forked from mindspore-Ecosystem/mindspore
104 lines
3.2 KiB
Python
104 lines
3.2 KiB
Python
# Copyright 2020 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 re
|
|
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
|
|
from mindspore.context import ParallelMode
|
|
from tests.dataset_mock import MindData
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
|
|
|
|
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):
|
|
super(AllToAllNet, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
self.matmul_weight = Parameter(Tensor(np.ones([128, 32]), dtype=ms.float32), name="weight")
|
|
self.transpose1 = P.Transpose()
|
|
|
|
def construct(self, x):
|
|
x = self.matmul(x, self.matmul_weight)
|
|
x = self.transpose1(x, (1, 0))
|
|
return x
|
|
|
|
|
|
def all_to_all_net():
|
|
return AllToAllNet()
|
|
|
|
|
|
def all_to_all_common():
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, device_num=1, global_rank=0)
|
|
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()
|
|
|
|
loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
|
|
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_one_dev():
|
|
_reset_op_id()
|
|
strategies = all_to_all_common()
|
|
for (k, v) in strategies.items():
|
|
if re.search('SoftmaxCrossEntropyWithLogits-op', k) is not None:
|
|
assert v == [[1, 1], [1, 1]]
|
|
elif re.search('Transpose-op', k) is not None:
|
|
assert v == [[1, 1]]
|
|
elif re.search('MatMul-op', k) is not None:
|
|
assert v == [[1, 1], [1, 1]]
|