GitLab

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* fix expected loss values
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* [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>

Co-authored-by: Guilhem Chéron <guilhemc@authentifier.com>

* NLLB tokenizer

* Apply suggestions from code review - Thanks Stefan!
Co-authored-by: Stefan Schweter <stefan@schweter.it>

* Final touches

* Style :)

* Update docs/source/en/model_doc/nllb.mdx
Co-authored-by: Stefan Schweter <stefan@schweter.it>

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* PR reviews

* Auto models
Co-authored-by: Stefan Schweter <stefan@schweter.it>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.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 ??!!)

* Fixing a bug where attention mask was not passed to generate.

* Fixing zero-size prompts.

* Comment on top.

* Initial TF DeiT implementation

* Fix copies naming issues

* Fix up + docs

* Properly same main layer

* Name layers properly

* Initial TF DeiT implementation

* Fix copies naming issues

* Fix up + docs

* Properly same main layer

* Name layers properly

* Fixup

* Fix import

* Fix import

* Fix import

* Fix weight loading for tests whilst not on hub

* Add doc tests and remove to_2tuple

* Add back to_2tuple
Removing to_2tuple results in many downstream changes needed because of the copies checks

* Incorporate updates in Improve vision models #17731 PR

* Don't hard code num_channels

* Copy PyTorch DeiT embeddings and remove pytorch operations with mask

* Fix patch embeddings & tidy up

* Update PixelShuffle to move logic into class layer

* Update doc strings - remove PT references

* Use NHWC format in internal layers

* Fix up

* Use linear activation layer

* Remove unused import

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Move dataclass to top of file

* Remove from_pt now weights on hub

* Fixup
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Amy Roberts <amyeroberts@users.noreply.github.com>

* enable fx2trt

* Update perf_train_gpu_one.mdx

* Update perf_train_gpu_one.mdx

* add lib check

* update

* format

* update

* fix import check

* fix isort

* improve doc

* refactor ctx manager

* fix isort

* black format

* isort fix

* fix format

* update args

* update black

* cleanups

* Update perf_train_gpu_one.mdx

* code refactor

* code refactor to init

* remove redundancy

* isort

* replace self.args with args
Co-authored-by: Stas Bekman <stas@stason.org>

* Make sharded checkpoints work in offline mode

* Add test

This reverts commit 3564c657.

* added metadata to training summary

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add summarization name mapping for MultiNews

* Add summarization name mapping for MultiNews

* supported python versions reference

* Update CONTRIBUTING.md

removing commit hash from link
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add fp16 option

* Fix BLOOM dtype

* Formatting

* Remove torch_dtype arg

* Revert formatting

* Apply formatting

* Add n_embed backward compat

* 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>

* enhance ipex import

* refine codes

* refine style

* add link

* style
Co-authored-by: Stas Bekman <stas@stason.org>

* 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

* fix dtype issue in _attn

* fix RotaryEmbedding

* fix RotaryEmbedding 2

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

* Fix some typos.
Signed-off-by: Yulv-git <yulvchi@qq.com>

* Fix typo.
Signed-off-by: Yulv-git <yulvchi@qq.com>

* make fixup.

* Make Trainer.predict call on_evaluate (#17952)

* Add on_predict

* Small fix

* Small and different fix

* Add tests

* Fix type issue in using bucketing with Trainer

- Fix type issues in LengthGrouperSampler,
  DistributedLengthGroupedSampler

refs: #18003

* Change logging type in LengthGroupedSampler

- Change `logger.warning` to `logger.info`
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Change logging type in DistributedLengthGroupedSampler

- Change `logger.warning` to `logger.info`
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Remove adundant clause in LengthGroupedSampler

- Use `elif`
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Remove adundant clause in DistributedLengthGroupedSampler

- Use `elif`
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Apply black, isort to modified codes in the script
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Fix slow CI by pinning resampy

* Actually put it in the speech dependencies

* Drop columns after loading samples, rather than before, to avoid breaking transforms

* make fixup

* Add workaround so this PR can work with current datasets version

* Added command for windows VENV activation

* changed linux and macos  specification