diff --git a/aten/src/ATen/native/cuda/Math.cuh b/aten/src/ATen/native/cuda/Math.cuh
index b99e9d0c94d..2fe8f5dd2e3 100644
--- a/aten/src/ATen/native/cuda/Math.cuh
+++ b/aten/src/ATen/native/cuda/Math.cuh
@@ -758,11 +758,10 @@ const auto sinc_string = jiterator_stringify(
   T sinc(T a) {
     if (a == T(0)) {
       return T(1);
-    } else {
-      constexpr T pi = T(3.14159265358979323846L);
-      T product = pi * a;
-      return std::sin(product) / product;
     }
+    constexpr T pi = T(3.14159265358979323846L);
+    T product = pi * a;
+    return std::sin(product) / product;
   }
 ); // sinc_string
 
diff --git a/test/inductor/test_flex_attention.py b/test/inductor/test_flex_attention.py
index 8b4382061b0..99440593c2b 100644
--- a/test/inductor/test_flex_attention.py
+++ b/test/inductor/test_flex_attention.py
@@ -2510,9 +2510,9 @@ def forward(self, arg0_1, arg1_1, arg2_1, arg3_1, arg4_1):
     @supported_platform
     def test_strided_backwards(self):
         shape = (1, 2, 4096, 64)
-        Q = torch.randn(shape, requires_grad=True, device="cuda", dtype=torch.bfloat16)
-        K = torch.randn(shape, requires_grad=True, device="cuda", dtype=torch.bfloat16)
-        V = torch.randn(shape, requires_grad=True, device="cuda", dtype=torch.bfloat16)
+        Q = torch.randn(shape, requires_grad=True, device="cuda")
+        K = torch.randn(shape, requires_grad=True, device="cuda")
+        V = torch.randn(shape, requires_grad=True, device="cuda")
         func = torch.compile(flex_attention, dynamic=True, fullgraph=True)
 
         K_sliced = K[:, :, :-128]