mindspore/tests/ut/python/parallel/test_transpose.py

# 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.nn as nn
from mindspore import Tensor, context
from mindspore.common.parameter import Parameter
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.nn.optim.momentum import Momentum
from mindspore.ops import operations as P
from mindspore.train import Model
from mindspore.context import ParallelMode
from tests.dataset_mock import MindData

class Dataset(MindData):
    def __init__(self, predict, label, length=3):
        super(Dataset, self).__init__(size=length)
        self.predict = predict
        self.label = label
        self.index = 0
        self.length = length

    def __iter__(self):
        return self

    def __next__(self):
        if self.index >= self.length:
            raise StopIteration
        self.index += 1
        return self.predict, self.label

    def reset(self):
        self.index = 0


class TransposeNet(nn.Cell):
    def __init__(self, strategy1, strategy2):
        super(TransposeNet, self).__init__()
        self.matmul = P.MatMul().shard(((8, 1), (1, 1)))
        self.matmul_weight = Parameter(Tensor(np.ones([128, 256]), dtype=ms.float32), name="weight")
        self.transpose1 = P.Transpose().shard(strategy1)
        self.transpose2 = P.Transpose().shard(strategy2)

    def construct(self, x):
        x = self.matmul(x, self.matmul_weight)
        x = self.transpose1(x, (1, 0))
        x = self.transpose2(x, (1, 0))
        return x


def transpose_net(strategy1, strategy2):
    return TransposeNet(strategy1=strategy1, strategy2=strategy2)


def transpose_common(strategy1, strategy2):
    learning_rate = 0.1
    momentum = 0.9
    epoch_size = 2

    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, device_num=8,
                                      parameter_broadcast=False)

    predict = Tensor(np.ones([32, 128]), dtype=ms.float32)
    label = Tensor(np.ones([32]), dtype=ms.int32)
    dataset = Dataset(predict, label, 2)
    net = transpose_net(strategy1, strategy2)

    loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    loss.softmax_cross_entropy.shard(((8, 1), (8, 1)))
    opt = Momentum(net.trainable_params(), learning_rate, momentum)
    context.set_context(mode=context.GRAPH_MODE)
    model = Model(net, loss, opt)

    model.train(epoch_size, dataset, dataset_sink_mode=False)


def test_transpose1():
    """
    Feature: distribute operator transpose in auto parallel.
    Description: run transpose distribute operator using model.
    Expectation: compile done without error.
    """
    strategy1 = ((1, 8),)
    strategy2 = ((1, 8),)
    transpose_common(strategy1, strategy2)


def test_transpose2():
    """
    Feature: distribute operator transpose in auto parallel.
    Description: run transpose distribute operator using model.
    Expectation: compile done without error.
    """
    strategy1 = ((1, 4),)
    strategy2 = ((1, 8),)
    transpose_common(strategy1, strategy2)


if __name__ == '__main__':
    test_transpose1()
    test_transpose2()