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85 lines
2.8 KiB
85 lines
2.8 KiB
# Copyright (c) 2018 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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from benchmark import BenchmarkSuite
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from op_test import OpTest
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# This is a demo op test case for operator benchmarking and high resolution number stability alignment.
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class TestSumOp(BenchmarkSuite):
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def setUp(self):
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self.op_type = "sum"
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self.customize_testcase()
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self.customize_fetch_list()
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def customize_fetch_list(self):
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"""
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customize fetch list, configure the wanted variables.
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>>> self.fetch_list = ["Out"]
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"""
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self.fetch_list = ["Out"]
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# pass
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def customize_testcase(self):
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# a test case
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x0 = np.random.random((300, 400)).astype('float32')
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x1 = np.random.random((300, 400)).astype('float32')
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x2 = np.random.random((300, 400)).astype('float32')
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# NOTE: if the output is empty, then it will autofilled by benchmarkSuite.
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# only the output dtype is used, the shape, lod and data is computed from input.
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self.inputs = {"X": [("x0", x0), ("x1", x1), ("x2", x2)]}
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self.outputs = {"Out": x0 + x1 + x2}
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def test_check_output(self):
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"""
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compare the output with customized output. In this case,
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you should set the correct output by hands.
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>>> self.outputs = {"Out": x0 + x1 + x2}
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"""
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self.check_output(atol=1e-8)
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def test_output_stability(self):
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# compare the cpu gpu output in high resolution.
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self.check_output_stability()
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def test_timeit_output(self):
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"""
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perf the op, time cost will be averged in iters.
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output example
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>>> One pass of (sum_op) at CPUPlace cost 0.000461330413818
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>>> One pass of (sum_op) at CUDAPlace(0) cost 0.000556070804596
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"""
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self.timeit_output(iters=100)
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def test_timeit_grad(self):
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"""
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perf the op gradient, time cost will be averged in iters.
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output example
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>>> One pass of (sum_grad_op) at CPUPlace cost 0.00279935121536
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>>> One pass of (sum_grad_op) at CUDAPlace(0) cost 0.00500632047653
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"""
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self.timeit_grad(iters=100)
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if __name__ == "__main__":
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unittest.main()
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