# Copyright 2019 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

import mindspore as ms
import mindspore.common.api as me
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore import context
from mindspore.ops import operations as P


def setup_function():
    context.set_auto_parallel_context(dataset_strategy="full_batch")


def test_get_parameter_layout():
    class Net(nn.Cell):
        def __init__(self, strategy1, strategy2, weight):
            super().__init__()
            self.weight = Parameter(weight, "w1")
            self.matmul = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
            self.relu = P.ReLU().shard(strategy2)

        def construct(self, x):
            out = self.matmul(x, self.weight)
            out = self.relu(out)
            return out

    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(device_num=8, global_rank=0)
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
    strategy1 = ((2, 1), (4, 1))
    strategy2 = ((2, 4),)
    context.set_context(mode=context.GRAPH_MODE)

    x = Tensor(np.ones([32, 32]), dtype=ms.float32)
    weight = Tensor(np.ones([64, 32]), dtype=ms.float32)

    net = Net(strategy1, strategy2, weight)
    net.set_train()
    exe = me._cell_graph_executor
    exe.compile(net, x, phase='train')
    x_layout = ([8], [0, -1], [32, 32], 0, True, '')  # device_arrangement = [2, 4], tensor_map = [1, -1]
    weight_layout = ([2, 4], [0, -1], [16, 32], 0, True, '')  # device_arrangement = [2, 4], tensor_map = [0, -1]
    expect_dict = {'x': x_layout, 'w1': weight_layout}
    # to be resovled: static local variable count_p is used in step_parallel.cc, it needs to be reset between each ut
    assert net.parameter_layout_dict == expect_dict


if __name__ == '__main__':
    test_get_parameter_layout()
