diff --git a/exir/passes/constant_prop_pass.py b/exir/passes/constant_prop_pass.py
index 0049e597f8d..cf864153ba8 100644
--- a/exir/passes/constant_prop_pass.py
+++ b/exir/passes/constant_prop_pass.py
@@ -61,6 +61,8 @@ def is_const(
         )
     elif isinstance(arg, _PRIMITIVE_TYPES):
         return True
+    elif arg is None:
+        return True
     elif not isinstance(arg, torch.fx.Node):
         return False
     elif arg in const_node_to_tensor:
diff --git a/exir/tests/test_passes.py b/exir/tests/test_passes.py
index d3c2d0a0936..6618c729987 100644
--- a/exir/tests/test_passes.py
+++ b/exir/tests/test_passes.py
@@ -1823,3 +1823,34 @@ def _do_checks(
         self.assertTrue(
             torch.allclose(output_no_dim_order[0], output_no_dim_order_revert[0])
         )
+
+    def test_constant_prop_pass_none(self) -> None:
+        """
+        This checks that None arguments are treated as constants in constant_prop_pass.
+        """
+
+        class M(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.cst = torch.ones(3, 3, 3, dtype=torch.int8)
+                self.w = torch.ones(3, 3, 3, dtype=torch.int8)
+
+            def forward(self, x):
+                # Note: using e.g aten.linear would not work as None is not in the graph
+                a = torch.ops.aten.convolution.default(
+                    self.cst, self.w, None, [1], [0], [1], False, [0], 1
+                )
+                return a + x
+
+        mod = M()
+        x = torch.randn([3, 3, 3])
+        mod(x)
+        edge = to_edge(
+            export(mod, (x,), strict=True),
+            compile_config=exir.EdgeCompileConfig(_check_ir_validity=False),
+        )
+        # 2 constants: self.w and self.cst
+        self.assertEqual(2, len(edge.exported_program().constants))
+        pass_result = constant_prop_pass(edge.exported_program())
+        # 1 constant: a (= self.w @ self.cst)
+        self.assertEqual(1, len(pass_result.constants))