# Copyright (c) 2019 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 os

import paddle.fluid as fluid
from utils import gen_data
from nets import mlp
from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy
from paddle.fluid.incubate.fleet.base import role_maker

input_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')

cost = mlp(input_x, input_y)
optimizer = fluid.optimizer.SGD(learning_rate=0.01)

dist_strategy = DistributedStrategy()
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)

optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy)
optimizer.minimize(cost, fluid.default_startup_program())

train_prog = fleet.main_program

gpu_id = int(os.getenv("FLAGS_selected_gpus", "0"))
place = fluid.CUDAPlace(gpu_id)

exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

step = 1001
for i in range(step):
    cost_val = exe.run(
        program=train_prog,
        feed=gen_data(),
        fetch_list=[cost.name])
    print("worker_index: %d, step%d cost = %f" %
          (fleet.worker_index(), i, cost_val[0]))
