[cherry-pick] Fix the error message of assign op (#20508) (#20542)

test=develop, test=release/1.6

* Fix the error message of assign op
test=develop

* Refine input type checking for assign op
test=develop

* Refine unittest of assign op
test=develop

python/paddle/fluid/layers/tensor.py

@@ -400,9 +400,17 @@ def assign(input, output=None):
           result3 = fluid.layers.assign(np.array([[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]], dtype='float32')) # result3 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
     """
     helper = LayerHelper('assign', **locals())
-    if output is None:
-        output = helper.create_variable_for_type_inference(dtype=input.dtype)
     if isinstance(input, Variable):
+        if convert_dtype(input.dtype) not in [
+                'float32', 'float64', 'int32', 'int64'
+        ]:
+            raise TypeError(
+                "When the type of 'input' in assign is Variable, the data "
+                "type of 'input' must be float32, float64, int32 or int64, "
+                "but received %s." % convert_dtype(input.dtype))
+        if output is None:
+            output = helper.create_variable_for_type_inference(
+                dtype=input.dtype)
         helper.append_op(
             type='assign', inputs={'X': [input]}, outputs={'Out': [output]})
     elif isinstance(input, numpy.ndarray):
@@ -414,11 +422,16 @@ def assign(input, output=None):
             value_name = "int32_values"
             values = [int(v) for v in input.flat]
         else:
-            raise ValueError("Unsupported dtype %s", input.dtype)
+            raise TypeError(
+                "When the type of 'input' in assign is numpy.ndarray, "
+                "the data type of 'input' must be float32 or int32, but "
+                "received %s." % convert_dtype(dtype))
         if input.size > 1024 * 1024:
             raise ValueError("The size of input is too big. Please consider "
                              "saving it to file and 'load_op' to load it")
-
+        if output is None:
+            output = helper.create_variable_for_type_inference(
+                dtype=input.dtype)
         helper.append_op(
             type='assign_value',
             outputs={'Out': [output]},
@@ -428,7 +441,8 @@ def assign(input, output=None):
                 value_name: values
             })
     else:
-        raise ValueError("Wrong type for assign input: %s" % type(input))
+        raise TypeError("The type of 'input' in assign must be Variable or "
+                        "numpy.ndarray, but received %s" % type(input))
 
     return output
 

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

@@ -15,14 +15,18 @@
 from __future__ import print_function
 
 import op_test
-import numpy
+import numpy as np
 import unittest
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
 
 
 class TestAssignOp(op_test.OpTest):
     def setUp(self):
         self.op_type = "assign"
-        x = numpy.random.random(size=(100, 10))
+        x = np.random.random(size=(100, 10))
         self.inputs = {'X': x}
         self.outputs = {'Out': x}
 
@@ -33,5 +37,32 @@ class TestAssignOp(op_test.OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestAssignOpError(op_test.OpTest):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # The type of input must be Variable or numpy.ndarray.
+            x1 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            self.assertRaises(TypeError, fluid.layers.assign, x1)
+            # When the type of input is Variable, the dtype of input must be float32, float64, int32, int64.
+            x2 = fluid.layers.data(name='x2', shape=[4], dtype="bool")
+            self.assertRaises(TypeError, fluid.layers.assign, x2)
+            x3 = fluid.layers.data(name='x3', shape=[4], dtype="float16")
+            self.assertRaises(TypeError, fluid.layers.assign, x3)
+            x4 = fluid.layers.data(name='x4', shape=[4], dtype="uint8")
+            self.assertRaises(TypeError, fluid.layers.assign, x4)
+            # When the type of input is numpy.ndarray, the dtype of input must be float32, int32.
+            x5 = np.array([[2.5, 2.5]], dtype='bool')
+            self.assertRaises(TypeError, fluid.layers.assign, x5)
+            x6 = np.array([[2.5, 2.5]], dtype='float16')
+            self.assertRaises(TypeError, fluid.layers.assign, x6)
+            x7 = np.array([[2.5, 2.5]], dtype='float64')
+            self.assertRaises(TypeError, fluid.layers.assign, x7)
+            x8 = np.array([[2.5, 2.5]], dtype='int64')
+            self.assertRaises(TypeError, fluid.layers.assign, x8)
+            x9 = np.array([[2.5, 2.5]], dtype='uint8')
+            self.assertRaises(TypeError, fluid.layers.assign, x9)
+
+
 if __name__ == '__main__':
     unittest.main()