Check and fix tensor and scalar type promotion (#28299)

* check and fix tensor and scalar type promotion

* fix else branch error

* fix scalar method error

* fix test_math_op_path unittest

* add future division for unittest

* rm useless bin file

python/paddle/fluid/dygraph/math_op_patch.py

@@ -149,28 +149,46 @@ def monkey_patch_math_varbase():
                          reverse=False,
                          scalar_method=None):
         def __impl__(self, other_var):
-            # tensor and ComplexVariable opetator
+            # 0. check tensor and ComplexVariable opetator
             if isinstance(other_var, ComplexVariable):
                 # need import paddle in closure
                 import paddle
                 math_op = getattr(paddle.incubate.complex.tensor, op_type)
                 return math_op(self, other_var)
 
-            # FIXME(zjl): elementwise_div between integers cannot be converted to scale,
-            # which may lose accuracy. This is a hot fix for release 1.6.
-            if scalar_method is not None and not (
-                    op_type == 'elementwise_div' and
-                    self.dtype in _supported_int_dtype_):
-                if isinstance(other_var, float):
-                    if self.dtype in _supported_int_dtype_:
-                        assert other_var == int(other_var), \
-                            "float value {} cannot convert to integer".format(other_var)
+            # 1. scalar exists cases
+            # we need combine the tensor.dtype and scalar.dtype, cast correct object
+            if isinstance(other_var, float):
+                # in all cases(+, -, *, /, **, //, %), we need cast tensor.dtype to float
+                if self.dtype in _supported_int_dtype_:
+                    self = astype(self, 'float32')
+                # here use `scale` replace `elementwise` to get better performance
+                # but only +, -, *, / can use this method
+                if scalar_method is not None:
                     return scalar_method(self, other_var)
-                elif isinstance(other_var, int):
-                    return scalar_method(self, float(other_var))
+            elif isinstance(other_var, int):
+                # in all cases(+, -, *, /, **, //, %), we can cast it to float
+                # because the output tensor.dtype depend on the type of input tensor
+                other_var = float(other_var)
+                # division is a special case
+                # NOTE(chenweihang): because we cast tensor to float32 instead float64,
+                # the division result can only guarantee the numerical accuracy of 6 digits 
+                # after the decimal point. The result of numpy calculation is of float64 type, 
+                # so the calculation result here and the calculation result of numpy are 
+                # different after 6 decimal point. If necessary, we can also use float64 here.
+                # torch's behavior here is consistent with ours
+                if op_type == 'elementwise_div' and self.dtype in _supported_int_dtype_:
+                    self = astype(self, 'float32')
+                # here use `scale` replace `elementwise` to get better performance
+                # but only +, -, *, / can use this method
+                if scalar_method is not None:
+                    return scalar_method(self, other_var)
+            else:
+                # do nothing
+                pass
 
+            # 2. create varbase for scalar
             lhs_dtype = self.dtype
-
             if not isinstance(other_var, core.VarBase):
                 if reverse:
                     other_var = create_tensor(
@@ -179,6 +197,7 @@ def monkey_patch_math_varbase():
                     # add fill_op 
                     other_var = create_scalar(value=other_var, dtype=lhs_dtype)
 
+            # 3. unify right var type to left var
             rhs_dtype = other_var.dtype
             if lhs_dtype != rhs_dtype:
                 other_var = astype(other_var, lhs_dtype)
@@ -187,6 +206,7 @@ def monkey_patch_math_varbase():
                 self = other_var
                 other_var = tmp
 
+            # 4. calculation
             axis = -1
             math_op = getattr(core.ops, op_type)
             return math_op(self, other_var, 'axis', axis)

python/paddle/fluid/layers/math_op_patch.py

@@ -215,21 +215,39 @@ def monkey_patch_variable():
                          reverse=False,
                          scalar_method=None):
         def __impl__(self, other_var):
-            # FIXME(zjl): elementwise_div between integers cannot be converted to scale,
-            # which may lose accuracy. This is a hot fix for release 1.6.
-            if scalar_method is not None and not (
-                    op_type == 'elementwise_div' and
-                    self.dtype in _supported_int_dtype_):
-                if isinstance(other_var, float):
-                    if self.dtype in _supported_int_dtype_:
-                        assert other_var == int(other_var), \
-                            "float value {} cannot convert to integer".format(other_var)
+            # 1. scalar exists cases
+            # we need combine the tensor.dtype and scalar.dtype, cast correct object
+            if isinstance(other_var, float):
+                # in all cases(+, -, *, /, **, //, %), we need cast tensor.dtype to float
+                if self.dtype in _supported_int_dtype_:
+                    self = astype(self, 'float32')
+                # here use `scale` replace `elementwise` to get better performance
+                # but only +, -, *, / can use this method
+                if scalar_method is not None:
                     return scalar_method(self, other_var)
-                elif isinstance(other_var, int):
-                    return scalar_method(self, float(other_var))
+            elif isinstance(other_var, int):
+                # in all cases(+, -, *, /, **, //, %), we can cast it to float
+                # because the output tensor.dtype depend on the type of input tensor
+                other_var = float(other_var)
+                # division is a special case
+                # NOTE(chenweihang): because we cast tensor to float32 instead float64,
+                # the division result can only guarantee the numerical accuracy of 6 digits 
+                # after the decimal point. The result of numpy calculation is of float64 type, 
+                # so the calculation result here and the calculation result of numpy are 
+                # different after 6 decimal point. If necessary, we can also use float64 here.
+                # torch's behavior here is consistent with ours
+                if op_type == 'elementwise_div' and self.dtype in _supported_int_dtype_:
+                    self = astype(self, 'float32')
+                # here use `scale` replace `elementwise` to get better performance
+                # but only +, -, *, / can use this method
+                if scalar_method is not None:
+                    return scalar_method(self, other_var)
+            else:
+                # do nothing
+                pass
 
+            # 2. create variable for scalar
             lhs_dtype = safe_get_dtype(self)
-
             if not isinstance(other_var, Variable):
                 if reverse:
                     has_batch_size = False
@@ -251,6 +269,7 @@ def monkey_patch_variable():
                     other_var = create_scalar(
                         current_block(self), value=other_var, dtype=lhs_dtype)
 
+            # 3. unify right var type to left var
             rhs_dtype = safe_get_dtype(other_var)
             if lhs_dtype != rhs_dtype:
                 other_var = astype(other_var, lhs_dtype)

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

@@ -12,15 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import print_function
+from __future__ import print_function, division
 
 import unittest
 from decorator_helper import prog_scope
+import paddle
 import paddle.fluid as fluid
 import numpy
 
 
 class TestMathOpPatches(unittest.TestCase):
+    def setUp(self):
+        paddle.enable_static()
+
     @prog_scope()
     def test_add_scalar(self):
         a = fluid.layers.data(name="a", shape=[1])
@@ -197,8 +201,8 @@ class TestMathOpPatches(unittest.TestCase):
                         feed={"a": a_np},
                         fetch_list=[b])
 
-        b_np_actual = (a_np / 7).astype('int64')
-        self.assertTrue(numpy.array_equal(b_np, b_np_actual))
+        b_np_actual = (a_np / 7).astype('float32')
+        self.assertTrue(numpy.allclose(b_np, b_np_actual))
 
     @prog_scope()
     def test_equal(self):

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

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function, division
+
+import unittest
+import numpy as np
+
+import paddle
+
+# Support types are ref from `paddle.tensor.math`
+# - Related paddle dtypes:
+#  - int type: int64, (no test here: uint8, int8, int16, int32)
+#  - float type: float32, (no test here: float64)
+# - Python scalar dtypes: 
+#  - int(64)
+#  - float(64)
+
+
+class TestTensorScalarTypePromotionDynamic(unittest.TestCase):
+    def check_operation(self, a, b, c, op):
+        if op == '+':
+            c_rlt = a + b
+        elif op == '-':
+            c_rlt = a - b
+        elif op == '*':
+            c_rlt = a * b
+        elif op == '/':
+            c_rlt = a / b
+        elif op == '**':
+            c_rlt = a**b
+        elif op == '//':
+            c_rlt = a // b
+        elif op == '%':
+            c_rlt = a % b
+        else:
+            raise ValueError("Unsupported operation.")
+
+        self.assertEqual(c_rlt.dtype, c.dtype)
+        self.assertTrue(np.array_equal(c_rlt.numpy(), c.numpy()))
+
+    def test_tensor_add_scalar(self):
+        # tensor(int64) + scalar(int)
+        a = paddle.ones([2, 2, 2], dtype='int64')
+        b = 1
+        c = paddle.full([2, 2, 2], 2, dtype="int64")
+        self.check_operation(a, b, c, '+')
+
+        # tensor(float32) + scalar(int)
+        a = paddle.ones([2, 2, 2], dtype='float32')
+        b = 1
+        c = paddle.full([2, 2, 2], 2, dtype="float32")
+        self.check_operation(a, b, c, '+')
+
+        # tensor(int64) + scalar(float, .0)
+        a = paddle.ones([2, 2, 2], dtype='int64')
+        b = 1.0
+        c = paddle.full([2, 2, 2], 2, dtype="float32")
+        self.check_operation(a, b, c, '+')
+
+        # tensor(int64) + scalar(float, .5)
+        a = paddle.ones([2, 2, 2], dtype='int64')
+        b = 1.5
+        c = paddle.full([2, 2, 2], 2.5, dtype="float32")
+        self.check_operation(a, b, c, '+')
+
+        # tensor(float32) + scalar(float)
+        a = paddle.ones([2, 2, 2], dtype='float32')
+        b = 1.5
+        c = paddle.full([2, 2, 2], 2.5, dtype="float32")
+        self.check_operation(a, b, c, '+')
+
+    def test_tensor_sub_scalar(self):
+        # tensor(int64) - scalar(int)
+        a = paddle.ones([2, 2, 2], dtype='int64')
+        b = 1
+        c = paddle.zeros([2, 2, 2], dtype="int64")
+        self.check_operation(a, b, c, '-')
+
+        # tensor(float32) - scalar(int)
+        a = paddle.ones([2, 2, 2], dtype='float32')
+        b = 1
+        c = paddle.zeros([2, 2, 2], dtype="float32")
+        self.check_operation(a, b, c, '-')
+
+        # tensor(int64) - scalar(float, .0)
+        a = paddle.ones([2, 2, 2], dtype='int64')
+        b = 1.0
+        c = paddle.zeros([2, 2, 2], dtype="float32")
+        self.check_operation(a, b, c, '-')
+
+        # tensor(int64) - scalar(float, .5)
+        a = paddle.full([2, 2, 2], 2, dtype='int64')
+        b = 1.5
+        c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+        self.check_operation(a, b, c, '-')
+
+        # tensor(float32) - scalar(float)
+        a = paddle.full([2, 2, 2], 2, dtype='float32')
+        b = 1.5
+        c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+        self.check_operation(a, b, c, '-')
+
+    def test_scalar_sub_tensor(self):
+        # scalar(int) - tensor(int64)
+        a = 1
+        b = paddle.ones([2, 2, 2], dtype='int64')
+        c = paddle.zeros([2, 2, 2], dtype="int64")
+        self.check_operation(a, b, c, '-')
+
+        # scalar(int) - tensor(float32)
+        a = 1
+        b = paddle.ones([2, 2, 2], dtype='float32')
+        c = paddle.zeros([2, 2, 2], dtype="float32")
+        self.check_operation(a, b, c, '-')
+
+        # scalar(float, .0) - tensor(int64)
+        a = 1.0
+        b = paddle.ones([2, 2, 2], dtype='int64')
+        c = paddle.zeros([2, 2, 2], dtype="float32")
+        self.check_operation(a, b, c, '-')
+
+        # scalar(float, .5) - tensor(int64)
+        a = 1.5
+        b = paddle.full([2, 2, 2], 2, dtype='int64')
+        c = paddle.full([2, 2, 2], -0.5, dtype="float32")
+        self.check_operation(a, b, c, '-')
+
+        # scalar(float) - tensor(float32)
+        a = 1.5
+        b = paddle.full([2, 2, 2], 2, dtype='float32')
+        c = paddle.full([2, 2, 2], -0.5, dtype="float32")
+        self.check_operation(a, b, c, '-')
+
+    def test_tensor_mul_tensor(self):
+        # tensor(int64) * scalar(int)
+        a = paddle.ones([2, 2, 2], dtype='int64')
+        b = 1
+        c = paddle.ones([2, 2, 2], dtype="int64")
+        self.check_operation(a, b, c, '*')
+
+        # tensor(float32) * scalar(int)
+        a = paddle.ones([2, 2, 2], dtype='float32')
+        b = 1
+        c = paddle.ones([2, 2, 2], dtype="float32")
+        self.check_operation(a, b, c, '*')
+
+        # tensor(int64) * scalar(float, .0)
+        a = paddle.ones([2, 2, 2], dtype='int64')
+        b = 1.0
+        c = paddle.ones([2, 2, 2], dtype="float32")
+        self.check_operation(a, b, c, '*')
+
+        # tensor(int64) * scalar(float, .5)
+        a = paddle.ones([2, 2, 2], dtype='int64')
+        b = 1.5
+        c = paddle.full([2, 2, 2], 1.5, dtype="float32")
+        self.check_operation(a, b, c, '*')
+
+        # tensor(float32) * scalar(float)
+        a = paddle.ones([2, 2, 2], dtype='float32')
+        b = 1.5
+        c = paddle.full([2, 2, 2], 1.5, dtype="float32")
+        self.check_operation(a, b, c, '*')
+
+    def test_tensor_div_scalar(self):
+        # tensor(int64) / scalar(int)
+        a = paddle.ones([2, 2, 2], dtype='int64')
+        b = 2
+        c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+        self.check_operation(a, b, c, '/')
+
+        # tensor(float32) / scalar(int)
+        a = paddle.ones([2, 2, 2], dtype='float32')
+        b = 2
+        c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+        self.check_operation(a, b, c, '/')
+
+        # tensor(int64) / scalar(float, .0)
+        a = paddle.ones([2, 2, 2], dtype='int64')
+        b = 2.0
+        c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+        self.check_operation(a, b, c, '/')
+
+        # tensor(int64) / scalar(float, .5)
+        a = paddle.ones([2, 2, 2], dtype='int64')
+        b = 0.5
+        c = paddle.full([2, 2, 2], 2, dtype="float32")
+        self.check_operation(a, b, c, '/')
+
+        # tensor(float32) / scalar(float)
+        a = paddle.ones([2, 2, 2], dtype='float32')
+        b = 0.5
+        c = paddle.full([2, 2, 2], 2, dtype="float32")
+        self.check_operation(a, b, c, '/')
+
+    def test_scalar_div_tensor(self):
+        # scalar(int) / tensor(int64)
+        a = 1
+        b = paddle.full([2, 2, 2], 2, dtype='int64')
+        c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+        self.check_operation(a, b, c, '/')
+
+        # scalar(int) / tensor(float32)
+        a = 1
+        b = paddle.full([2, 2, 2], 0.5, dtype='float32')
+        c = paddle.full([2, 2, 2], 2, dtype="float32")
+        self.check_operation(a, b, c, '/')
+
+        # scalar(float) / tensor(int64)
+        a = 1.0
+        b = paddle.full([2, 2, 2], 2, dtype='int64')
+        c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+        self.check_operation(a, b, c, '/')
+
+        # scalar(float) / tensor(float32)
+        a = 1.0
+        b = paddle.full([2, 2, 2], 0.5, dtype='float32')
+        c = paddle.full([2, 2, 2], 2, dtype="float32")
+        self.check_operation(a, b, c, '/')
+
+    def test_tensor_pow_scalar(self):
+        # tensor(int64) ** scalar(int)
+        a = paddle.full([2, 2, 2], 2, dtype='int64')
+        b = 3
+        c = paddle.full([2, 2, 2], 8, dtype="int64")
+        self.check_operation(a, b, c, '**')
+
+        # tensor(int64) ** scalar(float)
+        a = paddle.full([2, 2, 2], 2, dtype='int64')
+        b = 3.0
+        c = paddle.full([2, 2, 2], 8, dtype="float32")
+        self.check_operation(a, b, c, '**')
+
+        # tensor(float32) ** scalar(int)
+        a = paddle.full([2, 2, 2], 2, dtype='float32')
+        b = 3
+        c = paddle.full([2, 2, 2], 8, dtype="float32")
+        self.check_operation(a, b, c, '**')
+
+        # tensor(float32) ** scalar(float)
+        a = paddle.full([2, 2, 2], 2, dtype='float32')
+        b = 3.0
+        c = paddle.full([2, 2, 2], 8, dtype="float32")
+        self.check_operation(a, b, c, '**')
+
+    def test_scalar_pow_tensor(self):
+        # scalar(int) ** tensor(int64)
+        a = 3
+        b = paddle.full([2, 2, 2], 2, dtype='int64')
+        c = paddle.full([2, 2, 2], 9, dtype="int64")
+        self.check_operation(a, b, c, '**')
+
+        # scalar(float) ** tensor(int64)
+        a = 3.0
+        b = paddle.full([2, 2, 2], 2, dtype='int64')
+        c = paddle.full([2, 2, 2], 9, dtype="float32")
+        self.check_operation(a, b, c, '**')
+
+        # scalar(int) ** tensor(float32)
+        a = 3
+        b = paddle.full([2, 2, 2], 2, dtype='float32')
+        c = paddle.full([2, 2, 2], 9, dtype="float32")
+        self.check_operation(a, b, c, '**')
+
+        # tensor(float32) ** scalar(float)
+        a = 3.0
+        b = paddle.full([2, 2, 2], 2, dtype='float32')
+        c = paddle.full([2, 2, 2], 9, dtype="float32")
+        self.check_operation(a, b, c, '**')
+
+    ## TODO: floordiv op kernel doesn't support float
+    def test_tensor_floordiv_scalar(self):
+        # tensor(int64) // scalar(int)
+        a = paddle.full([2, 2, 2], 3, dtype='int64')
+        b = 2
+        c = paddle.full([2, 2, 2], 1, dtype="int64")
+        self.check_operation(a, b, c, '//')
+
+    def test_tensor_mod_scalar(self):
+        # tensor(int64) % scalar(int)
+        a = paddle.full([2, 2, 2], 3, dtype='int64')
+        b = 2
+        c = paddle.full([2, 2, 2], 1, dtype="int64")
+        self.check_operation(a, b, c, '%')
+
+        # tensor(int64) % scalar(float)
+        a = paddle.full([2, 2, 2], 3, dtype='int64')
+        b = 2.0
+        c = paddle.full([2, 2, 2], 1, dtype="float32")
+        self.check_operation(a, b, c, '%')
+
+        # tensor(float32) % scalar(int)
+        a = paddle.full([2, 2, 2], 3, dtype='float32')
+        b = 2
+        c = paddle.full([2, 2, 2], 1, dtype="float32")
+        self.check_operation(a, b, c, '%')
+
+        # tensor(float32) % scalar(float)
+        a = paddle.full([2, 2, 2], 3, dtype='float32')
+        b = 2.0
+        c = paddle.full([2, 2, 2], 1, dtype="float32")
+        self.check_operation(a, b, c, '%')
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function, division
+
+import unittest
+import numpy as np
+
+import paddle
+from paddle.static import program_guard
+from paddle.static import Program
+
+# Support types are ref from `paddle.tensor.math`
+# - Related paddle dtypes:
+#  - int type: int64, (no test here: uint8, int8, int16, int32)
+#  - float type: float32, (no test here: float64)
+# - Python scalar dtypes: 
+#  - int(64)
+#  - float(64)
+
+
+class TestTensorScalarTypePromotionStatic(unittest.TestCase):
+    def setUp(self):
+        paddle.enable_static()
+
+    def check_operation(self, a, b, c, op):
+        exe = paddle.static.Executor()
+
+        if op == '+':
+            c_rlt = a + b
+        elif op == '-':
+            c_rlt = a - b
+        elif op == '*':
+            c_rlt = a * b
+        elif op == '/':
+            c_rlt = a / b
+        elif op == '**':
+            c_rlt = a**b
+        elif op == '//':
+            c_rlt = a // b
+        elif op == '%':
+            c_rlt = a % b
+        else:
+            raise ValueError("Unsupported operation.")
+
+        rlt = exe.run(fetch_list=[c_rlt.name, c.name])
+
+        self.assertEqual(rlt[0].dtype, rlt[1].dtype)
+        self.assertTrue(np.array_equal(rlt[0], rlt[1]))
+
+    def test_tensor_add_scalar(self):
+        # tensor(int64) + scalar(int)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='int64')
+            b = 1
+            c = paddle.full([2, 2, 2], 2, dtype="int64")
+            self.check_operation(a, b, c, '+')
+
+        # tensor(float32) + scalar(int)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='float32')
+            b = 1
+            c = paddle.full([2, 2, 2], 2, dtype="float32")
+            self.check_operation(a, b, c, '+')
+
+        # tensor(int64) + scalar(float, .0)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='int64')
+            b = 1.0
+            c = paddle.full([2, 2, 2], 2, dtype="float32")
+            self.check_operation(a, b, c, '+')
+
+        # tensor(int64) + scalar(float, .5)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='int64')
+            b = 1.5
+            c = paddle.full([2, 2, 2], 2.5, dtype="float32")
+            self.check_operation(a, b, c, '+')
+
+        # tensor(float32) + scalar(float)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='float32')
+            b = 1.5
+            c = paddle.full([2, 2, 2], 2.5, dtype="float32")
+            self.check_operation(a, b, c, '+')
+
+    def test_tensor_sub_scalar(self):
+        # tensor(int64) - scalar(int)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='int64')
+            b = 1
+            c = paddle.zeros([2, 2, 2], dtype="int64")
+            self.check_operation(a, b, c, '-')
+
+        # tensor(float32) - scalar(int)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='float32')
+            b = 1
+            c = paddle.zeros([2, 2, 2], dtype="float32")
+            self.check_operation(a, b, c, '-')
+
+        # tensor(int64) - scalar(float, .0)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='int64')
+            b = 1.0
+            c = paddle.zeros([2, 2, 2], dtype="float32")
+            self.check_operation(a, b, c, '-')
+
+        # tensor(int64) - scalar(float, .5)
+        with program_guard(Program()):
+            a = paddle.full([2, 2, 2], 2, dtype='int64')
+            b = 1.5
+            c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+            self.check_operation(a, b, c, '-')
+
+        # tensor(float32) - scalar(float)
+        with program_guard(Program()):
+            a = paddle.full([2, 2, 2], 2, dtype='float32')
+            b = 1.5
+            c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+            self.check_operation(a, b, c, '-')
+
+    def test_scalar_sub_tensor(self):
+        # scalar(int) - tensor(int64)
+        with program_guard(Program()):
+            a = 1
+            b = paddle.ones([2, 2, 2], dtype='int64')
+            c = paddle.zeros([2, 2, 2], dtype="int64")
+            self.check_operation(a, b, c, '-')
+
+        # scalar(int) - tensor(float32)
+        with program_guard(Program()):
+            a = 1
+            b = paddle.ones([2, 2, 2], dtype='float32')
+            c = paddle.zeros([2, 2, 2], dtype="float32")
+            self.check_operation(a, b, c, '-')
+
+        # scalar(float, .0) - tensor(int64)
+        with program_guard(Program()):
+            a = 1.0
+            b = paddle.ones([2, 2, 2], dtype='int64')
+            c = paddle.zeros([2, 2, 2], dtype="float32")
+            self.check_operation(a, b, c, '-')
+
+        # scalar(float, .5) - tensor(int64)
+        with program_guard(Program()):
+            a = 1.5
+            b = paddle.full([2, 2, 2], 2, dtype='int64')
+            c = paddle.full([2, 2, 2], -0.5, dtype="float32")
+            self.check_operation(a, b, c, '-')
+
+        # scalar(float) - tensor(float32)
+        with program_guard(Program()):
+            a = 1.5
+            b = paddle.full([2, 2, 2], 2, dtype='float32')
+            c = paddle.full([2, 2, 2], -0.5, dtype="float32")
+            self.check_operation(a, b, c, '-')
+
+    def test_tensor_mul_tensor(self):
+        # tensor(int64) * scalar(int)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='int64')
+            b = 1
+            c = paddle.ones([2, 2, 2], dtype="int64")
+            self.check_operation(a, b, c, '*')
+
+        # tensor(float32) * scalar(int)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='float32')
+            b = 1
+            c = paddle.ones([2, 2, 2], dtype="float32")
+            self.check_operation(a, b, c, '*')
+
+        # tensor(int64) * scalar(float, .0)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='int64')
+            b = 1.0
+            c = paddle.ones([2, 2, 2], dtype="float32")
+            self.check_operation(a, b, c, '*')
+
+        # tensor(int64) * scalar(float, .5)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='int64')
+            b = 1.5
+            c = paddle.full([2, 2, 2], 1.5, dtype="float32")
+            self.check_operation(a, b, c, '*')
+
+        # tensor(float32) * scalar(float)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='float32')
+            b = 1.5
+            c = paddle.full([2, 2, 2], 1.5, dtype="float32")
+            self.check_operation(a, b, c, '*')
+
+    def test_tensor_div_scalar(self):
+        # tensor(int64) / scalar(int)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='int64')
+            b = 2
+            c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+            self.check_operation(a, b, c, '/')
+
+        # tensor(float32) / scalar(int)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='float32')
+            b = 2
+            c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+            self.check_operation(a, b, c, '/')
+
+        # tensor(int64) / scalar(float, .0)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='int64')
+            b = 2.0
+            c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+            self.check_operation(a, b, c, '/')
+
+        # tensor(int64) / scalar(float, .5)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='int64')
+            b = 0.5
+            c = paddle.full([2, 2, 2], 2, dtype="float32")
+            self.check_operation(a, b, c, '/')
+
+        # tensor(float32) / scalar(float)
+        with program_guard(Program()):
+            a = paddle.ones([2, 2, 2], dtype='float32')
+            b = 0.5
+            c = paddle.full([2, 2, 2], 2, dtype="float32")
+            self.check_operation(a, b, c, '/')
+
+    def test_scalar_div_tensor(self):
+        # scalar(int) / tensor(int64)
+        with program_guard(Program()):
+            a = 1
+            b = paddle.full([2, 2, 2], 2, dtype='int64')
+            c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+            self.check_operation(a, b, c, '/')
+
+        # scalar(int) / tensor(float32)
+        with program_guard(Program()):
+            a = 1
+            b = paddle.full([2, 2, 2], 0.5, dtype='float32')
+            c = paddle.full([2, 2, 2], 2, dtype="float32")
+            self.check_operation(a, b, c, '/')
+
+        # scalar(float) / tensor(int64)
+        with program_guard(Program()):
+            a = 1.0
+            b = paddle.full([2, 2, 2], 2, dtype='int64')
+            c = paddle.full([2, 2, 2], 0.5, dtype="float32")
+            self.check_operation(a, b, c, '/')
+
+        # scalar(float) / tensor(float32)
+        with program_guard(Program()):
+            a = 1.0
+            b = paddle.full([2, 2, 2], 0.5, dtype='float32')
+            c = paddle.full([2, 2, 2], 2, dtype="float32")
+            self.check_operation(a, b, c, '/')
+
+    def test_tensor_pow_scalar(self):
+        # tensor(int64) ** scalar(int)
+        with program_guard(Program()):
+            a = paddle.full([2, 2, 2], 2, dtype='int64')
+            b = 3
+            c = paddle.full([2, 2, 2], 8, dtype="int64")
+            self.check_operation(a, b, c, '**')
+
+        # tensor(int64) ** scalar(float)
+        with program_guard(Program()):
+            a = paddle.full([2, 2, 2], 2, dtype='int64')
+            b = 3.0
+            c = paddle.full([2, 2, 2], 8, dtype="float32")
+            self.check_operation(a, b, c, '**')
+
+        # tensor(float32) ** scalar(int)
+        with program_guard(Program()):
+            a = paddle.full([2, 2, 2], 2, dtype='float32')
+            b = 3
+            c = paddle.full([2, 2, 2], 8, dtype="float32")
+            self.check_operation(a, b, c, '**')
+
+        # tensor(float32) ** scalar(float)
+        with program_guard(Program()):
+            a = paddle.full([2, 2, 2], 2, dtype='float32')
+            b = 3.0
+            c = paddle.full([2, 2, 2], 8, dtype="float32")
+            self.check_operation(a, b, c, '**')
+
+    def test_scalar_pow_tensor(self):
+        # scalar(int) ** tensor(int64)
+        with program_guard(Program()):
+            a = 3
+            b = paddle.full([2, 2, 2], 2, dtype='int64')
+            c = paddle.full([2, 2, 2], 9, dtype="int64")
+            self.check_operation(a, b, c, '**')
+
+        # scalar(float) ** tensor(int64)
+        with program_guard(Program()):
+            a = 3.0
+            b = paddle.full([2, 2, 2], 2, dtype='int64')
+            c = paddle.full([2, 2, 2], 9, dtype="float32")
+            self.check_operation(a, b, c, '**')
+
+        # scalar(int) ** tensor(float32)
+        with program_guard(Program()):
+            a = 3
+            b = paddle.full([2, 2, 2], 2, dtype='float32')
+            c = paddle.full([2, 2, 2], 9, dtype="float32")
+            self.check_operation(a, b, c, '**')
+
+        # tensor(float32) ** scalar(float)
+        with program_guard(Program()):
+            a = 3.0
+            b = paddle.full([2, 2, 2], 2, dtype='float32')
+            c = paddle.full([2, 2, 2], 9, dtype="float32")
+            self.check_operation(a, b, c, '**')
+
+    # ## TODO: floordiv op kernel doesn't support float
+    def test_tensor_floordiv_scalar(self):
+        # tensor(int64) // scalar(int)
+        with program_guard(Program()):
+            a = paddle.full([2, 2, 2], 3, dtype='int64')
+            b = 2
+            c = paddle.full([2, 2, 2], 1, dtype="int64")
+            self.check_operation(a, b, c, '//')
+
+    def test_tensor_mod_scalar(self):
+        # tensor(int64) % scalar(int)
+        with program_guard(Program()):
+            a = paddle.full([2, 2, 2], 3, dtype='int64')
+            b = 2
+            c = paddle.full([2, 2, 2], 1, dtype="int64")
+            self.check_operation(a, b, c, '%')
+
+        # tensor(int64) % scalar(float)
+        with program_guard(Program()):
+            a = paddle.full([2, 2, 2], 3, dtype='int64')
+            b = 2.0
+            c = paddle.full([2, 2, 2], 1, dtype="float32")
+            self.check_operation(a, b, c, '%')
+
+        # tensor(float32) % scalar(int)
+        with program_guard(Program()):
+            a = paddle.full([2, 2, 2], 3, dtype='float32')
+            b = 2
+            c = paddle.full([2, 2, 2], 1, dtype="float32")
+            self.check_operation(a, b, c, '%')
+
+        # tensor(float32) % scalar(float)
+        with program_guard(Program()):
+            a = paddle.full([2, 2, 2], 3, dtype='float32')
+            b = 2.0
+            c = paddle.full([2, 2, 2], 1, dtype="float32")
+            self.check_operation(a, b, c, '%')
+
+
+if __name__ == '__main__':
+    unittest.main()