diff --git a/scripts/benchmark.py b/scripts/benchmark.py
index d3568f4..0193409 100644
--- a/scripts/benchmark.py
+++ b/scripts/benchmark.py
@@ -8,6 +8,7 @@
 import torch
 import numpy as np
 import pandas as pd
+import e3nn
 from e3nn.o3._spherical_harmonics import _spherical_harmonics
 
 from equitriton.sph_harm.bindings import *
@@ -82,11 +83,14 @@ def e3nn_benchmark(tensor_shape: list[int], device: str | torch.device, l_max: i
         joint_tensor[..., 1].contiguous(),
         joint_tensor[..., 2].contiguous(),
     )
-    output = _spherical_harmonics(l_max, x, y, z)
+    e3nn.set_optimization_defaults(jit_script_fx=False)
+    output = torch.compile(_spherical_harmonics, fullgraph=True, mode="max-autotune")(l_max, x, y, z)
     output.backward(gradient=torch.ones_like(output))
     # delete references to ensure memory gets cleared
     del output
     del joint_tensor
+    e3nn.set_optimization_defaults(jit_script_fx=True) # Turn it back on to avoid any issues 
+
 
 
 @benchmark(num_steps=args.num_steps, warmup_fraction=args.warmup_fraction)
@@ -131,4 +135,4 @@ def triton_benchmark(tensor_shape: list[int], device: str | torch.device, l_max:
     all_data.append(joint_results)
 
 df = pd.DataFrame(all_data)
-df.to_csv(f"{args.device}_lmax{args.l_max}_results.csv", index=False)
+df.to_csv(f"{args.device}_lmax{args.l_max}_results.csv", index=False)
\ No newline at end of file
diff --git a/scripts/measure_numerical_error.py b/scripts/measure_numerical_error.py
index 91d8929..03f69f9 100644
--- a/scripts/measure_numerical_error.py
+++ b/scripts/measure_numerical_error.py
@@ -6,6 +6,7 @@
 
 import torch
 import numpy as np
+import e3nn
 from e3nn.o3._spherical_harmonics import _spherical_harmonics
 
 from equitriton.sph_harm.bindings import *
@@ -73,7 +74,8 @@ def compare_e3nn_triton(
         joint_tensor[..., 1].contiguous(),
         joint_tensor[..., 2].contiguous(),
     )
-    e3nn_output = _spherical_harmonics(l_max, x, y, z)
+    e3nn.set_optimization_defaults(jit_script_fx=False)
+    e3nn_output = torch.compile(_spherical_harmonics, fullgraph=True, mode="max-autotune")(l_max, x, y, z)
     e3nn_output.backward(gradient=torch.ones_like(e3nn_output))
     e3nn_grad = joint_tensor.grad.detach().clone()
     joint_tensor.grad = None
@@ -95,6 +97,7 @@ def compare_e3nn_triton(
     # delete intermediate tensors to make sure we don't leak
     del e3nn_output
     del triton_output
+    e3nn.set_optimization_defaults(jit_script_fx=True) # Turn it back on to avoid any issues 
     return (signed_fwd_error, signed_bwd_error)
 
 
diff --git a/scripts/profile_script.py b/scripts/profile_script.py
index 9404177..1cdd0ae 100644
--- a/scripts/profile_script.py
+++ b/scripts/profile_script.py
@@ -7,6 +7,7 @@
 
 import torch
 from torch.profiler import record_function
+import e3nn
 from e3nn.o3._spherical_harmonics import _spherical_harmonics
 
 from equitriton.sph_harm.bindings import *
@@ -74,13 +75,15 @@ def e3nn_benchmark(tensor_shape: list[int], device: str | torch.device, l_max: i
         joint_tensor[..., 1].contiguous(),
         joint_tensor[..., 2].contiguous(),
     )
+    e3nn.set_optimization_defaults(jit_script_fx=False)
     with record_function("forward"):
-        output = _spherical_harmonics(l_max, x, y, z)
+        output = torch.compile(_spherical_harmonics, fullgraph=True, mode="max-autotune")(l_max, x, y, z)
     with record_function("backward"):
         output.backward(gradient=torch.ones_like(output))
     # delete references to ensure memory gets cleared
     del output
     del joint_tensor
+    e3nn.set_optimization_defaults(jit_script_fx=True) # Turn it back on to avoid any issues 
 
 
 @profile(