* Added StableAdamW as an optimizer option for Trainer. Also wrote tests to verify its behaviour.
* Fixed issue with
* Added docs for StableAdamW. Also fixed a typo in schedule free optimizers
---------
Co-authored-by: Gautham Krithiwas <gauthamkrithiwas2003@gmail.com>
* add cosine_with_min_lr_schedule_with_warmup_lr_rate scheduler in trainer
* Update src/transformers/optimization.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update optimization.py
fix the error of the unclosed "("
* Update optimization.py
remove whitespace in line 402 in order to pass the quality test
* Update src/transformers/optimization.py
* Update src/transformers/optimization.py
* Apply style fixes
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* more torch.hpu patches
* increase top_k because it results in flaky behavior when Tempreture, TopP and TopK are used together, which ends up killing beams early.
* remove temporal fix
* fix scatter operation when input and src are the same
* trigger
* fix and reduce
* skip finding batch size as it makes the hpu go loco
* fix fsdp (yay all are passing)
* fix checking equal nan values
* style
* remove models list
* order
* rename to cuda_extensions
* Update src/transformers/trainer.py
* feat: add flexible Liger Kernel configuration to TrainingArguments
Add support for granular Liger Kernel configuration through a new
`liger_kernel_config` parameter in TrainingArguments. This allows users
to selectively enable/disable specific kernels (rope, swiglu, cross_entropy,
etc.) instead of the current approach that rely on default configuration.
Features:
- Add `liger_kernel_config` dict parameter to TrainingArguments
- Support selective kernel application for all supported models
- Maintain full backward compatibility with existing `use_liger_kernel` flag
Example usage:
```python
TrainingArguments(
use_liger_kernel=True,
liger_kernel_config={
"rope": True,
"swiglu": True,
"cross_entropy": False,
"fused_linear_cross_entropy": True
}
)
Closes#38905
* Address comments and update Liger section in Trainer docs
* [fix] make legacy bnb code work
* [fix] use get with default instead of getter
* add test for bnb 8bit optim skip embed
* [fix] style
* add require annotation of bnb
---------
Co-authored-by: jaycha <jaycha@ncsoft.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* chore: fix typos in the tests
* chore: fix typos in the tests
* chore: fix typos in the tests
* chore: fix typos in the tests
* chore: fix typos in the tests
* chore: fix typos in the tests
* chore: fix typos in the tests
* chore: fix typos in the tests
* chore: fix typos in the tests
* chore: fix typos in the tests
* chore: fix typos in the tests
* chore: fix typos in the tests
* chore: fix typos in the tests
* fix: format codes
* chore: fix copy mismatch issue
* fix: format codes
* chore: fix copy mismatch issue
* chore: fix copy mismatch issue
* chore: fix copy mismatch issue
* chore: restore previous words
* chore: revert unexpected changes
* feat: Saving tokenizer in collator when processing_class is None
* chore: Style issue
* chore: Typo
* dbg: Check why test failed
* dbg: Remove logics and another test failed which successed before, so should be the stablibility issue
* test: Init unit-test
* chore: Style
* chore: Add err log
* fix: Case
* Update tests/trainer/test_trainer.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* chore: Try to use get_regression_trainer
* fix: Impl and style
* fix: Style
* fix: Case
* fix: Import err
* fix: Missed import
* fix: Import block un-sorted problem
* fix: Try another tokenizer
* fix: Test logic
* chore: Light updates
* chore: Reformat
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Just import torch AdamW instead
* Update docs too
* Make AdamW undocumented
* make fixup
* Add a basic wrapper class
* Add it back to the docs
* Just remove AdamW entirely
* Remove some AdamW references
* Drop AdamW from the public init
* make fix-copies
* Cleanup some references
* make fixup
* Delete lots of transformers.AdamW references
* Remove extra references to adamw_hf
* Set best_model_checkpoint only when ckpt exists.
Rather than set it explicitly without checking if the checkpoint directory even exists as before, now we moved the setting logic inside of _save_checkpoint and are only setting it if it exists.
* Added best_global_step to TrainerState.
* Added tests for best_model_checkpoint.
* Fixed hard-coded values in test to prevent fail.
* Added helper func and removed hard-coded best_step.
* Added side effect patch generator for _eval.
* Added evaluate side effect func.
* Removed erroneous patching.
* Fixed minor bug.
* Applied Ruff.
* Fixed Ruff problem in make style.
* Used Trainer.set_initial_training_values.
* Don't accidentally mutate the base_model_tp_plan
* Co-authored by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Trigger tests
* Marking grad accum test as slow
* Add a flaky decorator
* Add a flaky decorator
* Use cyril's codeblock
* Don't copy() when it's None
* Use cyril's new codeblock
* make fixup
* test
* fix
* fix
* skip some and run some first
* test fsdp
* fix
* patches for generate
* test distributed
* copy
* don't test distributed loss for hpu
* require fp16 and run first
* changes from marc's PR fixing zero3
* better alternative
* return True when fp16 support on gaudi without creating bridge
* fix
* fix tested dtype in deepspeed inference test
* test
* fix
* test
* fix
* skip
* require fp16
* run first fsdp
* Apply suggestions from code review
* address comments
* address comments and refactor test
* reduce precison
* avoid doing gaudi1 specific stuff in the genreation loop
* document test_gradient_accumulation_loss_alignment_with_model_loss test a bit more
* fix: prevent model access error during Optuna hyperparameter tuning
The `transformers.integrations.integration_utils.run_hp_search_optuna` function releases model memory and sets trainer.model to None after each trial. This causes an AttributeError when subsequent Trainer.train calls attempt to access the model before reinitialization. This is only an issue when `fp16_full_eval` or `bf16_full_eval` flags are enabled.
* Update src/transformers/trainer.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Add implementation for DataCollatorForMultipleChoice based on docs.
* Add DataCollatorForMultipleChoice to import structure.
* Remove custom DataCollatorForMultipleChoice implementations from example scripts.
* Remove custom implementations of DataCollatorForMultipleChoice from docs in English, Spanish, Japanese and Korean.
* Refactor torch version of DataCollatorForMultipleChoice to be more easily understandable.
* Apply suggested changes and run make fixup.
* fix copies, style and fixup
* add missing documentation
* nits
* fix docstring
* style
* nits
* isort
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
* Adding option to save/reload scaler
* Removing duplicate variable
* Adding save/reload test
* Small fixes on deterministic algorithm call
* Moving LLM test to another file to isolate its environment
* Moving back to old file and using subprocess to run test isolated
* Reverting back accidental change
* Reverting back accidental change
* add RAdamScheduleFree optimizer
* revert schedulefree version to the minimum requirement
* refine is_schedulefree_available so that it can take min_version
* refine documents
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* use torch.testing.assertclose instead to get more details about error in cis
* fix
* style
* test_all
* revert for I bert
* fixes and updates
* more image processing fixes
* more image processors
* fix mamba and co
* style
* less strick
* ok I won't be strict
* skip and be done
* up
* update codecarbon
* replace directly-specified-test-dirs with tmp_dir
* pass tmp_dir to all get_regression_trainer
* test_trainer.py: Use tmp_dir consistently for all output_dir arguments
* fix some with...as tmp_dir blocks
* reflect the comments to improve test_trainer.py
* refresh .gitignore
* Updated docstring for _determine_best_metric.
* Updated docstring for metric_for_best_model.
* Added test case for save strategy.
* Updated incorrect test case.
* Changed eval_strategy to match save_strategy.
* Separated test cases for metric.
* Allow load_best_model when save_strategy == "best".
* Updated docstring for metric_for_best_model.
* fix GA bugs and add unit test
* narrow down model loss unit test diff gap
* format code to make ruff happy
* send num_items_in_batch argument to decoder
* fix GA loss bug in BertLMHeadModel
* use TinyStories-33M to narrow down diff gap
* fotmat code
* missing .config
* avoid add extra args
---------
Co-authored-by: kangsheng <kangsheng@meituan.com>
* Update trainer for easier handling of accumulate + proper reporting
* test
* Fixup tests
* Full fix
* Fix style
* rm comment
* Fix tests
* Minimize test + remove py 311 check
* Unused import
* Forward contrib credits from discussions
* Fix reported metrics
* Refactor, good as it's going to get
* rm pad tok id check
* object detection and audio are being annoying
* Fin
* Fin x2
---------
Co-authored-by: Gyanateet Dutta <Ryukijano@users.noreply.github.com>
