test_mul_nn_grad.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.

import unittest
import numpy as np

import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core
import gradient_checker
from decorator_helper import prog_scope

paddle.enable_static()


class TestMulGradCheck(unittest.TestCase):

    @prog_scope()
    def func(self, place):
        prog = fluid.Program()
        with fluid.program_guard(prog):
            x = layers.create_parameter(dtype="float64", shape=[2, 8], name='x')
            y = layers.create_parameter(dtype="float64", shape=[8, 4], name='y')
            z = layers.mul(x=x, y=y)
            gradient_checker.grad_check([x, y], z, place=place)

    def test_grad(self):
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        for p in places:
            self.func(p)


class TestMulDoubleGradCheck(unittest.TestCase):

    @prog_scope()
    def func(self, place):
        # the shape of input variable should be clearly specified, not inlcude -1.
        x_shape = [7, 11]
        y_shape = [11, 9]
        eps = 0.005
        dtype = np.float64

        x = layers.data('x', x_shape, False, dtype)
        x.persistable = True
        y = layers.data('y', y_shape, False, dtype)
        y.persistable = True
        out = layers.mul(x, y)
        x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
        y_arr = np.random.uniform(-1, 1, y_shape).astype(dtype)

        gradient_checker.double_grad_check([x, y],
                                           out,
                                           x_init=[x_arr, y_arr],
                                           place=place,
                                           eps=eps)

    def test_grad(self):
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        for p in places:
            self.func(p)


class TestMatmulDoubleGradCheck(unittest.TestCase):

    def setUp(self):
        self.init_test()

    def init_test(self):
        self.x_shape = [2]
        self.y_shape = [2]
        self.transpose_x = False
        self.transpose_y = False

    @prog_scope()
    def func(self, place):
        eps = 0.005
        dtype = np.float64
        typename = "float64"
        x = layers.create_parameter(dtype=typename,
                                    shape=self.x_shape,
                                    name='x')
        y = layers.create_parameter(dtype=typename,
                                    shape=self.y_shape,
                                    name='y')
        out = layers.matmul(x,
                            y,
                            self.transpose_x,
                            self.transpose_y,
                            name='out')

        x_arr = np.random.uniform(-1, 1, self.x_shape).astype(dtype)
        y_arr = np.random.uniform(-1, 1, self.y_shape).astype(dtype)
        gradient_checker.double_grad_check([x, y],
                                           out,
                                           x_init=[x_arr, y_arr],
                                           place=place,
                                           eps=eps)

    def test_grad(self):
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        for p in places:
            self.func(p)


def TestMatmulDoubleGradCheckCase1(TestMatmulDoubleGradCheck):

    def init_test(self):
        self.x_shape = [2, 3]
        self.y_shape = [3, 2]
        self.transpose_x = True
        self.transpose_y = True


def TestMatmulDoubleGradCheckCase2(TestMatmulDoubleGradCheck):

    def init_test(self):
        self.x_shape = [2, 4, 3]
        self.y_shape = [2, 4, 5]
        self.transpose_x = True
        self.transpose_y = False


def TestMatmulDoubleGradCheckCase3(TestMatmulDoubleGradCheck):

    def init_test(self):
        self.x_shape = [2, 3, 4, 5]
        self.y_shape = [2, 3, 3, 5]
        self.transpose_x = False
        self.transpose_y = True


def TestMatmulDoubleGradCheckCase4(TestMatmulDoubleGradCheck):

    def init_test(self):
        self.x_shape = [2, 3, 4]
        self.y_shape = [4, 3]
        self.transpose_x = False
        self.transpose_y = False


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