Updated Trainer's liger-kernel integration to call correct patching API

src/transformers/trainer.py

@@ -468,19 +468,18 @@ def __init__(
 
         if self.args.use_liger_kernel:
             if is_liger_kernel_available():
-                from liger_kernel.transformers.trainer_integration import _apply_liger_kernel
+                from liger_kernel.transformers import _apply_liger_kernel_to_instance
 
-                model_type = getattr(model, "config", None) and getattr(model.config, "model_type", None)
-                if model_type:
-                    # Monkey patch the model with liger kernels. Use the default kernel configurations.
-                    _apply_liger_kernel(model_type=model_type)
+                if isinstance(model, PreTrainedModel):
+                    # Patch the model with liger kernels. Use the default kernel configurations.
+                    _apply_liger_kernel_to_instance(model=model)
                 else:
                     logger.warning(
-                        "The model does not have a valid `model_type` specified. No liger kernels will be applied."
+                        "The model is not an instance of PreTrainedModel. No liger kernels will be applied."
                     )
             else:
                 raise ImportError(
-                    "You have set `use_liger_kernel` to `True` but liger-kernel >= 0.1.0 is not available. "
+                    "You have set `use_liger_kernel` to `True` but liger-kernel >= 0.3.0 is not available. "
                     "Please install it with `pip install liger-kernel`"
                 )
 

src/transformers/utils/import_utils.py

@@ -1187,7 +1187,7 @@ def is_liger_kernel_available():
     if not _liger_kernel_available:
         return False
 
-    return version.parse(importlib.metadata.version("liger_kernel")) >= version.parse("0.1.0")
+    return version.parse(importlib.metadata.version("liger_kernel")) >= version.parse("0.3.0")
 
 
 # docstyle-ignore

tests/trainer/test_trainer.py

@@ -1344,22 +1344,28 @@ def test_get_eval_dataloader_with_persistent_workers(self):
 
     @require_liger_kernel
     def test_use_liger_kernel_patching(self):
-        # Test that the model code actually gets patched with Liger kernel
-        from liger_kernel.transformers.rms_norm import LigerRMSNorm
+        # Ensure any monkey patching is cleaned up for subsequent tests
+        with patch("transformers.models.llama.modeling_llama"):
+            from liger_kernel.transformers import LigerRMSNorm, liger_rotary_pos_emb
 
-        from transformers.models.llama import modeling_llama
+            from transformers.models.llama import modeling_llama
 
-        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
-        tiny_llama = LlamaForCausalLM(config)
+            config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
+            tiny_llama = LlamaForCausalLM(config)
 
-        args = TrainingArguments(
-            "./test",
-            use_liger_kernel=True,
-        )
-        Trainer(tiny_llama, args)
+            # Spot check that modeling code and model instance variables are not yet patched
+            self.assertNotEqual(modeling_llama.apply_rotary_pos_emb, liger_rotary_pos_emb)
+            self.assertFalse(isinstance(tiny_llama.model.norm, LigerRMSNorm))
+
+            args = TrainingArguments(
+                "./test",
+                use_liger_kernel=True,
+            )
+            Trainer(tiny_llama, args)
 
-        # Check that one of the Llama model layers has been correctly patched with Liger kernel
-        self.assertEqual(modeling_llama.LlamaRMSNorm, LigerRMSNorm)
+            # Spot check that modeling code and model instance variables are patched
+            self.assertEqual(modeling_llama.apply_rotary_pos_emb, liger_rotary_pos_emb)
+            self.assertTrue(isinstance(tiny_llama.model.norm, LigerRMSNorm))
 
     @require_liger_kernel
     @require_torch_gpu