GitLab

* updating fsdp documentation

* typo fix

* Clean up utils.hub

* Remove imports

* More fixes

* Last fix

* [DX fix] Fixing QA pipeline streaming a dataset.

QuestionAnsweringArgumentHandler would iterate over the whole dataset
effectively killing all properties of the pipeline.
This restores nice properties when using `Dataset` or `Generator` since
those are meant to be consumed lazily.

* Handling TF better.

* zero chance anyone's using that constant no?

* `transformers-cli login` => `huggingface-cli login`

* `transformers-cli repo create` => `huggingface-cli repo create`

* `make style`

* Delete valohai.yaml

* NLP => ML

* typo

* website supports https

* datasets

* 60k + modalities

* unrelated link fixing for accelerate

* Ok those links were actually broken

* Fix link

* Make `AutoTokenizer` auto-link

* wording tweak

* add at least one non-nlp task

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Move cache folder to just huggingface

* Thank you VsCode for this needless import

* Move to hub

* Forgot one

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Draft new cached_file

* Initial draft for config and model

* Small fixes

* Fix first batch of tests

* Look in cache when internet is down

* Fix last tests

* Bad black, not fixing all quality errors

* Make diff less

* Implement change for TF and Flax models

* Add tokenizer and feature extractor

* For compatibility with main

* Add utils to move the cache and auto-do it at first use.

* Quality

* Deal with empty commit shas

* Deal with empty etag

* Address review comments

* Fix pipeline tests

* Make sure all pipelines tests run with init changes

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Refactor `TFSwinLayer` to increase serving compatibility
Signed-off-by: Seunghwan Hong <seunghwan@scatterlab.co.kr>

* Fix missed parameters while refactoring
Signed-off-by: Seunghwan Hong <seunghwan@scatterlab.co.kr>

* Fix window_reverse to calculate batch size
Signed-off-by: Seunghwan Hong <harrydrippin@gmail.com>
Co-Authored-By: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

Signed-off-by: Seunghwan Hong <seunghwan@scatterlab.co.kr>

* Adding a better error message when the model is improperly configured

within transformers.

* Update src/transformers/pipelines/__init__.py

* Black version.

* Overriding task aliases so that tokenizer+feature_extractor

values are correct.

* Fixing task aliases by overriding their names early

* X.

* Fixing feature-extraction.

* black again.

* Normalizing `translation` too.

* Fixing last few corner cases.

translation need to use its non normalized name (translation_XX_to_YY,
so that the task_specific_params are correctly overloaded).
This can be removed and cleaned up in a later PR.

`speech-encode-decoder` actually REQUIRES to pass a `tokenizer` manually
so the error needs to be discarded when the `tokenizer` is already
there.

* doc-builder fix.

* Fixing the real issue.

* Removing dead code.

* Do not import the actual config classes.

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* swag_no_trainer updated for with gather_metrics

* Removed unused variable samples_seen

* updated examples with gather_for_metrics

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* First draft

* Add VideoMAEForVideoClassification

* Improve conversion script

* Add VideoMAEForPreTraining

* Add VideoMAEFeatureExtractor

* Improve VideoMAEFeatureExtractor

* Improve docs

* Add first draft of model tests

* Improve VideoMAEForPreTraining

* Fix base_model_prefix

* Make model take pixel_values of shape (B, T, C, H, W)

* Add loss computation of VideoMAEForPreTraining

* Improve tests

* Improve model testsé

* Make all tests pass

* Add VideoMAE to main README

* Add tests for VideoMAEFeatureExtractor

* Add integration test

* Improve conversion script

* Rename patch embedding class

* Remove VideoMAELayer from init

* Update design of patch embeddings

* Improve comments

* Improve conversion script

* Improve conversion script

* Add conversion of pretrained model

* Add loss verification of pretrained model

* Add loss verification of unnormalized targets

* Add integration test for pretraining model

* Apply suggestions from code review

* Fix bug to make feature extractor resize only shorter edge

* Address more comments

* Improve normalization of videos

* Add doc examples

* Move constants to dedicated script

* Remove scripts

* Transfer checkpoints, fix docs

* Update script

* Update image mean and std

* Fix doc tests

* Set return_tensors to NumPy by default

* Revert the previous change
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

* swag_no_trainer updated for with gather_metrics

* Removed unused variable samples_seen

* Enable HFTracer to trace with custom dummy inputs instead of pre-computed ones

* Add HFTracer.trace docstring, and make it possible to handle callable and torch.nn.Module in general

* Remove pdb comment

* Apply suggestions

* change shape to support dynamic batch input in tf.generate

* add tests
Co-authored-by: nlpcatcode <nlpcodecat@gmail.com>

* Cleanup some code

* Improve signatures

* Try to reduce the number of reshape/copies

* I don't think we actually need the layer_num scaling trick

* No need for duplication

* Try to fix beam_search

* Fix beam search

* Removing layer num normalization seems to be breaking

* Not sure self.layer_number normalization actually matters

* Try and be backward compatible

* Try to fix beam_search

* Revert attempt to be backward compatible

* Improve documentation on past_key_values format

* Optimize the device allocation in case of hidden_states in multiple devices

* No need to manually cast the values to a specific device

* Rename with long version of variables

* Improve type hinting

* Add comment that explains that some methods return views

* Actually i think the attention casting only makes sense when we use torch.float16

* We don't actually need layer_number to be passed anymore

* Fix FX test

* Bypass torch.baddbmm

* Apply suggestions from code review

* Add comment about support for torchScript v1.11

* fix ONNX support for bloom (#18456)
Co-authored-by: Niklas Muennighoff <n.muennighoff@gmail.com>
Co-authored-by: Nouamane Tazi <nouamane98@gmail.com>

Comparisons like
version.parse(torch.__version__) > version.parse("1.6")
are True for torch==1.6.0+cu101 or torch==1.6.0+cpu

version.parse(version.parse(torch.__version__).base_version) are preferred (and available in pytorch_utils.py

* fix: keras fit tests for segformer tf and minor refactors.

* refactor: test_keras_fit to make it simpler using the existing one.

* fix: styling issues.

* Fix failing test_xla_generate_slow tests

* Fix failing speech-to-text xla_generate tests

* Update pinned hhub version

* Make style

* Update no_trainer script for image-classification

* Update no_trainer scripts for language-modeling examples

* Remove unused variable

* Removing truncation from losses array for language modeling examples

* Add file in spanish docs to be translated

* Translate first two sections to Spanish

* Translate four additional sections to Spanish

* Finish translation to Spanish

* Improve writing style in Spanish

* Add suggested changes from reviewer

* support ONNX export of XDropout in deberta{,_v2}

* black

* copy to sew_d

* add test

* isort

* use pytest.mark.filterwarnings

* review comments

This PR moves GroupViT and LXMert to their correct sections. As pointed out by @NielsRogge and @LysandreJik, GroupViT and LXMert are both multimodal models.

The current wording makes it sound as if the programming languages are part of the 46 natural languages.