pytorch
diff --git a/‎test/inductor/test_torchinductor.py
Lines changed: 15 additions & 0 deletions b/‎test/inductor/test_torchinductor.py
Lines changed: 15 additions & 0 deletions
diff --git a/‎torch/_meta_registrations.py
Lines changed: 12 additions & 0 deletions b/‎torch/_meta_registrations.py
Lines changed: 12 additions & 0 deletions
@@ -6616,6 +6616,21 @@ def test_avg_pool_errors_with_uint(self):
                 ):
                     c_op(x, kernel_size=2, stride=2)
 
+    def test_replication_pad_errors_with_bool(self):
+        for dim in (1, 2, 3):
+
+            def fn(x):
+                x = torch.signbit(x)
+                x = eval(f"nn.ReplicationPad{dim}d(padding=1)")(x)
+                return x
+
+            c_fn = torch.compile(fn)
+            x = torch.randn([1] * (dim + 2))
+            with self.assertRaisesRegex(
+                RuntimeError, r".*(not implemented|aoti_torch_).*"
+            ):
+                c_fn(x)
+
     def test_log1p(self):
         def fn(x):
             return torch.log1p(x), torch.log1p(x) * 2
 
@@ -1853,6 +1853,10 @@ def meta_reflection_pad1d(input, padding):
 @register_meta(aten.replication_pad1d)
 @out_wrapper()
 def meta_replication_pad1d(input, padding):
+    torch._check(
+        input.dtype != torch.bool,
+        lambda: f""""replication_pad1d" not implemented for '{input.dtype.__str__()}'""",
+    )
     return _pad1d_common(input, padding, is_reflection=False)
 
 
@@ -1960,6 +1964,10 @@ def meta_reflection_pad2d(input, padding):
 @register_meta(aten.replication_pad2d)
 @out_wrapper()
 def meta_replication_pad2d(input, padding):
+    torch._check(
+        input.dtype != torch.bool,
+        lambda: f""""replication_pad2d" not implemented for '{input.dtype.__str__()}'""",
+    )
     return _pad2d_common(input, padding, is_reflection=False)
 
 
@@ -2073,6 +2081,10 @@ def meta_reflection_pad3d(input, padding):
 @register_meta(aten.replication_pad3d)
 @out_wrapper()
 def meta_replication_pad3d(input, padding):
+    torch._check(
+        input.dtype != torch.bool,
+        lambda: f""""replication_pad3d" not implemented for '{input.dtype.__str__()}'""",
+    )
     return _pad3d_common(input, padding, is_reflection=False)