* fix run_seq2seq.py; porting DeepSpeed tests to it
* unrefactor
* defensive programming
* defensive programming 2
* port the rest of the trainer tests
* style
* a cleaner scripts dir finder
* cleanup
* Add check-ops script
* Finish to implement check_tf_ops and start the test
* Make the test mandatory only for BERT
* Update tf_ops folder
* Remove useless classes
* Add the ONNX test for GPT2 and BART
* Add a onnxruntime slow test + better opset flexibility
* Fix test + apply style
* fix tests
* Switch min opset from 12 to 10
* Update src/transformers/file_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Fix GPT2
* Remove extra shape_list usage
* Fix GPT2
* Address Morgan's comments
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Conversion from slow to fast for BPE spm vocabs contained an error.
- There is only 1 test currently (tokenizers + slow) that used the modified path
and it's reformer, which does not contain any ids modification so the
bug was silent for now.
- The real issue is that vocab variable was overloaded by
SentencePieceExtractor, leading to Slow specific vocab oddities to be
completely ignored
- The bug was reported here https://github.com/huggingface/transformers/issues/9518
- Ran the complete tokenization test suite with slow without error
(`RUN_SLOW=1 pytest -sv tests/test_tokenization_*`)
* Remove rebase error.
* Adding the fixture.
* remove xnli_compute_metrics, add load_dataset, load_metric, set_seed,metric.compute,load_metric
* fix
* fix
* fix
* push
* fix
* everything works
* fix init
* fix
* special treatment for sepconv1d
* style
* 🙏🏽
* add doc and cleanup
* fix doc
* fix doc again
* fix doc again
* Apply suggestions from code review
* make style
* Proposal that should work
* Remove needless code
* Fix test
* Apply suggestions from code review
* remove xnli_compute_metrics, add load_dataset, load_metric, set_seed,metric.compute,load_metric
* amend README
* removed data_args.task_name and replaced with task_name = "xnli"; use split function to load train and validation dataset separately; remove __post_init__; remove flag --task_name from README.
* removed dict task_to_keys, use str "xnli" instead of variable task_name, change preprocess_function to use examples["premise"], examples["hypothesis"] directly, remove sentence1_key and sentence2_key, change compute_metrics function to cater only to accuracy metric, add condition for train_langauge is None when using dataset.load_dataset()
* removed `torch.distributed.barrier()` and `import torch` as `from_pretrained` is able to do the work; amend README
* fix rag generate and tests
* put back adjust_logits_during_generation
* tests are okay
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Stas Bekman
Julien Plu
Suraj Patil
Lysandre Debut
Nicolas Patry
Sylvain Gugger
Manuel Romero
Julien Chaumond
Mohamed Al Salti
Sylvain Gugger
Patrick von Platen
Tanmay Thakur
Qbiwan
Anthony MOI
Shiva Zamani
Boris Dayma
abhishek thakur
Daniel Stancl
Lysandre