# Copyright 2023 Huawei Technologies Co., Ltd
#
# 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 numpy as np
from mindspore.communication import init, get_rank
import mindspore as ms
import mindspore.nn as nn
import mindspore.ops as ops
from mindspore import context


context.set_context(mode=ms.GRAPH_MODE)
context.set_context(jit_level="O2")
init()


class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.all_reduce_sum = ops.AllReduce(ops.ReduceOp.SUM)

    def construct(self, x):
        return self.all_reduce_sum(x)

value = get_rank()
input_x = ms.Tensor(np.array([[value]]).astype(np.float32))
net = Net()
output = net(input_x)