diff --git a/src/relay/op/tensor/transform.cc b/src/relay/op/tensor/transform.cc
index 9e66301dfcbb2..fa8a778e80536 100644
--- a/src/relay/op/tensor/transform.cc
+++ b/src/relay/op/tensor/transform.cc
@@ -2002,7 +2002,7 @@ Array<Array<Layout> > StridedSliceInferCorrectLayout(
 
   auto layout = old_in_layouts[0];
   if (layout.defined() && new_in_layouts.defined()) {
-    CHECK_EQ(new_in_layouts.size(), 1);
+    CHECK_GE(new_in_layouts.size(), 1);
     auto new_layout = new_in_layouts[0];
     auto shape = old_in_shapes[0];
 
@@ -2059,9 +2059,25 @@ Array<Array<Layout> > StridedSliceInferCorrectLayout(
         new_end.push_back(tvm::Integer(ed / factor));
       }
     }
+
     layout = new_layout;
+
+    DLContext ctx;
+    ctx.device_type = kDLCPU;
+    ctx.device_id = 0;
+    auto begin_ndarray = runtime::NDArray::Empty({int64_t(new_begin.size())}, Type2TVMType(Int(64)), ctx);
+    auto end_ndarray = runtime::NDArray::Empty({int64_t(new_begin.size())}, Type2TVMType(Int(64)), ctx);
+    auto strides_ndarray = runtime::NDArray::Empty({int64_t(new_begin.size())}, Type2TVMType(Int(64)), ctx);
+    int64_t* begin_data = static_cast<int64_t*>(begin_ndarray->data);
+    int64_t* end_data = static_cast<int64_t*>(end_ndarray->data);
+    for (size_t i = 0; i < new_begin.size(); ++i) {
+      begin_data[i] = new_begin[i];
+      end_data[i] = new_end[i];
+    }
+    params->begin = ConstantNode::make(begin_ndarray);
+    params->end = ConstantNode::make(end_ndarray);
   }
-  return {{layout}, {layout}};
+  return {{layout, Layout("C"), Layout("C"), Layout("C")}, {layout}};
 }
 
 inline Tensor DynamicStridedSlice(const tvm::Tensor& input,
diff --git a/src/relay/pass/combine_parallel_conv2d.cc b/src/relay/pass/combine_parallel_conv2d.cc
index 8562a1ffa454a..763857fd02d98 100644
--- a/src/relay/pass/combine_parallel_conv2d.cc
+++ b/src/relay/pass/combine_parallel_conv2d.cc
@@ -192,9 +192,9 @@ class ParallelConv2DCombiner : public ParallelOpCombiner {
       DLContext ctx;
       ctx.device_type = kDLCPU;
       ctx.device_id = 0;
-      auto begin_ndarray = runtime::NDArray::Empty({1}, Type2TVMType(Int(64)), ctx);
-      auto end_ndarray = runtime::NDArray::Empty({1}, Type2TVMType(Int(64)), ctx);
-      auto strides_ndarray = runtime::NDArray::Empty({1}, Type2TVMType(Int(64)), ctx);
+      auto begin_ndarray = runtime::NDArray::Empty({int64_t(begin.size())}, Type2TVMType(Int(64)), ctx);
+      auto end_ndarray = runtime::NDArray::Empty({int64_t(begin.size())}, Type2TVMType(Int(64)), ctx);
+      auto strides_ndarray = runtime::NDArray::Empty({int64_t(begin.size())}, Type2TVMType(Int(64)), ctx);
       int64_t* begin_data = static_cast<int64_t*>(begin_ndarray->data);
       int64_t* end_data = static_cast<int64_t*>(end_ndarray->data);
 
diff --git a/tests/python/relay/test_pass_alter_op_layout.py b/tests/python/relay/test_pass_alter_op_layout.py
index 2738690025df6..8d93e5327c136 100644
--- a/tests/python/relay/test_pass_alter_op_layout.py
+++ b/tests/python/relay/test_pass_alter_op_layout.py
@@ -528,7 +528,7 @@ def before():
         x = relay.var("x", shape=(1, 32, 28, 28))
         weight = relay.var('weight', shape=(32, 32, 3, 3))
         y = relay.nn.conv2d(x, weight, channels=32, kernel_size=(3, 3), padding=(1, 1))
-        y = relay.strided_slice(y, begin=[0, 16], end=[None, None])
+        y = relay.strided_slice(y, begin=relay.const([0, 16], "int32"), end=relay.const([1, 32], "int32"))
         y = relay.Function(analysis.free_vars(y), y)
         return y
 
@@ -546,7 +546,7 @@ def expected():
         x = relay.layout_transform(x, "NCHW", "NCHW4c")
         y = relay.nn.conv2d(x, weight, channels=32, kernel_size=(3, 3), padding=(1, 1),
                             data_layout="NCHW4c")
-        y = relay.strided_slice(y, begin=[0, 4], end=[None, 8])
+        y = relay.strided_slice(y, begin=relay.const([0, 4], "int32"), end=relay.const([1, 8], "int32"))
         y = relay.layout_transform(y, "NCHW4c", "NCHW")
         y = relay.Function(analysis.free_vars(y), y)
         return y