Yi Huaijie
5 years ago
parent
5b14292f69
commit
2eb739de6e
@ -1,76 +0,0 @@
|
||||
# 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 pytest
|
||||
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.ops import operations as P
|
||||
import mindspore._ms_mpi as mpi
|
||||
# run comand:
|
||||
# mpirun -output-filename log -merge-stderr-to-stdout -np 3 python test_reduce_scatter.py
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
||||
context.set_mpi_config(enable_mpi=True)
|
||||
|
||||
class Net(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.op = "sum"
|
||||
|
||||
self.reducescatter = P.HostReduceScatter(op=self.op, group=[0,1,2])
|
||||
|
||||
def construct(self, x):
|
||||
return self.reducescatter(x)
|
||||
|
||||
class AllGatherNet(nn.Cell):
|
||||
def __init__(self):
|
||||
super(AllGatherNet, self).__init__()
|
||||
self.hostallgather = P.HostAllGather(group=(0, 1, 2))
|
||||
|
||||
def construct(self, x):
|
||||
return self.hostallgather(x)
|
||||
|
||||
def test_net_reduce_scatter():
|
||||
x = np.arange(12).astype(np.float32) * 0.1
|
||||
|
||||
reducescatter = Net()
|
||||
rankid = mpi.get_rank_id()
|
||||
print("self rankid:", rankid)
|
||||
output = reducescatter(Tensor(x, mstype.float32))
|
||||
print("output:\n", output)
|
||||
if rankid == 0:
|
||||
expect_result = np.arange(4).astype(np.float32) * 0.3
|
||||
if rankid == 1:
|
||||
expect_result = np.arange(4, 8).astype(np.float32) * 0.3
|
||||
if rankid == 2:
|
||||
expect_result = np.arange(8, 12).astype(np.float32) * 0.3
|
||||
diff = abs(output.asnumpy() - expect_result)
|
||||
error = np.ones(shape=expect_result.shape) * 1.0e-6
|
||||
assert np.all(diff < error)
|
||||
|
||||
allgather = AllGatherNet()
|
||||
allgather_output = allgather(output)
|
||||
print("allgather result:\n", allgather_output)
|
||||
expect_allgather_result = np.arange(12).astype(np.float32) * 0.3
|
||||
diff = abs(allgather_output.asnumpy() - expect_allgather_result)
|
||||
error = np.ones(shape=expect_allgather_result.shape) * 1.0e-6
|
||||
assert np.all(diff < error)
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_net_reduce_scatter()
|
||||
Loading…
Reference in new issue