Add Checking Type for "multiply" operation (#26508)

Co-authored-by: Nwujionghao <wujionghao@email.com>
As the title

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

@@ -44,8 +44,8 @@ class TestMultiplyAPI(unittest.TestCase):
 
     def __run_dynamic_graph_case(self, x_data, y_data, axis=-1):
         paddle.disable_static()
-        x = paddle.to_variable(x_data)
-        y = paddle.to_variable(y_data)
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
         res = paddle.multiply(x, y, axis=axis)
         return res.numpy()
 
@@ -126,17 +126,31 @@ class TestMultiplyError(unittest.TestCase):
         paddle.disable_static()
         x_data = np.random.randn(200).astype(np.int8)
         y_data = np.random.randn(200).astype(np.int8)
-        x = paddle.to_variable(x_data)
-        y = paddle.to_variable(y_data)
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
         self.assertRaises(fluid.core.EnforceNotMet, paddle.multiply, x, y)
 
         # test dynamic computation graph: inputs must be broadcastable
         x_data = np.random.rand(200, 5)
         y_data = np.random.rand(200)
-        x = paddle.to_variable(x_data)
-        y = paddle.to_variable(y_data)
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
         self.assertRaises(fluid.core.EnforceNotMet, paddle.multiply, x, y)
 
+        # test dynamic computation graph: inputs must be broadcastable(python)
+        x_data = np.random.rand(200, 5)
+        y_data = np.random.rand(200)
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
+        self.assertRaises(fluid.core.EnforceNotMet, paddle.multiply, x, y)
+
+        # test dynamic computation graph: dtype must be same
+        x_data = np.random.randn(200).astype(np.int64)
+        y_data = np.random.randn(200).astype(np.float64)
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
+        self.assertRaises(TypeError, paddle.multiply, x, y)
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/tensor/math.py

@@ -562,10 +562,23 @@ floor_mod = remainder  #DEFINE_ALIAS
 
 def multiply(x, y, axis=-1, name=None):
     """
-	:alias_main: paddle.multiply
-	:alias: paddle.multiply,paddle.tensor.multiply,paddle.tensor.math.multiply
+    multiply two tensors element-wise. The equation is:
 
-Examples:
+    .. math::
+        out = x * y
+
+    **Note**:
+    ``paddle.multiply`` supports broadcasting. If you would like to know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+
+    Args:
+        x (Tensor): the input tensor, its data type should be float32, float64, int32, int64.
+        y (Tensor): the input tensor, its data type should be float32, float64, int32, int64.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        N-D Tensor. A location into which the result is stored. Its dimension equals with $x$.
+
+    Examples:
 
         ..  code-block:: python
 
@@ -575,21 +588,26 @@ Examples:
             paddle.disable_static()
             x_data = np.array([[1, 2], [3, 4]], dtype=np.float32)
             y_data = np.array([[5, 6], [7, 8]], dtype=np.float32)
-        x = paddle.to_variable(x_data)
-        y = paddle.to_variable(y_data)
+            x = paddle.to_tensor(x_data)
+            y = paddle.to_tensor(y_data)
             res = paddle.multiply(x, y)
             print(res.numpy()) # [[5, 12], [21, 32]]
 
             x_data = np.array([[[1, 2, 3], [1, 2, 3]]], dtype=np.float32)
             y_data = np.array([1, 2], dtype=np.float32)
-        x = paddle.to_variable(x_data)
-        y = paddle.to_variable(y_data)
+            x = paddle.to_tensor(x_data)
+            y = paddle.to_tensor(y_data)
             res = paddle.multiply(x, y, axis=1)
             print(res.numpy()) # [[[1, 2, 3], [2, 4, 6]]]
 
     """
     op_type = 'elementwise_mul'
     act = None
+    if x.dtype != y.dtype:
+        raise TypeError(
+            'Input tensors must be same type, but received type of x: %s, type of y: %s '
+            % (x.dtype, y.dtype))
+
     if in_dygraph_mode():
         return _elementwise_op_in_dygraph(
             x, y, axis=axis, act=act, op_name=op_type)