diff --git a/paddle/fluid/operators/activation_op.h b/paddle/fluid/operators/activation_op.h
index 94cbaf76e942aae3ec299f6d0186418527c7ce96..018f515d81e93fc6b97c28e4872269c9da33ddbb 100644
--- a/paddle/fluid/operators/activation_op.h
+++ b/paddle/fluid/operators/activation_op.h
@@ -388,9 +388,9 @@ struct HardShrinkFunctor : public BaseActivationFunctor<T> {
   }
   template <typename Device, typename X, typename Out>
   void operator()(Device d, X x, Out out) const {
-    auto temp1 = (x < static_cast<T>(threshold * -1)).template cast<T>();
-    auto temp2 = (x > static_cast<T>(threshold)).template cast<T>();
-    out.device(d) = x * (temp1 + temp2);
+    auto temp1 = x < static_cast<T>(threshold * -1.f);
+    auto temp2 = x > static_cast<T>(threshold);
+    out.device(d) = x * (temp1 + temp2 > 0).template cast<T>();
   }
 };
 
@@ -405,9 +405,9 @@ struct HardShrinkGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Out, typename dOut,
             typename dX>
   void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
-    auto temp1 = (x < static_cast<T>(threshold * -1)).template cast<T>();
-    auto temp2 = (x > static_cast<T>(threshold)).template cast<T>();
-    dx.device(d) = dout * (temp1 + temp2).template cast<T>();
+    auto temp1 = x < static_cast<T>(threshold * -1.f);
+    auto temp2 = x > static_cast<T>(threshold);
+    dx.device(d) = dout * (temp1 + temp2 > 0).template cast<T>();
   }
 
   static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
diff --git a/python/paddle/fluid/tests/unittests/test_activation_op.py b/python/paddle/fluid/tests/unittests/test_activation_op.py
index db32976f046d36557f201621b651e57904f5a974..9704eb432cae9970761c648a38f7158ce32ba974 100644
--- a/python/paddle/fluid/tests/unittests/test_activation_op.py
+++ b/python/paddle/fluid/tests/unittests/test_activation_op.py
@@ -453,20 +453,29 @@ class TestHardShrink(TestActivation):
         self.op_type = "hard_shrink"
         self.init_dtype()
 
-        threshold = 0.5
+        self.threshold = 0.5
+        self.set_attrs()
         x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype) * 10
-        out = ref_hardshrink(x, threshold)
+        out = ref_hardshrink(x, self.threshold)
 
-        self.attrs = {'threshold': threshold}
+        self.attrs = {'threshold': self.threshold}
         self.inputs = {'X': x}
         self.outputs = {'Out': out}
 
+    def set_attrs(self):
+        pass
+
     def test_check_grad(self):
         if self.dtype == np.float16:
             return
         self.check_grad(['X'], 'Out')
 
 
+class TestHardShrink_threshold_negative(TestHardShrink):
+    def set_attrs(self):
+        self.threshold = -0.1
+
+
 class TestHardShrinkAPI(unittest.TestCase):
     # test paddle.nn.Hardshrink, paddle.nn.functional.hardshrink
     def setUp(self):