GitLab

* Patches the full import failure and adds a test

* Add comment

* Add DistributedSamplerWithLoop

* Fix typo

* Test and small fix

* make flax tests pytorch independent

* fix typo

* finish

* improve circle ci

* fix return tensors

* correct flax test

* re-add sentencepiece

* last tokenizer fixes

* finish maybe now

* Tests run on Docker
Co-authored-by: Morgan <funtowiczmo@gmail.com>

* Comments from code review

* Reply to itself

* Dependencies
Co-authored-by: Morgan <funtowiczmo@gmail.com>

* Adding required flags to non-default arguments.
Signed-off-by: Adam Pocock <adam.pocock@oracle.com>

* make style fix.

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

* GPT2DoubleHeadsModel made parallelizeable

* GPT2DoubleHeadsModel added as parallelizeable onto the GPT2 test suite

* TF model exists for Blenderbot 400M

* Marian

* RAG

* [WIP] Adding new parameter to `generate`:  `max_time`.

Generation by tokens number is sometimes a bit clunky because we don't
know how many tokens are good enough or even how many tokens are in
the payload (for pipelines users for instance). This leads to hard
to understand behavior.

This PR proposes a new argument `max_time` which is a float of seconds
for the allowed time for `generate` to run on.
Ideally combinations of `max_tokens=None`, `max_time=2` could be used to
generate as many tokens as possible within time budget.

NB: Another possible approach consists of passing a callback to `generate`
  putting the caller in charge of the actual decision of when to stop
  generating tokens. It opens the door to 'which args should we pass'
  to this callback. It's hard to imagine other use-cases for this
  early stopping behavior than time (that are not already covered by
  parameters of generate)

* Revamp with StoppingCriteria

* Removing deprecated mentions.

* Forgot arguments to stopping criteria.

* Readding max_length it's not just used as a stopping criteria.

* Default value for `stopping_criteria`.

* Address @patrickvonplaten comments.

- More docstrings
- Actual doc
- Include in global namespace
- Remove TF work.

* Put back `max_length` (deprecation different PR).

* Doc quality.

* Fixing old behavior without `stopping_criteria` but with `max_length`.

Making sure we don't break that in the future.

* Adding more tests for possible inconsistencies between

`max_length` and `stopping_criteria`.

* Fixing the torch imports.

* PoC

* Fix slow tests for the PT1.8 Embedding problem

* Allow to pass kwargs to model's from_pretrained when using pipeline.

* Disable the use of past_keys_values for GPT2 when exporting to ONNX.

* style

* Remove comment.

* Appease the documentation gods

* Fix style
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* Adds a @require_torch to a test that requires it

* Tokenizer too

* Style

* Move tokenizer files in each repo

* Fix mBART50 tests

* Fix mBART tests

* Fix Marian tests

* Update templates

* s2t

* fix config

* conversion script

* fix import

* add tokenizer

* fix tok init

* fix tokenizer

* first version working

* fix embeds

* fix lm head

* remove extra heads

* fix convert script

* handle encoder attn mask

* style

* better enc attn mask

* override _prepare_attention_mask_for_generation

* handle attn_maks in encoder and decoder

* input_ids => input_features

* enable use_cache

* remove old code

* expand embeddings if needed

* remove logits bias

* masked_lm_loss => loss

* hack tokenizer to support feature processing

* fix model_input_names

* style

* fix error message

* doc

* remove inputs_embeds

* remove input_embeds

* remove unnecessary docstring

* quality

* SpeechToText => Speech2Text

* style

* remove shared_embeds

* subsample => conv

* remove Speech2TextTransformerDecoderWrapper

* update output_lengths formula

* fix table

* remove max_position_embeddings

* update conversion scripts

* add possibility to do upper case for now

* add FeatureExtractor and Processor

* add tests for extractor

* require_torch_audio => require_torchaudio

* add processor test

* update import

* remove classification head

* attention mask is now 1D

* update docstrings

* attention mask should be of type long

* handle attention mask from generate

* alwyas return attention_mask

* fix test

* style

* doc

* Speech2TextTransformer => Speech2Text

* Speech2TextTransformerConfig => Speech2TextConfig

* remove dummy_inputs

* nit

* style

* multilinguial tok

* fix tokenizer

* add tgt_lang setter

* save lang_codes

* fix tokenizer

* add forced_bos_token_id to tokenizer

* apply review suggestions

* add torchaudio to extra deps

* add speech deps to CI

* fix dep

* add libsndfile to ci

* libsndfile1

* add speech to extras all

* libsndfile1 -> libsndfile1

* libsndfile

* libsndfile1-dev

* apt update

* add sudo to install

* update deps table

* install libsndfile1-dev on CI

* tuple to list

* init conv layer

* add model tests

* quality

* add integration tests

* skip_special_tokens

* add speech_to_text_transformer in toctree

* fix tokenizer

* fix fp16 tests

* add tokenizer tests

* fix copyright

* input_values => input_features

* doc

* add model in readme

* doc

* change checkpoint names

* fix copyright

* fix code example

* add max_model_input_sizes in tokenizer

* fix integration tests

* add do_lower_case to tokenizer

* remove clamp trick

* fix "Add modeling imports here"

* fix copyrights

* fix tests

* SpeechToTextTransformer => SpeechToText

* fix naming

* fix table formatting

* fix typo

* style

* fix typos

* remove speech dep from extras[testing]

* fix copies

* rename doc file,

* put imports under is_torch_available

* run feat extract tests when torch is available

* dummy objects for processor and extractor

* fix imports in tests

* fix import in modeling test

* fxi imports

* fix torch import

* fix imports again

* fix positional embeddings

* fix typo in import

* adapt new extractor refactor

* style

* fix torchscript test

* doc

* doc

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix docs, copied from, style

* fix docstring

* handle imports

* remove speech from all extra deps

* remove s2t from seq2seq lm mapping

* better names

* skip training tests

* add install instructions

* List => Tuple

* doc

* fix conversion script

* fix urls

* add instruction for libsndfile

* fix fp16 test
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Fix tests of TrainerCallback

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

* save first version

* finish refactor

* finish refactor

* correct naming

* correct naming

* shorter names

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

* change name

* finish
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Pipeline tests should be slow

* Temporarily mark some tests as slow

* Temporarily mark Barthez tests as slow

* Create modeling_tf_dpr.py

* Add TFDPR

* Add back TFPegasus, TFMarian, TFMBart, TFBlenderBot

last commit accidentally deleted these 4 lines, so I recover them back

* Add TFDPR

* Add TFDPR

* clean up some comments, add TF input-style doc string

* Add TFDPR

* Make return_dict=False as default

* Fix return_dict bug (in .from_pretrained)

* Add get_input_embeddings()

* Create test_modeling_tf_dpr.py

The current version is already passed all 27 tests!
Please see the test run at : 
https://colab.research.google.com/drive/1czS_m9zy5k-iSJbzA_DP1k1xAAC_sdkf?usp=sharing



* fix quality

* delete init weights

* run fix copies

* fix repo consis

* del config_class, load_tf_weights

They shoud be 'pytorch only'

* add config_class back

after removing it, test failed ... so totally only removing "use_tf_weights = None" on Lysandre suggestion

* newline after .. note::

* import tf, np (Necessary for ModelIntegrationTest)

* slow_test from_pretrained with from_pt=True

At the moment we don't have TF weights (since we don't have official official TF model)
Previously, I did not run slow test, so I missed this bug

* Add simple TFDPRModelIntegrationTest

Note that this is just a test that TF and Pytorch gives approx. the same output.
However, I could not test with the official DPR repo's output yet

* upload correct tf model

* remove position_ids as missing keys

* create modeling_tf_rag

* add tests for tf

* add tf tests

* revert wrong pt commit

* further refactor

* further refactor

* refactor

* Update modeling_tf_rag.py

- input_processing
- fix prepare_input_for_generation (mostly fix generate bug)
- bring back from_pretrained hack in order to test generate

* delete colab pieces of code

* Show case of greedy "generate"

Temporarily change from beam_search test to greedy_search test to show case that TF and PT do get equivalent output.

* cosmetic update

* correct typos

* update

* push some progress

* make easy check

* fix rag save from pretrained

* Update src/transformers/modeling_tf_utils.py

* remove commented out lines

* delete unnecessary lines

* add simple test case for nq_checkpoint

Add nq_checkpoint test to show that current version without hack still fails

* temporarily put ugly hack back again

* Add TFRagSequenceForGeneration!!

* __init__.py , import TFRagSequenceForGeneration

* Add TFRagSequence tests!

* rag init.py - add TFRagSequenceForGeneration

* fix from_pretrained

* fix prepare_inputs_for_generation

* Beam search for RagToken!

* minor clean up

* add tf.cast in TFRagModel

* More tf.cast

* Add all remaining tests (still have issues)

* delete all T5 related

* make style

* fix load weight prefix

* fix bart

* fix return_dict for tf_rag

make all tests pass .. Hooray

* fix some tests

* fix code quality

* fix qualtiy check

* finish tests tf rag

* add tf rag to docs

* remove TFT5 from docstring
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* remove TFT5 from docstring
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Delete outdated comments
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* improve doc strings

* add generative model classes

* fix adjust token logic

* refactor generate for TFRag

* using shape_list, not _get_shape
Co-authored-by: Julien Plu <plu.julien@gmail.com>

* axis=[1]->axis=1

* delete NEED_HELP comment

* improve readability
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* improve readability
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* improve readability
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Indicating model is in a developing state in docstrings

As suggested by Julien

* small last changes

* apply sylvains suggestions

* finish tf rag
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: patrickvonplaten <patrick@huggingface.co>
Co-authored-by: Julien Plu <plu.julien@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Check layer types for Optimizer construction

* Duplicate class

This reverts commit b35e7b68.

This reverts commit a8ec52ef.

* more readable test

* add all the missing places

* one more nltk

* better exception check

* revert

* fix tests

* emb should be a parameter

* fix positional embeddings

* fix make_weights

* don't save pos embeds

* add comment to describe the clamping

* m2m_100

* no layernorm_embedding

* sinusoidal positional embeddings

* update pos embeddings

* add default config values

* tokenizer

* add conversion script

* fix config

* fix pos embed

* remove _float_tensor

* update tokenizer

* update lang codes

* handle lang codes

* fix pos embeds

* fix spm key

* put embedding weights on device

* remove qa and seq classification heads

* fix convert script

* lang codes pn one line

* fix embeds

* fix tokenizer

* fix tokenizer

* add fast tokenizer

* style

* M2M100MT => M2M100

* fix copyright, style

* tokenizer converter

* vocab file

* remove fast tokenizer

* fix embeds

* fix tokenizer

* fix tests

* add tokenizer tests

* add integration test

* quality

* fix model name

* fix test

* doc

* doc

* fix doc

* add copied from statements

* fix tokenizer tests

* apply review suggestions

* fix urls

* fix shift_tokens_right

* apply review suggestions

* fix

* fix doc

* add lang code to id

* remove unused function

* update checkpoint names

* fix copy

* fix tokenizer

* fix checkpoint names

* fix merge issue

* style

* offline mode start

* add specific values

* fix fallback

* add test

* better values check and range

* test that actually works

* document the offline mode

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

* more strict check

* cleaner test

* pt-only test

* style
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Only run one test

* Patch segfault

* Fix summarization pipeline

* Ready for merge

* first step to refactor

* make all fast tests pass

* make all slow tests pass

* save intermediate

* correct cache

* finish PR

* make fp16 work

* Rework TPU checkpointing in Trainer

* Wraps the barrier in a dist test

* Address review comments

* Remove line

* add encode labels function to tokenizer

* start adding finetuning

* init dropout

* upload

* correct convert script

* apply changes

* fix second typo

* make first dummy training run

* adapt convert script

* push confg for comparison

* remove conf

* finish training

* adapt data collator

* add research folder

* update according to fairseq feedback

* some minor corrections

* refactor masking indices a bit

* some minor changes

* clean tokenizer

* finish clean-up

* remove previous logic

* update run script

* correct training

* finish changes

* finish model

* correct bug

* fix training a bit more

* add some tests

* finish gradient checkpointing

* finish example

* correct gradient checkpointing

* improve tokenization method

* revert changes in tokenizer

* revert general change

* adapt fine-tuning

* update

* save intermediate test

* Update README.md

* finish finetuning

* delete conversion script

* Update src/transformers/models/wav2vec2/configuration_wav2vec2.py

* Update src/transformers/models/wav2vec2/processing_wav2vec2.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* finish wav2vec2 script

* finish wav2vec2 fine-tuning

* finalize test

* correct test

* adapt tests

* finish

* remove test file
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>