add torch device

* fix workflow

* call ci

* Update .github/workflows/self-scheduled-caller.yml

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

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Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Remove unused augmentation

* Fix pad_if_smaller() and remove unused augmentation

* Add indentation

* Fix requirements

* Update dataset use instructions

* Replace transforms with albumentations

* Replace identity transform with None

* Fixing formatting

* Fixed comment place

* avoid generation length warning

* add tests

* Update src/transformers/generation/candidate_generator.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* add tests and minor fixes

* refine `min_new_tokens`

* Update src/transformers/generation/candidate_generator.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* add method to prepare length arguments

* add test for min length

* Update src/transformers/generation/candidate_generator.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* fix variable naming

* empty commit for tests

* trigger tests (empty)

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Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* [CI] fix setup

* fix

* test

* Revert "test"

This reverts commit 7df416d4.

fixes

* revert back to torch 2.1.1

* run test

* switch to torch 2.2.1

* udapte dockerfile

* fix awq tests

* fix test

* run quanto tests

* update tests

* split quantization tests

* fix

* fix again

* final fix

* fix report artifact

* build docker again

* Revert "build docker again"

This reverts commit 399a5f9d9308da071d79034f238c719de0f3532e.

* debug

* revert

* style

* new notification system

* testing notfication

* rebuild docker

* fix_prev_ci_results

* typo

* remove warning

* fix typo

* fix artifact name

* debug

* issue fixed

* debug again

* fix

* fix time

* test notif with faling test

* typo

* issues again

* final fix ?

* run all quantization tests again

* remove name to clear space

* revert modfiication done on workflow

* fix

* build docker

* build only quant docker

* fix quantization ci

* fix

* fix report

* better quantization_matrix

* add print

* revert to the basic one

Co-authored-by: Wauplin <lucainp@gmail.com>

* fix mistral and mixtral

* add pdb

* fix mixtral tesst

* fix

* fix mistral ?

* add fix gemma

* fix mistral

* fix

* test

* anoter test

* fix

* fix

* fix mistral tests

* fix them again

* final fixes for mistral

* fix padding right

* fix whipser fa2

* fix

* fix

* fix gemma

* test

* fix llama

* fix

* fix

* fix llama gemma

* add class attribute

* fix CI

* clarify whisper

* compute_capability

* rename names in some comments

* Add   # fmt: skip

* make style

* Update tests/models/mistral/test_modeling_mistral.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* update

* update

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Co-authored-by: Younes Belkada <younesbelkada@gmail.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Undo

* Use tokenizer

* Undo data collator

* Fix data collator

* Support feature extractors as well

* See if we can get tests to pass with the fixed weights

* See if we can get tests to pass with the fixed weights

* Replace the revisions now that we don't need them anymore

fix copies

* Add datasets.Dataset to Trainer's train_dataset and eval_dataset type hints

* Add is_datasets_available check for importing datasets under TYPE_CHECKING guard

https://github.com/huggingface/transformers/pull/30077/files#r1555939352

* fix sequence length errors

* fix label column name error for vit

* fix the lm_head embedding!=linear layer mismatches for Seq2Seq models

* init: add StableLm 2 support

* add integration test for parallel residual and qk layernorm

* update(modeling): match qk norm naming for consistency with phi/persimmon

* fix(tests): run fwd/bwd on random init test model to jitter norm weights off identity

* `use_parallel_residual`: add copy pointer to `GPTNeoXLayer.forward`

* refactor: rename head states var in `StableLmLayerNormPerHead`

* tests: update test model and add generate check

* adding env variable for mps and is_torch_mps_available for Pipeline

* fix linting errors

* Remove environment overide

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

fix typo at ImportError

* remove controlflows

* style

* rename patch_ to padded_ following review comment

* style

* Update trainer.py

* fix copies

* fix falcon without attention_mask & alibi

* add test

* Update tests/models/falcon/test_modeling_falcon.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

fix learning rate display issue in galore optimizer

* pass token to trainer.push_to_hub

* fmt

* Update src/transformers/trainer.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* pass token to create_repo, update_folder

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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* ImportError: Trainer with PyTorch requires accelerate>=0.20.1 Fix

Adding the evaluate and accelerate installs at the beginning of the cell to fix the issue

* ImportError Fix: Trainer with PyTorch requires accelerate>=0.20.1

* Import Error Fix

* Update installation.md

* Update quicktour.md

* rollback other lang changes

* Update _config.py

* updates for other languages

* fixing error

* Tutorial Update

* Update tokenization_utils_base.py

* Just use an optimizer string to pass the doctest?

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Co-authored-by: Matt <rocketknight1@gmail.com>

* Patch fix - don't use safetensors for TF models

* Skip test for TF for now

* Update for another test

* add bnb flag

* move maker

* add accelerator maker

updated examples/pytorch/language-modeling scripts and requirements.txt to require datasets>=2.14.0 (#30120)

updated requirements.txt and require_version() calls in examples/pytorch/language-modeling to require datasets>=2.14.0

* Make MLFlow version detection more robust and handles mlflow-skinny

* Make function name more clear and refactor the logic

* Further refactor

* add _torch_extract_fbank_features_batch function in feature_extractor_whisper

* reformat feature_extraction_whisper.py file

* handle batching in single function

* add gpu test & doc

* add batch test & device in each __call__

* add device arg in doc string

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Co-authored-by: vaibhav.aggarwal <vaibhav.aggarwal@sprinklr.com>

* clean-up whisper kwargs

* failing test