#   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 numpy as np
import argparse
import time
import math

import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
from paddle.fluid import core
import unittest
from multiprocessing import Process
import os
import signal
from functools import reduce
from test_dist_base import TestDistRunnerBase, runtime_main
import paddle.distributed.fleet as fleet

paddle.enable_static()

DTYPE = "float32"
paddle.dataset.mnist.fetch()

# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1


def cnn_model(data):
    conv_pool_1 = fluid.nets.simple_img_conv_pool(
        input=data,
        filter_size=5,
        num_filters=20,
        pool_size=2,
        pool_stride=2,
        act="relu",
        param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
            value=0.01)))
    conv_pool_2 = fluid.nets.simple_img_conv_pool(
        input=conv_pool_1,
        filter_size=5,
        num_filters=50,
        pool_size=2,
        pool_stride=2,
        act="relu",
        param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
            value=0.01)))

    SIZE = 10
    input_shape = conv_pool_2.shape
    param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
    scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5

    with fluid.device_guard("gpu:1"):
        predict = fluid.layers.fc(
            input=conv_pool_2,
            size=SIZE,
            act="softmax",
            param_attr=fluid.param_attr.ParamAttr(
                initializer=fluid.initializer.Constant(value=0.01)))
        # To cover @RENAMED@GRADIENT
        predict2 = fluid.layers.fc(
            input=conv_pool_1,
            size=SIZE,
            act="softmax",
            param_attr=fluid.param_attr.ParamAttr(
                initializer=fluid.initializer.Constant(value=0.01)))
        predict += predict2
    return predict


class TestDistMnist2x2(TestDistRunnerBase):

    def get_model(self, batch_size=2, use_dgc=False, dist_strategy=None):
        # Input data
        with fluid.device_guard("gpu:0"):
            images = fluid.layers.data(name='pixel',
                                       shape=[1, 28, 28],
                                       dtype=DTYPE)
            label = fluid.layers.data(name='label', shape=[1], dtype='int64')

            if dist_strategy:
                data_loader = fluid.io.DataLoader.from_generator(
                    feed_list=[images, label],
                    capacity=64,
                    use_double_buffer=False,
                    iterable=False)
            # Train program
            predict = cnn_model(images)
        with fluid.device_guard("gpu:1"):
            cost = fluid.layers.cross_entropy(input=predict, label=label)
            avg_cost = paddle.mean(x=cost)

        # Evaluator
        with fluid.device_guard("gpu:1"):
            batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
            batch_acc = fluid.layers.accuracy(input=predict,
                                              label=label,
                                              total=batch_size_tensor)

        inference_program = fluid.default_main_program().clone()
        base_lr = self.lr
        passes = [30, 60, 80, 90]
        steps_per_pass = 10
        bd = [steps_per_pass * p for p in passes]
        lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
        lr_val = paddle.optimizer.lr.PiecewiseDecay(boundaries=bd, values=lr)

        opt = paddle.optimizer.AdamW(
            learning_rate=lr_val,
            grad_clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=1.0))

        acc_steps = 2  # accumulated steps for pipeline
        if dist_strategy:
            # Reader
            train_reader = paddle.batch(paddle.dataset.mnist.test(),
                                        batch_size=batch_size)
            test_reader = paddle.batch(paddle.dataset.mnist.test(),
                                       batch_size=batch_size)
            fleet.init(is_collective=True)
            strategy = fleet.DistributedStrategy()
            strategy.pipeline = True
            strategy.amp = True
            strategy.pipeline_configs = {
                'micro_batch_size': batch_size,
                'schedule_mode': '1F1B',
                'accumulate_steps': acc_steps
            }
            dist_opt = fleet.distributed_optimizer(optimizer=opt,
                                                   strategy=strategy)
            dist_opt.minimize(avg_cost)
        else:
            opt.minimize(avg_cost)
            # Reader
            train_reader = paddle.batch(paddle.dataset.mnist.test(),
                                        batch_size=batch_size * acc_steps)
            test_reader = paddle.batch(paddle.dataset.mnist.test(),
                                       batch_size=batch_size * acc_steps)

        if dist_strategy:
            return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict, data_loader
        else:
            return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict


if __name__ == "__main__":
    runtime_main(TestDistMnist2x2)
