forked from mindspore-Ecosystem/mindspore
124 lines
4.2 KiB
Python
124 lines
4.2 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.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.common.initializer import initializer
|
|
from mindspore.common.parameter import Parameter
|
|
from mindspore.communication.management import init, NCCL_WORLD_COMM_GROUP, get_rank, get_group_size
|
|
from mindspore.ops import operations as P
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
|
|
init('nccl')
|
|
rank = get_rank()
|
|
size = get_group_size()
|
|
x = np.ones([3, 1, 3, 3]).astype(np.float32) * 0.01 * (rank + 1)
|
|
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.x1 = Parameter(initializer(Tensor(x), x.shape), name='x1')
|
|
self.x2 = Parameter(initializer(Tensor(x), x.shape), name='x2')
|
|
self.x3 = Parameter(initializer(Tensor(x), x.shape), name='x3')
|
|
|
|
self.op0 = "sum"
|
|
self.op1 = "sum"
|
|
self.op2 = "sum"
|
|
|
|
self.all_reduce1 = P.AllReduce(self.op0, group=NCCL_WORLD_COMM_GROUP)
|
|
self.all_reduce2 = P.AllReduce(self.op1, group=NCCL_WORLD_COMM_GROUP)
|
|
self.all_reduce3 = P.AllReduce(self.op2, group=NCCL_WORLD_COMM_GROUP)
|
|
|
|
def construct(self):
|
|
return (self.all_reduce1(self.x1),
|
|
self.all_reduce2(self.x2),
|
|
self.all_reduce3(self.x3))
|
|
|
|
|
|
def test_AllReduce():
|
|
all_reduce = Net()
|
|
output = all_reduce()
|
|
|
|
expect0 = np.ones([3, 1, 3, 3]).astype(np.float32) * 0
|
|
for i in range(size):
|
|
part = np.ones([3, 1, 3, 3]).astype(np.float32) * 0.01 * (i + 1)
|
|
expect0 += part
|
|
diff0 = output[0].asnumpy() - expect0
|
|
error0 = np.ones(shape=expect0.shape) * 1.0e-5
|
|
assert np.all(diff0 < error0)
|
|
assert output[0].shape == expect0.shape
|
|
|
|
expect1 = expect0
|
|
diff1 = output[1].asnumpy() - expect1
|
|
error1 = np.ones(shape=expect1.shape) * 1.0e-5
|
|
assert np.all(diff1 < error1)
|
|
assert output[1].shape == expect1.shape
|
|
|
|
expect2 = expect1
|
|
diff2 = output[2].asnumpy() - expect2
|
|
error2 = np.ones(shape=expect2.shape) * 1.0e-5
|
|
assert np.all(diff2 < error2)
|
|
assert output[2].shape == expect2.shape
|
|
|
|
|
|
class Net2(nn.Cell):
|
|
def __init__(self):
|
|
super(Net2, self).__init__()
|
|
self.x1 = Parameter(initializer(Tensor(x), x.shape), name='x1')
|
|
|
|
self.op0 = "sum"
|
|
self.op1 = "sum"
|
|
self.op2 = "sum"
|
|
|
|
self.all_reduce1 = P.AllReduce(self.op0, group=NCCL_WORLD_COMM_GROUP)
|
|
self.all_reduce2 = P.AllReduce(self.op1, group=NCCL_WORLD_COMM_GROUP)
|
|
self.all_reduce3 = P.AllReduce(self.op2, group=NCCL_WORLD_COMM_GROUP)
|
|
|
|
def construct(self):
|
|
x_ = self.all_reduce1(self.x1)
|
|
y = self.all_reduce2(x_)
|
|
z = self.all_reduce3(y)
|
|
return (x_, y, z)
|
|
|
|
|
|
def test_AllReduce2():
|
|
all_reduce = Net2()
|
|
output = all_reduce()
|
|
|
|
expect0 = np.ones([3, 1, 3, 3]).astype(np.float32) * 0
|
|
for i in range(size):
|
|
part = np.ones([3, 1, 3, 3]).astype(np.float32) * 0.01 * (i + 1)
|
|
expect0 += part
|
|
diff0 = abs(output[0].asnumpy() - expect0)
|
|
error0 = np.ones(shape=expect0.shape) * 1.0e-5
|
|
assert np.all(diff0 < error0)
|
|
assert output[0].shape == expect0.shape
|
|
|
|
expect1 = expect0 * size
|
|
diff1 = abs(output[1].asnumpy() - expect1)
|
|
error1 = np.ones(shape=expect1.shape) * 1.0e-5
|
|
assert np.all(diff1 < error1)
|
|
assert output[1].shape == expect1.shape
|
|
|
|
expect2 = expect1 * size
|
|
diff2 = abs(output[2].asnumpy() - expect2)
|
|
error2 = np.ones(shape=expect2.shape) * 1.0e-5
|
|
assert np.all(diff2 < error2)
|
|
assert output[2].shape == expect2.shape
|