should be private (requires a paid huggingface.co account)
-
-            exist_ok: Do not raise an error if repo already exists
-
             lfsmultipartthresh: Optional: internal param for testing purposes.
         """
         path = "{}/api/repos/create".format(self.endpoint)
-        json = {"name": name, "organization": organization, "private": private}
+        json = {"name": name, "organization": organization}
         if lfsmultipartthresh is not None:
             json["lfsmultipartthresh"] = lfsmultipartthresh
         r = requests.post(
@@ -257,8 +247,6 @@ class HfApi:
             headers={"authorization": "Bearer {}".format(token)},
             json=json,
         )
-        if exist_ok and r.status_code == 409:
-            return ""
         r.raise_for_status()
         d = r.json()
         return d["url"]

src/transformers/integrations.py

@@ -92,13 +92,6 @@ try:
 except ImportError:
     _has_mlflow = False
 
-try:
-    import fairscale  # noqa: F401
-
-    _has_fairscale = True
-except ImportError:
-    _has_fairscale = False
-
 # No transformer imports above this point
 
 from .file_utils import is_torch_tpu_available  # noqa: E402
@@ -135,10 +128,6 @@ def is_mlflow_available():
     return _has_mlflow
 
 
-def is_fairscale_available():
-    return _has_fairscale
-
-
 def hp_params(trial):
     if is_optuna_available():
         if isinstance(trial, optuna.Trial):

src/transformers/modeling_flax_utils.py

@@ -15,7 +15,6 @@
 
 import os
 from abc import ABC, abstractmethod
-from functools import partial
 from pickle import UnpicklingError
 from typing import Dict, Set, Tuple, Union
 
@@ -35,12 +34,17 @@ from .utils import logging
 logger = logging.get_logger(__name__)
 
 
+@jax.jit
+def gelu_new(x):
+    return nn.gelu(x, approximate=True)
+
+
 ACT2FN = {
     "gelu": nn.gelu,
     "relu": nn.relu,
     "silu": nn.swish,
     "swish": nn.swish,
-    "gelu_new": partial(nn.gelu, approximate=True),
+    "gelu_new": gelu_new,
 }
 
 
@@ -226,7 +230,6 @@ class FlaxPreTrainedModel(ABC):
         resume_download = kwargs.pop("resume_download", False)
         proxies = kwargs.pop("proxies", None)
         local_files_only = kwargs.pop("local_files_only", False)
-        use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
 
         # Load config if we don't provide a configuration
@@ -241,7 +244,6 @@ class FlaxPreTrainedModel(ABC):
                 resume_download=resume_download,
                 proxies=proxies,
                 local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
                 revision=revision,
                 **kwargs,
             )
@@ -285,7 +287,6 @@ class FlaxPreTrainedModel(ABC):
                     proxies=proxies,
                     resume_download=resume_download,
                     local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
                 )
             except EnvironmentError as err:
                 logger.error(err)

src/transformers/modeling_tf_utils.py

@@ -894,9 +894,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
                 Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
             local_files_only(:obj:`bool`, `optional`, defaults to :obj:`False`):
                 Whether or not to only look at local files (e.g., not try doanloading the model).
-            use_auth_token (:obj:`str` or `bool`, `optional`):
-                The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
-                generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
             revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
                 The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                 git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
@@ -919,10 +916,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
                       with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
                       attribute will be passed to the underlying model's ``__init__`` function.
 
-        .. note::
-
-            Passing :obj:`use_auth_token=True` is required when you want to use a private model.
-
         Examples::
 
             >>> from transformers import BertConfig, TFBertModel
@@ -946,7 +939,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
         proxies = kwargs.pop("proxies", None)
         output_loading_info = kwargs.pop("output_loading_info", False)
         local_files_only = kwargs.pop("local_files_only", False)
-        use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
         mirror = kwargs.pop("mirror", None)
 
@@ -962,7 +954,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
                 resume_download=resume_download,
                 proxies=proxies,
                 local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
                 revision=revision,
                 **kwargs,
             )
@@ -1005,7 +996,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
                     proxies=proxies,
                     resume_download=resume_download,
                     local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
                 )
             except EnvironmentError as err:
                 logger.error(err)

src/transformers/modeling_utils.py

@@ -886,9 +886,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
                 Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
             local_files_only(:obj:`bool`, `optional`, defaults to :obj:`False`):
                 Whether or not to only look at local files (i.e., do not try to download the model).
-            use_auth_token (:obj:`str` or `bool`, `optional`):
-                The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
-                generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
             revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
                 The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                 git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
@@ -911,10 +908,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
                       with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
                       attribute will be passed to the underlying model's ``__init__`` function.
 
-        .. note::
-
-            Passing :obj:`use_auth_token=True` is required when you want to use a private model.
-
         Examples::
 
             >>> from transformers import BertConfig, BertModel
@@ -938,7 +931,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
         proxies = kwargs.pop("proxies", None)
         output_loading_info = kwargs.pop("output_loading_info", False)
         local_files_only = kwargs.pop("local_files_only", False)
-        use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
         mirror = kwargs.pop("mirror", None)
 
@@ -954,7 +946,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
                 resume_download=resume_download,
                 proxies=proxies,
                 local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
                 revision=revision,
                 **kwargs,
             )
@@ -1007,7 +998,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
                     proxies=proxies,
                     resume_download=resume_download,
                     local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
                 )
             except EnvironmentError as err:
                 logger.error(err)

src/transformers/models/auto/__init__.py

@@ -31,7 +31,6 @@ if is_torch_available():
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
-        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         MODEL_MAPPING,
         MODEL_WITH_LM_HEAD_MAPPING,
@@ -44,7 +43,6 @@ if is_torch_available():
         AutoModelForQuestionAnswering,
         AutoModelForSeq2SeqLM,
         AutoModelForSequenceClassification,
-        AutoModelForTableQuestionAnswering,
         AutoModelForTokenClassification,
         AutoModelWithLMHead,
     )

src/transformers/models/auto/configuration_auto.py

@@ -51,7 +51,6 @@ from ..retribert.configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCH
 from ..roberta.configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
 from ..squeezebert.configuration_squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig
 from ..t5.configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
-from ..tapas.configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
 from ..transfo_xl.configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
 from ..xlm.configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
 from ..xlm_prophetnet.configuration_xlm_prophetnet import (
@@ -96,7 +95,6 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
         XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
         PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
-        TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ]
     for key, value, in pretrained_map.items()
 )
@@ -143,7 +141,6 @@ CONFIG_MAPPING = OrderedDict(
         ("dpr", DPRConfig),
         ("layoutlm", LayoutLMConfig),
         ("rag", RagConfig),
-        ("tapas", TapasConfig),
     ]
 )
 
@@ -188,7 +185,6 @@ MODEL_NAMES_MAPPING = OrderedDict(
         ("prophetnet", "ProphetNet"),
         ("mt5", "mT5"),
         ("mpnet", "MPNet"),
-        ("tapas", "TAPAS"),
     ]
 )
 

src/transformers/models/auto/modeling_auto.py

@@ -165,12 +165,6 @@ from ..squeezebert.modeling_squeezebert import (
     SqueezeBertModel,
 )
 from ..t5.modeling_t5 import T5ForConditionalGeneration, T5Model
-from ..tapas.modeling_tapas import (
-    TapasForMaskedLM,
-    TapasForQuestionAnswering,
-    TapasForSequenceClassification,
-    TapasModel,
-)
 from ..transfo_xl.modeling_transfo_xl import TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel
 from ..xlm.modeling_xlm import (
     XLMForMultipleChoice,
@@ -236,7 +230,6 @@ from .configuration_auto import (
     RobertaConfig,
     SqueezeBertConfig,
     T5Config,
-    TapasConfig,
     TransfoXLConfig,
     XLMConfig,
     XLMProphetNetConfig,
@@ -284,7 +277,6 @@ MODEL_MAPPING = OrderedDict(
         (XLMProphetNetConfig, XLMProphetNetModel),
         (ProphetNetConfig, ProphetNetModel),
         (MPNetConfig, MPNetModel),
-        (TapasConfig, TapasModel),
     ]
 )
 
@@ -316,7 +308,6 @@ MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
         (LxmertConfig, LxmertForPreTraining),
         (FunnelConfig, FunnelForPreTraining),
         (MPNetConfig, MPNetForMaskedLM),
-        (TapasConfig, TapasForMaskedLM),
     ]
 )
 
@@ -349,7 +340,6 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
         (ReformerConfig, ReformerModelWithLMHead),
         (FunnelConfig, FunnelForMaskedLM),
         (MPNetConfig, MPNetForMaskedLM),
-        (TapasConfig, TapasForMaskedLM),
     ]
 )
 
@@ -396,7 +386,6 @@ MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
         (ReformerConfig, ReformerForMaskedLM),
         (FunnelConfig, FunnelForMaskedLM),
         (MPNetConfig, MPNetForMaskedLM),
-        (TapasConfig, TapasForMaskedLM),
     ]
 )
 
@@ -442,7 +431,6 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
         (CTRLConfig, CTRLForSequenceClassification),
         (TransfoXLConfig, TransfoXLForSequenceClassification),
         (MPNetConfig, MPNetForSequenceClassification),
-        (TapasConfig, TapasForSequenceClassification),
     ]
 )
 
@@ -470,13 +458,6 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
     ]
 )
 
-MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = OrderedDict(
-    [
-        # Model for Table Question Answering mapping
-        (TapasConfig, TapasForQuestionAnswering),
-    ]
-)
-
 MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
     [
         # Model for Token Classification mapping
@@ -1390,106 +1371,6 @@ class AutoModelForQuestionAnswering:
         )
 
 
-class AutoModelForTableQuestionAnswering:
-    r"""
-    This is a generic model class that will be instantiated as one of the model classes of the library---with a table
-    question answering head---when created with the when created with the
-    :meth:`~transformers.AutoModeForTableQuestionAnswering.from_pretrained` class method or the
-    :meth:`~transformers.AutoModelForTableQuestionAnswering.from_config` class method.
-
-    This class cannot be instantiated directly using ``__init__()`` (throws an error).
-    """
-
-    def __init__(self):
-        raise EnvironmentError(
-            "AutoModelForQuestionAnswering is designed to be instantiated "
-            "using the `AutoModelForTableQuestionAnswering.from_pretrained(pretrained_model_name_or_path)` or "
-            "`AutoModelForTableQuestionAnswering.from_config(config)` methods."
-        )
-
-    @classmethod
-    @replace_list_option_in_docstrings(MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, use_model_types=False)
-    def from_config(cls, config):
-        r"""
-        Instantiates one of the model classes of the library---with a table question answering head---from a
-        configuration.
-
-        Note:
-            Loading a model from its configuration file does **not** load the model weights. It only affects the
-            model's configuration. Use :meth:`~transformers.AutoModelForTableQuestionAnswering.from_pretrained` to load
-            the model weights.
-
-        Args:
-            config (:class:`~transformers.PretrainedConfig`):
-                The model class to instantiate is selected based on the configuration class:
-
-                List options
-
-        Examples::
-
-            >>> from transformers import AutoConfig, AutoModelForTableQuestionAnswering
-            >>> # Download configuration from huggingface.co and cache.
-            >>> config = AutoConfig.from_pretrained('google/tapas-base-finetuned-wtq')
-            >>> model = AutoModelForTableQuestionAnswering.from_config(config)
-        """
-        if type(config) in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys():
-            return MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING[type(config)](config)
-
-        raise ValueError(
-            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
-            "Model type should be one of {}.".format(
-                config.__class__,
-                cls.__name__,
-                ", ".join(c.__name__ for c in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys()),
-            )
-        )
-
-    @classmethod
-    @replace_list_option_in_docstrings(MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING)
-    @add_start_docstrings(
-        "Instantiate one of the model classes of the library---with a table question answering head---from a "
-        "pretrained model.",
-        AUTO_MODEL_PRETRAINED_DOCSTRING,
-    )
-    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
-        r"""
-        Examples::
-
-            >>> from transformers import AutoConfig, AutoModelForTableQuestionAnswering
-
-            >>> # Download model and configuration from huggingface.co and cache.
-            >>> model = AutoModelForTableQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wtq')
-
-            >>> # Update configuration during loading
-            >>> model = AutoModelForTableQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wtq', output_attentions=True)
-            >>> model.config.output_attentions
-            True
-
-            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
-            >>> config = AutoConfig.from_json_file('./tf_model/tapas_tf_checkpoint.json')
-            >>> model = AutoModelForQuestionAnswering.from_pretrained('./tf_model/tapas_tf_checkpoint.ckpt.index', from_tf=True, config=config)
-        """
-        config = kwargs.pop("config", None)
-        if not isinstance(config, PretrainedConfig):
-            config, kwargs = AutoConfig.from_pretrained(
-                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
-            )
-
-        if type(config) in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys():
-            return MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING[type(config)].from_pretrained(
-                pretrained_model_name_or_path, *model_args, config=config, **kwargs
-            )
-
-        raise ValueError(
-            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
-            "Model type should be one of {}.".format(
-                config.__class__,
-                cls.__name__,
-                ", ".join(c.__name__ for c in MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.keys()),
-            )
-        )
-
-
 class AutoModelForTokenClassification:
     r"""
     This is a generic model class that will be instantiated as one of the model classes of the library---with a token

src/transformers/models/auto/modeling_tf_auto.py

@@ -120,7 +120,7 @@ from ..mpnet.modeling_tf_mpnet import (
     TFMPNetModel,
 )
 from ..mt5.modeling_tf_mt5 import TFMT5ForConditionalGeneration, TFMT5Model
-from ..openai.modeling_tf_openai import TFOpenAIGPTForSequenceClassification, TFOpenAIGPTLMHeadModel, TFOpenAIGPTModel
+from ..openai.modeling_tf_openai import TFOpenAIGPTLMHeadModel, TFOpenAIGPTModel
 from ..pegasus.modeling_tf_pegasus import TFPegasusForConditionalGeneration
 from ..roberta.modeling_tf_roberta import (
     TFRobertaForMaskedLM,
@@ -341,7 +341,6 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
         (FunnelConfig, TFFunnelForSequenceClassification),
         (GPT2Config, TFGPT2ForSequenceClassification),
         (MPNetConfig, TFMPNetForSequenceClassification),
-        (OpenAIGPTConfig, TFOpenAIGPTForSequenceClassification),
     ]
 )
 

src/transformers/models/auto/tokenization_auto.py

@@ -47,7 +47,6 @@ from ..rag.tokenization_rag import RagTokenizer
 from ..retribert.tokenization_retribert import RetriBertTokenizer
 from ..roberta.tokenization_roberta import RobertaTokenizer
 from ..squeezebert.tokenization_squeezebert import SqueezeBertTokenizer
-from ..tapas.tokenization_tapas import TapasTokenizer
 from ..transfo_xl.tokenization_transfo_xl import TransfoXLTokenizer
 from ..xlm.tokenization_xlm import XLMTokenizer
 from .configuration_auto import (
@@ -85,7 +84,6 @@ from .configuration_auto import (
     RobertaConfig,
     SqueezeBertConfig,
     T5Config,
-    TapasConfig,
     TransfoXLConfig,
     XLMConfig,
     XLMProphetNetConfig,
@@ -225,7 +223,6 @@ TOKENIZER_MAPPING = OrderedDict(
         (XLMProphetNetConfig, (XLMProphetNetTokenizer, None)),
         (ProphetNetConfig, (ProphetNetTokenizer, None)),
         (MPNetConfig, (MPNetTokenizer, MPNetTokenizerFast)),
-        (TapasConfig, (TapasTokenizer, None)),
     ]
 )
 

src/transformers/models/bart/__init__.py

@@ -36,4 +36,4 @@ if is_torch_available():
     )
 
 if is_tf_available():
-    from .modeling_tf_bart import TFBartForConditionalGeneration, TFBartModel, TFBartPretrainedModel
+    from .modeling_tf_bart import TFBartForConditionalGeneration, TFBartModel

src/transformers/models/bart/modeling_bart.py

@@ -67,16 +67,9 @@ def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int):
     Shift input ids one token to the right, and wrap the last non pad token (usually <eos>).
     """
     prev_output_tokens = input_ids.clone()
-
-    assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
-    # replace possible -100 values in labels by `pad_token_id`
-    prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id)
-
-    index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
-    decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
-    prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
-    prev_output_tokens[:, 0] = decoder_start_tokens
-
+    index_of_eos = (input_ids.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
+    prev_output_tokens[:, 0] = input_ids.gather(1, index_of_eos).squeeze()
+    prev_output_tokens[:, 1:] = input_ids[:, :-1]
     return prev_output_tokens
 
 
@@ -222,10 +215,10 @@ class BartAttention(nn.Module):
 
     def forward(
         self,
-        hidden_states: torch.Tensor,
+        hidden_states,
         key_value_states: Optional[torch.Tensor] = None,
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
+        attn_mask: Optional[torch.Tensor] = None,
         output_attentions: bool = False,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         """Input shape: Batch x Time x Channel"""
@@ -281,14 +274,14 @@ class BartAttention(nn.Module):
             src_len,
         ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
 
-        if attention_mask is not None:
-            assert attention_mask.size() == (
+        if attn_mask is not None:
+            assert attn_mask.size() == (
                 bsz,
                 1,
                 tgt_len,
                 src_len,
-            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
+            ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attn_mask.size()}"
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_mask
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = F.softmax(attn_weights, dim=-1)
@@ -342,19 +335,23 @@ class BartEncoderLayer(nn.Module):
         self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
         self.final_layer_norm = BartLayerNorm(self.embed_dim)
 
-    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, output_attentions: bool = False):
+    def forward(
+        self, hidden_states: torch.Tensor, encoder_padding_mask: torch.Tensor, output_attentions: bool = False
+    ):
         """
         Args:
             hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
-            attention_mask (:obj:`torch.FloatTensor`): attention mask of size
+            encoder_padding_mask (:obj:`torch.FloatTensor`): attention mask of size
                 `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            output_attentions (:obj:`bool`): Whether the base model outputs attentions. This requires the attentions tensor to be reshaped in this function.
+
+        Returns:
+            encoded output of shape `(seq_len, batch, embed_dim)`
         """
         residual = hidden_states
         if self.normalize_before:
             hidden_states = self.self_attn_layer_norm(hidden_states)
         hidden_states, attn_weights, _ = self.self_attn(
-            hidden_states=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
+            hidden_states=hidden_states, attn_mask=encoder_padding_mask, output_attentions=output_attentions
         )
         hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
         hidden_states = residual + hidden_states
@@ -408,35 +405,24 @@ class BartDecoderLayer(nn.Module):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
+        encoder_hidden_states: torch.Tensor,
+        encoder_attn_mask: Optional[torch.Tensor] = None,
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        attn_mask: Optional[torch.Tensor] = None,
         output_attentions: Optional[torch.Tensor] = False,
     ):
-        """
-        Args:
-            hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
-            attention_mask (:obj:`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            encoder_hidden_states (:obj:`torch.FloatTensor`): cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
-            encoder_attention_mask (:obj:`torch.FloatTensor`): encoder attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            past_key_value (:obj:`Tuple(torch.FloatTensor)`): cached past key and value projection states
-            output_attentions (:obj:`bool`): Whether the base model outputs attentions. This requires the attentions tensor to be reshaped in this function.
-        """
         residual = hidden_states
         if self.normalize_before:
             hidden_states = self.self_attn_layer_norm(hidden_states)
-
         # Self Attention
+
         # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
         self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
         # add present self-attn cache to positions 1,2 of present_key_value tuple
         hidden_states, self_attn_weights, present_key_value = self.self_attn(
             hidden_states=hidden_states,
             past_key_value=self_attn_past_key_value,
-            attention_mask=attention_mask,
+            attn_mask=attn_mask,
             output_attentions=output_attentions,
         )
         hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
@@ -457,7 +443,7 @@ class BartDecoderLayer(nn.Module):
             hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
                 hidden_states=hidden_states,
                 key_value_states=encoder_hidden_states,
-                attention_mask=encoder_attention_mask,
+                attn_mask=encoder_attn_mask,
                 past_key_value=cross_attn_past_key_value,
                 output_attentions=output_attentions,
             )
@@ -919,9 +905,9 @@ class BartDecoder(BartPretrainedModel):
 
         # create causal mask
         # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
+        attn_mask = None
         if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
+            attn_mask = _make_causal_mask(
                 input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
             ).to(self.device)
 
@@ -942,9 +928,9 @@ class BartDecoder(BartPretrainedModel):
             # never mask leading token, even if it is pad
             attention_mask[:, 0] = attention_mask[:, 1]
 
-        if attention_mask is not None and combined_attention_mask is not None:
+        if attention_mask is not None and attn_mask is not None:
             # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            combined_attention_mask = combined_attention_mask + _expand_mask(
+            attn_mask = attn_mask + _expand_mask(
                 attention_mask, inputs_embeds.dtype, past_key_values_length=past_key_values_length
             )
 
@@ -982,9 +968,9 @@ class BartDecoder(BartPretrainedModel):
 
             hidden_states, layer_self_attn, present_key_value, layer_cross_attn = decoder_layer(
                 hidden_states,
-                attention_mask=combined_attention_mask,
-                encoder_hidden_states=encoder_hidden_states,
-                encoder_attention_mask=encoder_attention_mask,
+                encoder_hidden_states,
+                encoder_attn_mask=encoder_attention_mask,
+                attn_mask=attn_mask,
                 past_key_value=past_key_value,
                 output_attentions=output_attentions,
             )

src/transformers/models/distilbert/modeling_distilbert.py

@@ -627,7 +627,7 @@ class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
         pooled_output = self.pre_classifier(pooled_output)  # (bs, dim)
         pooled_output = nn.ReLU()(pooled_output)  # (bs, dim)
         pooled_output = self.dropout(pooled_output)  # (bs, dim)
-        logits = self.classifier(pooled_output)  # (bs, num_labels)
+        logits = self.classifier(pooled_output)  # (bs, dim)
 
         loss = None
         if labels is not None:

src/transformers/models/openai/__init__.py

@@ -39,7 +39,6 @@ if is_tf_available():
     from .modeling_tf_openai import (
         TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFOpenAIGPTDoubleHeadsModel,
-        TFOpenAIGPTForSequenceClassification,
         TFOpenAIGPTLMHeadModel,
         TFOpenAIGPTMainLayer,
         TFOpenAIGPTModel,

src/transformers/models/openai/modeling_tf_openai.py

@@ -28,12 +28,11 @@ from ...file_utils import (
     add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
-from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput, TFSequenceClassifierOutput
+from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput
 from ...modeling_tf_utils import (
     TFCausalLanguageModelingLoss,
     TFConv1D,
     TFPreTrainedModel,
-    TFSequenceClassificationLoss,
     TFSequenceSummary,
     TFSharedEmbeddings,
     get_initializer,
@@ -763,154 +762,3 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
-
-
-@add_start_docstrings(
-    """
-    The OpenAI GPT Model transformer with a sequence classification head on top (linear layer).
-
-    :class:`~transformers.TFOpenAIGPTForSequenceClassification` uses the last token in order to do the classification,
-    as other causal models (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    :obj:`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each
-    row. If no :obj:`pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot
-    guess the padding tokens when :obj:`inputs_embeds` are passed instead of :obj:`input_ids`, it does the same (take
-    the last value in each row of the batch).
-    """,
-    OPENAI_GPT_START_DOCSTRING,
-)
-class TFOpenAIGPTForSequenceClassification(TFOpenAIGPTPreTrainedModel, TFSequenceClassificationLoss):
-    def __init__(self, config, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
-        self.num_labels = config.num_labels
-        self.score = tf.keras.layers.Dense(
-            config.num_labels,
-            kernel_initializer=get_initializer(config.initializer_range),
-            name="score",
-            use_bias=False,
-        )
-        self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
-
-    def get_output_embeddings(self):
-        return self.transformer.tokens_embed
-
-    @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="openai-gpt",
-        output_type=TFSequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def call(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        labels=None,
-        training=False,
-        **kwargs,
-    ):
-        r"""
-        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
-            config.vocab_size - 1]``.
-        """
-        inputs = input_processing(
-            func=self.call,
-            config=self.config,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            labels=labels,
-            training=training,
-            kwargs_call=kwargs,
-        )
-
-        transformer_outputs = self.transformer(
-            input_ids=inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=inputs["return_dict"],
-            training=inputs["training"],
-        )
-
-        hidden_states = transformer_outputs[0]
-        logits = self.score(hidden_states)
-        logits_shape = shape_list(logits)
-        in_logits = None
-        if self.config.pad_token_id is None:
-            sequence_lengths = -1
-        else:
-            if inputs["input_ids"] is not None:
-                sequence_lengths = (
-                    tf.reduce_sum(
-                        tf.cast(tf.math.not_equal(inputs["input_ids"], self.config.pad_token_id), tf.int32),
-                        -1,
-                        keepdims=False,
-                    )
-                    - 1
-                )
-
-                def get_seq_element(sequence_position, input_batch):
-                    return tf.strided_slice(
-                        input_batch, [sequence_position, 0], [sequence_position + 1, input_batch.shape[-1]], [1, 1]
-                    )
-
-                result = tf.map_fn(
-                    fn=lambda t: get_seq_element(t[0], t[1]), elems=[sequence_lengths, logits], dtype="float"
-                )
-                in_logits = tf.reshape(result, [logits_shape[0], logits_shape[-1]])
-            else:
-                sequence_lengths = -1
-                logger.warning(
-                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
-                    f"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
-                )
-        loss = None
-
-        if inputs["labels"] is not None:
-            if input_ids is not None:
-                batch_size, sequence_length = shape_list(inputs["input_ids"])[:2]
-            else:
-                batch_size, sequence_length = shape_list(inputs["inputs_embeds"])[:2]
-            assert (
-                self.config.pad_token_id is not None or batch_size == 1
-            ), "Cannot handle batch sizes > 1 if no padding token is defined."
-
-            if not tf.is_tensor(sequence_lengths):
-                in_logits = logits[0:batch_size, sequence_lengths]
-
-            loss = self.compute_loss(
-                tf.reshape(inputs["labels"], [-1, 1]), tf.reshape(in_logits, [-1, self.num_labels])
-            )
-
-        pooled_logits = in_logits if in_logits is not None else logits
-
-        if not inputs["return_dict"]:
-            output = (pooled_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TFSequenceClassifierOutput(
-            loss=loss,
-            logits=pooled_logits,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )

src/transformers/models/t5/modeling_tf_t5.py

@@ -1305,6 +1305,14 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
             # get decoder inputs from shifting lm labels to the right
             inputs["decoder_input_ids"] = self._shift_right(inputs["labels"])
 
+        # If decoding with past key value states, only the last tokens
+        # should be given as an input
+        if inputs["past_key_values"] is not None:
+            if inputs["decoder_input_ids"] is not None:
+                inputs["decoder_input_ids"] = inputs["decoder_input_ids"][:, -1:]
+            if inputs["decoder_inputs_embeds"] is not None:
+                inputs["decoder_inputs_embeds"] = inputs["decoder_inputs_embeds"][:, -1:]
+
         # Decode
         decoder_outputs = self.decoder(
             inputs["decoder_input_ids"],

src/transformers/models/tapas/__init__.py

@@ -1,31 +0,0 @@
-# flake8: noqa
-# There's no way to ignore "F401 '...' imported but unused" warnings in this
-# module, but to preserve other warnings. So, don't check this module at all.
-
-# Copyright 2020 The HuggingFace Team. 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.
-
-from ...file_utils import is_torch_available
-from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
-from .tokenization_tapas import TapasTokenizer
-
-
-if is_torch_available():
-    from .modeling_tapas import (
-        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
-        TapasForMaskedLM,
-        TapasForQuestionAnswering,
-        TapasForSequenceClassification,
-        TapasModel,
-    )

src/transformers/models/tapas/configuration_tapas.py

@@ -1,236 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Google Research and The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-TAPAS configuration. Based on the BERT configuration with added parameters.
-
-Hyperparameters are taken from run_task_main.py and hparam_utils.py of the original implementation. URLS:
-
-- https://github.com/google-research/tapas/blob/master/tapas/run_task_main.py
-- https://github.com/google-research/tapas/blob/master/tapas/utils/hparam_utils.py
-
-"""
-
-
-from ...configuration_utils import PretrainedConfig
-
-
-TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "google/tapas-base-finetuned-sqa": "https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json",
-    "google/tapas-base-finetuned-wtq": "https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json",
-    "google/tapas-base-finetuned-wikisql-supervised": "https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json",
-    "google/tapas-base-finetuned-tabfact": "https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json",
-}
-
-
-class TapasConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.TapasModel`. It is used to
-    instantiate a TAPAS model according to the specified arguments, defining the model architecture. Instantiating a
-    configuration with the defaults will yield a similar configuration to that of the TAPAS `tapas-base-finetuned-sqa`
-    architecture. Configuration objects inherit from :class:`~transformers.PreTrainedConfig` and can be used to control
-    the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
-
-    Hyperparameters additional to BERT are taken from run_task_main.py and hparam_utils.py of the original
-    implementation. Original implementation available at https://github.com/google-research/tapas/tree/master.
-
-    Args:
-        vocab_size (:obj:`int`, `optional`, defaults to 30522):
-            Vocabulary size of the TAPAS model. Defines the number of different tokens that can be represented by the
-            :obj:`inputs_ids` passed when calling :class:`~transformers.TapasModel`.
-        hidden_size (:obj:`int`, `optional`, defaults to 768):
-            Dimensionality of the encoder layers and the pooler layer.
-        num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (:obj:`int`, `optional`, defaults to 12):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (:obj:`int`, `optional`, defaults to 3072):
-            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
-        hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string,
-            :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
-        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
-            The dropout ratio for the attention probabilities.
-        max_position_embeddings (:obj:`int`, `optional`, defaults to 1024):
-            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_sizes (:obj:`List[int]`, `optional`, defaults to :obj:`[3, 256, 256, 2, 256, 256, 10]`):
-            The vocabulary sizes of the :obj:`token_type_ids` passed when calling :class:`~transformers.TapasModel`.
-        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
-            The epsilon used by the layer normalization layers.
-        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to use gradient checkpointing to save memory at the expense of a slower backward pass.
-        positive_label_weight (:obj:`float`, `optional`, defaults to 10.0):
-            Weight for positive labels.
-        num_aggregation_labels (:obj:`int`, `optional`, defaults to 0):
-            The number of aggregation operators to predict.
-        aggregation_loss_weight (:obj:`float`, `optional`, defaults to 1.0):
-            Importance weight for the aggregation loss.
-        use_answer_as_supervision (:obj:`bool`, `optional`):
-            Whether to use the answer as the only supervision for aggregation examples.
-        answer_loss_importance (:obj:`float`, `optional`, defaults to 1.0):
-            Importance weight for the regression loss.
-        use_normalized_answer_loss (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to normalize the answer loss by the maximum of the predicted and expected value.
-        huber_loss_delta (:obj:`float`, `optional`):
-            Delta parameter used to calculate the regression loss.
-        temperature (:obj:`float`, `optional`, defaults to 1.0):
-            Value used to control (OR change) the skewness of cell logits probabilities.
-        aggregation_temperature (:obj:`float`, `optional`, defaults to 1.0):
-            Scales aggregation logits to control the skewness of probabilities.
-        use_gumbel_for_cells (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to apply Gumbel-Softmax to cell selection.
-        use_gumbel_for_aggregation (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to apply Gumbel-Softmax to aggregation selection.
-        average_approximation_function (:obj:`string`, `optional`, defaults to :obj:`"ratio"`):
-            Method to calculate the expected average of cells in the weak supervision case. One of :obj:`"ratio"`,
-            :obj:`"first_order"` or :obj:`"second_order"`.
-        cell_selection_preference (:obj:`float`, `optional`):
-            Preference for cell selection in ambiguous cases. Only applicable in case of weak supervision for
-            aggregation (WTQ, WikiSQL). If the total mass of the aggregation probabilities (excluding the "NONE"
-            operator) is higher than this hyperparameter, then aggregation is predicted for an example.
-        answer_loss_cutoff (:obj:`float`, `optional`):
-            Ignore examples with answer loss larger than cutoff.
-        max_num_rows (:obj:`int`, `optional`, defaults to 64):
-            Maximum number of rows.
-        max_num_columns (:obj:`int`, `optional`, defaults to 32):
-            Maximum number of columns.
-        average_logits_per_cell (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to average logits per cell.
-        select_one_column (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            Whether to constrain the model to only select cells from a single column.
-        allow_empty_column_selection (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to allow not to select any column.
-        init_cell_selection_weights_to_zero (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to initialize cell selection weights to 0 so that the initial probabilities are 50%.
-        reset_position_index_per_cell (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            Whether to restart position indexes at every cell (i.e. use relative position embeddings).
-        disable_per_token_loss (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to disable any (strong or weak) supervision on cells.
-        aggregation_labels (:obj:`Dict[int, label]`, `optional`):
-            The aggregation labels used to aggregate the results. For example, the WTQ models have the following
-            aggregation labels: :obj:`{0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"}`
-        no_aggregation_label_index (:obj:`int`, `optional`):
-            If the aggregation labels are defined and one of these labels represents "No aggregation", this should be
-            set to its index. For example, the WTQ models have the "NONE" aggregation label at index 0, so that value
-            should be set to 0 for these models.
-
-
-    Example::
-
-        >>> from transformers import TapasModel, TapasConfig
-        >>> # Initializing a default (SQA) Tapas configuration
-        >>> configuration = TapasConfig()
-        >>> # Initializing a model from the configuration
-        >>> model = TapasModel(configuration)
-        >>> # Accessing the model configuration
-        >>> configuration = model.config
-    """
-
-    model_type = "tapas"
-
-    def __init__(
-        self,
-        vocab_size=30522,
-        hidden_size=768,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        intermediate_size=3072,
-        hidden_act="gelu",
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        max_position_embeddings=1024,
-        type_vocab_sizes=[3, 256, 256, 2, 256, 256, 10],
-        initializer_range=0.02,
-        layer_norm_eps=1e-12,
-        pad_token_id=0,
-        gradient_checkpointing=False,
-        positive_label_weight=10.0,
-        num_aggregation_labels=0,
-        aggregation_loss_weight=1.0,
-        use_answer_as_supervision=None,
-        answer_loss_importance=1.0,
-        use_normalized_answer_loss=False,
-        huber_loss_delta=None,
-        temperature=1.0,
-        aggregation_temperature=1.0,
-        use_gumbel_for_cells=False,
-        use_gumbel_for_aggregation=False,
-        average_approximation_function="ratio",
-        cell_selection_preference=None,
-        answer_loss_cutoff=None,
-        max_num_rows=64,
-        max_num_columns=32,
-        average_logits_per_cell=False,
-        select_one_column=True,
-        allow_empty_column_selection=False,
-        init_cell_selection_weights_to_zero=False,
-        reset_position_index_per_cell=True,
-        disable_per_token_loss=False,
-        aggregation_labels=None,
-        no_aggregation_label_index=None,
-        **kwargs
-    ):
-
-        super().__init__(pad_token_id=pad_token_id, **kwargs)
-
-        # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.hidden_act = hidden_act
-        self.intermediate_size = intermediate_size
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.max_position_embeddings = max_position_embeddings
-        self.type_vocab_sizes = type_vocab_sizes
-        self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.gradient_checkpointing = gradient_checkpointing
-
-        # Fine-tuning task hyperparameters
-        self.positive_label_weight = positive_label_weight
-        self.num_aggregation_labels = num_aggregation_labels
-        self.aggregation_loss_weight = aggregation_loss_weight
-        self.use_answer_as_supervision = use_answer_as_supervision
-        self.answer_loss_importance = answer_loss_importance
-        self.use_normalized_answer_loss = use_normalized_answer_loss
-        self.huber_loss_delta = huber_loss_delta
-        self.temperature = temperature
-        self.aggregation_temperature = aggregation_temperature
-        self.use_gumbel_for_cells = use_gumbel_for_cells
-        self.use_gumbel_for_aggregation = use_gumbel_for_aggregation
-        self.average_approximation_function = average_approximation_function
-        self.cell_selection_preference = cell_selection_preference
-        self.answer_loss_cutoff = answer_loss_cutoff
-        self.max_num_rows = max_num_rows
-        self.max_num_columns = max_num_columns
-        self.average_logits_per_cell = average_logits_per_cell
-        self.select_one_column = select_one_column
-        self.allow_empty_column_selection = allow_empty_column_selection
-        self.init_cell_selection_weights_to_zero = init_cell_selection_weights_to_zero
-        self.reset_position_index_per_cell = reset_position_index_per_cell
-        self.disable_per_token_loss = disable_per_token_loss
-
-        # Aggregation hyperparameters
-        self.aggregation_labels = aggregation_labels
-        self.no_aggregation_label_index = no_aggregation_label_index
-
-        if isinstance(self.aggregation_labels, dict):
-            self.aggregation_labels = {int(k): v for k, v in aggregation_labels.items()}

src/transformers/models/tapas/convert_tapas_original_tf_checkpoint_to_pytorch.py

@@ -1,137 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convert TAPAS checkpoint."""
-
-
-import argparse
-
-from transformers.models.tapas.modeling_tapas import (
-    TapasConfig,
-    TapasForMaskedLM,
-    TapasForQuestionAnswering,
-    TapasForSequenceClassification,
-    TapasModel,
-    load_tf_weights_in_tapas,
-)
-from transformers.models.tapas.tokenization_tapas import TapasTokenizer
-from transformers.utils import logging
-
-
-logging.set_verbosity_info()
-
-
-def convert_tf_checkpoint_to_pytorch(
-    task, reset_position_index_per_cell, tf_checkpoint_path, tapas_config_file, pytorch_dump_path
-):
-    # Initialise PyTorch model.
-    # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
-    # TapasConfig to False.
-
-    # initialize configuration from json file
-    config = TapasConfig.from_json_file(tapas_config_file)
-    # set absolute/relative position embeddings parameter
-    config.reset_position_index_per_cell = reset_position_index_per_cell
-
-    # set remaining parameters of TapasConfig as well as the model based on the task
-    if task == "SQA":
-        model = TapasForQuestionAnswering(config=config)
-    elif task == "WTQ":
-        # run_task_main.py hparams
-        config.num_aggregation_labels = 4
-        config.use_answer_as_supervision = True
-        # hparam_utils.py hparams
-        config.answer_loss_cutoff = 0.664694
-        config.cell_selection_preference = 0.207951
-        config.huber_loss_delta = 0.121194
-        config.init_cell_selection_weights_to_zero = True
-        config.select_one_column = True
-        config.allow_empty_column_selection = False
-        config.temperature = 0.0352513
-
-        model = TapasForQuestionAnswering(config=config)
-    elif task == "WIKISQL_SUPERVISED":
-        # run_task_main.py hparams
-        config.num_aggregation_labels = 4
-        config.use_answer_as_supervision = False
-        # hparam_utils.py hparams
-        config.answer_loss_cutoff = 36.4519
-        config.cell_selection_preference = 0.903421
-        config.huber_loss_delta = 222.088
-        config.init_cell_selection_weights_to_zero = True
-        config.select_one_column = True
-        config.allow_empty_column_selection = True
-        config.temperature = 0.763141
-
-        model = TapasForQuestionAnswering(config=config)
-    elif task == "TABFACT":
-        model = TapasForSequenceClassification(config=config)
-    elif task == "MLM":
-        model = TapasForMaskedLM(config=config)
-    elif task == "INTERMEDIATE_PRETRAINING":
-        model = TapasModel(config=config)
-
-    print("Building PyTorch model from configuration: {}".format(str(config)))
-
-    # Load weights from tf checkpoint
-    load_tf_weights_in_tapas(model, config, tf_checkpoint_path)
-
-    # Save pytorch-model (weights and configuration)
-    print("Save PyTorch model to {}".format(pytorch_dump_path))
-    model.save_pretrained(pytorch_dump_path[:-17])
-
-    # Save tokenizer files
-    dir_name = r"C:\Users\niels.rogge\Documents\Python projecten\tensorflow\Tensorflow models\SQA\Base\tapas_sqa_inter_masklm_base_reset"
-    tokenizer = TapasTokenizer(vocab_file=dir_name + r"\vocab.txt", model_max_length=512)
-
-    print("Save tokenizer files to {}".format(pytorch_dump_path))
-    tokenizer.save_pretrained(pytorch_dump_path[:-17])
-
-    print("Used relative position embeddings:", model.config.reset_position_index_per_cell)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
-    )
-    parser.add_argument(
-        "--reset_position_index_per_cell",
-        default=False,
-        action="store_true",
-        help="Whether to use relative position embeddings or not. Defaults to True.",
-    )
-    parser.add_argument(
-        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
-    )
-    parser.add_argument(
-        "--tapas_config_file",
-        default=None,
-        type=str,
-        required=True,
-        help="The config json file corresponding to the pre-trained TAPAS 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.task,
-        args.reset_position_index_per_cell,
-        args.tf_checkpoint_path,
-        args.tapas_config_file,
-        args.pytorch_dump_path,
-    )

src/transformers/models/transfo_xl/modeling_tf_transfo_xl.py

@@ -623,9 +623,9 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
         core_out = tf.transpose(core_out, perm=(1, 0, 2))
 
         if inputs["output_hidden_states"]:
-            # Transpose to library standard shape [bsz, len, hidden_dim] and add last layer
+            # Add last layer and transpose to library standard shape [bsz, len, hidden_dim]
+            hids.append(core_out)
             hids = tuple(tf.transpose(t, perm=(1, 0, 2)) for t in hids)
-            hids = hids + (core_out,)
         else:
             hids = None
         if inputs["output_attentions"]:

src/transformers/pipelines.py

@@ -12,7 +12,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import collections
+
+
 import csv
 import json
 import os
@@ -31,7 +32,7 @@ import numpy as np
 
 from .configuration_utils import PretrainedConfig
 from .data import SquadExample, SquadFeatures, squad_convert_examples_to_features
-from .file_utils import add_end_docstrings, is_tf_available, is_torch_available, requires_pandas
+from .file_utils import add_end_docstrings, is_tf_available, is_torch_available
 from .modelcard import ModelCard
 from .models.auto.configuration_auto import AutoConfig
 from .models.auto.tokenization_auto import AutoTokenizer
@@ -67,7 +68,6 @@ if is_torch_available():
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
-        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         AutoModel,
         AutoModelForCausalLM,
@@ -75,7 +75,6 @@ if is_torch_available():
         AutoModelForQuestionAnswering,
         AutoModelForSeq2SeqLM,
         AutoModelForSequenceClassification,
-        AutoModelForTableQuestionAnswering,
         AutoModelForTokenClassification,
     )
 
@@ -745,8 +744,8 @@ class TextGenerationPipeline(Pipeline):
     task identifier: :obj:`"text-generation"`.
 
     The models that this pipeline can use are models that have been trained with an autoregressive language modeling
-    objective, which includes the uni-directional models in the library (e.g. gpt2). See the list of available models
-    on `huggingface.co/models <https://huggingface.co/models?filter=causal-lm>`__.
+    objective, which includes the uni-directional models in the library (e.g. gpt2). See the list of available
+    community models on `huggingface.co/models <https://huggingface.co/models?filter=causal-lm>`__.
     """
 
     # Prefix text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
@@ -2059,274 +2058,6 @@ class QuestionAnsweringPipeline(Pipeline):
         }
 
 
-class TableQuestionAnsweringArgumentHandler(ArgumentHandler):
-    """
-    Handles arguments for the TableQuestionAnsweringPipeline
-    """
-
-    def __call__(self, table=None, query=None, sequential=False, padding=True, truncation=True):
-        # Returns tqa_pipeline_inputs of shape:
-        # [
-        #   {"table": pd.DataFrame, "query": List[str]},
-        #   ...,
-        #   {"table": pd.DataFrame, "query" : List[str]}
-        # ]
-        requires_pandas(self)
-        import pandas as pd
-
-        if table is None:
-            raise ValueError("Keyword argument `table` cannot be None.")
-        elif query is None:
-            if isinstance(table, dict) and table.get("query") is not None and table.get("table") is not None:
-                tqa_pipeline_inputs = [table]
-            elif isinstance(table, list) and len(table) > 0:
-                if not all(isinstance(d, dict) for d in table):
-                    raise ValueError(
-                        f"Keyword argument `table` should be a list of dict, but is {(type(d) for d in table)}"
-                    )
-
-                if table[0].get("query") is not None and table[0].get("table") is not None:
-                    tqa_pipeline_inputs = table
-                else:
-                    raise ValueError(
-                        f"If keyword argument `table` is a list of dictionaries, each dictionary should have a `table` "
-                        f"and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."
-                    )
-            else:
-                raise ValueError(
-                    f"Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
-                    f"is {type(table)})"
-                )
-        else:
-            tqa_pipeline_inputs = [{"table": table, "query": query}]
-
-        for tqa_pipeline_input in tqa_pipeline_inputs:
-            if not isinstance(tqa_pipeline_input["table"], pd.DataFrame):
-                if tqa_pipeline_input["table"] is None:
-                    raise ValueError("Table cannot be None.")
-
-                tqa_pipeline_input["table"] = pd.DataFrame(tqa_pipeline_input["table"])
-
-        return tqa_pipeline_inputs, sequential, padding, truncation
-
-
-@add_end_docstrings(PIPELINE_INIT_ARGS)
-class TableQuestionAnsweringPipeline(Pipeline):
-    """
-    Table Question Answering pipeline using a :obj:`ModelForTableQuestionAnswering`. This pipeline is only available in
-    PyTorch.
-
-    This tabular question answering pipeline can currently be loaded from :func:`~transformers.pipeline` using the
-    following task identifier: :obj:`"table-question-answering"`.
-
-    The models that this pipeline can use are models that have been fine-tuned on a tabular question answering task.
-    See the up-to-date list of available models on `huggingface.co/models
-    <https://huggingface.co/models?filter=table-question-answering>`__.
-    """
-
-    default_input_names = "table,query"
-
-    def __init__(self, args_parser=TableQuestionAnsweringArgumentHandler(), *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._args_parser = args_parser
-
-        if self.framework == "tf":
-            raise ValueError("The TableQuestionAnsweringPipeline is only available in PyTorch.")
-
-        self.check_model_type(MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING)
-
-        self.aggregate = bool(getattr(self.model.config, "aggregation_labels")) and bool(
-            getattr(self.model.config, "num_aggregation_labels")
-        )
-
-    def batch_inference(self, **inputs):
-        with torch.no_grad():
-            return self.model(**inputs)
-
-    def sequential_inference(self, **inputs):
-        """
-        Inference used for models that need to process sequences in a sequential fashion, like the SQA models which
-        handle conversational query related to a table.
-        """
-        with torch.no_grad():
-            all_logits = []
-            all_aggregations = []
-            prev_answers = None
-            batch_size = inputs["input_ids"].shape[0]
-
-            input_ids = inputs["input_ids"].to(self.device)
-            attention_mask = inputs["attention_mask"].to(self.device)
-            token_type_ids = inputs["token_type_ids"].to(self.device)
-            token_type_ids_example = None
-
-            for index in range(batch_size):
-                # If sequences have already been processed, the token type IDs will be created according to the previous
-                # answer.
-                if prev_answers is not None:
-                    prev_labels_example = token_type_ids_example[:, 3]  # shape (seq_len,)
-                    model_labels = np.zeros_like(prev_labels_example.cpu().numpy())  # shape (seq_len,)
-
-                    token_type_ids_example = token_type_ids[index]  # shape (seq_len, 7)
-                    for i in range(model_labels.shape[0]):
-                        segment_id = token_type_ids_example[:, 0].tolist()[i]
-                        col_id = token_type_ids_example[:, 1].tolist()[i] - 1
-                        row_id = token_type_ids_example[:, 2].tolist()[i] - 1
-
-                        if row_id >= 0 and col_id >= 0 and segment_id == 1:
-                            model_labels[i] = int(prev_answers[(col_id, row_id)])
-
-                    token_type_ids_example[:, 3] = torch.from_numpy(model_labels).type(torch.long).to(self.device)
-
-                input_ids_example = input_ids[index]
-                attention_mask_example = attention_mask[index]  # shape (seq_len,)
-                token_type_ids_example = token_type_ids[index]  # shape (seq_len, 7)
-                outputs = self.model(
-                    input_ids=input_ids_example.unsqueeze(0),
-                    attention_mask=attention_mask_example.unsqueeze(0),
-                    token_type_ids=token_type_ids_example.unsqueeze(0),
-                )
-                logits = outputs.logits
-
-                if self.aggregate:
-                    all_aggregations.append(outputs.logits_aggregation)
-
-                all_logits.append(logits)
-
-                dist_per_token = torch.distributions.Bernoulli(logits=logits)
-                probabilities = dist_per_token.probs * attention_mask_example.type(torch.float32).to(
-                    dist_per_token.probs.device
-                )
-
-                coords_to_probs = collections.defaultdict(list)
-                for i, p in enumerate(probabilities.squeeze().tolist()):
-                    segment_id = token_type_ids_example[:, 0].tolist()[i]
-                    col = token_type_ids_example[:, 1].tolist()[i] - 1
-                    row = token_type_ids_example[:, 2].tolist()[i] - 1
-                    if col >= 0 and row >= 0 and segment_id == 1:
-                        coords_to_probs[(col, row)].append(p)
-
-                prev_answers = {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}
-
-            logits_batch = torch.cat(tuple(all_logits), 0)
-
-            return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(all_aggregations), 0))
-
-    def __call__(self, *args, **kwargs):
-        r"""
-        Answers queries according to a table. The pipeline accepts several types of inputs which are detailed below:
-
-        - ``pipeline(table, query)``
-        - ``pipeline(table, [query])``
-        - ``pipeline(table=table, query=query)``
-        - ``pipeline(table=table, query=[query])``
-        - ``pipeline({"table": table, "query": query})``
-        - ``pipeline({"table": table, "query": [query]})``
-        - ``pipeline([{"table": table, "query": query}, {"table": table, "query": query}])``
-
-        The :obj:`table` argument should be a dict or a DataFrame built from that dict, containing the whole table:
-
-        Example::
-
-            data = {
-                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
-                "age": ["56", "45", "59"],
-                "number of movies": ["87", "53", "69"],
-                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
-            }
-
-        This dictionary can be passed in as such, or can be converted to a pandas DataFrame:
-
-        Example::
-
-            import pandas as pd
-            table = pd.DataFrame.from_dict(data)
-
-
-        Args:
-            table (:obj:`pd.DataFrame` or :obj:`Dict`):
-                Pandas DataFrame or dictionary that will be converted to a DataFrame containing all the table values.
-                See above for an example of dictionary.
-            query (:obj:`str` or :obj:`List[str]`):
-                Query or list of queries that will be sent to the model alongside the table.
-            sequential (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
-                inference to be done sequentially to extract relations within sequences, given their conversational
-                nature.
-            padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`False`):
-                Activates and controls padding. Accepts the following values:
-
-                * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
-                  single sequence if provided).
-                * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
-                  maximum acceptable input length for the model if that argument is not provided.
-                * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
-                  different lengths).
-
-            truncation (:obj:`bool`, :obj:`str` or :class:`~transformers.TapasTruncationStrategy`, `optional`, defaults to :obj:`False`):
-                Activates and controls truncation. Accepts the following values:
-
-                * :obj:`True` or :obj:`'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument
-                  :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not
-                  provided. This will truncate row by row, removing rows from the table.
-                * :obj:`False` or :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with
-                  sequence lengths greater than the model maximum admissible input size).
-
-
-        Return:
-            A dictionary or a list of dictionaries containing results: Each result is a dictionary with the following
-            keys:
-
-            - **answer** (:obj:`str`) -- The answer of the query given the table. If there is an aggregator, the answer
-              will be preceded by :obj:`AGGREGATOR >`.
-            - **coordinates** (:obj:`List[Tuple[int, int]]`) -- Coordinates of the cells of the answers.
-            - **cells** (:obj:`List[str]`) -- List of strings made up of the answer cell values.
-            - **aggregator** (:obj:`str`) -- If the model has an aggregator, this returns the aggregator.
-        """
-        pipeline_inputs, sequential, padding, truncation = self._args_parser(*args, **kwargs)
-        batched_answers = []
-        for pipeline_input in pipeline_inputs:
-            table, query = pipeline_input["table"], pipeline_input["query"]
-            inputs = self.tokenizer(
-                table, query, return_tensors=self.framework, truncation="drop_rows_to_fit", padding=padding
-            )
-
-            outputs = self.sequential_inference(**inputs) if sequential else self.batch_inference(**inputs)
-
-            if self.aggregate:
-                logits, logits_agg = outputs[:2]
-                predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits.detach(), logits_agg)
-                answer_coordinates_batch, agg_predictions = predictions
-                aggregators = {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(agg_predictions)}
-
-                no_agg_label_index = self.model.config.no_aggregation_label_index
-                aggregators_prefix = {
-                    i: aggregators[i] + " > " for i, pred in enumerate(agg_predictions) if pred != no_agg_label_index
-                }
-            else:
-                logits = outputs[0]
-                predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits.detach())
-                answer_coordinates_batch = predictions[0]
-                aggregators = {}
-                aggregators_prefix = {}
-
-            answers = []
-            for index, coordinates in enumerate(answer_coordinates_batch):
-                cells = [table.iat[coordinate] for coordinate in coordinates]
-                aggregator = aggregators.get(index, "")
-                aggregator_prefix = aggregators_prefix.get(index, "")
-                answer = {
-                    "answer": aggregator_prefix + ", ".join(cells),
-                    "coordinates": coordinates,
-                    "cells": [table.iat[coordinate] for coordinate in coordinates],
-                }
-                if aggregator:
-                    answer["aggregator"] = aggregator
-
-                answers.append(answer)
-            batched_answers.append(answers if len(answers) > 1 else answers[0])
-        return batched_answers if len(batched_answers) > 1 else batched_answers[0]
-
-
 @add_end_docstrings(PIPELINE_INIT_ARGS)
 class SummarizationPipeline(Pipeline):
     """
@@ -3021,18 +2752,6 @@ SUPPORTED_TASKS = {
             "model": {"pt": "distilbert-base-cased-distilled-squad", "tf": "distilbert-base-cased-distilled-squad"},
         },
     },
-    "table-question-answering": {
-        "impl": TableQuestionAnsweringPipeline,
-        "pt": AutoModelForTableQuestionAnswering if is_torch_available() else None,
-        "tf": None,
-        "default": {
-            "model": {
-                "pt": "nielsr/tapas-base-finetuned-wtq",
-                "tokenizer": "nielsr/tapas-base-finetuned-wtq",
-                "tf": "nielsr/tapas-base-finetuned-wtq",
-            },
-        },
-    },
     "fill-mask": {
         "impl": FillMaskPipeline,
         "tf": TFAutoModelForMaskedLM if is_tf_available() else None,
@@ -3287,12 +3006,6 @@ def pipeline(
                 "Model might be a PyTorch model (ending with `.bin`) but PyTorch is not available. "
                 "Trying to load the model with Tensorflow."
             )
-
-        if model_class is None:
-            raise ValueError(
-                f"Pipeline using {framework} framework, but this framework is not supported by this pipeline."
-            )
-
         model = model_class.from_pretrained(model, config=config, revision=revision, **model_kwargs)
         if task == "translation" and model.config.task_specific_params:
             for key in model.config.task_specific_params:

src/transformers/testing_utils.py

@@ -28,8 +28,6 @@ from .file_utils import (
     _datasets_available,
     _faiss_available,
     _flax_available,
-    _pandas_available,
-    _scatter_available,
     _sentencepiece_available,
     _tf_available,
     _tokenizers_available,
@@ -172,19 +170,6 @@ def require_torch(test_case):
         return test_case
 
 
-def require_torch_scatter(test_case):
-    """
-    Decorator marking a test that requires PyTorch scatter.
-
-    These tests are skipped when PyTorch scatter isn't installed.
-
-    """
-    if not _scatter_available:
-        return unittest.skip("test requires PyTorch scatter")(test_case)
-    else:
-        return test_case
-
-
 def require_tf(test_case):
     """
     Decorator marking a test that requires TensorFlow.
@@ -236,27 +221,6 @@ def require_tokenizers(test_case):
         return test_case
 
 
-def require_pandas(test_case):
-    """
-    Decorator marking a test that requires pandas. These tests are skipped when pandas isn't installed.
-    """
-    if not _pandas_available:
-        return unittest.skip("test requires pandas")(test_case)
-    else:
-        return test_case
-
-
-def require_scatter(test_case):
-    """
-    Decorator marking a test that requires PyTorch Scatter. These tests are skipped when PyTorch Scatter isn't
-    installed.
-    """
-    if not _scatter_available:
-        return unittest.skip("test requires PyTorch Scatter")(test_case)
-    else:
-        return test_case
-
-
 def require_torch_multi_gpu(test_case):
     """
     Decorator marking a test that requires a multi-GPU setup (in PyTorch).

src/transformers/tokenization_utils_base.py

@@ -262,7 +262,7 @@ class BatchEncoding(UserDict):
         :class:`~transformers.BatchEncoding`. Currently can be one of :obj:`None` (unknown), :obj:`1` (a single
         sentence) or :obj:`2` (a pair of sentences)
         """
-        return self._n_sequences
+        return self.n_sequences
 
     @property
     def is_fast(self) -> bool:
@@ -1648,9 +1648,6 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
             proxies (:obj:`Dict[str, str], `optional`):
                 A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
                 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            use_auth_token (:obj:`str` or `bool`, `optional`):
-                The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
-                generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
             revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
                 The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                 git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
@@ -1665,10 +1662,6 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
                 ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``,
                 ``mask_token``, ``additional_special_tokens``. See parameters in the ``__init__`` for more details.
 
-        .. note::
-
-            Passing :obj:`use_auth_token=True` is required when you want to use a private model.
-
         Examples::
 
             # We can't instantiate directly the base class `PreTrainedTokenizerBase` so let's show our examples on a derived class: BertTokenizer
@@ -1696,7 +1689,6 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
         resume_download = kwargs.pop("resume_download", False)
         proxies = kwargs.pop("proxies", None)
         local_files_only = kwargs.pop("local_files_only", False)
-        use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
         subfolder = kwargs.pop("subfolder", None)
 
@@ -1778,7 +1770,6 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
                         proxies=proxies,
                         resume_download=resume_download,
                         local_files_only=local_files_only,
-                        use_auth_token=use_auth_token,
                     )
                 except requests.exceptions.HTTPError as err:
                     if "404 Client Error" in str(err):

src/transformers/trainer.py

@@ -33,7 +33,6 @@ from .integrations import (  # isort: split
     hp_params,
     is_azureml_available,
     is_comet_available,
-    is_fairscale_available,
     is_mlflow_available,
     is_optuna_available,
     is_ray_available,
@@ -94,7 +93,8 @@ from .training_args import TrainingArguments
 from .utils import logging
 
 
-_is_native_amp_available = False
+_use_native_amp = False
+_use_apex = False
 
 DEFAULT_CALLBACKS = [DefaultFlowCallback]
 DEFAULT_PROGRESS_CALLBACK = ProgressCallback
@@ -110,10 +110,16 @@ if version.parse(torch.__version__) < version.parse("1.6"):
 
     if is_apex_available():
         from apex import amp
+    _use_apex = True
 else:
-    _is_native_amp_available = True
+    _use_native_amp = True
     from torch.cuda.amp import autocast
 
+if version.parse(torch.__version__) < version.parse("1.2"):
+    _use_ddp_no_sync = False
+else:
+    _use_ddp_no_sync = True
+
 if is_datasets_available():
     import datasets
 
@@ -154,11 +160,6 @@ if is_azureml_available():
 
     DEFAULT_CALLBACKS.append(AzureMLCallback)
 
-if is_fairscale_available():
-    from fairscale.nn.data_parallel import ShardedDataParallel as ShardedDDP
-    from fairscale.optim import OSS
-    from fairscale.optim.grad_scaler import ShardedGradScaler
-
 logger = logging.get_logger(__name__)
 
 
@@ -291,40 +292,13 @@ class Trainer:
             if isinstance(eval_dataset, datasets.Dataset):
                 self._remove_unused_columns(self.eval_dataset, description="evaluation")
 
-        # Setup Sharded DDP training
-        self.sharded_dpp = False
-        if args.sharded_ddp:
-            if args.local_rank == -1:
-                raise ValueError("Using sharded DDP only works in distributed training.")
-            elif not is_fairscale_available():
-                raise ImportError("Sharded DDP training requires fairscale: `pip install fairscale`.")
-            else:
-                self.sharded_dpp = True
-
-        # Mixed precision setup
-        self.use_apex = False
-        self.use_amp = False
-        if args.fp16:
-            if args.fp16_backend == "auto":
-                backend = "amp" if _is_native_amp_available else "apex"
-            else:
-                backend = args.fp16_backend
-
-            if backend == "amp":
-                self.use_amp = True
-                self.scaler = ShardedGradScaler() if self.sharded_dpp else torch.cuda.amp.GradScaler()
-            else:
-                if not is_apex_available():
-                    raise ImportError(
-                        "Using FP16 with APEX but APEX is not installed, please refer to https://www.github.com/nvidia/apex."
-                    )
-                self.use_apex = True
-
         self.state = TrainerState()
         self.control = TrainerControl()
         # Internal variable for total_flos used to count as tensors (for distributed + TPU), will be sent in the
         # state at each call to self.log.
         self._total_flos = None
+        if self.args.fp16 and _use_native_amp:
+            self.scaler = torch.cuda.amp.GradScaler()
         self.hp_search_backend = None
         self.use_tune_checkpoints = False
         default_label_names = (
@@ -507,21 +481,12 @@ class Trainer:
                     "weight_decay": 0.0,
                 },
             ]
-            if self.sharded_dpp:
-                self.optimizer = OSS(
-                    params=optimizer_grouped_parameters,
-                    optim=AdamW,
-                    lr=self.args.learning_rate,
-                    betas=(self.args.adam_beta1, self.args.adam_beta2),
-                    eps=self.args.adam_epsilon,
-                )
-            else:
-                self.optimizer = AdamW(
-                    optimizer_grouped_parameters,
-                    lr=self.args.learning_rate,
-                    betas=(self.args.adam_beta1, self.args.adam_beta2),
-                    eps=self.args.adam_epsilon,
-                )
+            self.optimizer = AdamW(
+                optimizer_grouped_parameters,
+                lr=self.args.learning_rate,
+                betas=(self.args.adam_beta1, self.args.adam_beta2),
+                eps=self.args.adam_epsilon,
+            )
         if self.lr_scheduler is None:
             self.lr_scheduler = get_linear_schedule_with_warmup(
                 self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=num_training_steps
@@ -660,7 +625,9 @@ class Trainer:
 
         # Mixed precision training with apex (torch < 1.6)
         model = self.model
-        if self.use_apex:
+        if self.args.fp16 and _use_apex:
+            if not is_apex_available():
+                raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
             model, self.optimizer = amp.initialize(model, self.optimizer, opt_level=self.args.fp16_opt_level)
 
         # Multi-gpu training (should be after apex fp16 initialization)
@@ -668,9 +635,7 @@ class Trainer:
             model = torch.nn.DataParallel(model)
 
         # Distributed training (should be after apex fp16 initialization)
-        if self.sharded_dpp:
-            model = ShardedDDP(model, self.optimizer)
-        elif self.args.local_rank != -1:
+        if self.args.local_rank != -1:
             model = torch.nn.parallel.DistributedDataParallel(
                 model,
                 device_ids=[self.args.local_rank],
@@ -681,8 +646,8 @@ class Trainer:
                     else True
                 ),
             )
-            # find_unused_parameters breaks checkpointing as per
-            # https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021
+        # find_unused_parameters breaks checkpointing as per
+        # https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021
 
         # Train!
         if is_torch_tpu_available():
@@ -791,8 +756,11 @@ class Trainer:
                 if (step + 1) % self.args.gradient_accumulation_steps == 0:
                     self.control = self.callback_handler.on_step_begin(self.args, self.state, self.control)
 
-                if ((step + 1) % self.args.gradient_accumulation_steps != 0) and self.args.local_rank != -1:
-                    # Avoid unnecessary DDP synchronization since there will be no backward pass on this example.
+                if (
+                    ((step + 1) % self.args.gradient_accumulation_steps != 0)
+                    and self.args.local_rank != -1
+                    and _use_ddp_no_sync
+                ):
                     with model.no_sync():
                         tr_loss += self.training_step(model, inputs)
                 else:
@@ -804,26 +772,17 @@ class Trainer:
                     steps_in_epoch <= self.args.gradient_accumulation_steps
                     and (step + 1) == steps_in_epoch
                 ):
-                    # Gradient clipping
-                    if self.args.max_grad_norm is not None and self.args.max_grad_norm > 0:
-                        if self.use_amp:
-                            # AMP: gradients need unscaling
-                            self.scaler.unscale_(self.optimizer)
+                    if self.args.fp16 and _use_native_amp:
+                        self.scaler.unscale_(self.optimizer)
+                        torch.nn.utils.clip_grad_norm_(model.parameters(), self.args.max_grad_norm)
+                    elif self.args.fp16 and _use_apex:
+                        torch.nn.utils.clip_grad_norm_(amp.master_params(self.optimizer), self.args.max_grad_norm)
+                    else:
+                        torch.nn.utils.clip_grad_norm_(model.parameters(), self.args.max_grad_norm)
 
-                        if hasattr(self.optimizer, "clip_grad_norm"):
-                            # Some optimizers (like the sharded optimizer) have a specific way to do gradient clipping
-                            self.optimizer.clip_grad_norm(self.args.max_grad_norm)
-                        else:
-                            # Revert to normal clipping otherwise, handling Apex or full precision
-                            torch.nn.utils.clip_grad_norm_(
-                                amp.master_params(self.optimizer) if self.use_apex else model.parameters(),
-                                self.args.max_grad_norm,
-                            )
-
-                    # Optimizer step
                     if is_torch_tpu_available():
                         xm.optimizer_step(self.optimizer)
-                    elif self.use_amp:
+                    elif self.args.fp16 and _use_native_amp:
                         self.scaler.step(self.optimizer)
                         self.scaler.update()
                     else:
@@ -931,8 +890,6 @@ class Trainer:
         self.save_model(output_dir)
 
         # Save optimizer and scheduler
-        if self.sharded_dpp:
-            self.optimizer.consolidate_state_dict()
         if is_torch_tpu_available():
             xm.rendezvous("saving_optimizer_states")
             xm.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
@@ -1132,7 +1089,7 @@ class Trainer:
         model.train()
         inputs = self._prepare_inputs(inputs)
 
-        if self.use_amp:
+        if self.args.fp16 and _use_native_amp:
             with autocast():
                 loss = self.compute_loss(model, inputs)
         else:
@@ -1144,9 +1101,9 @@ class Trainer:
         if self.args.gradient_accumulation_steps > 1:
             loss = loss / self.args.gradient_accumulation_steps
 
-        if self.use_amp:
+        if self.args.fp16 and _use_native_amp:
             self.scaler.scale(loss).backward()
-        elif self.use_apex:
+        elif self.args.fp16 and _use_apex:
             with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                 scaled_loss.backward()
         else:
@@ -1286,10 +1243,7 @@ class Trainer:
             shutil.rmtree(checkpoint)
 
     def evaluate(
-        self,
-        eval_dataset: Optional[Dataset] = None,
-        ignore_keys: Optional[List[str]] = None,
-        metric_key_prefix: str = "eval",
+        self, eval_dataset: Optional[Dataset] = None, ignore_keys: Optional[List[str]] = None
     ) -> Dict[str, float]:
         """
         Run evaluation and returns metrics.
@@ -1307,9 +1261,6 @@ class Trainer:
             ignore_keys (:obj:`Lst[str]`, `optional`):
                 A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                 gathering predictions.
-            metric_key_prefix (:obj:`str`, `optional`, defaults to :obj:`"eval"`):
-                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
-                "eval_bleu" if the prefix is "eval" (default)
 
         Returns:
             A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
@@ -1327,7 +1278,6 @@ class Trainer:
             # self.args.prediction_loss_only
             prediction_loss_only=True if self.compute_metrics is None else None,
             ignore_keys=ignore_keys,
-            metric_key_prefix=metric_key_prefix,
         )
 
         self.log(output.metrics)
@@ -1339,9 +1289,7 @@ class Trainer:
         self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, output.metrics)
         return output.metrics
 
-    def predict(
-        self, test_dataset: Dataset, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = "eval"
-    ) -> PredictionOutput:
+    def predict(self, test_dataset: Dataset, ignore_keys: Optional[List[str]] = None) -> PredictionOutput:
         """
         Run prediction and returns predictions and potential metrics.
 
@@ -1355,9 +1303,6 @@ class Trainer:
             ignore_keys (:obj:`Lst[str]`, `optional`):
                 A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                 gathering predictions.
-            metric_key_prefix (:obj:`str`, `optional`, defaults to :obj:`"eval"`):
-                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
-                "eval_bleu" if the prefix is "eval" (default)
 
         .. note::
 
@@ -1377,9 +1322,7 @@ class Trainer:
 
         test_dataloader = self.get_test_dataloader(test_dataset)
 
-        return self.prediction_loop(
-            test_dataloader, description="Prediction", ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix
-        )
+        return self.prediction_loop(test_dataloader, description="Prediction", ignore_keys=ignore_keys)
 
     def prediction_loop(
         self,
@@ -1387,7 +1330,6 @@ class Trainer:
         description: str,
         prediction_loss_only: Optional[bool] = None,
         ignore_keys: Optional[List[str]] = None,
-        metric_key_prefix: str = "eval",
     ) -> PredictionOutput:
         """
         Prediction/evaluation loop, shared by :obj:`Trainer.evaluate()` and :obj:`Trainer.predict()`.
@@ -1479,12 +1421,12 @@ class Trainer:
             metrics = {}
 
         if eval_loss is not None:
-            metrics[f"{metric_key_prefix}_loss"] = eval_loss.mean().item()
+            metrics["eval_loss"] = eval_loss.mean().item()
 
-        # Prefix all keys with metric_key_prefix + '_'
+        # Prefix all keys with eval_
         for key in list(metrics.keys()):
-            if not key.startswith(f"{metric_key_prefix}_"):
-                metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
+            if not key.startswith("eval_"):
+                metrics[f"eval_{key}"] = metrics.pop(key)
 
         return PredictionOutput(predictions=preds, label_ids=label_ids, metrics=metrics)
 
@@ -1541,7 +1483,7 @@ class Trainer:
                 ignore_keys = []
 
         with torch.no_grad():
-            if self.use_amp:
+            if self.args.fp16 and _use_native_amp:
                 with autocast():
                     outputs = model(**inputs)
             else:

src/transformers/trainer_tf.py

@@ -574,7 +574,7 @@ class TFTrainer:
 
                     if (
                         self.args.eval_steps > 0
-                        and self.args.evaluation_strategy == EvaluationStrategy.STEPS
+                        and self.args.evaluate_strategy == EvaluationStrategy.STEPS
                         and self.global_step % self.args.eval_steps == 0
                     ):
                         self.evaluate()

src/transformers/training_args.py

@@ -51,9 +51,8 @@ class TrainingArguments:
     TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
     itself**.
 
-    Using :class:`~transformers.HfArgumentParser` we can turn this class into `argparse
-    <https://docs.python.org/3/library/argparse.html#module-argparse>`__ arguments that can be specified on the command
-    line.
+    Using :class:`~transformers.HfArgumentParser` we can turn this class into argparse arguments to be able to specify
+    them on the command line.
 
 
 
@@ -69,11 +68,10 @@ class TrainingArguments:
             intended to be used by your training/evaluation scripts instead. See the `example scripts
             <https://github.com/huggingface/transformers/tree/master/examples>`__ for more details.
         do_eval (:obj:`bool`, `optional`):
-            Whether to run evaluation on the validation set or not. Will be set to :obj:`True` if
-            :obj:`evaluation_strategy` is different from :obj:`"no"`. This argument is not directly used by
-            :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
-            the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`__ for more
-            details.
+            Whether to run evaluation on the dev set or not. Will be set to :obj:`True` if :obj:`evaluation_strategy`
+            is different from :obj:`"no"`. This argument is not directly used by :class:`~transformers.Trainer`, it's
+            intended to be used by your training/evaluation scripts instead. See the `example scripts
+            <https://github.com/huggingface/transformers/tree/master/examples>`__ for more details.
         do_predict (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to run predictions on the test set or not. This argument is not directly used by
             :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
@@ -87,7 +85,7 @@ class TrainingArguments:
                 * :obj:`"epoch"`: Evaluation is done at the end of each epoch.
 
         prediction_loss_only (:obj:`bool`, `optional`, defaults to `False`):
-            When performing evaluation and generating predictions, only returns the loss.
+            When performing evaluation and predictions, only returns the loss.
         per_device_train_batch_size (:obj:`int`, `optional`, defaults to 8):
             The batch size per GPU/TPU core/CPU for training.
         per_device_eval_batch_size (:obj:`int`, `optional`, defaults to 8):
@@ -109,11 +107,11 @@ class TrainingArguments:
         weight_decay (:obj:`float`, `optional`, defaults to 0):
             The weight decay to apply (if not zero).
         adam_beta1 (:obj:`float`, `optional`, defaults to 0.9):
-            The beta1 hyperparameter for the Adam optimizer.
+            The beta1 for the Adam optimizer.
         adam_beta2 (:obj:`float`, `optional`, defaults to 0.999):
-            The beta2 hyperparameter for the Adam optimizer.
+            The beta2 for the Adam optimizer.
         adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8):
-            The epsilon hyperparameter for the Adam optimizer.
+            Epsilon for the Adam optimizer.
         max_grad_norm (:obj:`float`, `optional`, defaults to 1.0):
             Maximum gradient norm (for gradient clipping).
         num_train_epochs(:obj:`float`, `optional`, defaults to 3.0):
@@ -125,8 +123,7 @@ class TrainingArguments:
         warmup_steps (:obj:`int`, `optional`, defaults to 0):
             Number of steps used for a linear warmup from 0 to :obj:`learning_rate`.
         logging_dir (:obj:`str`, `optional`):
-            `TensorBoard <https://www.tensorflow.org/tensorboard>`__ log directory. Will default to
-            `runs/**CURRENT_DATETIME_HOSTNAME**`.
+            Tensorboard log directory. Will default to `runs/**CURRENT_DATETIME_HOSTNAME**`.
         logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to log and evaluate the first :obj:`global_step` or not.
         logging_steps (:obj:`int`, `optional`, defaults to 500):
@@ -141,12 +138,12 @@ class TrainingArguments:
         seed (:obj:`int`, `optional`, defaults to 42):
             Random seed for initialization.
         fp16 (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training.
+            Whether to use 16-bit (mixed) precision training (through NVIDIA apex) instead of 32-bit training.
         fp16_opt_level (:obj:`str`, `optional`, defaults to 'O1'):
-            For :obj:`fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details
-            on the `Apex documentation <https://nvidia.github.io/apex/amp.html>`__.
+            For :obj:`fp16` training, apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details
+            on the `apex documentation <https://nvidia.github.io/apex/amp.html>`__.
         local_rank (:obj:`int`, `optional`, defaults to -1):
-            Rank of the process during distributed training.
+            During distributed training, the rank of the process.
         tpu_num_cores (:obj:`int`, `optional`):
             When training on TPU, the number of TPU cores (automatically passed by launcher script).
         debug (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -166,14 +163,13 @@ class TrainingArguments:
             ``Trainer`` will use the corresponding output (usually index 2) as the past state and feed it to the model
             at the next training step under the keyword argument ``mems``.
         run_name (:obj:`str`, `optional`):
-            A descriptor for the run. Typically used for `wandb <https://www.wandb.com/>`_ logging.
+            A descriptor for the run. Notably used for wandb logging.
         disable_tqdm (:obj:`bool`, `optional`):
-            Whether or not to disable the tqdm progress bars and table of metrics produced by
-            :class:`~transformers.notebook.NotebookTrainingTracker` in Jupyter Notebooks. Will default to :obj:`True`
-            if the logging level is set to warn or lower (default), :obj:`False` otherwise.
+            Whether or not to disable the tqdm progress bars. Will default to :obj:`True` if the logging level is set
+            to warn or lower (default), :obj:`False` otherwise.
         remove_unused_columns (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            If using :obj:`datasets.Dataset` datasets, whether or not to automatically remove the columns unused by the
-            model forward method.
+            If using `nlp.Dataset` datasets, whether or not to automatically remove the columns unused by the model
+            forward method.
 
             (Note that this behavior is not implemented for :class:`~transformers.TFTrainer` yet.)
         label_names (:obj:`List[str]`, `optional`):
@@ -205,19 +201,12 @@ class TrainingArguments:
               :obj:`"eval_loss"`.
             - :obj:`False` if :obj:`metric_for_best_model` is not set, or set to :obj:`"loss"` or :obj:`"eval_loss"`.
         model_parallel (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            If there is more than one device, whether to use model parallelism to distribute the model's modules across
-            devices or not.
-        ignore_skip_data (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            If there are more than one devices, whether to use model parallelism to distribute the model's modules
+            across devices or not.
+        ignore_data_skip (:obj:`bool`, `optional`, defaults to :obj:`False`):
             When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
             stage as in the previous training. If set to :obj:`True`, the training will begin faster (as that skipping
             step can take a long time) but will not yield the same results as the interrupted training would have.
-        fp16_backend (:obj:`str`, `optional`, defaults to :obj:`"auto"`):
-            The backend to use for mixed precision training. Must be one of :obj:`"auto"`, :obj:`"amp"` or
-            :obj:`"apex"`. :obj:`"auto"` will use AMP or APEX depending on the PyTorch version detected, while the
-            other choices will force the requested backend.
-        sharded_ddp (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Use Sharded DDP training from `FairScale <https://github.com/facebookresearch/fairscale>`__ (in distributed
-            training only). This is an experimental feature.
     """
 
     output_dir: str = field(
@@ -317,7 +306,7 @@ class TrainingArguments:
 
     fp16: bool = field(
         default=False,
-        metadata={"help": "Whether to use 16-bit (mixed) precision (through NVIDIA Apex) instead of 32-bit"},
+        metadata={"help": "Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"},
     )
     fp16_opt_level: str = field(
         default="O1",
@@ -385,14 +374,6 @@ class TrainingArguments:
             "help": "When resuming training, whether or not to skip the first epochs and batches to get to the same training data."
         },
     )
-    fp16_backend: str = field(
-        default="auto",
-        metadata={"help": "The backend to be used for mixed precision.", "choices": ["auto", "amp", "apex"]},
-    )
-    sharded_ddp: bool = field(
-        default=False,
-        metadata={"help": "Whether or not to use sharded DDP training (in distributed training only)."},
-    )
 
     def __post_init__(self):
         if self.disable_tqdm is None:

src/transformers/training_args_tf.py

@@ -33,9 +33,8 @@ class TFTrainingArguments(TrainingArguments):
     TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
     itself**.
 
-    Using :class:`~transformers.HfArgumentParser` we can turn this class into `argparse
-    <https://docs.python.org/3/library/argparse.html#module-argparse>`__ arguments that can be specified on the command
-    line.
+    Using :class:`~transformers.HfArgumentParser` we can turn this class into argparse arguments to be able to specify
+    them on the command line.
 
     Parameters:
         output_dir (:obj:`str`):
@@ -44,26 +43,16 @@ class TFTrainingArguments(TrainingArguments):
             If :obj:`True`, overwrite the content of the output directory. Use this to continue training if
             :obj:`output_dir` points to a checkpoint directory.
         do_train (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to run training or not. This argument is not directly used by :class:`~transformers.Trainer`, it's
-            intended to be used by your training/evaluation scripts instead. See the `example scripts
-            <https://github.com/huggingface/transformers/tree/master/examples>`__ for more details.
-        do_eval (:obj:`bool`, `optional`):
-            Whether to run evaluation on the validation set or not. Will be set to :obj:`True` if
-            :obj:`evaluation_strategy` is different from :obj:`"no"`. This argument is not directly used by
-            :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
-            the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`__ for more
-            details.
+            Whether to run training or not.
+        do_eval (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether to run evaluation on the dev set or not.
         do_predict (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to run predictions on the test set or not. This argument is not directly used by
-            :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
-            the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`__ for more
-            details.
+            Whether to run predictions on the test set or not.
         evaluation_strategy (:obj:`str` or :class:`~transformers.trainer_utils.EvaluationStrategy`, `optional`, defaults to :obj:`"no"`):
             The evaluation strategy to adopt during training. Possible values are:
 
                 * :obj:`"no"`: No evaluation is done during training.
                 * :obj:`"steps"`: Evaluation is done (and logged) every :obj:`eval_steps`.
-                * :obj:`"epoch"`: Evaluation is done at the end of each epoch.
 
         per_device_train_batch_size (:obj:`int`, `optional`, defaults to 8):
             The batch size per GPU/TPU core/CPU for training.
@@ -81,12 +70,8 @@ class TFTrainingArguments(TrainingArguments):
             The initial learning rate for Adam.
         weight_decay (:obj:`float`, `optional`, defaults to 0):
             The weight decay to apply (if not zero).
-        adam_beta1 (:obj:`float`, `optional`, defaults to 0.9):
-            The beta1 hyperparameter for the Adam optimizer.
-        adam_beta2 (:obj:`float`, `optional`, defaults to 0.999):
-            The beta2 hyperparameter for the Adam optimizer.
         adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8):
-            The epsilon hyperparameter for the Adam optimizer.
+            Epsilon for the Adam optimizer.
         max_grad_norm (:obj:`float`, `optional`, defaults to 1.0):
             Maximum gradient norm (for gradient clipping).
         num_train_epochs(:obj:`float`, `optional`, defaults to 3.0):
@@ -97,8 +82,7 @@ class TFTrainingArguments(TrainingArguments):
         warmup_steps (:obj:`int`, `optional`, defaults to 0):
             Number of steps used for a linear warmup from 0 to :obj:`learning_rate`.
         logging_dir (:obj:`str`, `optional`):
-            `TensorBoard <https://www.tensorflow.org/tensorboard>`__ log directory. Will default to
-            `runs/**CURRENT_DATETIME_HOSTNAME**`.
+            Tensorboard log directory. Will default to `runs/**CURRENT_DATETIME_HOSTNAME**`.
         logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to log and evaluate the first :obj:`global_step` or not.
         logging_steps (:obj:`int`, `optional`, defaults to 500):
@@ -113,10 +97,10 @@ class TFTrainingArguments(TrainingArguments):
         seed (:obj:`int`, `optional`, defaults to 42):
             Random seed for initialization.
         fp16 (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training.
+            Whether to use 16-bit (mixed) precision training (through NVIDIA apex) instead of 32-bit training.
         fp16_opt_level (:obj:`str`, `optional`, defaults to 'O1'):
-            For :obj:`fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details
-            on the `Apex documentation <https://nvidia.github.io/apex/amp.html>`__.
+            For :obj:`fp16` training, apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details
+            on the `apex documentation <https://nvidia.github.io/apex/amp.html>`__.
         local_rank (:obj:`int`, `optional`, defaults to -1):
             During distributed training, the rank of the process.
         tpu_num_cores (:obj:`int`, `optional`):

src/transformers/utils/dummy_pt_objects.py

@@ -303,9 +303,6 @@ MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = None
 MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None
 
 
-MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = None
-
-
 MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None
 
 
@@ -396,15 +393,6 @@ class AutoModelForSequenceClassification:
         requires_pytorch(self)
 
 
-class AutoModelForTableQuestionAnswering:
-    def __init__(self, *args, **kwargs):
-        requires_pytorch(self)
-
-    @classmethod
-    def from_pretrained(self, *args, **kwargs):
-        requires_pytorch(self)
-
-
 class AutoModelForTokenClassification:
     def __init__(self, *args, **kwargs):
         requires_pytorch(self)
@@ -1879,45 +1867,6 @@ def load_tf_weights_in_t5(*args, **kwargs):
     requires_pytorch(load_tf_weights_in_t5)
 
 
-TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = None
-
-
-class TapasForMaskedLM:
-    def __init__(self, *args, **kwargs):
-        requires_pytorch(self)
-
-    @classmethod
-    def from_pretrained(self, *args, **kwargs):
-        requires_pytorch(self)
-
-
-class TapasForQuestionAnswering:
-    def __init__(self, *args, **kwargs):
-        requires_pytorch(self)
-
-    @classmethod
-    def from_pretrained(self, *args, **kwargs):
-        requires_pytorch(self)
-
-
-class TapasForSequenceClassification:
-    def __init__(self, *args, **kwargs):
-        requires_pytorch(self)
-
-    @classmethod
-    def from_pretrained(self, *args, **kwargs):
-        requires_pytorch(self)
-
-
-class TapasModel:
-    def __init__(self, *args, **kwargs):
-        requires_pytorch(self)
-
-    @classmethod
-    def from_pretrained(self, *args, **kwargs):
-        requires_pytorch(self)
-
-
 TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

src/transformers/utils/dummy_tf_objects.py

@@ -256,15 +256,6 @@ class TFBartModel:
         requires_tf(self)
 
 
-class TFBartPretrainedModel:
-    def __init__(self, *args, **kwargs):
-        requires_tf(self)
-
-    @classmethod
-    def from_pretrained(self, *args, **kwargs):
-        requires_tf(self)
-
-
 TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
@@ -1125,15 +1116,6 @@ class TFOpenAIGPTDoubleHeadsModel:
         requires_tf(self)
 
 
-class TFOpenAIGPTForSequenceClassification:
-    def __init__(self, *args, **kwargs):
-        requires_tf(self)
-
-    @classmethod
-    def from_pretrained(self, *args, **kwargs):
-        requires_tf(self)
-
-
 class TFOpenAIGPTLMHeadModel:
     def __init__(self, *args, **kwargs):
         requires_tf(self)

tests/test_modeling_auto.py

@@ -17,13 +17,7 @@
 import unittest
 
 from transformers import is_torch_available
-from transformers.testing_utils import (
-    DUMMY_UNKWOWN_IDENTIFIER,
-    SMALL_MODEL_IDENTIFIER,
-    require_scatter,
-    require_torch,
-    slow,
-)
+from transformers.testing_utils import DUMMY_UNKWOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, require_torch, slow
 
 
 if is_torch_available():
@@ -36,7 +30,6 @@ if is_torch_available():
         AutoModelForQuestionAnswering,
         AutoModelForSeq2SeqLM,
         AutoModelForSequenceClassification,
-        AutoModelForTableQuestionAnswering,
         AutoModelForTokenClassification,
         AutoModelWithLMHead,
         BertConfig,
@@ -51,8 +44,6 @@ if is_torch_available():
         RobertaForMaskedLM,
         T5Config,
         T5ForConditionalGeneration,
-        TapasConfig,
-        TapasForQuestionAnswering,
     )
     from transformers.models.auto.modeling_auto import (
         MODEL_FOR_CAUSAL_LM_MAPPING,
@@ -61,7 +52,6 @@ if is_torch_available():
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
-        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         MODEL_MAPPING,
         MODEL_WITH_LM_HEAD_MAPPING,
@@ -69,7 +59,6 @@ if is_torch_available():
     from transformers.models.bert.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.gpt2.modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.t5.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_LIST
-    from transformers.models.tapas.modeling_tapas import TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
 
 
 @require_torch
@@ -179,21 +168,6 @@ class AutoModelTest(unittest.TestCase):
             self.assertIsNotNone(model)
             self.assertIsInstance(model, BertForQuestionAnswering)
 
-    @slow
-    @require_scatter
-    def test_table_question_answering_model_from_pretrained(self):
-        for model_name in TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
-            config = AutoConfig.from_pretrained(model_name)
-            self.assertIsNotNone(config)
-            self.assertIsInstance(config, TapasConfig)
-
-            model = AutoModelForTableQuestionAnswering.from_pretrained(model_name)
-            model, loading_info = AutoModelForTableQuestionAnswering.from_pretrained(
-                model_name, output_loading_info=True
-            )
-            self.assertIsNotNone(model)
-            self.assertIsInstance(model, TapasForQuestionAnswering)
-
     @slow
     def test_token_classification_model_from_pretrained(self):
         for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
@@ -226,7 +200,6 @@ class AutoModelTest(unittest.TestCase):
             MODEL_MAPPING,
             MODEL_FOR_PRETRAINING_MAPPING,
             MODEL_FOR_QUESTION_ANSWERING_MAPPING,
-            MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
             MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
             MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
             MODEL_WITH_LM_HEAD_MAPPING,

tests/test_modeling_bart.py

@@ -207,7 +207,7 @@ class BartModelTester:
 
 
 @require_torch
-class BartModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class BARTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
     all_model_classes = (
         (BartModel, BartForConditionalGeneration, BartForSequenceClassification, BartForQuestionAnswering)
         if is_torch_available()

tests/test_modeling_t5.py

@@ -484,7 +484,9 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
 
     all_model_classes = (T5Model, T5ForConditionalGeneration) if is_torch_available() else ()
     all_generative_model_classes = (T5ForConditionalGeneration,) if is_torch_available() else ()
-    all_parallelizable_model_classes = (T5Model, T5ForConditionalGeneration) if is_torch_available() else ()
+    all_parallelizable_model_classes = (
+        (T5Model, T5ForConditionalGeneration, T5EncoderModel) if is_torch_available() else ()
+    )
     test_pruning = False
     test_torchscript = True
     test_resize_embeddings = True
@@ -610,9 +612,6 @@ class T5EncoderOnlyModelTester:
         self.scope = None
         self.is_training = is_training
 
-    def get_large_model_config(self):
-        return T5Config.from_pretrained("t5-base")
-
     def prepare_config_and_inputs(self):
         input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
 
@@ -690,8 +689,6 @@ class T5EncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase):
     test_pruning = False
     test_torchscript = True
     test_resize_embeddings = False
-    test_model_parallel = True
-    all_parallelizable_model_classes = (T5EncoderModel,) if is_torch_available() else ()
 
     def setUp(self):
         self.model_tester = T5EncoderOnlyModelTester(self)

tests/test_modeling_tf_bart.py

@@ -30,7 +30,7 @@ if is_tf_available():
     import tensorflow as tf
 
     from transformers import TFBartForConditionalGeneration, TFBartModel
-    from transformers.models.bart.modeling_tf_bart import TFBartSinusoidalPositionalEmbedding
+    from transformers.models.bart.modeling_tf_bart import TFSinusoidalPositionalEmbedding
 
 
 @require_tf
@@ -85,38 +85,6 @@ class TFBartModelTester:
         inputs_dict = prepare_bart_inputs_dict(config, input_ids)
         return config, inputs_dict
 
-    def check_decoder_model_past_large_inputs(self, config, inputs_dict):
-        model = TFBartModel(config=config).get_decoder()
-        input_ids = inputs_dict["input_ids"]
-
-        input_ids = input_ids[:1, :]
-        self.batch_size = 1
-
-        # first forward pass
-        outputs = model(input_ids, use_cache=True)
-
-        output, past_key_values = outputs.to_tuple()
-        past_key_values = past_key_values[1]
-
-        # create hypothetical next token and extent to next_input_ids
-        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
-
-        # append to next input_ids and
-        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
-
-        output_from_no_past = model(next_input_ids)[0]
-        output_from_past = model(next_tokens, past_key_values=past_key_values)[0]
-
-        self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
-
-        # select random slice
-        random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
-        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
-        output_from_past_slice = output_from_past[:, :, random_slice_idx]
-
-        # test that outputs are equal for slice
-        tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
-
 
 def prepare_bart_inputs_dict(
     config,
@@ -146,9 +114,9 @@ class TFBartModelTest(TFModelTesterMixin, unittest.TestCase):
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    def test_decoder_model_past_large_inputs(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
-        self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)
+    def test_inputs_embeds(self):
+        # inputs_embeds not supported
+        pass
 
     def test_model_common_attributes(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -317,11 +285,13 @@ class FasterTFBartModelIntegrationTests(unittest.TestCase):
         model = self.xsum_1_1_model
         assert model.model.decoder.embed_tokens._layer == model.model.shared
         ARTICLE = 'The Palestinian Authority officially became the 123rd member of the International Criminal Court on Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based. The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the situation in Palestinian territories, paving the way for possible war crimes investigations against Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the world is also a step closer to ending a long era of impunity and injustice," he said, according to an ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine acquires all the rights as well as responsibilities that come with being a State Party to the Statute. These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should immediately end their pressure, and countries that support universal acceptance of the court\'s treaty should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the group. "What\'s objectionable is the attempts to undermine international justice, not Palestine\'s decision to join a treaty to which over 100 countries around the world are members." In January, when the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an outrage, saying the court was overstepping its boundaries. The United States also said it "strongly" disagreed with the court\'s decision. "As we have said repeatedly, we do not believe that Palestine is a state and therefore we do not believe that it is eligible to join the ICC," the State Department said in a statement. It urged the warring sides to resolve their differences through direct negotiations. "We will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace," it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou Bensouda said her office would "conduct its analysis in full independence and impartiality." The war between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry will include alleged war crimes committed since June. The International Criminal Court was set up in 2002 to prosecute genocide, crimes against humanity and war crimes.'
-        EXPECTED = " The International Criminal Court (ICC) has announced that it has been announced by the International Criminal court."
         dct = self.tok(ARTICLE, return_tensors="tf")
         generated_ids = model.generate(**dct, num_beams=4)
         result = self.tok.batch_decode(generated_ids, skip_special_tokens=True)[0]
-        assert result == EXPECTED
+        assert (
+            result
+            == " The International Criminal Court (ICC) has announced that it has been announced by the International Criminal court."
+        )
 
     def test_xsum_1_1_batch_generation(self):
         batch = self.tok(
@@ -355,6 +325,7 @@ class FasterTFBartModelIntegrationTests(unittest.TestCase):
             truncation=True,
         )
         features = self.xsum_1_1_model.get_encoder()(**batch).last_hidden_state
+        import numpy as np
 
         expected = np.array([[-0.0828, -0.0251, -0.0674], [0.1277, 0.3311, -0.0255], [0.2613, -0.0840, -0.2763]])
         assert np.allclose(features[0, :3, :3].numpy(), expected, atol=1e-3)
@@ -369,14 +340,16 @@ class TestTFSinusoidalPositionalEmbeddings(unittest.TestCase):
     ]
 
     def test_positional_emb_cache_logic(self):
-        emb1 = TFBartSinusoidalPositionalEmbedding(num_positions=32, embedding_dim=6)
-        no_cache = emb1((4, 10), past_key_values_length=0)
-        yes_cache = emb1((4, 10), past_key_values_length=2)
-        self.assertTrue(no_cache.shape == yes_cache.shape == (10, 6))
-        self.assertListEqual(no_cache[2:].numpy().tolist(), yes_cache[:-2].numpy().tolist())
+        input_ids = _long_tensor([[4, 10]])
+        emb1 = TFSinusoidalPositionalEmbedding(num_positions=32, embedding_dim=6)
+        no_cache = emb1(input_ids, use_cache=False)
+        yes_cache = emb1(input_ids, use_cache=True)
+        self.assertEqual((1, 1, 6), yes_cache.shape)  # extra dim to allow broadcasting, feel free to delete!
+
+        np.testing.assert_almost_equal(no_cache[-1].numpy(), yes_cache[0][0].numpy())
 
     def test_positional_emb_weights_against_marian(self):
-        emb1 = TFBartSinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512)
+        emb1 = TFSinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512)
         emb1.build(None)
         weights = emb1.embeddings.numpy()
         for i, (expected_weight, actual_weight) in enumerate(zip(self.desired_weights, weights)):

tests/test_modeling_tf_common.py

@@ -434,14 +434,14 @@ class TFModelTesterMixin:
                 num_labels = 2
 
             X = tf.data.Dataset.from_tensor_slices(
-                (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)))
+                (inputs_dict, np.random.randint(0, num_labels, (self.model_tester.batch_size, 1)))
             ).batch(1)
 
             hidden_states = main_layer(symbolic_inputs)[0]
             outputs = tf.keras.layers.Dense(num_labels, activation="softmax", name="outputs")(hidden_states)
             model = tf.keras.models.Model(inputs=symbolic_inputs, outputs=[outputs])
 
-            model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["binary_accuracy"])
+            model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["acc"])
             model.fit(X, epochs=1)
 
             with tempfile.TemporaryDirectory() as tmpdirname:

tests/test_modeling_tf_openai.py

@@ -29,7 +29,6 @@ if is_tf_available():
     from transformers.models.openai.modeling_tf_openai import (
         TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFOpenAIGPTDoubleHeadsModel,
-        TFOpenAIGPTForSequenceClassification,
         TFOpenAIGPTLMHeadModel,
         TFOpenAIGPTModel,
     )
@@ -63,7 +62,6 @@ class TFOpenAIGPTModelTester:
         self.num_labels = 3
         self.num_choices = 4
         self.scope = None
-        self.pad_token_id = self.vocab_size - 1
 
     def prepare_config_and_inputs(self):
         input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
@@ -101,7 +99,6 @@ class TFOpenAIGPTModelTester:
             n_ctx=self.max_position_embeddings,
             # type_vocab_size=self.type_vocab_size,
             # initializer_range=self.initializer_range,
-            pad_token_id=self.pad_token_id,
         )
 
         head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
@@ -157,21 +154,6 @@ class TFOpenAIGPTModelTester:
         )
         self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices))
 
-    def create_and_check_openai_gpt_for_sequence_classification(
-        self, config, input_ids, input_mask, head_mask, token_type_ids, *args
-    ):
-        config.num_labels = self.num_labels
-        sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
-        inputs = {
-            "input_ids": input_ids,
-            "attention_mask": input_mask,
-            "token_type_ids": token_type_ids,
-            "labels": sequence_labels,
-        }
-        model = TFOpenAIGPTForSequenceClassification(config)
-        result = model(inputs)
-        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
-
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
 
@@ -195,9 +177,7 @@ class TFOpenAIGPTModelTester:
 class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase):
 
     all_model_classes = (
-        (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
-        if is_tf_available()
-        else ()
+        (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel) if is_tf_available() else ()
     )
     all_generative_model_classes = (
         (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
@@ -233,10 +213,6 @@ class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase):
             name = model.get_prefix_bias_name()
             assert name is None
 
-    def test_openai_gpt_sequence_classification_model(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*config_and_inputs)
-
     @slow
     def test_model_from_pretrained(self):
         for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

tests/test_optimization_tf.py

@@ -14,8 +14,6 @@
 
 import unittest
 
-from packaging import version
-
 from transformers import is_tf_available
 from transformers.testing_utils import require_tf
 
@@ -78,18 +76,12 @@ class OptimizationFTest(unittest.TestCase):
                 local_variables = strategy.experimental_local_results(gradient_placeholder)
                 local_variables[0].assign(grad1)
                 local_variables[1].assign(grad2)
-                if version.parse(tf.version.VERSION) >= version.parse("2.2"):
-                    strategy.run(accumulate_on_replica, args=(gradient_placeholder,))
-                else:
-                    strategy.experimental_run_v2(accumulate_on_replica, args=(gradient_placeholder,))
+                strategy.experimental_run_v2(accumulate_on_replica, args=(gradient_placeholder,))
 
         @tf.function
         def apply_grad():
             with strategy.scope():
-                if version.parse(tf.version.VERSION) >= version.parse("2.2"):
-                    strategy.run(apply_on_replica)
-                else:
-                    strategy.experimental_run_v2(apply_on_replica)
+                strategy.experimental_run_v2(apply_on_replica)
 
         def _check_local_values(grad1, grad2):
             values = strategy.experimental_local_results(accumulator._gradients[0])

tests/test_pipelines_table_question_answering.py

@@ -1,234 +0,0 @@
-# Copyright 2020 The HuggingFace Team. 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.
-
-import unittest
-
-from transformers.pipelines import Pipeline, pipeline
-from transformers.testing_utils import require_pandas, require_torch, require_torch_scatter, slow
-
-from .test_pipelines_common import CustomInputPipelineCommonMixin
-
-
-@require_torch_scatter
-@require_torch
-@require_pandas
-class TQAPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
-    pipeline_task = "table-question-answering"
-    pipeline_running_kwargs = {
-        "padding": "max_length",
-    }
-    small_models = [
-        "lysandre/tiny-tapas-random-wtq",
-        "lysandre/tiny-tapas-random-sqa",
-    ]
-    large_models = ["nielsr/tapas-base-finetuned-wtq"]  # Models tested with the @slow decorator
-    valid_inputs = [
-        {
-            "table": {
-                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
-                "age": ["56", "45", "59"],
-                "number of movies": ["87", "53", "69"],
-                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
-            },
-            "query": "how many movies has george clooney played in?",
-        },
-        {
-            "table": {
-                "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
-                "age": ["56", "45", "59"],
-                "number of movies": ["87", "53", "69"],
-                "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
-            },
-            "query": ["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
-        },
-        {
-            "table": {
-                "Repository": ["Transformers", "Datasets", "Tokenizers"],
-                "Stars": ["36542", "4512", "3934"],
-                "Contributors": ["651", "77", "34"],
-                "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
-            },
-            "query": [
-                "What repository has the largest number of stars?",
-                "Given that the numbers of stars defines if a repository is active, what repository is the most active?",
-                "What is the number of repositories?",
-                "What is the average number of stars?",
-                "What is the total amount of stars?",
-            ],
-        },
-    ]
-
-    def _test_pipeline(self, table_querier: Pipeline):
-        output_keys = {"answer", "coordinates", "cells"}
-        valid_inputs = self.valid_inputs
-        invalid_inputs = [
-            {"query": "What does it do with empty context ?", "table": ""},
-            {"query": "What does it do with empty context ?", "table": None},
-        ]
-        self.assertIsNotNone(table_querier)
-
-        mono_result = table_querier(valid_inputs[0])
-        self.assertIsInstance(mono_result, dict)
-
-        for key in output_keys:
-            self.assertIn(key, mono_result)
-
-        multi_result = table_querier(valid_inputs)
-        self.assertIsInstance(multi_result, list)
-        for result in multi_result:
-            self.assertIsInstance(result, (list, dict))
-
-        for result in multi_result:
-            if isinstance(result, list):
-                for _result in result:
-                    for key in output_keys:
-                        self.assertIn(key, _result)
-            else:
-                for key in output_keys:
-                    self.assertIn(key, result)
-        for bad_input in invalid_inputs:
-            self.assertRaises(ValueError, table_querier, bad_input)
-        self.assertRaises(ValueError, table_querier, invalid_inputs)
-
-    def test_aggregation(self):
-        table_querier = pipeline(
-            "table-question-answering",
-            model="lysandre/tiny-tapas-random-wtq",
-            tokenizer="lysandre/tiny-tapas-random-wtq",
-        )
-        self.assertIsInstance(table_querier.model.config.aggregation_labels, dict)
-        self.assertIsInstance(table_querier.model.config.no_aggregation_label_index, int)
-
-        mono_result = table_querier(self.valid_inputs[0])
-        multi_result = table_querier(self.valid_inputs)
-
-        self.assertIn("aggregator", mono_result)
-
-        for result in multi_result:
-            if isinstance(result, list):
-                for _result in result:
-                    self.assertIn("aggregator", _result)
-            else:
-                self.assertIn("aggregator", result)
-
-    def test_aggregation_with_sequential(self):
-        table_querier = pipeline(
-            "table-question-answering",
-            model="lysandre/tiny-tapas-random-wtq",
-            tokenizer="lysandre/tiny-tapas-random-wtq",
-        )
-        self.assertIsInstance(table_querier.model.config.aggregation_labels, dict)
-        self.assertIsInstance(table_querier.model.config.no_aggregation_label_index, int)
-
-        mono_result = table_querier(self.valid_inputs[0], sequential=True)
-        multi_result = table_querier(self.valid_inputs, sequential=True)
-
-        self.assertIn("aggregator", mono_result)
-
-        for result in multi_result:
-            if isinstance(result, list):
-                for _result in result:
-                    self.assertIn("aggregator", _result)
-            else:
-                self.assertIn("aggregator", result)
-
-    def test_sequential(self):
-        table_querier = pipeline(
-            "table-question-answering",
-            model="lysandre/tiny-tapas-random-sqa",
-            tokenizer="lysandre/tiny-tapas-random-sqa",
-        )
-        sequential_mono_result_0 = table_querier(self.valid_inputs[0], sequential=True)
-        sequential_mono_result_1 = table_querier(self.valid_inputs[1], sequential=True)
-        sequential_multi_result = table_querier(self.valid_inputs, sequential=True)
-        mono_result_0 = table_querier(self.valid_inputs[0])
-        mono_result_1 = table_querier(self.valid_inputs[1])
-        multi_result = table_querier(self.valid_inputs)
-
-        # First valid input has a single question, the dict should be equal
-        self.assertDictEqual(sequential_mono_result_0, mono_result_0)
-
-        # Second valid input has several questions, the questions following the first one should not be equal
-        self.assertNotEqual(sequential_mono_result_1, mono_result_1)
-
-        # Assert that we get the same results when passing in several sequences.
-        for index, (sequential_multi, multi) in enumerate(zip(sequential_multi_result, multi_result)):
-            if index == 0:
-                self.assertDictEqual(sequential_multi, multi)
-            else:
-                self.assertNotEqual(sequential_multi, multi)
-
-    @slow
-    def test_integration_wtq(self):
-        tqa_pipeline = pipeline("table-question-answering")
-
-        data = {
-            "Repository": ["Transformers", "Datasets", "Tokenizers"],
-            "Stars": ["36542", "4512", "3934"],
-            "Contributors": ["651", "77", "34"],
-            "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
-        }
-        queries = [
-            "What repository has the largest number of stars?",
-            "Given that the numbers of stars defines if a repository is active, what repository is the most active?",
-            "What is the number of repositories?",
-            "What is the average number of stars?",
-            "What is the total amount of stars?",
-        ]
-
-        results = tqa_pipeline(data, queries)
-
-        expected_results = [
-            {"answer": "Transformers", "coordinates": [(0, 0)], "cells": ["Transformers"]},
-            {"answer": "Transformers", "coordinates": [(0, 0)], "cells": ["Transformers"]},
-            {
-                "answer": "Transformers, Datasets, Tokenizers",
-                "coordinates": [(0, 0), (1, 0), (2, 0)],
-                "cells": ["Transformers", "Datasets", "Tokenizers"],
-            },
-            {
-                "answer": "36542, 4512, 3934",
-                "coordinates": [(0, 1), (1, 1), (2, 1)],
-                "cells": ["36542", "4512", "3934"],
-            },
-            {
-                "answer": "36542, 4512, 3934",
-                "coordinates": [(0, 1), (1, 1), (2, 1)],
-                "cells": ["36542", "4512", "3934"],
-            },
-        ]
-        self.assertListEqual(results, expected_results)
-
-    @slow
-    def test_integration_sqa(self):
-        tqa_pipeline = pipeline(
-            "table-question-answering",
-            model="nielsr/tapas-base-finetuned-sqa",
-            tokenizer="nielsr/tapas-base-finetuned-sqa",
-        )
-        data = {
-            "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
-            "Age": ["56", "45", "59"],
-            "Number of movies": ["87", "53", "69"],
-            "Date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
-        }
-        queries = ["How many movies has George Clooney played in?", "How old is he?", "What's his date of birth?"]
-        results = tqa_pipeline(data, queries, sequential=True)
-
-        expected_results = [
-            {"answer": "69", "coordinates": [(2, 2)], "cells": ["69"]},
-            {"answer": "59", "coordinates": [(2, 1)], "cells": ["59"]},
-            {"answer": "28 november 1967", "coordinates": [(2, 3)], "cells": ["28 november 1967"]},
-        ]
-        self.assertListEqual(results, expected_results)

tests/test_tokenization_common.py

@@ -584,7 +584,7 @@ class TokenizerTesterMixin:
 
                 # We want to have sequence 0 and sequence 1 are tagged
                 # respectively with 0 and 1 token_ids
-                # (regardless of whether the model use token type ids)
+                # (regardeless of weither the model use token type ids)
                 # We use this assumption in the QA pipeline among other place
                 output = tokenizer(seq_0, return_token_type_ids=True)
                 self.assertIn(0, output["token_type_ids"])
@@ -600,7 +600,7 @@ class TokenizerTesterMixin:
 
                 # We want to have sequence 0 and sequence 1 are tagged
                 # respectively with 0 and 1 token_ids
-                # (regardless of whether the model use token type ids)
+                # (regardeless of weither the model use token type ids)
                 # We use this assumption in the QA pipeline among other place
                 output = tokenizer(seq_0)
                 self.assertIn(0, output.sequence_ids())

tests/test_trainer.py

@@ -798,38 +798,34 @@ class TrainerIntegrationTest(unittest.TestCase):
 
     def test_early_stopping_callback(self):
         # early stopping stops training before num_training_epochs
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            trainer = get_regression_trainer(
-                output_dir=tmp_dir,
-                num_train_epochs=20,
-                gradient_accumulation_steps=1,
-                per_device_train_batch_size=16,
-                load_best_model_at_end=True,
-                evaluation_strategy=EvaluationStrategy.EPOCH,
-                compute_metrics=AlmostAccuracy(),
-                metric_for_best_model="accuracy",
-            )
-            trainer.add_callback(EarlyStoppingCallback(1, 0.0001))
-            train_output = trainer.train()
-            self.assertLess(train_output.global_step, 20 * 64 / 16)
+        trainer = get_regression_trainer(
+            num_train_epochs=20,
+            gradient_accumulation_steps=1,
+            per_device_train_batch_size=16,
+            load_best_model_at_end=True,
+            evaluation_strategy=EvaluationStrategy.EPOCH,
+            compute_metrics=AlmostAccuracy(),
+            metric_for_best_model="accuracy",
+        )
+        trainer.add_callback(EarlyStoppingCallback(1, 0.0001))
+        train_output = trainer.train()
+        self.assertLess(train_output.global_step, 20 * 64 / 16)
 
         # Invalid inputs to trainer with early stopping callback result in assertion error
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            trainer = get_regression_trainer(
-                output_dir=tmp_dir,
-                num_train_epochs=20,
-                gradient_accumulation_steps=1,
-                per_device_train_batch_size=16,
-                evaluation_strategy=EvaluationStrategy.EPOCH,
-                compute_metrics=AlmostAccuracy(),
-                metric_for_best_model="accuracy",
-            )
-            trainer.add_callback(EarlyStoppingCallback(1))
+        trainer = get_regression_trainer(
+            num_train_epochs=20,
+            gradient_accumulation_steps=1,
+            per_device_train_batch_size=16,
+            evaluation_strategy=EvaluationStrategy.EPOCH,
+            compute_metrics=AlmostAccuracy(),
+            metric_for_best_model="accuracy",
+        )
+        trainer.add_callback(EarlyStoppingCallback(1))
+        self.assertEqual(trainer.state.global_step, 0)
+        try:
+            trainer.train()
+        except AssertionError:
             self.assertEqual(trainer.state.global_step, 0)
-            try:
-                trainer.train()
-            except AssertionError:
-                self.assertEqual(trainer.state.global_step, 0)
 
     def test_flos_extraction(self):
         trainer = get_regression_trainer(learning_rate=0.1)