I create an archive of older checkpoints during training the checkpoint has a name with `f"{checkpoint_prefix}-*.zip/.tar `
previously `glob(f"{checkpoint_prefix}-*")` takes all files/folders starting with the name checkpoint, and later `shutil.rmtree(checkpoint)` takes a folder name; since at some point it my get a zip file; it crashes training; adding this `if os.path.isdir(x)` allows only folders on `glob_checkpoints`
* add simple multi gpu complet
* add human_eval_multi_gpu
* use copy strategy to distribute across gpu, to avoid padding
* add doc string
* update code style
* use task id to arrange output
* truncate input to avoid zero pad
* Stop the copy mechanism
* update style
* restore copies to scale better in distributed mode
* update style
* replace human eval
* Apply suggestions from code review
1. Tokenize all input at the same time
2. use attention_mask to get the input length
3. other small fixes
Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com>
* correct typo and update docstring
* update code style
* remove num sample division constraint
* remove max len calculation
* use accelerator.gather once to speed up
* use accelerate set_seed; update accelerate version
* correct gather bug
Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com>
* update proto sentencepiece model
* Revert "update proto sentencepiece model"
This reverts commit b07f671747.
* add check
* add test
* Revert "Revert "update proto sentencepiece model""
This reverts commit 46108257b8.
* test for log level
* test for log level 2
* warning at the warning level
* clean
* format
* add explanation in docstring
* Fixed some bugs involving saving during epochs
* Added tests mimicking the existing examples tests
* Added in json exporting to all `no_trainer` examples for consistency
* Add TapexTokenizer
* Improve docstrings and provide option to provide answer
* Remove option for pretokenized inputs
* Add TAPEX to README
* Fix copies
* Remove option for pretokenized inputs
* Initial commit: add tapex fine-tuning examples on both table-based question answering and table-based fact verification.
* - Draft a README file for running the script and introducing some background.
- Remove unused code lines in tabfact script.
- Disable the deafult `pad_to_max_length` option which is memory-consuming.
* * Support `as_target_tokenizer` function for TapexTokenizer.
* Fix the do_lower_case behaviour of TapexTokenizer.
* Add unit tests for target scenarios and cased/uncased scenarios for both source and target.
* * Replace the label BartTokenizer with TapexTokenizer's as_target_tokenizer function.
* Fix typos in tapex example README.
* * fix the evaluation script - remove the property `task_name`
* * Make the label space more clear for tabfact tasks
* * Using a new fine-tuning script for tapex-base on tabfact.
* * Remove the lowercase code outside the tokenizer - we use the tokenizer to control whether do_lower_case
* Guarantee the hyper-parameter can be run without out-of-memory on 16GB card and report the new reproduced number on wikisql
* * Remove the default tokenizer_name option.
* Provide evaluation command.
* * Support for WikiTableQuestion dataset.
* Fix a typo in README.
* * Fix the datasets's key name in WikiTableQuestions
* Run make fixup and move test to folder
* Fix quality
* Apply suggestions from code review
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Apply suggestions from code review
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply some more suggestions from code review
* Improve docstrings
* Overwrite failing test
* Improve comment in example scripts
* Fix rebase
* Add TAPEX to Auto mapping
* Add TAPEX to auto config mappings
* Put TAPEX higher than BART in auto mapping
* Add TAPEX to doc tests
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>
Co-authored-by: SivilTaram <qianlxc@outlook.com>
Co-authored-by: Niels Rogge <nielsrogge@nielss-mbp.home>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Update README.md Support Image
Updates the Support image linking to our EAP page (to give it a refresh + help avoid image fatigue).
Slack thread checking in with #open-source-internal on this update (https://huggingface.slack.com/archives/C021H1P1HKR/p1648838903316709)
* Compressed Updated Support image
* Improves Support Image Logo + Height
Updated the image based on logo + size feedback. Big thanks to Bibi for making quick edits to this image.
* Add inputs vector to calculate metric method
* Include inputs for evaluation metrics with backwards compatibility
* Prevent inputs create OOM issue and documentation details
* Update style and code documentation
* Fix style formatting issues
* Update files format with make style
* Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* 📝 add image/vision classification and asr
* 🖍 minor formatting fixes
* Fixed a typo in legacy seq2seq_trainer.py (#16531)
* Add ONNX export for BeiT (#16498)
* Add beit onnx conversion support
* Updated docs
* Added cross reference to ViT ONNX config
* call on_train_end when trial is pruned (#16536)
* Type hints added (#16529)
* Fix Bart type hints (#16297)
* Add type hints to PLBart PyTorch
* Remove pending merge conflicts
* Fix PLBart Type Hints
* Add changes from review
* Add VisualBert type hints (#16544)
* Adding missing type hints for mBART model (PyTorch) (#16429)
* added type hints for mbart tensorflow tf implementation
* Adding missing type hints for mBART model
Tensorflow Implementation model added with missing type hints
* Missing Type hints - correction
For TF model
* Code fixup using make quality tests
* Hint types - typo error
* make fix-copies and make fixup
* type hints
* updated files
* type hints update
* making dependent modesls coherent
Co-authored-by: matt <rocketknight1@gmail.com>
* Remove MBart subclass of XLMRoberta in tokenzier docs (#16546)
* Remove MBart subclass of XLMRoberta in tokenzier
* Fix style
* Copy docs from MBart50 tokenizer
* Use random_attention_mask for TF tests (#16517)
* use random_attention_mask for TF tests
* Fix for TFCLIP test (for now).
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Improve code example (#16450)
Co-authored-by: Niels Rogge <nielsrogge@nielss-mbp.home>
* Pin tokenizers version <0.13 (#16539)
* Pin tokenizers version <0.13
* Style
* Add code samples for TF speech models (#16494)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* [FlaxSpeechEncoderDecoder] Fix dtype bug (#16581)
* [FlaxSpeechEncoderDecoder] Fix dtype bug
* more fixes
* Making the impossible to connect error actually report the right URL. (#16446)
* Fix flax import in __init__.py: modeling_xglm -> modeling_flax_xglm (#16556)
* Add utility to find model labels (#16526)
* Add utility to find model labels
* Use it in the Trainer
* Update src/transformers/utils/generic.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Quality
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Enable doc in Spanish (#16518)
* Reorganize doc for multilingual support
* Fix style
* Style
* Toc trees
* Adapt templates
* Add use_auth to load_datasets for private datasets to PT and TF examples (#16521)
* fix formatting and remove use_auth
* Add use_auth_token to Flax examples
* add a test checking the format of `convert_tokens_to_string`'s output (#16540)
* add new tests
* add comment to overridden tests
* TF: Finalize `unpack_inputs`-related changes (#16499)
* Add unpack_inputs to remaining models
* removed kwargs to `call()` in TF models
* fix TF T5 tests
* [SpeechEncoderDecoderModel] Correct Encoder Last Hidden State Output (#16586)
* initialize the default rank set on TrainerState (#16530)
* initialize the default rank set on TrainerState
* fix style
* Trigger doc build
* Fix CI: test_inference_for_pretraining in ViTMAEModelTest (#16591)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* add a template to add missing tokenization test (#16553)
* add a template to add missing tokenization test
* add cookiecutter setting
* improve doc
* Update templates/adding_a_missing_tokenization_test/README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* made _load_pretrained_model_low_mem static + bug fix (#16548)
* handle torch_dtype in low cpu mem usage (#16580)
* [Doctests] Correct filenaming (#16599)
* [Doctests] Correct filenaming
* improve quicktour
* make style
* Adding new train_step logic to make things less confusing for users (#15994)
* Adding new train_step logic to make things less confusing for users
* DO NOT ASK WHY WE NEED THAT SUBCLASS
* Metrics now working, at least for single-output models with type annotations!
* Updates and TODOs for the new train_step
* Make fixup
* Temporary test workaround until T5 has types
* Temporary test workaround until T5 has types
* I think this actually works! Needs a lot of tests though
* MAke style/quality
* Revert changes to T5 tests
* Deleting the aforementioned unmentionable subclass
* Deleting the aforementioned unmentionable subclass
* Adding a Keras API test
* Style fixes
* Removing unneeded TODO and comments
* Update test_step too
* Stop trying to compute metrics with the dummy_loss, patch up test
* Make style
* make fixup
* Docstring cleanup
* make fixup
* make fixup
* Stop expanding 1D input tensors when using dummy loss
* Adjust T5 test given the new compile()
* make fixup
* Skipping test for convnext
* Removing old T5-specific Keras test now that we have a common one
* make fixup
* make fixup
* Only skip convnext test on CPU
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Avoiding TF import issues
* make fixup
* Update compile() to support TF 2.3
* Skipping model.fit() on template classes for now
* Skipping model.fit() on template class tests for now
* Replace ad-hoc solution with find_labels
* make fixup
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Adding missing type hints for BigBird model (#16555)
* added type hints for mbart tensorflow tf implementation
* Adding missing type hints for mBART model
Tensorflow Implementation model added with missing type hints
* Missing Type hints - correction
For TF model
* Code fixup using make quality tests
* Hint types - typo error
* make fix-copies and make fixup
* type hints
* updated files
* type hints update
* making dependent modesls coherent
* Type hints for BigBird
* removing typos
Co-authored-by: matt <rocketknight1@gmail.com>
* [deepspeed] fix typo, adjust config name (#16597)
* 🖍 apply feedback
Co-authored-by: Cathy <815244047@qq.com>
Co-authored-by: Jim Rohrer <jrohrer1@gmail.com>
Co-authored-by: Ferdinand Schlatt <fschlatt@gmail.com>
Co-authored-by: Dahlbomii <101373053+Dahlbomii@users.noreply.github.com>
Co-authored-by: Gunjan Chhablani <chhablani.gunjan@gmail.com>
Co-authored-by: Rishav Chandra Varma <rishavchandra.v16@iiits.in>
Co-authored-by: matt <rocketknight1@gmail.com>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Niels Rogge <nielsrogge@nielss-mbp.home>
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Daniel Stancl <46073029+stancld@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Karim Foda <35491698+KMFODA@users.noreply.github.com>
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Andres Codas <andrescodas@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
Co-authored-by: Francesco Saverio Zuppichini <francesco.zuppichini@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
If global_attention_mask is found in the models inputs (used by certain
models, like LED) in the prediction_step method of Seq2SeqTrainer,
it is added to the gen_kwargs, which are passed to model.decode().
This allows us to properly set the global attention when decoding.
* added type hints for mbart tensorflow tf implementation
* Adding missing type hints for mBART model
Tensorflow Implementation model added with missing type hints
* Missing Type hints - correction
For TF model
* Code fixup using make quality tests
* Hint types - typo error
* make fix-copies and make fixup
* type hints
* updated files
* type hints update
* making dependent modesls coherent
* Type hints for BigBird
* removing typos
Co-authored-by: matt <rocketknight1@gmail.com>
* Adding new train_step logic to make things less confusing for users
* DO NOT ASK WHY WE NEED THAT SUBCLASS
* Metrics now working, at least for single-output models with type annotations!
* Updates and TODOs for the new train_step
* Make fixup
* Temporary test workaround until T5 has types
* Temporary test workaround until T5 has types
* I think this actually works! Needs a lot of tests though
* MAke style/quality
* Revert changes to T5 tests
* Deleting the aforementioned unmentionable subclass
* Deleting the aforementioned unmentionable subclass
* Adding a Keras API test
* Style fixes
* Removing unneeded TODO and comments
* Update test_step too
* Stop trying to compute metrics with the dummy_loss, patch up test
* Make style
* make fixup
* Docstring cleanup
* make fixup
* make fixup
* Stop expanding 1D input tensors when using dummy loss
* Adjust T5 test given the new compile()
* make fixup
* Skipping test for convnext
* Removing old T5-specific Keras test now that we have a common one
* make fixup
* make fixup
* Only skip convnext test on CPU
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Avoiding TF import issues
* make fixup
* Update compile() to support TF 2.3
* Skipping model.fit() on template classes for now
* Skipping model.fit() on template class tests for now
* Replace ad-hoc solution with find_labels
* make fixup
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
