GitLab

* Generalize problem_type to all classification models

* Missing import

* Deberta BC and fix tests

* Fix template

* Missing imports

* Revert change to reformer test

* Fix style

* Adding `handle_long_generation` paramters for `text-generation` pipeline.

* More error handling

* Fixing tests by dropping tf support on this functionality, it needs

`max_new_tokens` to make it possible to understand user's intent.
Otherwise, `max_length` == `tokenizer.model_max_length` <
input_ids.shape[0].

* Fixing doc ?

* Doc ?

* Remove link from doc.

* Catched an issue on roberta.

* Damn doc.

* Non BC proposal ?

* Cleaning the fix ?

* Finally using only a test override.

* Don't need to modify this.

* Bad print.

* Add the support for the fast (rust) implementation of BlenbderbotTokenizer

* Fix a converter and a typo in a doc

* Apply the patil-suraj's suggestion

* (Nitpick) Fast tokenization -> Fast Tokenization in doc

* Apply the SaulLu's suggestion

* Apply Narsil's suggestion to fix test pipelines

* Add encoder_no_repeat_ngram_size according to the Narsil's suggestion

* Revert the last (unnecessary) commit

* Override pipeline config for Blenderbot to allow for larger pos. emb.

* make fix-copies

* Remove n_ctx from configs

* Fix GPTJ and OpenAIGPT, both are acceptable breaking changes as there are no configs such that it breaks

* Remove unecessary n_positions from TFOpenAIGPT

* Tentative enabling of `batch_size` for pipelines.

* Add systematic test for pipeline batching.

* Enabling batch_size on almost all pipelines

- Not `zero-shot` (it's already passing stuff as batched so trickier)
- Not `QA` (preprocess uses squad features, we need to switch to real
tensors at this boundary.

* Adding `min_length_for_response` for conversational.

* Making CTC, speech mappings avaiable regardless of framework.

* Attempt at fixing automatic tests (ffmpeg not enabled for fast tests)

* Removing ffmpeg dependency in tests.

* Small fixes.

* Slight cleanup.

* Adding docs

and adressing comments.

* Quality.

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

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

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

* Improving docs.

* Update docs/source/main_classes/pipelines.rst
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>

* N -> oberved_batch_size

softmax trick.

* Follow `padding_side`.

* Supporting image pipeline batching (and padding).

* Rename `unbatch` -> `loader_batch`.

* unbatch_size forgot.

* Custom padding for offset mappings.

* Attempt to remove librosa.

* Adding require_audio.

* torchaudio.

* Back to using datasets librosa.

* Adding help to set a pad_token on the tokenizer.

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

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

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

* Quality.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>

* Fix SEW-D

* Update tests

* isort

* First draft

* Make style & quality

* Improve conversion script

* Add print statement to see actual slice

* Make absolute tolerance smaller

* Fix image classification models

* Add post_process_semantic method

* Disable padding

* Improve conversion script

* Rename to ForSemanticSegmentation, add integration test, remove post_process methods

* Improve docs

* Fix code quality

* Fix feature extractor tests

* Fix tests for image classification model

* Delete file

* Add is_torch_available to feature extractor

* Improve documentation of feature extractor methods

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

* Apply some more suggestions of code review

* Rebase with master

* Fix rebase issues

* Make sure model only outputs hidden states when the user wants to

* Apply suggestions from code review

* Add pad method

* Support padding of 2d images

* Add print statement

* Add print statement

* Move padding method to SegformerFeatureExtractor

* Fix issue

* Add casting of segmentation maps

* Add test for padding

* Add small note about padding
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* up

* up

* fix

* up

* Update examples/pytorch/test_xla_examples.py

* correct labels

* up

* up

* up

* up

* up

* up

* Add conversion

* Rename

* Add an integration test and remove layer_norm

* Remove layer_norm from the converter

* wording

* Fix imports

* Add SEW CTC models

* Update paths

* Update paths

* unispeech

* add copy from

* remove hubert copy from

* finish for today

* add unispeech-sat

* adapt more

* up

* up

* up

* up

* add modeling

* add tests

* up

* up

* finish

* up

* Apply suggestions from code review

* up

* up

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

* up

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

* up

* up

* up

* up

* up

* up

* up

* add clip

* fix clip PyTorch

* fix clip PyTorch

* up

* up

* up

* up

* up

* up

* up

* Fix

* Style

* Name

* Fix tests

* Style

* Remove embed sizes checking

* Disable some tests

* Fix

* Apply suggestion

* add `ContextManagers` for lists of contexts

* fix import sorting

* add `ContextManagers` tests

* TF Model train and eval step metrics for seq2seq models.

When using a model with a seq2seq output compute metrics against logits.

* Removing vestigial code
Co-authored-by: matt <rocketknight1@gmail.com>

* adapt_examples

* up

* up

* up

* up

* add auto models

* finish

* Add API to register a new object in auto classes

* Fix test

* Documentation

* Add to tokenizers and test

* Add cleanup after tests

* Be more careful

* Move import

* Move import

* Cleanup in TF test too

* Add consistency check

* Add documentation

* Style

* Update docs/source/model_doc/auto.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

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

* Add the pre-trained BARTpho model

* Add the pre-trained BARTpho model

* Add the pre-trained BARTpho model

* Fix incorrectly sorted and/or formatted imports

* Fix incorrectly sorted and/or formatted style

* Fix check_dummies

* Fix check_dummies

* Fix check_dummies

* Update docs/source/model_doc/bartpho.rst
Co-authored-by: Suraj Patil <surajp815@gmail.com>

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

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

* Update tests/test_tokenization_bartpho.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

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

* Update tests/test_tokenization_bartpho.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

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

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

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

* Add the pre-trained BARTpho model

* Add Tips section in doc and details of monolingual_vocab_file

* Fix conflicts

* Add another tip related to monolingual_vocab_file

* Readd dependency_versions_table.py

* Handle failing checks

* Remove test_list.txt

* Remove md5sum.saved

* Revise Readme.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* up

* up

* up

* finish

* Working encoder

* SEW-D and tests

* Further conv fixes

* Automodels and conv inits

* Update integration tests, add docs

* Docs cleanup, resolve todos

* Conf fix

* Fix docs

* Fix tests, apply suggestions

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

* Model conversion and updated no-mask tests

* Remove copy of feature_proj

* Style

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

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

* Move orgs
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Scatter dummies + skip pipeline tests

* Add torch scatter to build docs

* Add strong test for configuration attributes

* Add fake modif to trigger all tests

* Add a better fake modif

* Ignore is_encoder_decoder

* Fix faulty configs

* Remove fake modif

* First draft

* Update self-attention of RoBERTa as proposition

* Improve conversion script

* Add TrOCR decoder-only model

* More improvements

* Make forward pass with pretrained weights work

* More improvements

* Some more improvements

* More improvements

* Make conversion work

* Clean up print statements

* Add documentation, processor

* Add test files

* Small improvements

* Some more improvements

* Make fix-copies, improve docs

* Make all vision encoder decoder model tests pass

* Make conversion script support other models

* Update URL for OCR image

* Update conversion script

* Fix style & quality

* Add support for the large-printed model

* Fix some issues

* Add print statement for debugging

* Add print statements for debugging

* Make possible fix for sinusoidal embedding

* Further debugging

* Potential fix v2

* Add more print statements for debugging

* Add more print statements for debugging

* Deubg more

* Comment out print statements

* Make conversion of large printed model possible, address review comments

* Make it possible to convert the stage1 checkpoints

* Clean up code, apply suggestions from code review

* Apply suggestions from code review, use Microsoft models in tests

* Rename encoder_hidden_size to cross_attention_hidden_size

* Improve docs

* Add cross attentions to TFGPT2Model

* Add TFEncoderDecoderModel

* Add TFBaseModelOutputWithPoolingAndCrossAttentions

* Add cross attentions to TFBertModel

* Fix past or past_key_values argument issue

* Fix generation

* Fix save and load

* Add some checks and comments

* Clean the code that deals with past keys/values

* Add kwargs to processing_inputs

* Add serving_output to TFEncoderDecoderModel

* Some cleaning + fix use_cache value issue

* Fix tests + add bert2bert/bert2gpt2 tests

* Fix more tests

* Ignore crossattention.bias when loading GPT2 weights into TFGPT2

* Fix return_dict_in_generate in tf generation

* Fix is_token_logit_eos_token bug in tf generation

* Finalize the tests after fixing some bugs

* Fix another is_token_logit_eos_token bug in tf generation

* Add/Update docs

* Add TFBertEncoderDecoderModelTest

* Clean test script

* Add TFEncoderDecoderModel to the library

* Add cross attentions to TFRobertaModel

* Add TFRobertaEncoderDecoderModelTest

* make style

* Change the way of position_ids computation

* bug fix

* Fix copies in tf_albert

* Remove some copied from and apply some fix-copies

* Remove some copied

* Add cross attentions to some other TF models

* Remove encoder_hidden_states from TFLayoutLMModel.call for now

* Make style

* Fix TFRemBertForCausalLM

* Revert the change to longformer + Remove copies

* Revert the change to albert and convbert + Remove copies

* make quality

* make style

* Add TFRembertEncoderDecoderModelTest

* make quality and fix-copies

* test TFRobertaForCausalLM

* Fixes for failed tests

* Fixes for failed tests

* fix more tests

* Fixes for failed tests

* Fix Auto mapping order

* Fix TFRemBertEncoder return value

* fix tf_rembert

* Check copies are OK

* Fix missing TFBaseModelOutputWithPastAndCrossAttentions is not defined

* Add TFEncoderDecoderModelSaveLoadTests

* fix tf weight loading

* check the change of use_cache

* Revert the change

* Add missing test_for_causal_lm for TFRobertaModelTest

* Try cleaning past

* fix _reorder_cache

* Revert some files to original versions

* Keep as many copies as possible

* Apply suggested changes - Use raise ValueError instead of assert

* Move import to top

* Fix wrong require_torch

* Replace more assert by raise ValueError

* Add test_pt_tf_model_equivalence (the test won't pass for now)

* add test for loading/saving

* finish

* finish

* Remove test_pt_tf_model_equivalence

* Update tf modeling template

* Remove pooling, added in the prev. commit, from MainLayer

* Update tf modeling test template

* Move inputs["use_cache"] = False to modeling_tf_utils.py

* Fix torch.Tensor in the comment

* fix use_cache

* Fix missing use_cache in ElectraConfig

* Add a note to from_pretrained

* Fix style

* Change test_encoder_decoder_save_load_from_encoder_decoder_from_pt

* Fix TFMLP (in TFGPT2) activation issue

* Fix None past_key_values value in serving_output

* Don't call get_encoderdecoder_model in TFEncoderDecoderModelTest.test_configuration_tie until we have a TF checkpoint on Hub

* Apply review suggestions - style for cross_attns in serving_output

* Apply review suggestions - change assert + docstrings

* break the error message to respect the char limit

* deprecate the argument past

* fix docstring style

* Update the encoder-decoder rst file

* fix Unknown interpreted text role "method"

* fix typo
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* correct long to bool

* up

* correct code

* adapt wav2vec2

* add example

* add files

* adapt

* remove bogus file

* Apply suggestions from code review

* adapt files more

* upload changes

* del old files

* up

* up

* up

* up

* up

* correct gradient checkpoitning

* add readme

* finish

* finish

* up

* more fixes

* up

* up

* add demo run to readme

* up

[Gradient checkpoining] Correct disabling `find_unused_parameters` in Trainer when gradient checkpointing is enabled (#13961)

* up

* correct test

* Honor existing attention mask in tokenzier.pad

* Fix initialization of attention mask

* Roll the implem on all subclasses

* Fix tests

* up

* Update src/transformers/generation_stopping_criteria.py

* finish

* Adding support for tokens being suffixes or part of each other.

* Better test name.