#   Copyright (c) 2018 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 collections
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.initializer import ConstantInitializer
from paddle.fluid.param_attr import WeightNormParamAttr


class TestWeightNormalization(unittest.TestCase):
    batch_size = 3
    hidden_size = 5
    data_desc = (['x', [10], 0], )

    @classmethod
    def setUpClass(cls):
        cls.set_program()

    @classmethod
    def set_program(cls):
        data = fluid.layers.data(name=cls.data_desc[0][0],
                                 shape=cls.data_desc[0][1])
        out = fluid.layers.fc(input=data,
                              size=cls.hidden_size,
                              param_attr=WeightNormParamAttr(
                                  dim=None,
                                  name='weight_norm_param',
                                  initializer=ConstantInitializer(1.0)),
                              bias_attr=False,
                              act=None)
        loss = fluid.layers.reduce_sum(out)
        fluid.backward.append_backward(loss=loss)
        cls.fetch_list = [
            'weight_norm_param_g', 'weight_norm_param_v',
            'weight_norm_param_g@GRAD'
        ]

    def run_program(self):
        outputs = []
        places = [core.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(core.CUDAPlace(0))
        for place in places:
            self.set_inputs(place)
            exe = fluid.Executor(place)
            exe.run(fluid.default_startup_program())
            output = exe.run(fluid.default_main_program(),
                             feed=self.inputs,
                             fetch_list=self.fetch_list,
                             return_numpy=False)
            outputs.append(output)
        self.actual_outputs = outputs

    def set_data(self):
        self.data = collections.OrderedDict()
        for desc in self.data_desc:
            data_name = desc[0]
            data_shape = desc[1]
            data_lod_level = desc[2]
            data_lod = []
            for i in range(data_lod_level):
                lod_level_i = np.random.randint(low=1,
                                                high=5,
                                                size=self.batch_size if i == 0
                                                else sum(lod_level_i)).tolist()
                data_lod.append(lod_level_i)
            data_value = np.random.random(
                size=[sum(data_lod[-1]) if data_lod else self.batch_size] +
                data_shape).astype('float32')
            self.data[data_name] = (data_value, data_lod)

    def set_inputs(self, place):
        self.inputs = {}
        for desc in self.data_desc:
            tensor = fluid.Tensor()
            tensor.set(self.data[desc[0]][0], place)
            if self.data[desc[0]][1]:
                tensor.set_recursive_sequence_lengths(self.data[desc[0]][1])
            self.inputs[desc[0]] = tensor

    def weight_normalize(self):
        v = np.ones(
            (self.data[self.data_desc[0][0]][0].shape[-1], self.hidden_size))
        g = np.linalg.norm(v, axis=None, keepdims=True)
        w = g * v / np.linalg.norm(v, axis=None, keepdims=True)
        x = self.data[self.data_desc[0][0]][0]
        out = np.dot(x, w)
        g_grad = (np.dot(x.T, np.ones_like(out)) *
                  (v / np.linalg.norm(v, axis=None, keepdims=True))).sum(
                      axis=None, keepdims=True)
        return g, v, g_grad

    def test_weight_normalization(self):
        self.set_data()
        self.run_program()
        expect_output = self.weight_normalize()
        for actual_output in self.actual_outputs:
            [
                np.testing.assert_allclose(np.array(actual),
                                           expect,
                                           rtol=1e-05,
                                           atol=0.001)
                for expect, actual in zip(expect_output, actual_output)
            ]


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