diff --git a/test/test_ops.py b/test/test_ops.py
index e260e86f0f..d73ae536ac 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -5,10 +5,12 @@
 from torchao.quantization.utils import TORCH_VERSION_AFTER_2_4
 import unittest
 from parameterized import parameterized
+import pytest
 
 
 # torch.testing._internal.optests.generate_tests.OpCheckError: opcheck(op, ...):
 # test_faketensor failed with module 'torch' has no attribute '_custom_ops' (scroll up for stack trace)
+@pytest.mark.filterwarnings("ignore:create_unbacked_symint is deprecated, please use new_dynamic_size instead:UserWarning")
 @unittest.skipIf(IS_FBCODE, "Skipping the test in fbcode since we don't have TARGET file for kernels")
 class TestOps(TestCase):
     def _create_tensors_with_iou(self, N, iou_thresh):
diff --git a/torchao/ops.py b/torchao/ops.py
index 3a25dbf6db..fcc6ae9364 100644
--- a/torchao/ops.py
+++ b/torchao/ops.py
@@ -1,5 +1,14 @@
 import torch
 from torch import Tensor
+from torchao.quantization.utils import TORCH_VERSION_AFTER_2_4
+
+def register_custom_op(name):
+    def decorator(func):
+        if TORCH_VERSION_AFTER_2_4:
+            return torch.library.register_fake(f"{name}")(func)
+        else:
+            return torch.library.impl_abstract(f"{name}")(func)
+    return decorator
 
 def nms(boxes: Tensor, scores: Tensor, iou_threshold: float) -> Tensor:
     """
@@ -9,7 +18,7 @@ def nms(boxes: Tensor, scores: Tensor, iou_threshold: float) -> Tensor:
 
 
 # Defines the meta kernel / fake kernel / abstract impl
-@torch.library.impl_abstract("torchao::nms")
+@register_custom_op("torchao::nms")
 def _(dets, scores, iou_threshold):
     torch._check(dets.dim() == 2, lambda: f"boxes should be a 2d tensor, got {dets.dim()}D")
     torch._check(dets.size(1) == 4, lambda: f"boxes should have 4 elements in dimension 1, got {dets.size(1)}")
@@ -36,7 +45,7 @@ def prepack_fp6_weight(fp6_weight: Tensor) -> Tensor:
     return torch.ops.torchao.prepack_fp6_weight.default(fp6_weight)
 
 
-@torch.library.impl_abstract("torchao::prepack_fp6_weight")
+@register_custom_op("torchao::prepack_fp6_weight")
 def _(fp6_weight):
     torch._check(fp6_weight.dim() == 2, lambda: f"weight should be a 2d tensor, got {fp6_weight.dim()}D")
     return torch.empty_like(fp6_weight)
@@ -49,7 +58,7 @@ def fp16_to_fp6(fp16_tensor: Tensor) -> Tensor:
     return torch.ops.torchao.fp16_to_fp6.default(fp16_tensor)
 
 
-@torch.library.impl_abstract("torchao::fp16_to_fp6")
+@register_custom_op("torchao::fp16_to_fp6")
 def _(fp16_tensor):
     torch._check(fp16_tensor.dim() == 2, lambda: f"weight should be a 2d tensor, got {fp16_tensor.dim()}D")
     torch._check(fp16_tensor.dtype is torch.float16, lambda: f"weight must be FP16, got {fp16_tensor.dtype}")
@@ -74,7 +83,7 @@ def fp16act_fp6weight_linear(_in_feats: Tensor, _weights: Tensor, _scales: Tenso
     return torch.ops.torchao.fp16act_fp6weight_linear.default(_in_feats, _weights, _scales, splitK)
 
 
-@torch.library.impl_abstract("torchao::fp16act_fp6weight_linear")
+@register_custom_op("torchao::fp16act_fp6weight_linear")
 def _(_in_feats, _weights, _scales, splitK = 1):
     torch._check(_in_feats.dim() == 2, lambda: f"input should be a 2d tensor, got {_in_feats.dim()}D")
     torch._check(_in_feats.dtype is torch.float16, lambda: f"weight must be FP16, got {_in_feats.dtype}")
@@ -95,7 +104,7 @@ def fp6_weight_dequant(fp6_tensor: Tensor, fp16_scale: Tensor) -> Tensor:
     return torch.ops.torchao.fp6_weight_dequant.default(fp6_tensor, fp16_scale)
 
 
-@torch.library.impl_abstract("torchao::fp6_weight_dequant")
+@register_custom_op("torchao::fp6_weight_dequant")
 def _(fp6_tensor, fp16_scale):
     torch._check(fp6_tensor.dim() == 2, lambda: f"weight should be a 2d tensor, got {fp6_tensor.dim()}D")
     torch._check(fp6_tensor.dtype is torch.int32, lambda: f"weight must be INT32, got {fp6_tensor.dtype}")