#   Copyright (c) 2021 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"
MODEL_PARALLEL_SIZE = 2
IN_SIZE = 2 * MODEL_PARALLEL_SIZE
OUT_SIZE = 2 * MODEL_PARALLEL_SIZE

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


def get_param_attr(weight, bias):
    weight_attr = paddle.ParamAttr(
        initializer=fluid.initializer.NumpyArrayInitializer(weight))
    bias_attr = paddle.ParamAttr(
        initializer=fluid.initializer.NumpyArrayInitializer(bias))
    return weight_attr, bias_attr


def create_model(data, rank):
    np.random.seed(2021)
    np_weight = np.random.uniform(-1, 1, size=(IN_SIZE, OUT_SIZE)).astype(DTYPE)
    np_bias = np.random.uniform(-1, 1, size=(OUT_SIZE, )).astype(DTYPE)
    if rank is not None:
        start_col = 0 if rank == 0 else OUT_SIZE // 2
        np_weight_part = np_weight[:, start_col:start_col + OUT_SIZE // 2]
        np_bias_part = np_bias[start_col:start_col + OUT_SIZE // 2]

        weight_attr, bias_attr = get_param_attr(np_weight_part, np_bias_part)
        result = paddle.distributed.split(
            data,
            size=(IN_SIZE, OUT_SIZE),
            operation='linear',
            axis=1,
            num_partitions=MODEL_PARALLEL_SIZE,
            weight_attr=weight_attr,
            bias_attr=bias_attr)
    else:
        weight_attr, bias_attr = get_param_attr(np_weight, np_bias)
        result = fluid.layers.fc(data,
                                 size=OUT_SIZE,
                                 param_attr=weight_attr,
                                 bias_attr=bias_attr)

    predict = paddle.sum(result)
    return predict


class TestModelParallel(TestDistRunnerBase):
    def get_model(self, batch_size=2, use_dgc=False, dist_strategy=None):
        # Input data
        data_in = fluid.data(
            name='data_in', shape=[batch_size, IN_SIZE], dtype=DTYPE)

        if dist_strategy:
            data_loader = fluid.io.DataLoader.from_generator(
                feed_list=[data_in],
                capacity=64,
                use_double_buffer=False,
                iterable=False)

        if dist_strategy:
            fleet.init(is_collective=True)
            strategy = fleet.DistributedStrategy()
            strategy.tensor_parallel = True
            strategy.tensor_parallel_configs = {'tensor_parallel_degree': 2}

        rank = fleet.worker_index() if dist_strategy else None
        avg_cost = create_model(data_in, rank)
        opt = fluid.optimizer.SGD(0.1)

        if dist_strategy:
            dist_opt = fleet.distributed_optimizer(
                optimizer=opt, strategy=strategy)
            dist_opt.minimize(avg_cost)
        else:
            opt.minimize(avg_cost)

        def gen_data():
            np.random.seed(2021)
            while True:
                data = [np.random.random([IN_SIZE]).astype(DTYPE)]
                yield data

        train_reader = paddle.batch(gen_data, batch_size=batch_size)

        if dist_strategy:
            return None, avg_cost, train_reader, None, None, None, data_loader
        else:
            return None, avg_cost, train_reader, None, None, None


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