chore: import upstream snapshot with attribution
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# Copyright (c) 2024 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 op_test import is_custom_device
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import paddle
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from paddle.distributed.fleet.utils.timer_helper import get_timers, set_timers
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class TestGPUEventTimer(unittest.TestCase):
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def test_main(self):
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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if paddle.is_compiled_with_rocm():
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return
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set_timers()
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key = "matmul"
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x = paddle.randn([1024, 1024])
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timers = get_timers()
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use_event = True
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timers(key, use_event=use_event).pre_alloc(5)
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for _ in range(2):
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for _ in range(3):
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timers(key, use_event=use_event).start()
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paddle.matmul(x, x)
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timers(key, use_event=use_event).stop()
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times = timers(key, use_event=use_event).elapsed_list(reset=False)
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assert isinstance(times, np.ndarray), times
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times2 = timers(key, use_event=use_event).elapsed_list(reset=False)
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np.testing.assert_array_equal(times, times2)
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timers.log(timers.timers.keys())
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assert timers(key, use_event=use_event).size() == 0
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assert timers(key, use_event=use_event).capacity() > 0
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timers(key, use_event=use_event).shrink_to_fit()
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assert timers(key, use_event=use_event).size() == 0
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assert timers(key, use_event=use_event).capacity() == 0
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if __name__ == "__main__":
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unittest.main()
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