wehub-resource-sync
229 lines
7.4 KiB
Python
229 lines
7.4 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 ctypes
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import unittest
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import numpy as np
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import paddle
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from paddle.device import xpu
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class TestCurrentStream(unittest.TestCase):
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def test_current_stream(self):
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if paddle.is_compiled_with_xpu():
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s = xpu.current_stream()
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self.assertTrue(isinstance(s, xpu.Stream))
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s1 = xpu.current_stream(0)
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self.assertTrue(isinstance(s1, xpu.Stream))
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s2 = xpu.current_stream(paddle.XPUPlace(0))
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self.assertTrue(isinstance(s2, xpu.Stream))
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self.assertEqual(s1, s2)
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s3 = xpu.current_stream('xpu:0')
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self.assertTrue(isinstance(s3, xpu.Stream))
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class TestSynchronize(unittest.TestCase):
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def test_synchronize(self):
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if paddle.is_compiled_with_xpu():
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self.assertIsNone(xpu.synchronize())
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self.assertIsNone(xpu.synchronize(0))
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self.assertIsNone(xpu.synchronize(paddle.XPUPlace(0)))
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self.assertIsNone(xpu.synchronize("xpu:0"))
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self.assertIsNone(xpu.synchronize("xpu"))
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self.assertRaises(ValueError, xpu.synchronize, "gpu")
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class TestXPUStream(unittest.TestCase):
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def test_xpu_stream(self):
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if paddle.is_compiled_with_xpu():
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s = paddle.device.xpu.Stream()
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self.assertIsNotNone(s)
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def test_xpu_stream_synchronize(self):
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if paddle.is_compiled_with_xpu():
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s = paddle.device.xpu.Stream()
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e1 = paddle.device.xpu.Event()
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e2 = paddle.device.xpu.Event()
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e1.record(s)
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e1.query()
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tensor1 = paddle.to_tensor(paddle.rand([1000, 1000]))
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tensor2 = paddle.matmul(tensor1, tensor1)
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s.synchronize()
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e2.record(s)
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e2.synchronize()
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self.assertTrue(e2.query())
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def test_xpu_stream_wait_event_and_record_event(self):
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if paddle.is_compiled_with_xpu():
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s1 = xpu.Stream(0)
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tensor1 = paddle.to_tensor(paddle.rand([1000, 1000]))
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tensor2 = paddle.matmul(tensor1, tensor1)
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e1 = xpu.Event()
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s1.record_event(e1)
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s2 = xpu.Stream(0)
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s2.wait_event(e1)
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s2.synchronize()
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self.assertTrue(e1.query())
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class TestXPUStream_paddle_device(unittest.TestCase):
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def test_xpu_stream(self):
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if paddle.is_compiled_with_xpu():
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s = paddle.device.Stream()
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self.assertIsNotNone(s)
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def test_xpu_stream_synchronize(self):
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if paddle.is_compiled_with_xpu():
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s = paddle.device.Stream()
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e1 = paddle.device.Event()
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e2 = paddle.device.Event()
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e1.record(s)
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e1.query()
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tensor1 = paddle.to_tensor(paddle.rand([1000, 1000]))
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tensor2 = paddle.matmul(tensor1, tensor1)
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s.synchronize()
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e2.record(s)
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e2.synchronize()
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self.assertTrue(e2.query())
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def test_xpu_stream_wait_event_and_record_event(self):
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if paddle.is_compiled_with_xpu():
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s1 = paddle.device.Stream(0)
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tensor1 = paddle.to_tensor(paddle.rand([1000, 1000]))
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tensor2 = paddle.matmul(tensor1, tensor1)
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e1 = paddle.device.Event()
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s1.record_event(e1)
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s2 = paddle.device.Stream(0)
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s2.wait_event(e1)
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s2.synchronize()
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self.assertTrue(e1.query())
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class TestXPUEvent(unittest.TestCase):
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def test_xpu_event(self):
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if paddle.is_compiled_with_xpu():
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e = paddle.device.xpu.Event()
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self.assertIsNotNone(e)
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s = paddle.device.xpu.current_stream()
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def test_xpu_event_methods(self):
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if paddle.is_compiled_with_xpu():
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e = paddle.device.xpu.Event()
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s = paddle.device.xpu.current_stream()
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event_query_1 = e.query()
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tensor1 = paddle.to_tensor(paddle.rand([1000, 1000]))
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tensor2 = paddle.matmul(tensor1, tensor1)
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s.record_event(e)
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e.synchronize()
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event_query_2 = e.query()
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self.assertTrue(event_query_1)
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self.assertTrue(event_query_2)
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class TestXPUEvent_paddle_device(unittest.TestCase):
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def test_xpu_event(self):
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if paddle.is_compiled_with_xpu():
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e = paddle.device.Event()
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self.assertIsNotNone(e)
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s = paddle.device.current_stream()
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def test_xpu_event_methods(self):
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if paddle.is_compiled_with_xpu():
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e = paddle.device.Event()
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s = paddle.device.current_stream()
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event_query_1 = e.query()
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tensor1 = paddle.to_tensor(paddle.rand([1000, 1000]))
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tensor2 = paddle.matmul(tensor1, tensor1)
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s.record_event(e)
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e.synchronize()
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event_query_2 = e.query()
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self.assertTrue(event_query_1)
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self.assertTrue(event_query_2)
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class TestStreamGuard(unittest.TestCase):
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'''
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Note:
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The asynchronous execution property of XPU Stream can only be tested offline.
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'''
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def test_stream_guard_normal(self):
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if paddle.is_compiled_with_xpu():
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s = paddle.device.Stream()
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a = paddle.to_tensor(np.array([0, 2, 4], dtype="int32"))
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b = paddle.to_tensor(np.array([1, 3, 5], dtype="int32"))
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c = a + b
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with paddle.device.stream_guard(s):
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d = a + b
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s.synchronize()
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np.testing.assert_array_equal(np.array(c), np.array(d))
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def test_stream_guard_default_stream(self):
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if paddle.is_compiled_with_xpu():
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s1 = paddle.device.current_stream()
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with paddle.device.stream_guard(s1):
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pass
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s2 = paddle.device.current_stream()
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self.assertTrue(id(s1.stream_base) == id(s2.stream_base))
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def test_set_current_stream_default_stream(self):
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if paddle.is_compiled_with_xpu():
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cur_stream = paddle.device.current_stream()
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new_stream = paddle.device.set_stream(cur_stream)
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self.assertTrue(
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id(cur_stream.stream_base) == id(new_stream.stream_base)
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)
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def test_stream_guard_raise_error(self):
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if paddle.is_compiled_with_xpu():
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def test_not_correct_stream_guard_input():
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tmp = np.zeros(5)
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with paddle.device.stream_guard(tmp):
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pass
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self.assertRaises(TypeError, test_not_correct_stream_guard_input)
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class TestRawStream(unittest.TestCase):
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def test_xpu_stream(self):
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if paddle.is_compiled_with_xpu():
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xpu_stream = paddle.device.xpu.current_stream().xpu_stream
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print(xpu_stream)
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self.assertTrue(type(xpu_stream) is int)
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ptr = ctypes.c_void_p(xpu_stream)
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if __name__ == "__main__":
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unittest.main()
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