wehub-resource-sync
98 lines
2.6 KiB
Python
98 lines
2.6 KiB
Python
# 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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import paddle
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from paddle.incubate.tensor.manipulation import (
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async_offload,
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async_offload_with_offset,
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async_reload,
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create_async_load,
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)
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class TestSaveLoadLargeParameters(unittest.TestCase):
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def offload_and_reload(self, data0):
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loader = create_async_load()
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data1 = paddle.randn([10, 10])
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cpu_data, task = async_offload(data0, loader)
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res = paddle.matmul(data1, data1)
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task.cpu_wait()
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gpu_data, task = async_reload(cpu_data, loader)
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res = paddle.matmul(data1, data1)
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task.cuda_wait()
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task.cpu_wait()
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np.testing.assert_array_equal(
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data0.numpy(),
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cpu_data.numpy(),
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)
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np.testing.assert_array_equal(
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data0.numpy(),
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gpu_data.numpy(),
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)
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def test_large_parameters_paddle_save_tensor(self):
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data0 = paddle.randn([10, 5])
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self.offload_and_reload(data0)
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def test_large_parameters_paddle_save_model_weight(self):
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model = paddle.nn.Linear(10, 5)
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data0 = model.weight
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self.offload_and_reload(data0)
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def test_offload_with_offset(self):
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loader = create_async_load()
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data1 = paddle.randn(
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[
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100,
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]
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)
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data2 = paddle.randn(
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[
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100,
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]
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).cpu()
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task1 = async_offload_with_offset(
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src_tensor=data1,
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dst_tensor=data2,
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src_offset=0,
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dst_offset=0,
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offload_size=50,
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async_loader=loader,
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)
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task2 = async_offload_with_offset(
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src_tensor=data1,
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dst_tensor=data2,
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src_offset=50,
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dst_offset=50,
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offload_size=50,
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async_loader=loader,
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)
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task1.cuda_wait()
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task2.cpu_wait()
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np.testing.assert_array_equal(
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data1.numpy(),
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data2.numpy(),
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)
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if __name__ == '__main__':
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unittest.main()
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