forked from mindspore-Ecosystem/mindspore
88 lines
2.8 KiB
Python
88 lines
2.8 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 _cell_graph_executor
|
|
from mindspore.ops import composite as C
|
|
from mindspore.ops import operations as P
|
|
|
|
|
|
grad_all = C.GradOperation(get_all=True)
|
|
|
|
|
|
class GradWrap(nn.Cell):
|
|
def __init__(self, network):
|
|
super(GradWrap, self).__init__()
|
|
self.network = network
|
|
|
|
def construct(self, x, y):
|
|
return grad_all(self.network)(x, y)
|
|
|
|
|
|
def compile_net(net, x, y):
|
|
net.set_auto_parallel()
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|
|
|
|
|
|
def test_sum_as_loss():
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy0, strategy1):
|
|
super().__init__()
|
|
self.fc_nobias = P.MatMul(transpose_b=True).shard(strategy0)
|
|
self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy1)
|
|
|
|
def construct(self, x, y):
|
|
out = self.fc_nobias(x, y)
|
|
out = self.reduce_sum(out, (0, 1))
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=16, global_rank=0)
|
|
strategy0 = ((4, 1), (4, 1))
|
|
strategy1 = ((4, 1),)
|
|
net = GradWrap(Net(strategy0, strategy1))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
compile_net(net, x, y)
|
|
|
|
|
|
def test_sum_as_loss2():
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy0, strategy1):
|
|
super().__init__()
|
|
self.fc_nobias = P.MatMul(transpose_b=True).shard(strategy0)
|
|
self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy1)
|
|
|
|
def construct(self, x, y):
|
|
out = self.fc_nobias(x, y)
|
|
out = self.reduce_sum(out, (0, 1))
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=16, global_rank=0)
|
|
strategy0 = ((4, 1), (4, 1))
|
|
strategy1 = ((1, 1),)
|
|
net = GradWrap(Net(strategy0, strategy1))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
compile_net(net, x, y)
|