wehub-resource-sync
84 lines
3.1 KiB
Python
84 lines
3.1 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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import paddle
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from paddle import static
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paddle.enable_static()
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class TestCrossStepOverlap(unittest.TestCase):
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def setUp(self):
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self.shape = [16, 513, 513, 19]
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self.x_value = 2
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self.y_value = 3
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self.overlap_op_num = 1500
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self.step_num = 3
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def test_cross_step_overlap(self):
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if not paddle.base.core.is_compiled_with_cuda():
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return
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# In this test case, z=x+y is calculated in the default stream,
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# and at the same time, numerous reduce_min ops that output to y
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# are executed in another stream (i.e., the custom stream).
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# These reduce_min ops are carefully designed that their kernel
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# calculation will overlap with the fill_constant kernels (output
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# to x and y) in the next step, and therefore cross-step multi-stream
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# synchronization is required. An Event should be recorded after the
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# last reduce_min in the first step and waited before the fill_constant
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# in the second step. Otherwise, the result of z will be wrong.
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with paddle.pir_utils.OldIrGuard():
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program = static.Program()
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with static.program_guard(program):
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x = paddle.full(
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self.shape, fill_value=self.x_value, dtype='float64'
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)
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y = paddle.full(
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self.shape, fill_value=self.y_value, dtype='float64'
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)
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z = paddle.add(x, y)
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block = program.global_block()
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block.var(x.name).desc.set_persistable(True)
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block.var(y.name).desc.set_persistable(True)
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for i in range(self.overlap_op_num):
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block.append_op(
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type='reduce_min',
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inputs={'X': x.name},
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outputs={'Out': y.name},
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attrs={'axis': 0, 'keepdim': True},
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)
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block.ops[-1].dist_attr.execution_stream = "custom"
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exe = static.Executor()
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results = []
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for i in range(self.step_num):
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result = exe.run(program, fetch_list=[z])
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results.append(result)
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for result in results:
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self.assertAlmostEqual(
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np.sum(result),
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(self.x_value + self.y_value) * np.prod(self.shape),
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)
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if __name__ == "__main__":
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unittest.main()
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