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

116 lines
3.6 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.ops import composite as C
from mindspore.ops import operations as P
from tests.ut.python.ops.test_math_ops import VirtualLoss
grad_all = C.GradOperation(get_all=True)
class NetWithLoss(nn.Cell):
def __init__(self, network):
super(NetWithLoss, self).__init__()
self.loss = VirtualLoss()
self.network = network
def construct(self, x, w1, w2):
predict = self.network(x, w1, w2)
return self.loss(predict)
class GradWrap(nn.Cell):
def __init__(self, network):
super(GradWrap, self).__init__()
self.network = network
def construct(self, x, w1, w2):
return grad_all(self.network)(x, w1, w2)
class NetConv(nn.Cell):
def __init__(self,
cin,
cout,
kernel_size,
stride=1,
pad_mode='pad',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros',
strategy=None):
super(NetConv, self).__init__()
self.conv = nn.Conv2d(cin,
cout,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init)
self.conv.conv2d.set_strategy(strategy)
def construct(self, input_x):
return self.conv(input_x)
def test_batch():
class Net(nn.Cell):
def __init__(self, strategy1, strategy2, strategy3):
super().__init__()
self.conv1 = NetConv(16, 8, (3, 3), bias_init='zeros', strategy=strategy1)
self.mul1 = P.Mul().set_strategy(strategy2)
self.conv2 = NetConv(8, 64, (9, 9), bias_init='zeros', strategy=strategy1)
self.mul2 = P.Mul().set_strategy(strategy3)
def construct(self, x, w1, w2):
out1 = self.conv1(x)
out2 = self.mul1(out1, w1)
out3 = self.conv2(out2)
out4 = self.mul2(out3, w2)
return out4
context.set_auto_parallel_context(device_num=8, global_rank=0)
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
strategy2 = ((1, 1, 1, 8), (1, 1, 1, 8))
strategy3 = ((4, 1, 1, 2), (4, 1, 1, 2))
net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
net.set_auto_parallel()
x = Tensor(np.ones([128, 16, 34, 34]), dtype=ms.float32)
w1 = Tensor(np.ones([128, 8, 32, 32]), dtype=ms.float32)
w2 = Tensor(np.ones([128, 64, 24, 24]), dtype=ms.float32)
_executor.compile(net, x, w1, w2)
if __name__ == '__main__':
test_batch()