diff --git a/py/torch_tensorrt/dynamo/lowering/_decompositions.py b/py/torch_tensorrt/dynamo/lowering/_decompositions.py
index 0c4e19d902..6784461694 100644
--- a/py/torch_tensorrt/dynamo/lowering/_decompositions.py
+++ b/py/torch_tensorrt/dynamo/lowering/_decompositions.py
@@ -420,6 +420,18 @@ def instance_norm_decomposition(
         )
 
 
+@register_torch_trt_decomposition(
+    torch.ops.aten.full_like, registry=TORCH_TRT_DECOMPOSITIONS
+)
+def full_like_decomposition(*args, **kwargs) -> torch.Tensor:
+    input = args[0]
+    shape = args[0].shape
+    fill_value = args[1]
+    kwargs["dtype"] = input.dtype
+    kwargs["device"] = to_torch_device(default_device())
+    return torch.full(shape, fill_value, dtype=kwargs["dtype"], device=kwargs["device"])
+
+
 def get_decompositions(
     enable_experimental_decompositions: bool = False,
 ) -> Dict[OpOverload, Callable[[Any], Any]]:
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py b/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py
index a472e52d03..2c7d6d1c02 100644
--- a/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py
+++ b/py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py
@@ -14,7 +14,6 @@
 from .remove_detach import remove_detach
 from .remove_input_alias_fixing_clones import remove_input_alias_fixing_clones
 from .repair_input_as_output import repair_input_as_output
-from .replace_full_like_with_full import replace_full_like_with_full
 from .replace_max_pool_with_indices import replace_max_pool_with_indices
 from .view_to_reshape import view_to_reshape
 
@@ -27,7 +26,6 @@
         lower_linear,
         fuse_prims_broadcast,
         replace_max_pool_with_indices,
-        replace_full_like_with_full,
         view_to_reshape,
         remove_assert_scalar,
         accumulate_fp32_matmul,
diff --git a/py/torch_tensorrt/dynamo/lowering/passes/replace_full_like_with_full.py b/py/torch_tensorrt/dynamo/lowering/passes/replace_full_like_with_full.py
deleted file mode 100644
index 9303aafd60..0000000000
--- a/py/torch_tensorrt/dynamo/lowering/passes/replace_full_like_with_full.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import logging
-
-import torch
-import torch.fx
-from torch_tensorrt.dynamo._defaults import default_device
-from torch_tensorrt.dynamo._settings import CompilationSettings
-from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
-    clean_up_graph_after_modifications,
-)
-from torch_tensorrt.dynamo.utils import to_torch_device
-
-logger = logging.getLogger(__name__)
-
-
-def replace_full_like_with_full(
-    gm: torch.fx.GraphModule, settings: CompilationSettings
-) -> torch.fx.GraphModule:
-    """Replace full_like nodes with equivalent full nodes"""
-    modified_graph = False
-
-    for node in gm.graph.nodes:
-        if node.target == torch.ops.aten.full_like.default:
-            modified_graph = True
-
-            # Extract arguments from full_like
-            input_tensor = node.args[0]
-            fill_value = node.args[1]
-            input_dtype = None
-            input_shape = None
-            input_device = to_torch_device(default_device())
-            if "val" in input_tensor.meta:
-                input_dtype = input_tensor.meta["val"].dtype
-                input_device = input_tensor.meta["val"].device
-                input_shape = list(input_tensor.meta["val"].shape)
-            elif "tensor_meta" in input_tensor.meta:
-                input_dtype = input_tensor.meta["tensor_meta"].dtype
-                input_shape = list(input_tensor.meta["tensor_meta"].shape)
-
-            # There's no memory format argument for torch.full.
-            # Set the input_device and dtype correspondingly.
-            new_kwargs = {}
-            for key, val in node.kwargs.items():
-                if key != "memory_format":
-                    new_kwargs[key] = val
-            new_kwargs["device"] = input_device
-            new_kwargs["dtype"] = input_dtype
-            # Replace full_like with full, using the shape as a list
-            input_nodes = (input_shape, fill_value)
-            with gm.graph.inserting_after(node):
-                full_node = gm.graph.call_function(
-                    torch.ops.aten.full.default,
-                    args=input_nodes,
-                    kwargs=new_kwargs,
-                )
-                full_node.meta = node.meta
-
-            node.replace_all_uses_with(full_node)
-            gm.graph.erase_node(node)
-
-    if modified_graph:
-        gm = clean_up_graph_after_modifications(gm)
-
-    return gm
diff --git a/tests/py/dynamo/lowering/test_decompositions.py b/tests/py/dynamo/lowering/test_decompositions.py
index b5225dc5c9..fc4a3de855 100644
--- a/tests/py/dynamo/lowering/test_decompositions.py
+++ b/tests/py/dynamo/lowering/test_decompositions.py
@@ -427,11 +427,13 @@ def __init__(self, *args, **kwargs) -> None:
                 super().__init__(*args, **kwargs)
 
             def forward(self, x):
-                y = torch.full_like(x, 2.0)
-                return y
+                c = torch.ops.aten.add(x, x)
+                y = torch.ops.aten.full_like.default(c, 2)
+                d = y + c
+                return d
 
         # Operations expected to be removed in the traced graph after decompositions
-        expected_ops = {torch.ops.aten.full.default}
+        expected_ops = {torch.ops.aten.add.Tensor}
         unexpected_ops = {torch.ops.aten.full_like.default}
 
         inputs = [torch.randn(3, 3, dtype=torch.float32).cuda()]