lichenever
5 years ago
parent
1c3fc5c49b
commit
d22f506431
@ -0,0 +1,76 @@
|
||||
# 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 numpy as np
|
||||
|
||||
import mindspore as ms
|
||||
from mindspore import Tensor
|
||||
from mindspore import context
|
||||
from mindspore.common.api import _executor
|
||||
from mindspore.common.parameter import Parameter
|
||||
from mindspore.ops import composite as C
|
||||
from mindspore.ops import operations as P
|
||||
import mindspore.nn as nn
|
||||
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, y, b):
|
||||
predict = self.network(x, y, b)
|
||||
return self.loss(predict)
|
||||
|
||||
|
||||
class GradWrap(nn.Cell):
|
||||
def __init__(self, network):
|
||||
super(GradWrap, self).__init__()
|
||||
self.network = network
|
||||
|
||||
def construct(self, x, y, b):
|
||||
return grad_all(self.network)(x, y, b)
|
||||
|
||||
|
||||
# model_parallel test
|
||||
def test_two_matmul_batchnorm_ex():
|
||||
class Net(nn.Cell):
|
||||
def __init__(self, strategy1, strategy2):
|
||||
super().__init__()
|
||||
self.matmul1 = P.MatMul().set_strategy(strategy1)
|
||||
self.norm = P.FusedBatchNormEx()
|
||||
self.gamma = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="gamma")
|
||||
self.beta = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="beta")
|
||||
self.mean = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="mean")
|
||||
self.var = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="var")
|
||||
self.matmul2 = P.MatMul().set_strategy(strategy2)
|
||||
|
||||
def construct(self, x, y, b):
|
||||
out = self.matmul1(x, y)
|
||||
out = self.norm(out, self.gamma, self.beta, self.mean, self.var)[0]
|
||||
out = self.matmul2(out, b)
|
||||
return out
|
||||
|
||||
context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8)
|
||||
strategy1 = ((4, 2), (2, 1))
|
||||
strategy2 = ((1, 8), (8, 1))
|
||||
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
||||
net.set_auto_parallel()
|
||||
x = Tensor(np.ones([128, 32]), dtype=ms.float32)
|
||||
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
||||
b = Tensor(np.ones([64, 64]), dtype=ms.float32)
|
||||
_executor.compile(net, x, y, b)
|
||||
Loading…
Reference in new issue