wehub-resource-sync
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test import OpTest
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from op_test_xpu import XPUOpTest
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import paddle
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paddle.enable_static()
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import random
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class XPUTestElementwiseModOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'elementwise_floordiv'
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self.use_dynamic_create_class = False
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class TestElementwiseModOp(XPUOpTest):
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def init_kernel_type(self):
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self.use_onednn = False
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def setUp(self):
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self.op_type = "elementwise_floordiv"
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self.dtype = self.in_type
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self.axis = -1
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self.init_input_output()
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self.init_kernel_type()
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self.init_axis()
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
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self.outputs = {'Out': self.out}
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def init_input_output(self):
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self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
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self.y = np.random.uniform(1, 1000, [10, 10]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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def init_axis(self):
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pass
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class TestElementwiseModOp_scalar(TestElementwiseModOp):
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def init_input_output(self):
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scale_x = random.randint(0, 100000)
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scale_y = random.randint(1, 100000)
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self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
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self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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class TestElementwiseModOpInverse(TestElementwiseModOp):
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def init_input_output(self):
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self.x = np.random.uniform(0, 10000, [10]).astype(self.dtype)
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self.y = np.random.uniform(1, 1000, [10, 10]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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support_types = get_xpu_op_support_types('elementwise_floordiv')
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for stype in support_types:
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create_test_class(globals(), XPUTestElementwiseModOp, stype)
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if __name__ == '__main__':
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unittest.main()
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