diff --git a/src/relay/op/tensor/transform.cc b/src/relay/op/tensor/transform.cc
index 3f7da4e954a4..d225d93fe394 100644
--- a/src/relay/op/tensor/transform.cc
+++ b/src/relay/op/tensor/transform.cc
@@ -2711,6 +2711,13 @@ InferCorrectLayoutOutput StridedSliceInferCorrectLayout(
             new_begin.push_back(begin[i]);
             new_end.push_back(end[i]);
           } else {
+            if (strides.defined() && i < strides.size()) {
+              auto stride = strides[i];
+              // arbitrary stride is not supported
+              if (stride.defined() && stride->value != 1) {
+                return out_default;
+              }
+            }
             int64_t bg = begin[i];
             int64_t ed = end[i];
             if (bg % factor || ed % factor) {
diff --git a/tests/python/relay/test_pass_alter_op_layout.py b/tests/python/relay/test_pass_alter_op_layout.py
index 250c177f65bb..cffc33b0bc24 100644
--- a/tests/python/relay/test_pass_alter_op_layout.py
+++ b/tests/python/relay/test_pass_alter_op_layout.py
@@ -1499,6 +1499,27 @@ def alter_conv2d(attrs, inputs, tinfos, out_type):
         assert tvm.ir.structural_equal(a, b)
 
 
+def test_conv2d_strided_slice_arbitrary_stride():
+    """Test rewriting strided_slice with arbitrary stride"""
+
+    def before():
+        x = relay.var("x", shape=(4, 12, 1, 1))
+        weight = relay.var("weight", shape=(9, 12, 1, 1))
+        y = relay.nn.conv2d(x, weight, channels=9, kernel_size=(1, 1), padding=(0, 0))
+        y = relay.strided_slice(y, begin=[3], end=[6], strides=[3], axes=[1])
+        y = relay.Function(analysis.free_vars(y), y)
+        return y
+
+    def alter_conv2d(attrs, inputs, tinfos, out_type):
+        data, weight = inputs
+        new_attrs = dict(attrs)
+        new_attrs["data_layout"] = "NCHW3c"
+        return relay.nn.conv2d(data, weight, **new_attrs)
+
+    with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
+        run_opt_pass(before(), transform.AlterOpLayout())
+
+
 def test_conv2d_reduce_channels():
     x = relay.var("data", shape=(1, 8, 48, 48))
     y = relay.nn.conv2d(