diff --git a/aten/src/ATen/native/cuda/ActivationSiluKernel.cu b/aten/src/ATen/native/cuda/ActivationSiluKernel.cu
index e9b495ca70b..82096b96dbb 100644
--- a/aten/src/ATen/native/cuda/ActivationSiluKernel.cu
+++ b/aten/src/ATen/native/cuda/ActivationSiluKernel.cu
@@ -15,12 +15,13 @@
 #include <ATen/cuda/ApplyGridUtils.cuh>
 #include <ATen/cuda/detail/OffsetCalculator.cuh>
 #include <ATen/native/cuda/Loops.cuh>
+#include <c10/util/complex.h>
 
 namespace at::native {
 namespace {
 
 void silu_kernel(TensorIteratorBase& iter) {
-  AT_DISPATCH_FLOATING_TYPES_AND2(
+  AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2(
       at::ScalarType::Half,
       at::ScalarType::BFloat16,
       iter.dtype(),
@@ -29,7 +30,7 @@ void silu_kernel(TensorIteratorBase& iter) {
         gpu_kernel(iter, [] GPU_LAMBDA(scalar_t x) -> scalar_t {
           using opmath_t = at::opmath_type<scalar_t>;
           const opmath_t x_acc = static_cast<opmath_t>(x);
-          return x_acc / (opmath_t(1) + c10::cuda::compat::exp(-x_acc));
+          return x_acc / (opmath_t(1) + ::exp(-x_acc));
         });
       });
 }
diff --git a/test/test_unary_ufuncs.py b/test/test_unary_ufuncs.py
index 76a2c9ebf5f..6c6744b6b78 100644
--- a/test/test_unary_ufuncs.py
+++ b/test/test_unary_ufuncs.py
@@ -1054,6 +1054,30 @@ class TestUnaryUfuncs(TestCase):
             rtol=rtol,
         )
 
+    @dtypes(torch.complex64, torch.complex128)
+    def test_silu_complex(self, device, dtype):
+        atol = 1e-6
+        rtol = 1e-6
+        inouts = [
+            (0.2 + 0.3j, 0.08775215595960617065 + 0.18024823069572448730j),
+            (1e-19 + 1e-18j, 4.99999984132761269448e-20 + 5.00000022906852482872e-19j),
+            (-1.0 + 2.0j, -0.78546208143234252930 + -0.44626939296722412109j),
+            (0.0 + 0.5j, -0.06383547931909561157 + 0.25000000000000000000j),
+            (2.0j, -1.55740761756896972656 + 0.99999988079071044922j)
+        ]
+
+        for inp, out in inouts:
+            res = torch.nn.functional.silu(torch.tensor(inp, dtype=dtype, device=device))
+            self.assertFalse(torch.any(torch.isnan(res)))
+            self.assertEqual(res.real, out.real, atol=atol, rtol=rtol)
+            self.assertEqual(res.imag, out.imag, atol=atol, rtol=rtol)
+
+        for inp, out in inouts:
+            res = torch.nn.functional.silu(torch.tensor(inp, dtype=dtype, device=device), inplace=True)
+            self.assertFalse(torch.any(torch.isnan(res)))
+            self.assertEqual(res.real, out.real, atol=atol, rtol=rtol)
+            self.assertEqual(res.imag, out.imag, atol=atol, rtol=rtol)
+
     # It is not obvious how to merge this into OpInfo becuase these inputs
     # succeed for gradcheck but are expected to fail for gradgradcheck
     @dtypes(torch.double)
diff --git a/torch/testing/_internal/common_methods_invocations.py b/torch/testing/_internal/common_methods_invocations.py
index 735f6c8ad29..977536e5876 100644
--- a/torch/testing/_internal/common_methods_invocations.py
+++ b/torch/testing/_internal/common_methods_invocations.py
@@ -13528,7 +13528,7 @@ op_db: List[OpInfo] = [
         ref=lambda x, inplace=False:
             x / (1 + np.exp(-x)),
         dtypes=complex_types(),
-        dtypesIfCUDA=empty_types(),
+        dtypesIfCUDA=complex_types(),
         supports_forward_ad=False,
         supports_autograd=False,
         assert_autodiffed=False,
@@ -13544,7 +13544,7 @@ op_db: List[OpInfo] = [
             ), ],
         skips=(
             DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_normal',
-                         dtypes=(torch.cfloat,), device_type='cpu'),
+                         dtypes=(torch.cfloat,)),
             # FIXME: intentionally misreports dtypes
             DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_dtypes'),
             # FIXME: numpy reference diverges: Comparing (nan+nanj) and (-0+0j)