Fix the api input type and data dtype check, cherry-pick from develop(#20138) (#20429)

Fix the api input type and data dtype check in the op of sign
test=release/1.6

python/paddle/fluid/layers/nn.py

@@ -15400,7 +15400,17 @@ def sign(x):
     helper = LayerHelper("sign", **locals())
 
     if not isinstance(x, Variable):
-        x = assign(x)
+        if isinstance(x, np.ndarray):
+            x = assign(x)
+        else:
+            raise TypeError(
+                "The type of 'x' in sign_op must be Variable or numpy.ndarray, but received %s."
+                % (type(x)))
+
+    if convert_dtype(x.dtype) not in ['float32', 'float64']:
+        raise TypeError(
+            "The data type of 'x' in sign_op must be float32 or float64, but received %s."
+            % (convert_dtype(x.dtype)))
 
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
 

python/paddle/fluid/tests/unittests/test_sign_op.py

@@ -17,6 +17,8 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
 
 
 class TestSignOp(OpTest):
@@ -34,5 +36,17 @@ class TestSignOp(OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestSignOpError(OpTest):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # The input type of sign_op must be Variable or numpy.ndarray.
+            input1 = 12
+            self.assertRaises(TypeError, fluid.layers.sign, input1)
+            # The input dtype of sign_op must be float32, float64.
+            input2 = fluid.layers.data(
+                name='input2', shape=[12, 10], dtype="int32")
+            self.assertRaises(TypeError, fluid.layers.sign, input2)
+
+
 if __name__ == "__main__":
     unittest.main()