wehub-resource-sync
90 lines
2.9 KiB
Python
90 lines
2.9 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 unittest
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from unittest import TestCase
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import numpy as np
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import paddle
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from paddle import base
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from paddle.base.wrapped_decorator import wrap_decorator
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def _dygraph_guard_(func):
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def __impl__(*args, **kwargs):
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if base.in_dygraph_mode():
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return func(*args, **kwargs)
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else:
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with base.dygraph.guard():
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return func(*args, **kwargs)
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return __impl__
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dygraph_guard = wrap_decorator(_dygraph_guard_)
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class TestDygraphClearGradient(TestCase):
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def setUp(self):
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self.input_shape = [10, 2]
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@dygraph_guard
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def test_tensor_method_clear_gradient_case1(self):
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input = paddle.uniform(self.input_shape)
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linear = paddle.nn.Linear(2, 3)
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out = linear(input)
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out.backward()
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if not base.framework.in_dygraph_mode():
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linear.weight.clear_gradient()
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else:
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linear.weight._zero_grads()
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# actual result
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gradient_actual = linear.weight.grad
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# expected result
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gradient_expected = np.zeros([2, 3]).astype('float64')
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np.testing.assert_allclose(gradient_actual.numpy(), gradient_expected)
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@dygraph_guard
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def test_tensor_method_clear_gradient_case2(self):
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input = paddle.uniform(self.input_shape)
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linear = paddle.nn.Linear(2, 3)
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out = linear(input)
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out.backward()
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# default arg set_to_zero is true
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# so, False means real clear gradient
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linear.weight.clear_gradient(False)
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# before ._gradient_set_empty(False),
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# the return of ._is_gradient_set_empty() should be True
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if not base.framework.in_dygraph_mode():
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self.assertTrue(linear.weight._is_gradient_set_empty())
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else:
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self.assertIsNone(linear.weight.grad)
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# reset, because ClearGradient will call SetIsEmpty(True), but this is not our expectation.
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if not base.framework.in_dygraph_mode():
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linear.weight._gradient_set_empty(False)
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# after ._gradient_set_empty(False),
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# the return of ._is_gradient_set_empty() should be False
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self.assertFalse(linear.weight._is_gradient_set_empty())
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# actual result
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gradient_actual = linear.weight.grad
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# expected result
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self.assertIsNone(gradient_actual)
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if __name__ == '__main__':
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unittest.main()
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