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

112 lines
3.9 KiB
Python

# 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
from mindspore import context
from mindspore.common.api import _executor
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.parallel._utils import _reset_op_id
from mindspore.train import Model, 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 AllToAllNet(nn.Cell):
def __init__(self, strategy1):
super(AllToAllNet, self).__init__()
self.matmul = P.MatMul().set_strategy(((1, 1), (1, 8)))
self.matmul_weight = Parameter(Tensor(np.ones([128, 256]), dtype=ms.float32), name="weight")
self.transpose1 = P.Transpose().set_strategy(strategy1)
def construct(self, x):
x = self.matmul(x, self.matmul_weight)
x = self.transpose1(x, (1, 0))
return x
def all_to_all_net(strategy1):
return AllToAllNet(strategy1=strategy1)
def all_to_all_common(strategy1):
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)
predict = Tensor(np.ones([32, 128]), dtype=ms.float32)
label = Tensor(np.ones([32]), dtype=ms.int32)
dataset = Dataset(predict, label, 2)
net = all_to_all_net(strategy1)
loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
loss.softmax_cross_entropy.set_strategy(((8, 1), (8, 1)))
loss.one_hot.set_strategy(((8, 1), (), ()))
opt = Momentum(net.trainable_params(), learning_rate, momentum)
model = Model(net, loss, opt)
model.train(epoch_size, dataset, dataset_sink_mode=False)
strategys = _executor._get_strategy(model._train_network)
return strategys
def test_all_to_all():
strategy1 = ((8, 1),)
context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
_reset_op_id()
strategys = all_to_all_common(strategy1)
print(strategys)
expect_dict = {'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits'
'/SoftmaxCrossEntropyWithLogits-op3': [[8, 1], [8, 1]],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits/'
'OneHot-op4': [[8, 1], [], []],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/Transpose-op1': [
[8, 1]],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/MatMul-op0': [
[1, 1], [1, 8]]}
assert strategys == expect_dict
context.set_context(save_graphs=False)
if __name__ == '__main__':
test_all_to_all()