* fix deberta issues
* add different code paths for gpu and tpu
* shorter gpu take along axis
* Stable Dropout without tf cond
* variable must be float
* first commit
* correct replace function
* add final changes
- works like charm!
- cannot implement tests yet
- tested
* clean up a bit
* add bitsandbytes dependencies
* working version
- added import function
- added bitsandbytes utils file
* small fix
* small fix
- fix import issue
* fix import issues
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* refactor a bit
- move bitsandbytes utils to utils
- change comments on functions
* reformat docstring
- reformat docstring on init_empty_weights_8bit
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* revert bad formatting
* change to bitsandbytes
* refactor a bit
- remove init8bit since it is useless
* more refactoring
- fixed init empty weights issue
- added threshold param
* small hack to make it work
* Update src/transformers/modeling_utils.py
* Update src/transformers/modeling_utils.py
* revmoe the small hack
* modify utils file
* make style + refactor a bit
* create correctly device map
* add correct dtype for device map creation
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* apply suggestions
- remove with torch.grad
- do not rely on Python bool magic!
* add docstring
- add docstring for new kwargs
* add docstring
- comment `replace_8bit_linear` function
- fix weird formatting
* - added more documentation
- added new utility function for memory footprint tracking
- colab demo to add
* few modifs
- typo doc
- force cast into float16 when load_in_8bit is enabled
* added colab link
* add test architecture + docstring a bit
* refactor a bit testing class
* make style + refactor a bit
* enhance checks
- add more checks
- start writing saving test
* clean up a bit
* male style
* add more details on doc
* add more tests
- still needs to fix 2 tests
* replace by "or"
- could not fix it from GitHub GUI
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* refactor a bit testing code + add readme
* make style
* fix import issue
* Update src/transformers/modeling_utils.py
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* add few comments
* add more doctring + make style
* more docstring
* raise error when loaded in 8bit
* make style
* add warning if loaded on CPU
* add small sanity check
* fix small comment
* add bitsandbytes on dockerfile
* Improve documentation
- improve documentation from comments
* add few comments
* slow tests pass on the VM but not on the CI VM
* Fix merge conflict
* make style
* another test should pass on a multi gpu setup
* fix bad import in testing file
* Fix slow tests
- remove dummy batches
- no more CUDA illegal memory errors
* odify dockerfile
* Update docs/source/en/main_classes/model.mdx
* Update Dockerfile
* Update model.mdx
* Update Dockerfile
* Apply suggestions from code review
* few modifications
- lm head can stay on disk/cpu
- change model name so that test pass
* change test value
- change test value to the correct output
- torch bmm changed to baddmm in bloom modeling when merging
* modify installation guidelines
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* replace `n`by `name`
* merge `load_in_8bit` and `low_cpu_mem_usage`
* first try - keep the lm head in full precision
* better check
- check the attribute `base_model_prefix` instead of computing the number of parameters
* added more tests
* Update src/transformers/utils/bitsandbytes.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Merge branch 'integration-8bit' of https://github.com/younesbelkada/transformers into integration-8bit
* improve documentation
- fix typos for installation
- change title in the documentation
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* update features
* MT5OnnxConfig added with updated with tests and docs
* fix imports
* fix onnc_config_cls for mt5
Co-authored-by: Thomas Chaigneau <thomas.deeptools.ai>
* add correct dtypes when checking for params dtype
* forward contrib credits
* Update src/transformers/modeling_utils.py
Co-authored-by: Thomas Wang <24695242+thomasw21@users.noreply.github.com>
* more comments
- added more comments on why we cast only floating point parameters
* Update src/transformers/modeling_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: sgugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Thomas Wang <24695242+thomasw21@users.noreply.github.com>
* Added accelerate gradient accumulation wrapper to run_image_classification_no_trainer.py example script
* make fixup changes
* PR comments
* changed input to Acceletor based on PR comment, ran make fixup
* Added comment explaining the sync_gradients statement
* Fixed lr scheduler max steps
* Changed run_clm_no_trainer.py script to use accelerate gradient accum wrapper
* Fixed all scripts except wav2vec2 pretraining to use accelerate gradient accum wrapper
* Added accelerate gradient accum wrapper for wav2vec2_pretraining_no_trainer.py script
* make fixup and lr_scheduler step inserted back into run_qa_beam_search_no_trainer.py
* removed changes to run_wav2vec2_pretraining_no_trainer.py script and fixed using wrong constant in qa_beam_search_no_trainer.py script
* [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.
* 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
* 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
* 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.
* 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>
Sylvain Gugger