Remove cast from paddle.pow api (#29134)

As the title

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

@@ -13,12 +13,13 @@
 # limitations under the License.
 
 from __future__ import print_function
+import unittest
+
+import numpy as np
+
 import paddle
 import paddle.tensor as tensor
-import paddle.fluid as fluid
 from paddle.static import Program, program_guard
-import numpy as np
-import unittest
 
 DYNAMIC = 1
 STATIC = 2
@@ -49,8 +50,8 @@ def _run_power(mode, x, y):
                 x_ = paddle.static.data(name="x", shape=x.shape, dtype=x.dtype)
                 y_ = y
                 res = paddle.pow(x_, y_)
-                place = fluid.CPUPlace()
-                exe = fluid.Executor(place)
+                place = paddle.CPUPlace()
+                exe = paddle.static.Executor(place)
                 outs = exe.run(feed={'x': x}, fetch_list=[res])
                 return outs[0]
         # y is tensor
@@ -59,8 +60,8 @@ def _run_power(mode, x, y):
                 x_ = paddle.static.data(name="x", shape=x.shape, dtype=x.dtype)
                 y_ = paddle.static.data(name="y", shape=y.shape, dtype=y.dtype)
                 res = paddle.pow(x_, y_)
-                place = fluid.CPUPlace()
-                exe = fluid.Executor(place)
+                place = paddle.CPUPlace()
+                exe = paddle.static.Executor(place)
                 outs = exe.run(feed={'x': x, 'y': y}, fetch_list=[res])
                 return outs[0]
 
@@ -105,24 +106,6 @@ class TestPowerAPI(unittest.TestCase):
         res = _run_power(STATIC, x, y)
         self.assertTrue(np.allclose(res, np.power(x, y)))
 
-        # test 1-d float tensor ** 1-d int tensor
-        dims = (np.random.randint(200, 300), )
-        x = (np.random.rand(*dims) * 10).astype(np.float64)
-        y = (np.random.rand(*dims) * 10).astype(np.int64)
-        res = _run_power(DYNAMIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-        res = _run_power(STATIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-
-        # test 1-d int tensor ** 1-d float tensor
-        dims = (np.random.randint(200, 300), )
-        x = (np.random.rand(*dims) * 10).astype(np.int64)
-        y = (np.random.rand(*dims) * 10).astype(np.float64)
-        res = _run_power(DYNAMIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-        res = _run_power(STATIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-
         # test 1-d int tensor ** 1-d int tensor
         dims = (np.random.randint(200, 300), )
         x = (np.random.rand(*dims) * 10).astype(np.int64)
@@ -141,24 +124,6 @@ class TestPowerAPI(unittest.TestCase):
         res = _run_power(STATIC, x, y)
         self.assertTrue(np.allclose(res, np.power(x, y)))
 
-        # test 1-d int tensor ** 1-d int tensor
-        dims = (np.random.randint(200, 300), )
-        x = (np.random.rand(*dims) * 10).astype(np.int64)
-        y = (np.random.rand(*dims) * 10).astype(np.int32)
-        res = _run_power(DYNAMIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-        res = _run_power(STATIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-
-        # test 1-d int tensor ** 1-d int tensor
-        dims = (np.random.randint(200, 300), )
-        x = (np.random.rand(*dims) * 10).astype(np.int32)
-        y = (np.random.rand(*dims) * 10).astype(np.int64)
-        res = _run_power(DYNAMIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-        res = _run_power(STATIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-
         # test 1-d int tensor ** 1-d int tensor
         dims = (np.random.randint(200, 300), )
         x = (np.random.rand(*dims) * 10).astype(np.float32)
@@ -168,33 +133,6 @@ class TestPowerAPI(unittest.TestCase):
         res = _run_power(STATIC, x, y)
         self.assertTrue(np.allclose(res, np.power(x, y)))
 
-        # test 1-d int tensor ** 1-d int tensor
-        dims = (np.random.randint(200, 300), )
-        x = (np.random.rand(*dims) * 10).astype(np.float64)
-        y = (np.random.rand(*dims) * 10).astype(np.float32)
-        res = _run_power(DYNAMIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-        res = _run_power(STATIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-
-        # test 1-d int tensor ** 1-d int tensor
-        dims = (np.random.randint(200, 300), )
-        x = (np.random.rand(*dims) * 10).astype(np.float64)
-        y = (np.random.rand(*dims) * 10).astype(np.int32)
-        res = _run_power(DYNAMIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-        res = _run_power(STATIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-
-        # test 1-d int tensor ** 1-d int tensor
-        dims = (np.random.randint(200, 300), )
-        x = (np.random.rand(*dims) * 10).astype(np.float32)
-        y = (np.random.rand(*dims) * 10).astype(np.int64)
-        res = _run_power(DYNAMIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-        res = _run_power(STATIC, x, y)
-        self.assertTrue(np.allclose(res, np.power(x, y)))
-
         # test broadcast
         dims = (np.random.randint(1, 10), np.random.randint(5, 10),
                 np.random.randint(5, 10))

python/paddle/tensor/math.py

@@ -184,9 +184,6 @@ def pow(x, y, name=None):
         if isinstance(y, (int, float)):
             return core.ops.pow(x, 'factor', y)
         elif isinstance(y, (paddle.Tensor, Variable)):
-            if x.dtype != y.dtype:
-                y = cast(y, dtype='float64')
-                x = cast(x, dtype='float64')
             return _elementwise_op_in_dygraph(
                 x, y, axis=-1, act=None, op_name='elementwise_pow')
         else:
@@ -204,9 +201,6 @@ def pow(x, y, name=None):
         elif isinstance(y, (paddle.Tensor, Variable)):
             # TODO A potential speed improvement is supporting different types in C++ and removing the cast ops here
             helper = LayerHelper('elementwise_pow', **locals())
-            if x.dtype != y.dtype:
-                y = cast(y, dtype='float64')
-                x = cast(x, dtype='float64')
             out = helper.create_variable_for_type_inference(dtype=x.dtype)
             return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
         else: