Monkey patch layer norm in mllama (#302)

## Summary
<!--- This is a required section; please describe the main purpose of
this proposed code change. --->
Monkey patches layer norm in mllama for conditional generation
<!---
## Details
This is an optional section; is there anything specific that reviewers
should be aware of?
--->

## Testing Done
<!--- This is a required section; please describe how this change was
tested. --->
Tested monkey patching works as intended
<!-- 
Replace BLANK with your device type. For example, A100-80G-PCIe

Complete the following tasks before sending your PR, and replace `[ ]`
with
`[x]` to indicate you have done them. 
-->

- Hardware Type: <BLANK>
- [x] run `make test` to ensure correctness
- [x] run `make checkstyle` to ensure code style
- [x] run `make test-convergence` to ensure convergence

---------

Co-authored-by: Shivam Sahni <shisahni@linkedin.com>

src/liger_kernel/transformers/monkey_patch.py

@@ -121,6 +121,7 @@ def apply_liger_kernel_to_mllama(
     rope: bool = True,
     cross_entropy: bool = False,
     fused_linear_cross_entropy: bool = True,
+    layer_norm: bool = True,
     rms_norm: bool = True,
     swiglu: bool = True,
     model: PreTrainedModel = None,
@@ -151,12 +152,15 @@ def apply_liger_kernel_to_mllama(
         MllamaForCausalLM,
         MllamaForConditionalGeneration,
         MllamaTextModel,
+        MllamaVisionModel,
     )
 
     from liger_kernel.transformers.model.mllama import lce_forward as mllama_lce_forward
 
     if rope:
         modeling_mllama.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if layer_norm:
+        modeling_mllama.nn.LayerNorm = LigerLayerNorm
     if rms_norm:
         modeling_mllama.MllamaTextRMSNorm = LigerRMSNorm
     if swiglu:
@@ -174,11 +178,14 @@ def apply_liger_kernel_to_mllama(
 
         if isinstance(model, MllamaForConditionalGeneration):
             language_model: MllamaForCausalLM = model.language_model
+            vision_model: MllamaVisionModel = model.vision_model
             text_model: MllamaTextModel = language_model.model
         elif isinstance(model, MllamaForCausalLM):
             text_model = model.model
+            vision_model = None
         elif isinstance(model, MllamaTextModel):
             text_model = model
+            vision_model = None
         else:
             raise ValueError(f"Unsupported Mllama model type: {type(model)}")
 
@@ -194,6 +201,20 @@ def apply_liger_kernel_to_mllama(
                     _patch_rms_norm_module(decoder_layer.input_layernorm)
                     _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
 
+        if vision_model:
+            _patch_layer_norm_module(vision_model.layernorm_pre)
+            _patch_layer_norm_module(vision_model.layernorm_post)
+
+            for layer in vision_model.transformer.layers:
+                if layer_norm:
+                    _patch_layer_norm_module(layer.input_layernorm)
+                    _patch_layer_norm_module(layer.post_attention_layernorm)
+
+            for layer in vision_model.global_transformer.layers:
+                if layer_norm:
+                    _patch_layer_norm_module(layer.input_layernorm)
+                    _patch_layer_norm_module(layer.post_attention_layernorm)
+
 
 def apply_liger_kernel_to_mistral(
     rope: bool = True,
@@ -767,7 +788,6 @@ def _apply_liger_kernel_to_instance(model: PreTrainedModel, **kwargs) -> None:
         for key, value in kwargs.items()
         if key in apply_fn_signature.parameters
     }
-
     logger.info(
         f"Applying Liger kernels to model instance with model type: {model_type} with kwargs: {applicable_kwargs}"
     )

test/convergence/test_mini_models_multimodal.py

@@ -316,15 +316,12 @@ def run_mini_model_multimodal(
         kwargs = {
             "rms_norm": True,
             "cross_entropy": True,
+            "layer_norm": True,
         }
         model_supports_rope = "qwen2_vl" not in model_name
         if model_supports_rope:
             kwargs["rope"] = True
 
-        model_supports_layer_norm = "qwen2_vl" in model_name
-        if model_supports_layer_norm:
-            kwargs["layer_norm"] = True
-
         if "gemma" in model_name:
             kwargs["geglu"] = True
         else:

test/transformers/test_monkey_patch.py

@@ -302,6 +302,27 @@ def test_apply_liger_kernel_to_instance_for_mllama_for_conditional_generation():
                 layer.post_attention_layernorm.forward
             ) != inspect.getsource(LigerRMSNorm.forward)
 
+        assert inspect.getsource(
+            dummy_model_instance.vision_model.layernorm_pre.forward
+        ) != inspect.getsource(LigerLayerNorm.forward)
+        assert inspect.getsource(
+            dummy_model_instance.vision_model.layernorm_post.forward
+        ) != inspect.getsource(LigerLayerNorm.forward)
+        for layer in dummy_model_instance.vision_model.transformer.layers:
+            assert inspect.getsource(
+                layer.input_layernorm.forward
+            ) != inspect.getsource(LigerLayerNorm.forward)
+            assert inspect.getsource(
+                layer.post_attention_layernorm.forward
+            ) != inspect.getsource(LigerLayerNorm.forward)
+        for layer in dummy_model_instance.vision_model.global_transformer.layers:
+            assert inspect.getsource(
+                layer.input_layernorm.forward
+            ) != inspect.getsource(LigerLayerNorm.forward)
+            assert inspect.getsource(
+                layer.post_attention_layernorm.forward
+            ) != inspect.getsource(LigerLayerNorm.forward)
+
         # Test applying kernels to the model instance
         _apply_liger_kernel_to_instance(model=dummy_model_instance)
 
@@ -320,6 +341,27 @@ def test_apply_liger_kernel_to_instance_for_mllama_for_conditional_generation():
                 layer.post_attention_layernorm.forward
             ) == inspect.getsource(LigerRMSNorm.forward)
 
+        assert inspect.getsource(
+            dummy_model_instance.vision_model.layernorm_pre.forward
+        ) == inspect.getsource(LigerLayerNorm.forward)
+        assert inspect.getsource(
+            dummy_model_instance.vision_model.layernorm_post.forward
+        ) == inspect.getsource(LigerLayerNorm.forward)
+        for layer in dummy_model_instance.vision_model.transformer.layers:
+            assert inspect.getsource(
+                layer.input_layernorm.forward
+            ) == inspect.getsource(LigerLayerNorm.forward)
+            assert inspect.getsource(
+                layer.post_attention_layernorm.forward
+            ) == inspect.getsource(LigerLayerNorm.forward)
+        for layer in dummy_model_instance.vision_model.global_transformer.layers:
+            assert inspect.getsource(
+                layer.input_layernorm.forward
+            ) == inspect.getsource(LigerLayerNorm.forward)
+            assert inspect.getsource(
+                layer.post_attention_layernorm.forward
+            ) == inspect.getsource(LigerLayerNorm.forward)
+
 
 def test_apply_liger_kernel_to_instance_for_mllama_for_causal_lm():
     # Ensure any monkey patching is cleaned up for subsequent tests

test/utils.py

@@ -222,8 +222,10 @@ def revert_liger_kernel_to_mllama():
     Revert all Liger kernel patches applied to MLlama.
     """
 
+    import torch.nn as nn
     from transformers.models.mllama import modeling_mllama
 
+    importlib.reload(nn)
     importlib.reload(modeling_mllama)
     print("Liger kernel patches have been reverted.")