You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
109 lines
3.4 KiB
109 lines
3.4 KiB
# 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
|
|
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, 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)
|
|
|
|
|
|
def compile_net(net, x, y, b):
|
|
net.set_auto_parallel()
|
|
net.set_train()
|
|
_executor.compile(net, x, y, b)
|
|
|
|
|
|
# it has not redistribution
|
|
def test_tensoradd_reshape_matmul():
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.add = P.Add().shard(strategy1)
|
|
self.reshape = P.Reshape()
|
|
self.matmul = P.MatMul().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.add(x, y)
|
|
out = self.reshape(out, (256, 16))
|
|
out = self.matmul(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=64, global_rank=0, gradients_mean=True)
|
|
strategy1 = ((8, 1, 1), (8, 1, 1))
|
|
strategy2 = ((8, 1), (1, 8))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
context.set_context(save_graphs=True)
|
|
|
|
x = Tensor(np.ones([32, 8, 16]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 8, 16]), dtype=ms.float32)
|
|
b = Tensor(np.ones([16, 16]), dtype=ms.float32)
|
|
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_two_matmul():
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super().__init__()
|
|
self.matmul1 = P.MatMul().shard(strategy1)
|
|
self.matmul2 = P.MatMul().shard(strategy2)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.matmul1(x, y)
|
|
out = self.matmul2(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=64, global_rank=0, gradients_mean=True)
|
|
strategy1 = ((8, 8), (8, 1))
|
|
strategy2 = ((8, 1), (1, 1))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
context.set_context(save_graphs=True)
|
|
|
|
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)
|
|
|
|
compile_net(net, x, y, b)
|