wehub-resource-sync
115 lines
4.1 KiB
Python
115 lines
4.1 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, convert_float_to_uint16
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from op_test_xpu import XPUOpTest
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import paddle
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class XPUTestElementwisePowOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'elementwise_pow'
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self.use_dynamic_create_class = False
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class TestElementwisePowOp(XPUOpTest):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.dtype = self.in_type
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self.compute_input_output()
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if self.dtype == np.uint16:
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# bfloat16 actually
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self.x = convert_float_to_uint16(self.tmp_x)
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self.y = convert_float_to_uint16(self.tmp_y)
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else:
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self.x = self.tmp_x.astype(self.dtype)
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self.y = self.tmp_y.astype(self.dtype)
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self.inputs = {
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'X': self.x,
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'Y': self.y,
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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def compute_input_output(self):
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self.tmp_x = np.random.uniform(1, 2, [20, 5])
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self.tmp_y = np.random.uniform(1, 2, [20, 5])
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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 test_check_grad(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_grad_with_place(place, ['X', 'Y'], 'Out')
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class TestElementwisePowOp_big_shape_1(TestElementwisePowOp):
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def compute_input_output(self):
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self.tmp_x = np.random.uniform(1, 2, [10, 10])
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self.tmp_y = np.random.uniform(0.1, 1, [10, 10])
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class TestElementwisePowOp_big_shape_2(TestElementwisePowOp):
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def compute_input_output(self):
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self.tmp_x = np.random.uniform(1, 2, [10, 10])
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self.tmp_y = np.random.uniform(0.2, 2, [10, 10])
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class TestElementwisePowOp_scalar(TestElementwisePowOp):
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def compute_input_output(self):
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self.tmp_x = np.random.uniform(0.1, 1, [3, 3, 4])
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self.tmp_y = np.random.uniform(0.1, 1, [1])
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class TestElementwisePowOp_tensor(TestElementwisePowOp):
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def compute_input_output(self):
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self.tmp_x = np.random.uniform(0.1, 1, [100])
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self.tmp_y = np.random.uniform(1, 3, [100])
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class TestElementwisePowOp_broadcast_0(TestElementwisePowOp):
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def compute_input_output(self):
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self.tmp_x = np.random.uniform(0.1, 1, [2, 1, 100])
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self.tmp_y = np.random.uniform(0.1, 1, [100])
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class TestElementwisePowOp_broadcast_4(TestElementwisePowOp):
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def compute_input_output(self):
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self.tmp_x = np.random.uniform(0.1, 1, [2, 10, 3, 5])
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self.tmp_y = np.random.uniform(0.1, 1, [2, 10, 1, 5])
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class TestElementwisePowOpInt(OpTest):
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def setUp(self):
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self.op_type = "elementwise_pow"
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self.inputs = {
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'X': np.asarray([1, 3, 6]),
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'Y': np.asarray([1, 1, 1]),
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}
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self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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support_types = get_xpu_op_support_types('elementwise_pow')
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for stype in support_types:
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create_test_class(globals(), XPUTestElementwisePowOp, stype)
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if __name__ == '__main__':
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unittest.main()
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