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100 lines
3.5 KiB
100 lines
3.5 KiB
# Copyright (c) 2020 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle
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import paddle.fluid.core as core
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class TestInplace(unittest.TestCase):
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def test_forward_version(self):
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with paddle.fluid.dygraph.guard():
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var = paddle.to_tensor(np.ones((4, 2, 3)).astype(np.float32))
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self.assertEqual(var.inplace_version, 0)
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var[0] = 1.1
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self.assertEqual(var.inplace_version, 1)
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paddle.assign(paddle.ones(shape=[3]), var)
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# NOTE(liym27): assign(input, output) is an inplace operation for output.
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# There is inplace-related processing for api assign, var.inplace_version should be 2 not 1.
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self.assertEqual(var.inplace_version, 2)
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var[2] = 3
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self.assertEqual(var.inplace_version, 3)
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def test_backward_error(self):
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# It raises an error because the inplace operator will result
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# in incorrect gradient computation.
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with paddle.fluid.dygraph.guard():
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var_a = paddle.ones(shape=[4, 2, 3], dtype="float32")
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var_a.stop_gradient = False
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var_b = var_a**2
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# Here, the gradient computation will use the value of var_b
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var_c = var_b**2
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var_b[1:2] = 3.3 # var_b is modified inplace after using it
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var_d = var_b**2
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loss = paddle.nn.functional.relu(var_c + var_d)
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with self.assertRaisesRegexp(
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RuntimeError,
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"received tensor_version:{} != wrapper_version_snapshot:{}".
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format(1, 0)):
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loss.backward()
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def test_backward_success_1(self):
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# var_b is modified inplace before using it, the inplace operator doesn't result
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# in incorrect gradient computation.
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with paddle.fluid.dygraph.guard():
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var_a = paddle.ones(shape=[4, 2, 3], dtype="float32")
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var_a.stop_gradient = False
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var_b = var_a**2
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var_b[1:2] = 3 # var_b is modified inplace before using it
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# Here, the gradient computation will use the value of var_b
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var_c = var_b**2
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loss = var_c.sum()
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loss.backward()
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def test_backward_success_2(self):
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# Although var_b is modified inplace after using it, it does not used in gradient computation.
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# The inplace operator doesn't result in incorrect gradient computation.
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with paddle.fluid.dygraph.guard():
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var_a = paddle.ones(shape=[4, 2, 3], dtype="float32")
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var_a.stop_gradient = False
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var_b = var_a**2
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var_b[1:2] = 3 # var_b is modified inplace before using it
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var_c = var_b + var_b # Here, the grad op of sum doesn't use the value of var_b
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loss = var_c.sum()
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var_b[1:2] = 3 # var_b is modified inplace after using it
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loss.backward()
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if __name__ == '__main__':
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unittest.main()
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