add nested dynamic shape test

tests/python/relay/test_pass_dynamic_to_static.py

@@ -85,3 +85,38 @@ def verify_reshape(shape, newshape):
 
     verify_reshape((2, 3, 4), (8, 3))
     verify_reshape((4, 7), (2, 7, 2))
+
+def test_dynamic_to_static_quad_reshape():
+    def verify_reshape(shape, newshape):
+        x = relay.var("x", relay.TensorType(shape, "float32"))
+        y = relay.var("y", relay.TensorType(newshape, "float32"))
+        z1 = relay.dynamic.reshape(x, relay.shape_of(y))
+        z2 = relay.dynamic.reshape(z1, relay.shape_of(x))
+        z3 = relay.dynamic.reshape(z2, relay.shape_of(z1))
+        z4 = relay.dynamic.reshape(z3, relay.shape_of(z2))
+        func = run_infer_type(relay.Function([x, y], z4))
+        func2 = run_opt_pass(run_opt_pass(func, transform.DynamicToStatic()), transform.InferType())
+
+        zz = func2.body
+        assert isinstance(zz, relay.Call)
+        assert zz.op == relay.op.get("reshape")
+        assert "newshape=" in zz.astext()
+        assert zz.checked_type == relay.ty.TensorType(shape, "float32")
+
+        x_data = np.random.uniform(low=-1, high=1, size=shape).astype("float32")
+        y_data = np.random.uniform(low=-1, high=1, size=newshape).astype("float32")
+        for target, ctx in ctx_list():
+            for kind in ["graph", "debug"]:
+                mod = tvm.ir.IRModule.from_expr(func2)
+                intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+                op_res = intrp.evaluate()(x_data, y_data)
+                tvm.testing.assert_allclose(op_res.asnumpy(), x_data, rtol=1e-5)
+
+    verify_reshape((2, 3, 4), (8, 3))
+    verify_reshape((4, 7), (2, 7, 2))
+
+if __name__=="__main__":
+    test_dynamic_to_static_reshape()
+    test_dynamic_to_static_double_reshape()
+    test_dynamic_to_static_quad_reshape()
+