diff --git a/aten/src/ATen/native/TensorShape.cpp b/aten/src/ATen/native/TensorShape.cpp
index aea88dd1cc94..c2d0856c3cd4 100644
--- a/aten/src/ATen/native/TensorShape.cpp
+++ b/aten/src/ATen/native/TensorShape.cpp
@@ -3063,7 +3063,7 @@ Tensor slice(
   }
   auto storage_offset = self.storage_offset() + start_val * strides[dim];
   auto len = end_val - start_val;
-  sizes[dim] = (len / step) + (len % step != 0); // safely round-up
+  sizes[dim] = (len + step - 1) / step; // round-up
   strides[dim] *= step;
 
   Tensor result;
diff --git a/test/inductor/test_torchinductor.py b/test/inductor/test_torchinductor.py
index ac5b538189b3..6a6e3c674179 100644
--- a/test/inductor/test_torchinductor.py
+++ b/test/inductor/test_torchinductor.py
@@ -4309,31 +4309,6 @@ class CommonTemplate:
         self.assertEqual(torch.compile(fn)(x1, y), fn(x1, y))
         self.assertEqual(torch.compile(fn)(x2, y), fn(x2, y))
 
-    def test_slice_copy(self):
-        class Model(nn.Module):
-            def __init__(self, start=449, step=(2**63 - 1)):
-                super().__init__()
-                self.start = start
-                self.step = step
-
-            def forward(self, x: torch.Tensor):
-                sliced = torch.slice_copy(
-                    x, dim=0, start=self.start, end=None, step=self.step
-                )
-                return torch.reciprocal(sliced)
-
-        with config.patch({"implicit_fallbacks": True}):
-            # bad case
-            self.common(
-                Model(),
-                (torch.randn(875),),
-            )
-            # normal case
-            self.common(
-                Model(step=10),
-                (torch.randn(875),),
-            )
-
     def test_slice1(self):
         def fn(a):
             return (
diff --git a/torch/_decomp/decompositions.py b/torch/_decomp/decompositions.py
index 1918373b342e..ba09c6173c5f 100644
--- a/torch/_decomp/decompositions.py
+++ b/torch/_decomp/decompositions.py
@@ -759,8 +759,7 @@ def slice_forward(
 
     storage_offset = self.storage_offset() + start_val * strides[dim]
     len = end_val - start_val
-    # safely round-up for corresponding c++ impl
-    sizes[dim] = (len // step) + (1 if len % step != 0 else 0)
+    sizes[dim] = (len + step - 1) // step
     strides[dim] *= step
 
     if self.is_quantized: