* enable optuna multi-objectives feature
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update hpo doc
* update docstring
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* extend direction to List[str] type
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fix issues in test_exponential_decay_length_penalty
Fix tests which were broken and add validation of negative scores.
Current test didn't take into account that ExponentialDecayLengthPenalty updates the score inplace, resulting in updates to base tested Tensor.
In addition, the gt assert had empty Tensors due to indexing along the batch dimension.
Test is currently expected to fail to show ExponentialDecayLengthPenalty issues with negative scores
* Fix ExponentialDecayLengthPenalty negative logits issue
In cases where the scores are negative, ExponentialDecayLengthPenalty decreases the score of eos_token_id instead of increasing it.
To fix this issue we compute the penalty of the absolute value and add it to the original score.
* Add examples for ExponentialDecayLengthPenalty
* Fix styling issue in ExponentialDecayLengthPenalty doc
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Style and quality fix
* Fix example outputs
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* intiial commit
* updates
* nits
* update conversion script
* update conversion script
* use path to load
* add tips etc
* some modeling logic
* modeling update
* more nits
* nits
* normal layer norm
* update config and doc
* nits
* update doc remove unused
* update
* fix inits and stuff
* fixup
* revert wrong changes
* updates
* more nits
* add default config values to the configuration file
* fixup happy
* update
* 2 tests left
* update readmes
* more nits
* slow test and more documentation
* update readme
* fix licences
* styling
* use fast if possible when saving tokenizer
* remove todo
* remove tokenization tests
* small last nits
* Apply suggestions from code review
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* nits to skip the timout doctest
* fix integration test
* fix test
* update eos token
* update to allow fast tokenization
* styling
* fix codeLlama as well for the update post processor
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add more copied from statements
* update
* doc passes doctest
* remove `# final layer norm?`
* change docstring prompot
* update
* Update README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* don't doctest the conversion script as it requires more packages
* don't init a model in the config
* oups
* fix doctest
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Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit corrects the dropout implementation in Graphormer, aligning it with the original implementation and improving performance. Specifically:
1. The `attention_dropout` variable, intended for use in GraphormerMultiheadAttention, was defined but not used. This has been corrected to use `attention_dropout` instead of the regular `dropout`.
2. The `activation_dropout` for the activations in the feed-forward layers was missing. Instead, the regular `dropout` was used. This commit adds `activation_dropout` to the feed-forward layers.
These changes ensure the dropout implementation matches the original Graphormer and delivers empirically better performance.
* Added HerBERT to README.md
* Update README.md to contain HerBERT (#26016)
* Resolved#26016: Updated READMEs and index.md to contain Herbert
Updated READMEs and ran make fix-copies
* add new arg for gptq
* add tests
* add min version autogptq
* fix order
* skip test
* fix
* Update src/transformers/modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix style
* change model path
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* docs: feat: model resources for llama
* fix: resolve suggestion
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>
Co-authored-by: Wonhyeong Seo <wonhseo@kakao.com>
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Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>
Co-authored-by: Wonhyeong Seo <wonhseo@kakao.com>
* Add support for deepspeed optimizer and HF scheduler
* fix bug
* fix the import
* fix issue with deepspeed scheduler saving for hf optim + hf scheduler scenario
* fix loading of hf scheduler when loading deepspeed checkpoint
* fix import of `DeepSpeedSchedulerWrapper`
* add tests
* add the comment and skip the failing tests
* address comment
This cl iterates through a list of keys rather than dict items while updating the dict elements. Fixes the following error:
File "..../transformers/training_args.py", line 1544, in post_init
for k, v in self.fsdp_config.items():
RuntimeError: dictionary keys changed during iteration