GitLab

* add raw scaffold

* implement feat extract layers

* make style

* remove +

* correctly convert weights

* make feat extractor work

* make feature extraction proj work

* run forward pass

* finish forward pass

* Succesful decoding example

* remove unused files

* more changes

* add wav2vec tokenizer

* add new structure

* fix run forward

* add other layer norm architecture

* finish 2nd structure

* add model tests

* finish tests for tok and model

* clean-up

* make style

* finish docstring for model and config

* make style

* correct docstring

* correct tests

* change checkpoints to fairseq

* fix examples

* finish wav2vec2

* make style

* apply sylvains suggestions

* apply lysandres suggestions

* change print to log.info

* re-add assert statement

* add input_values as required input name

* finish wav2vec2 tokenizer

* Update tests/test_tokenization_wav2vec2.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* apply sylvains suggestions
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* ALBERT Tokenizer integration test

* Batching

* Style

* change tokenizer requirement

* split line

* Correct typo from list to str

* improve style

* make other function pretty as well

* add comment

* correct typo

* add new test

* pass tests for tok without padding token

* Apply suggestions from code review

* Add {decoder_,}head_mask to fsmt_modeling.py

* Enable test_headmasking and some changes to docs

* Remove test_head_masking flag from fsmt test file

Remove test_head_masking flag from test_modeling_fsmt.py
since test_head_masking is set to be True by default (thus it is redundant to store).

* Merge master and remove test_head_masking = True

* Rebase necessary due to an update of jaxlib

* Remove test_head_masking=True in tests/test_modeling_fsmt.py
as it is redundant.

* Adding a new `return_full_text` parameter to TextGenerationPipeline.

For text-generation, it's sometimes used as prompting text.
In that context, prefixing `generated_text` with the actual input
forces the caller to take an extra step to remove it.

The proposed change adds a new parameter (for backward compatibility).
`return_full_text` that enables the caller to prevent adding the prefix.

* Doc quality.

* Remove redundant test_head_masking = True flags

* Remove all redundant test_head_masking flags in PyTorch test_modeling_* files

* Make test_head_masking = True as a default choice in test_modeling_tf_commong.py

* Remove all redundant test_head_masking flags in TensorFlow
test_modeling_tf_* files

* Put back test_head_masking=False fot TFT5 models

* tests: add integration tests for new Bort model

* bort: add conversion script from Gluonnlp to Transformers 🚀



* bort: minor cleanup (BORT -> Bort)

* add docs

* make fix-copies

* clean doc a bit

* correct docs

* Update docs/source/model_doc/bort.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update docs/source/model_doc/bort.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* correct dialogpt doc

* correct link

* Update docs/source/model_doc/bort.rst

* Update docs/source/model_doc/dialogpt.rst
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix --lr_scheduler_type choices

* rewrite to fix for all enum-based cl args

* cleanup

* adjust test

* style

* Proposal that should work

* Remove needless code

* Fix test
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>

* Allow --arg Value for booleans in HfArgumentParser

* Update last test

* Better error message

* Whenresuming training from checkpoint, Trainer loads model

* Finish cleaning tests

* Address review comment

* Use global_step from state

pipeline.

- If table is empty then the line that contain `answer[0]` will fail.
- This PR add a check to prevent `answer[0]`.
- Also adds an early check for presence of `table` and `query` to
prevent late failure and give better error message.
- Adds a few tests to make sure these errors are correctly raised.

* finalize convbert

* finalize convbert

* fix

* fix

* fix

* push

* fix

* tf image patches

* fix torch model

* tf tests

* conversion

* everything aligned

* remove print

* tf tests

* fix tf

* make tf tests pass

* everything works

* fix init

* fix

* special treatment for sepconv1d

* style

* 🙏🏽



* add doc and cleanup

* add electra test again

* fix doc

* fix doc again

* fix doc again

* Update src/transformers/modeling_tf_pytorch_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/conv_bert/configuration_conv_bert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update docs/source/model_doc/conv_bert.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/auto/configuration_auto.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/conv_bert/configuration_conv_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* conv_bert -> convbert

* more fixes from review

* add conversion script

* dont use pretrained embed

* unused config

* suggestions from julien

* some more fixes

* p -> param

* fix copyright

* fix doc

* Update src/transformers/models/convbert/configuration_convbert.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* comments from reviews

* fix-copies

* fix style

* revert shape_list
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* We most likely don't want special tokens in this output.

* Adding `skip_special_tokens=True` to FillMaskPipeline

- It's backward incompatible.
- It makes for sense for pipelines to remove references to
special_tokens (all of the other pipelines do that).
- Keeping special tokens makes it hard for users to actually remove them
  because all models have different tokens (<s>, <cls>, [CLS], ....)

* Fixing `token_str` in the same vein, and actually fix the tests too !

* Add head_mask/decoder_head_mask for TF BART models

* Add head_mask and decoder_head_mask input arguments for TF BART-based
models as a TF counterpart to the PR #9569

* Add test_headmasking functionality to tests/test_modeling_tf_common.py

* TODO: Add a test to verify that we can get a gradient back for
importance score computation

* Remove redundant #TODO note

Remove redundant #TODO note from tests/test_modeling_tf_common.py

* Fix assertions

* Make style

* Fix ...Model input args and adjust one new test

* Add back head_mask and decoder_head_mask to BART-based ...Model
after the last commit

* Remove head_mask ande decoder_head_mask from input_dict
in TF test_train_pipeline_custom_model as these two have different
shape than other input args (Necessary for passing this test)

* Revert adding global_rng in test_modeling_tf_common.py

* fix ci

* fix ci

* renaming

* fix dup line

* onnx triu workaround

* style

* working this time

* add test

* more efficient version

* Fix saved model tests + fix a graph issue in longformer

* Apply style

* Fix memory regression in Seq2Seq example

* Fix test and properly deal with -100

* Easier condition with device safety

* Patch for MBartTokenzierFast

* Fix Seq2Seq models for serving

* Apply style

* Fix lonfgormer

* Fix mBart/Pegasus/Blenderbot

* Apply style

* Add a main intermediate layer

* Apply style

* Remove import

* Apply tf.function to Longformer

* Fix utils check_copy

* Update S2S template

* Fix BART + Blenderbot

* Fix BlenderbotSmall

* Fix BlenderbotSmall

* Fix BlenderbotSmall

* Fix MBart

* Fix Marian

* Fix Pegasus + template

* Apply style

* Fix common attributes test

* Forgot to fix the LED test

* Apply Patrick's comment on LED Decoder

* Changing model default for TableQuestionAnsweringPipeline.

- Discussion: https://discuss.huggingface.co/t/table-question-answering-is-not-an-available-task-under-pipeline/3284/6

* Updating slow tests that were out of sync.

* Moved ProphetNetForCausalLM's parent initialization after config update

* Added unit tests for generation for ProphetNetForCausalLM

* Add DebertaForMaskedLM, DebertaForTokenClassification, DebertaForQuestionAnswering

* Add docs and fix quality

* Fix Deberta not having pooler

* Create new embeddings + add to BERT

* Add Albert

* Add DistilBert

* Add Albert + Electra + Funnel

* Add Longformer + Lxmert

* Add last models

* Apply style

* Update the template

* Remove unused imports

* Rename attribute

* Import embeddings in their own model file

* Replace word_embeddings per weight

* fix naming

* Fix Albert

* Fix Albert

* Fix Longformer

* Fix Lxmert Mobilebert and MPNet

* Fix copy

* Fix template

* Update the get weights function

* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/electra/modeling_tf_electra.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* address Sylvain's comments
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add decoder_head_mask for PyTorch T5 model

* Add decoder_head_mask args into T5Model and T5ForConditionalGeneration

* Slightly change the order of input args to be in accordance
with the convention from BART-based models introduced within the PR #9569.

* Make style for modeling_t5.py

* Add decoder_head_mask for TF T5 models

* Separate head_mask and decoder_head_mask args in TF T5 models

* Slightly change the order of input args to follow convention
of BART-based models updated in PR #9569

* Update test_forward_signature tests/test_modeling_tf_common.py
w.r.t. the changed order of input args

* Add FutureWarnings for T5 and TFT5 models

* Add FutureWarnings for T5 and TFT5 models warning a user that
input argument `head_mask` was split into two arguments -
`head_mask` and `decoder_head_mask`

* Add default behaviour - `decoder_head_mask` is set to copy
`head_mask`

* Fix T5 modeling and FutureWarning

* Make proper usage of head_mask and decoder_head_mask
in cross_attention

* Fix conditions for raising FutureWarning

* Reformat FutureWarning in T5 modeling

* Refactor the warning message

* New run_seq2seq script

* Add tests

* Mark as slow

* Update examples/seq2seq/run_seq2seq.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/transformers/data/data_collator.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update src/transformers/data/data_collator.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Address review comments
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update past_key_values in gpt2 (#9391)

* Update generation_utils, and rename some items

* Update modeling_gpt2 to avoid an error in gradient_checkpointing

* Remove 'reorder_cache' from util and add variations to XLNet, TransfoXL, GPT-2

* Change the location of '_reorder_cache' in modeling files

* Add '_reorder_cache' in modeling_ctrl

* Fix a bug of my last commit in CTRL

* Add '_reorder_cache' to GPT2DoubleHeadsModel

* Manage 'use_cache' in config of test_modeling_gpt2

* Clean up the doc string

* Update src/transformers/models/gpt2/modeling_gpt2.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Fix the doc string (GPT-2, CTRL)

* improve gradient_checkpointing_behavior
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Fix the attention_mask in DPRReaderTokenizer

* Add an integration test for DPRReader inference

* Run make style

* Add head_mask/decoder_head_mask for BART

This branch implement head_mask and decoder_head_mask
for BART-based models. Full list below:
- BART
- MBart
- Blenderbot
- BlenderbotSmall
- Marian
- Pegasus

Everything is accompanied with updated testing.

* Fix test_headmasking for BART models

* Fix text_headmasking for BART-like models
which has only 2 layers in each modules.
The condition
```
self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
```
is, therefore, invalid for encoder-decoder models considering
the `head_mask`
```
head_mask = torch.ones(
    self.model_tester.num_hidden_layers,
    self.model_tester.num_attention_heads,
    device=torch_device,
)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
```
specified in the `test_headmasking` test/function.

* Adjust test_modeling_common.py to reflect T5 input args

* Update tests/test_modeling_common.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* make style

* make fix-copies
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Upstream (and rename) sortish sampler

* Use proper sampler

* Update src/transformers/trainer_pt_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Fix Trainer with a parallel model

* More clean up

* Fix conversational pipeline test

* LayoutLM

* ProphetNet

* BART

* Blenderbot & small

* Marian

* mBART

* Pegasus

* Tapas tokenizer

* BERT2BERT test

* Style

* Example requirements

* TF BERT2BERT test

* Fix data parallelism in Trainer

* Update src/transformers/training_args.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>