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d10603f701
* init swissai model
* AutoModelForCausalLM
* AutoModelForCausalLM mapping
* qk norm and post ln optional
* fix wrong shape of qk norm: megatron uses head_dim
* automodel fixes
* minor fix in forward
* fix rope validation to accept llama3 scaling
* `SwissAIForTokenClassification` support
* Align `SwissAI` to v4.52.4
* Align `SwissAI` to v4.53.1
* Init CUDA xIELU
* `SwissAI*`->`Apertus*`
* ci fix
* check_docstring ignore ApertusConfig
* Licensing and placeholder tests
* Placeholder doc
* XIELU syntax
* `_xielu_python` optimization
* Fix xIELU
* [tmp] `{beta,eps}` persistent=False
until {beta,eps} saved in checkpoint
* Modular `Apertus`
* CUDA xIELU logging
* ci fix
* ci fix
* ci fix
* Update license
Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
* Update tests/models/apertus/test_modeling_apertus.py
Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
* `.utils.import_utils.is_torchdynamo_compiling`
* `Apertus` class ordering
* `past_key_value{->s}`, `make fix-copies`
* ci fix
* Remove unused configuration parameters
* `{beta,eps}` saved in checkpoint
* `{beta,eps}` Temporarily on CPU
* Suggestions
Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
* ci fix
* remove fx_compatible (deprecated)
* remove `rotary_embedding_layer`
As the tests are written for a config without default scaling (which is not the case in Apertus) - besides, rope scaling is tested in other models so it's all safe.
* fully removing `Mask4DTestHard` class
Not needed (for now)
* switch to `dtype` instead of `torch_dtype`
Following this:
https://github.com/huggingface/transformers/pull/39782
* remove unused imports
* remove `cache_implementation="static"`
* +Apertus to `docs/source/en/_toctree.yml` for the doc builder
---------
Co-authored-by: Alexander Hagele <alexanderhagele@gmail.com>
Co-authored-by: dhia680 <garbayad@gmail.com>
Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
Co-authored-by: Dhia Garbaya <84809366+dhia680@users.noreply.github.com>