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test_elementwise_mod_op_xpu.py
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test_elementwise_mod_op_xpu.py
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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 sys
sys.path.append("..")
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard
import paddle
from op_test_xpu import XPUOpTest
from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
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_fluid_dtype(self.x),
'Y': OpTest.np_dtype_to_fluid_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 TestElementwiseModOp_broadcast_1(ElementwiseModOp):
def init_input_output(self):
self.inputs = {
'X': np.random.rand(2, 100, 3).astype(self.dtype),
'Y': np.random.rand(2, 100, 3).astype(self.dtype)
}
self.attrs = {'axis': 1}
self.outputs = {'Out': self.inputs['X'] % self.inputs['Y']}
class TestElementwiseModOp_broadcast_2(ElementwiseModOp):
def init_input_output(self):
self.inputs = {
'X': np.random.rand(22, 128, 3).astype(self.dtype),
'Y': np.random.rand(22, 128, 3).astype(self.dtype)
}
self.attrs = {'axis': 1}
self.outputs = {'Out': self.inputs['X'] % self.inputs['Y']}
class TestRemainderOp(unittest.TestCase):
def test_dygraph(self):
with fluid.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., 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])
self.assertEqual(np.allclose(z_expected, z.numpy()), True)
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])
self.assertEqual(np.allclose(z_expected, z.numpy()), True)
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()