diff --git a/crates/ratchet-core/src/ops/norm/mod.rs b/crates/ratchet-core/src/ops/norm/mod.rs
index 9ed488ec..8acda16f 100644
--- a/crates/ratchet-core/src/ops/norm/mod.rs
+++ b/crates/ratchet-core/src/ops/norm/mod.rs
@@ -413,7 +413,6 @@ mod tests {
         let ln_prg = r#"
 import torch
 import torch.nn.functional as F
-
 def layer_norm(input, scale, bias):
     (input, scale, bias) = (torch.from_numpy(input), torch.from_numpy(scale), torch.from_numpy(bias))
     return F.layer_norm(input, (input.shape[-1],), weight=scale, bias=bias).numpy()
@@ -427,7 +426,6 @@ def manual_rms_norm(input, scale):
     input = input * torch.rsqrt(variance + 1e-5)
     return (scale * input).numpy()
 "#;
-
         let prg = match var {
             NormVariant::LayerNorm => ln_prg,
             NormVariant::RMSNorm => rms_prg,
@@ -489,7 +487,7 @@ def manual_rms_norm(input, scale):
 
     #[test]
     fn debug_norm() {
-        let device = Device::request_device(DeviceRequest::GPU).unwrap();
+        let device = Device::request_device(DeviceRequest::CPU).unwrap();
         let prob = NormProblem {
             var: NormVariant::LayerNorm,
             B: 2,
@@ -501,9 +499,14 @@ def manual_rms_norm(input, scale):
     }
 
     #[proptest(cases = 64)]
-    fn test_norm(prob: NormProblem) {
+    fn test_norm_gpu(prob: NormProblem) {
         let device = Device::request_device(DeviceRequest::GPU).unwrap();
-        println!("prob = {:#?}", prob);
+        run_norm_trial(&device, prob).unwrap();
+    }
+
+    #[proptest(cases = 64)]
+    fn test_norm_cpu(prob: NormProblem) {
+        let device = Device::request_device(DeviceRequest::CPU).unwrap();
         run_norm_trial(&device, prob).unwrap();
     }
 }
diff --git a/crates/ratchet-core/src/ops/unary.rs b/crates/ratchet-core/src/ops/unary.rs
index e8818477..346e053e 100644
--- a/crates/ratchet-core/src/ops/unary.rs
+++ b/crates/ratchet-core/src/ops/unary.rs
@@ -409,7 +409,7 @@ def {}(a):
     }
 
     fn run_unary_trial(prob: UnaryProblem, device: Device) -> anyhow::Result<()> {
-        let UnaryProblem { op, B, M, N } = prob;
+        let UnaryProblem { op, B, M, N: _ } = prob;
         let a = Tensor::randn::<f32>(shape![B, M], Device::CPU);
 
         let args = match op {