test_inplace.py

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function

import unittest
import numpy as np

import paddle
import paddle.fluid.core as core


class TestInplace(unittest.TestCase):
    def test_forward_version(self):
        with paddle.fluid.dygraph.guard():
            var = paddle.to_tensor(np.ones((4, 2, 3)).astype(np.float32))
            self.assertEqual(var.inplace_version, 0)

            var[0] = 1.1
            self.assertEqual(var.inplace_version, 1)

            paddle.nn.functional.assign(paddle.ones(shape=[3]), var)

            # NOTE(liym27): assign(input, output) is an inplace operation for output.
            # There is inplace-related processing for api assign, var.inplace_version should be 2 not 1.
            self.assertEqual(var.inplace_version, 2)

            var[2] = 3
            self.assertEqual(var.inplace_version, 3)

    def test_backward_error(self):
        # It raises an error because the inplace operator will result
        # in incorrect gradient computation.
        with paddle.fluid.dygraph.guard():
            var_a = paddle.ones(shape=[4, 2, 3], dtype="float32")
            var_a.stop_gradient = False

            var_b = var_a**2

            # Here, the gradient computation will use the value of var_b
            var_c = var_b**2
            var_b[1:2] = 3.3  # var_b is modified inplace after using it

            var_d = var_b**2

            loss = paddle.nn.functional.relu(var_c + var_d)
            with self.assertRaisesRegexp(
                    RuntimeError,
                    "received tensor_version:{} != wrapper_version_snapshot:{}".
                    format(1, 0)):
                loss.backward()

    def test_backward_success_1(self):
        # var_b is modified inplace before using it, the inplace operator doesn't result
        # in incorrect gradient computation.
        with paddle.fluid.dygraph.guard():
            var_a = paddle.ones(shape=[4, 2, 3], dtype="float32")
            var_a.stop_gradient = False

            var_b = var_a**2
            var_b[1:2] = 3  # var_b is modified inplace before using it

            # Here, the gradient computation will use the value of var_b
            var_c = var_b**2
            loss = var_c.sum()
            loss.backward()

    def test_backward_success_2(self):
        # Although var_b is modified inplace after using it, it does not used in gradient computation.
        # The inplace operator doesn't result in incorrect gradient computation.
        with paddle.fluid.dygraph.guard():
            var_a = paddle.ones(shape=[4, 2, 3], dtype="float32")
            var_a.stop_gradient = False

            var_b = var_a**2

            var_b[1:2] = 3  # var_b is modified inplace before using it

            var_c = var_b + var_b  # Here, the grad op of sum doesn't use the value of var_b
            loss = var_c.sum()

            var_b[1:2] = 3  # var_b is modified inplace after using it

            loss.backward()


if __name__ == '__main__':
    unittest.main()