* Initial work
* More work
* Add tests for custom pipelines on the Hub
* Protect import
* Make the test work for TF as well
* Last PyTorch specific bit
* Add documentation
* Style
* Title in toc
* Bad names!
* Update docs/source/en/add_new_pipeline.mdx
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
* Auto stash before merge of "custom_pipeline" and "origin/custom_pipeline"
* Address review comments
* Address more review comments
* Update src/transformers/pipelines/__init__.py
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
* [HPO] update to sigopt new experiment api
* follow https://docs.sigopt.com/experiments
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* [HPO] use new API if sigopt version >= 8.0.0
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Adding support for `device_map` directly in `pipeline(..)` function.
* Updating the docstring.
* Adding a better docstring
* Put back type hints.
* Blacked. (`make fixup` didn't work ??!!)
* Report value for a step instead of epoch.
Report an objective function value for a step instead of epoch to optuna.
I made this modification for the following reason:
If "eval_steps" is less than steps per epoch, there maybe warnings like this: "optuna/trial/_trial.py:592: UserWarning: The reported value is ignored because this `step` 0 is already reported.". So "step" are more appropriate than "epoch" here.
* MOD: make style.
Co-authored-by: zhaowei01 <zhaowei01@yuanfudao.com>
* fix tolerance for a bloom slow test
* enhance alibi padding
- get rid of for loops
- deals better with padded batched input
- avoid useless cpu/gpu communication when creating alibi
Co-authored-by: justheuristic <justheuristic@gmail.com>
* optimize attention mask
* fix scaled softmax limit values
* optimize building alibi tensor
Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com>
* fix attention_mask shape when it's None
* minor fixes
- fix docstring + arg names
* remove colons in docstring
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* apply suggestion
* remove unsued arg
* refactor a bit
- use [:, None] for consistency
* refactor attention block
Co-authored-by: Nouamane Tazi <nouamane98@gmail.com>
* quick fixes
* first attempt
* refactor attention block and fix all tests except "test_simple_generation"
- added comments to better explain attention block
* remove debug lines and add TODO comment
* change `torch.bmm` to `torch.baddbmm`
- fixes `test_simple_generation`but breaks `test_batch_generation_padd`
* styling
* all tests are passing now
- use `bmm`
- add explanation for `allow_fp16_reduced_precision_reduction`
Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com>
* styling
Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com>
* fix support for accelerate
Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* remove attn softmax in fp32
* refactor comments
* refactor a bit
- remove warning message
- remove print on test
* refer to pytorch t5
* change the slow tests
- do the tests in fp32
- remove some comments
- keep large comments
* update expected output for `test_simple_generation`
- we now test using fp32
* make style + change comments a bit
* fix dtype padd test
Co-authored-by: justheuristic <justheuristic@gmail.com>
Co-authored-by: Nouamane Tazi <nouamane98@gmail.com>
Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix RESOURCE_EXHAUSTED error for large datasets on Flax example scripts
* using np.permutation for creating batch_idx
* train_samples_idx -> training_samples_idx
* fix type hints
