diff --git a/paddle/fluid/pybind/eager_utils.cc b/paddle/fluid/pybind/eager_utils.cc
index b3912748433680cb8e0210d80ea81c810a025401..d07cbd5ee21a2bf0227cc3634ebcb7f8ee87e0ba 100644
--- a/paddle/fluid/pybind/eager_utils.cc
+++ b/paddle/fluid/pybind/eager_utils.cc
@@ -1025,7 +1025,20 @@ paddle::experimental::Scalar CastNumpy2Scalar(PyObject* obj,
   PyTypeObject* type = obj->ob_type;
   auto type_name = std::string(type->tp_name);
   VLOG(1) << "type_name: " << type_name;
-  if (type_name == "numpy.float64") {
+  if (type_name == "numpy.ndarray" && PySequence_Check(obj)) {
+    PyObject* item = nullptr;
+    item = PySequence_GetItem(obj, 0);
+    if (PyObject_CheckFloatOrToFloat(&item)) {
+      float value = static_cast<float>(PyFloat_AsDouble(item));
+      return paddle::experimental::Scalar(value);
+    } else {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "%s(): argument (position %d) is numpy.ndarry, the inner elements "
+          "must be "
+          "numpy.float32/float64 now, but got %s",
+          op_type, arg_pos + 1, type_name));  // NOLINT
+    }
+  } else if (type_name == "numpy.float64") {
     double value = CastPyArg2Double(obj, op_type, arg_pos);
     return paddle::experimental::Scalar(value);
   } else if (type_name == "numpy.float32") {
diff --git a/python/paddle/fluid/tests/unittests/test_bfgs.py b/python/paddle/fluid/tests/unittests/test_bfgs.py
index 4bf6de3eee51099b370cb3a0baa108fc189f7bb7..1a12913bc72e9a76782d86ce4060642e4bf13d96 100644
--- a/python/paddle/fluid/tests/unittests/test_bfgs.py
+++ b/python/paddle/fluid/tests/unittests/test_bfgs.py
@@ -20,6 +20,7 @@ import paddle
 import paddle.nn.functional as F
 
 from paddle.incubate.optimizer.functional.bfgs import minimize_bfgs
+from paddle.fluid.framework import _test_eager_guard
 
 from paddle.fluid.framework import _enable_legacy_dygraph
 _enable_legacy_dygraph()
@@ -120,7 +121,7 @@ class TestBfgs(unittest.TestCase):
         results = test_static_graph(func, x0, dtype='float64')
         self.assertTrue(np.allclose(0.8, results[2]))
 
-    def test_rosenbrock(self):
+    def func_rosenbrock(self):
         # The Rosenbrock function is a standard optimization test case.
         a = np.random.random(size=[1]).astype('float32')
         minimum = [a.item(), (a**2).item()]
@@ -139,6 +140,11 @@ class TestBfgs(unittest.TestCase):
         results = test_dynamic_graph(func, x0)
         self.assertTrue(np.allclose(minimum, results[2]))
 
+    def test_rosenbrock(self):
+        with _test_eager_guard():
+            self.func_rosenbrock()
+        self.func_rosenbrock()
+
     def test_exception(self):
         def func(x):
             return paddle.dot(x, x)