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* 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
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| .. | ||
| bettertransformer | ||
| deepspeed | ||
| extended | ||
| fixtures | ||
| fsdp | ||
| generation | ||
| models | ||
| optimization | ||
| peft_integration | ||
| pipelines | ||
| quantization | ||
| repo_utils | ||
| sagemaker | ||
| tensor_parallel | ||
| tokenization | ||
| trainer | ||
| utils | ||
| __init__.py | ||
| causal_lm_tester.py | ||
| test_backbone_common.py | ||
| test_configuration_common.py | ||
| test_feature_extraction_common.py | ||
| test_image_processing_common.py | ||
| test_image_transforms.py | ||
| test_modeling_common.py | ||
| test_pipeline_mixin.py | ||
| test_processing_common.py | ||
| test_sequence_feature_extraction_common.py | ||
| test_tokenization_common.py | ||
| test_training_args.py | ||
| test_video_processing_common.py | ||