wehub-resource-sync
96 lines
3.6 KiB
Python
96 lines
3.6 KiB
Python
# Copyright (c) 2021 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 os
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import tempfile
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import unittest
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import numpy as np
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from test_imperative_base import new_program_scope
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import paddle
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from paddle import base
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from paddle.base import framework
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from paddle.framework.io_utils import is_pir_fetch_var
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LARGE_PARAM = 2**26
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class TestStaticSaveLoadLargeParameters(unittest.TestCase):
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def test_large_parameters_static_save(self):
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# enable static graph mode
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paddle.enable_static()
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with new_program_scope():
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# create network
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x = paddle.static.data(
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name="static_save_load_large_x",
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shape=[None, 10],
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dtype='float32',
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)
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z = paddle.static.nn.fc(x, LARGE_PARAM, bias_attr=False)
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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exe.run(paddle.static.default_startup_program())
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prog = paddle.static.default_main_program()
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base_map = {}
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for var in prog.list_vars():
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if isinstance(var, framework.Parameter) or var.persistable:
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if is_pir_fetch_var(var):
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continue
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t = np.array(
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base.global_scope().find_var(var.name).get_tensor()
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)
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# make sure all the parameter or optimizer var have been update
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self.assertTrue(np.sum(np.abs(t)) != 0)
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base_map[var.name] = t
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temp_dir = tempfile.TemporaryDirectory()
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path = os.path.join(
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temp_dir.name, "test_static_save_load_large_param"
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)
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path = os.path.join(path, "static_save")
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protocol = 4
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paddle.static.save(prog, path, pickle_protocol=protocol)
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load_prog1 = paddle.static.Program()
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paddle.static.load(load_prog1, path)
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for var in load_prog1.list_vars():
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if isinstance(var, framework.Parameter) or var.persistable:
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if is_pir_fetch_var(var):
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continue
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new_t = np.array(
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base.global_scope().find_var(var.name).get_tensor()
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)
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base_t = base_map[var.name]
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np.testing.assert_array_equal(new_t, base_t)
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load_prog2 = paddle.static.Program()
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program_state = paddle.static.load_program_state(path)
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paddle.static.set_program_state(load_prog2, program_state)
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for var in load_prog2.list_vars():
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if isinstance(var, framework.Parameter) or var.persistable:
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if is_pir_fetch_var(var):
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continue
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new_t = np.array(
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base.global_scope().find_var(var.name).get_tensor()
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)
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base_t = base_map[var.name]
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np.testing.assert_array_equal(new_t, base_t)
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temp_dir.cleanup()
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if __name__ == '__main__':
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unittest.main()
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