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- # Copyright (c) 2022 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.
-
- import unittest
-
- import numpy as np
- from get_test_cover_info import (
- XPUOpTestWrapper,
- create_test_class,
- get_xpu_op_support_types,
- )
- from op_test import OpTest
- from op_test_xpu import XPUOpTest
-
- import paddle
- from paddle import base
-
- paddle.enable_static()
-
-
- class XPUTestElementwiseModOp(XPUOpTestWrapper):
- def __init__(self) -> None:
- self.op_name = 'elementwise_mod'
- self.use_dynamic_create_class = False
-
- class ElementwiseModOp(XPUOpTest):
- def init_kernel_type(self):
- self.use_mkldnn = False
-
- def init_input_output(self):
- self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
- self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
- self.out = np.mod(self.x, self.y)
- self.inputs = {
- 'X': OpTest.np_dtype_to_base_dtype(self.x),
- 'Y': OpTest.np_dtype_to_base_dtype(self.y),
- }
- self.outputs = {'Out': self.out}
- self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
-
- def init_dtype(self):
- pass
-
- def init_axis(self):
- pass
-
- def setUp(self):
- self.op_type = 'elementwise_mod'
- self.use_xpu = True
- self.dtype = self.in_type
- self.axis = -1
- self.init_dtype()
- self.init_input_output()
- self.init_kernel_type()
- self.init_axis()
-
- def test_check_output(self):
- if paddle.is_compiled_with_xpu():
- place = paddle.XPUPlace(0)
- self.check_output_with_place(place)
-
- class TestRemainderOp(unittest.TestCase):
- def test_dygraph(self):
- with base.dygraph.guard():
- np_x = np.random.rand(22, 128, 3).astype('int64')
- np_y = np.random.rand(22, 128, 3).astype('int64')
- x = paddle.to_tensor(np_x)
- y = paddle.to_tensor(np_y)
- z = paddle.remainder(x, y)
- np_z = z.numpy()
- z_expected = np.mod(np_x, np_y)
- self.assertEqual((np_z == z_expected).all(), True)
-
- np_x = np.array([-3.3, 11.5, -2, 3.5])
- np_y = np.array([-1.2, 2.0, 3.3, -2.3])
- x = paddle.to_tensor(np_x)
- y = paddle.to_tensor(np_y)
- z = x % y
- z_expected = np.array([-0.9, 1.5, 1.3, -1.1])
- np.testing.assert_allclose(z_expected, z.numpy(), rtol=1e-05)
-
- np_x = np.random.rand(22, 128, 3).astype('int32')
- np_y = np.random.rand(22, 128, 3).astype('int32')
- x = paddle.to_tensor(np_x)
- y = paddle.to_tensor(np_y)
- z = paddle.remainder(x, y)
- np_z = z.numpy()
- z_expected = np.mod(np_x, np_y)
- self.assertEqual((np_z == z_expected).all(), True)
-
- np_x = np.array([-3, 11, -2, 3])
- np_y = np.array([-1, 2, 3, -2])
- x = paddle.to_tensor(np_x, dtype="float16")
- y = paddle.to_tensor(np_y, dtype="float16")
- z = x % y
- z_expected = np.array([0, 1, 1, -1])
- np.testing.assert_allclose(z_expected, z.numpy(), rtol=1e-05)
-
-
- support_types = get_xpu_op_support_types('elementwise_mod')
- for stype in support_types:
- create_test_class(globals(), XPUTestElementwiseModOp, stype)
-
- if __name__ == '__main__':
- unittest.main()
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