wehub-resource-sync
83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
# Copyright (c) 2025 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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@unittest.skipIf(
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(not paddle.is_compiled_with_cuda()) or paddle.is_compiled_with_rocm(),
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'should compile with cuda.',
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)
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class TestVmmCompact(unittest.TestCase):
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def setUp(self):
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self.GB = 1000**3
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self.MB = 1000**2
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paddle.set_flags({'FLAGS_use_virtual_memory_auto_growth': True})
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self.cmds = [
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["Alloc", 1 * self.GB, "0x100000000"],
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["Alloc", 2 * self.GB, "0x100000001"],
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["Alloc", 2 * self.GB, "0x100000002"],
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["Alloc", 1 * self.GB, "0x100000003"],
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["Free", 1 * self.GB, "0x100000000"],
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["Free", 1 * self.GB, "0x100000003"],
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]
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def test_compact_api(self):
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params = {}
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(
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old_tensor1,
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new_tensor1,
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) = 0, 0
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for op, size, ptr in self.cmds:
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paddle.device.synchronize()
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paddle_reserved1 = paddle.device.cuda.memory_reserved() // self.MB
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if op == "Alloc":
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params[ptr] = paddle.randn(
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[int(int(size) / 4)], dtype='float32'
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)
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if op == "Free" and ptr in params:
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del params[ptr]
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if ptr == '0x100000001':
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old_tensor1 = params['0x100000001'].numpy()[0:100]
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paddle.device.synchronize()
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paddle_reserved2 = paddle.device.cuda.memory_reserved() // self.MB
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paddle_allocated2 = paddle.device.cuda.memory_allocated() // self.MB
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paddle_max_reserved = (
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paddle.device.cuda.max_memory_reserved() // self.MB
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)
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paddle_max_allocated = (
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paddle.device.cuda.max_memory_allocated() // self.MB
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)
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print(
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f"reserved = {paddle_reserved2} allocated = {paddle_allocated2} auto growth = {paddle_reserved2 - paddle_reserved1} max_allocated = {paddle_max_allocated} max_reserved = {paddle_max_reserved}"
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)
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paddle.device.cuda.vmm_compact()
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new_tensor1 = params['0x100000001'].numpy()[0:100]
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np.testing.assert_array_equal(old_tensor1, new_tensor1)
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assert len(paddle.core.get_compact_size(0)) == 1
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if __name__ == '__main__':
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unittest.main()
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