rename use_liger to use_liger_kernel

docs/source/en/trainer.md

@@ -395,7 +395,7 @@ First make sure to install Liger official repository:
 pip install liger-kernel
 ```
 
-You should pass `use_liger=True` to apply liger kernel on your model, for example:
+You should pass `use_liger_kernel=True` to apply liger kernel on your model, for example:
 
 ```py
 from transformers import TrainingArguments
@@ -411,11 +411,11 @@ training_args = TrainingArguments(
     save_strategy="epoch",
     load_best_model_at_end=True,
     push_to_hub=True,
-    use_liger=True
+    use_liger_kernel=True
 )
 ```
 
-The kernel supports the Llama, Gemma, Mistral, and Mixtral model architectures. The most up-to-date list of supported models can be found [here](https://github.com/linkedin/Liger-Kernel). When `use_liger` is set to `True`, the corresponding layers in the original model will be patched with Liger's efficient implementation, so you don't need to do anything extra other than setting the argument value.
+The kernel supports the Llama, Gemma, Mistral, and Mixtral model architectures. The most up-to-date list of supported models can be found [here](https://github.com/linkedin/Liger-Kernel). When `use_liger_kernel` is set to `True`, the corresponding layers in the original model will be patched with Liger's efficient implementation, so you don't need to do anything extra other than setting the argument value.
 
 ## LOMO optimizer
 

src/transformers/trainer.py

@@ -464,7 +464,7 @@ def __init__(
                     " to `True` to avoid any unexpected behavior such as device placement mismatching."
                 )
 
-        if self.args.use_liger:
+        if self.args.use_liger_kernel:
             if is_liger_kernel_available():
                 from liger_kernel.transformers.trainer_integration import _apply_liger_kernel
 
@@ -478,7 +478,7 @@ def __init__(
                     )
             else:
                 raise ImportError(
-                    "You have set `use_liger` to `True` but liger-kernel >= 0.1.0 is not available. "
+                    "You have set `use_liger_kernel` to `True` but liger-kernel >= 0.1.0 is not available. "
                     "Please install it with `pip install liger-kernel`"
                 )
 

src/transformers/training_args.py

@@ -792,7 +792,7 @@ class TrainingArguments:
         eval_use_gather_object (`bool`, *optional*, defaults to `False`):
             Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices. This should only be enabled if users are not just returning tensors, and this is actively discouraged by PyTorch.
 
-        use_liger (`bool`, *optional*, defaults to `False`):
+        use_liger_kernel (`bool`, *optional*, defaults to `False`):
             Whether enable [Liger](https://github.com/linkedin/Liger-Kernel) Kernel for LLM model training.
             It can effectively increase multi-GPU training throughput by ~20% and reduces memory usage by ~60%, works out of the box with
             flash attention, PyTorch FSDP, and Microsoft DeepSpeed. Currently, it supports llama, mistral, mixtral and gemma models.
@@ -1496,7 +1496,7 @@ class TrainingArguments:
         },
     )
 
-    use_liger: Optional[bool] = field(
+    use_liger_kernel: Optional[bool] = field(
         default=False,
         metadata={"help": "Whether or not to enable the Liger Kernel for model training."},
     )

tests/trainer/test_trainer.py

@@ -1337,7 +1337,7 @@ def test_apply_liger_kernel(self):
 
         args = TrainingArguments(
             "./test",
-            use_liger=True,
+            use_liger_kernel=True,
         )
         Trainer(tiny_model, args)