forked from mindspore-Ecosystem/mindspore
118 lines
3.9 KiB
Python
118 lines
3.9 KiB
Python
# 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 as ms
|
|
from mindspore import context, Tensor, Parameter
|
|
from mindspore.common.api import _executor
|
|
from mindspore.nn import Cell, TrainOneStepCell, Momentum
|
|
from mindspore.ops import operations as P
|
|
|
|
|
|
class Net(Cell):
|
|
def __init__(self, mul_weight, strategy1=None, strategy2=None):
|
|
super().__init__()
|
|
self.mul = P.Mul().shard(strategy1)
|
|
self.mul2 = P.Mul().shard(strategy2)
|
|
self.mul_weight = Parameter(mul_weight, "w1")
|
|
|
|
def construct(self, x, b):
|
|
out = self.mul(x, self.mul_weight)
|
|
out = self.mul2(out, self.mul_weight)
|
|
return out
|
|
|
|
|
|
class Net2(Cell):
|
|
def __init__(self, mul_weight, strategy1=None, strategy2=None):
|
|
super().__init__()
|
|
self.mul = P.Mul().shard(strategy1)
|
|
self.mul2 = P.Mul().shard(strategy2)
|
|
self.mul_weight = Parameter(mul_weight, "w1")
|
|
|
|
def construct(self, x, b):
|
|
out = self.mul(x, self.mul_weight)
|
|
out = self.mul2(x, out)
|
|
return out
|
|
|
|
|
|
class Net3(Cell):
|
|
def __init__(self, mul_weight, strategy1=None, strategy2=None):
|
|
super().__init__()
|
|
self.mul = P.MatMul().shard(strategy1)
|
|
self.mul2 = P.MatMul().shard(strategy2)
|
|
self.mul_weight = Parameter(mul_weight, "w1")
|
|
|
|
def construct(self, x, b):
|
|
out = self.mul(x, self.mul_weight)
|
|
out = self.mul2(out, self.mul_weight)
|
|
return out
|
|
|
|
|
|
_x = Tensor(np.ones([16, 16]), dtype=ms.float32)
|
|
_w = Tensor(np.ones([16, 16]), dtype=ms.float32)
|
|
_b = Tensor(np.ones([16, 16]), dtype=ms.float32)
|
|
|
|
|
|
def compile_net(net):
|
|
optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
|
|
train_net = TrainOneStepCell(net, optimizer)
|
|
train_net.set_auto_parallel()
|
|
train_net.set_train()
|
|
_executor.compile(train_net, _x, _b)
|
|
context.reset_auto_parallel_context()
|
|
|
|
|
|
def test_parameter_same_split():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
|
|
strategy1 = ((16, 1), (16, 1))
|
|
strategy2 = ((16, 1), (16, 1))
|
|
net = Net(_w, strategy1, strategy2)
|
|
compile_net(net)
|
|
|
|
|
|
def test_parameter_different_split():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
|
|
strategy1 = ((16, 1), (16, 1))
|
|
strategy2 = ((4, 4), (4, 4))
|
|
net = Net(_w, strategy1, strategy2)
|
|
with pytest.raises(RuntimeError):
|
|
compile_net(net)
|
|
|
|
|
|
def test_input_same_split():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
|
|
strategy1 = ((16, 1), (16, 1))
|
|
strategy2 = ((16, 1), (16, 1))
|
|
net = Net(_w, strategy1, strategy2)
|
|
compile_net(net)
|
|
|
|
|
|
def test_input_different_split():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
|
|
strategy1 = ((16, 1), (16, 1))
|
|
strategy2 = ((4, 4), (4, 4))
|
|
net = Net2(_w, strategy1, strategy2)
|
|
with pytest.raises(RuntimeError):
|
|
compile_net(net)
|
|
|
|
|
|
def test_parameter_different_group():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
|
|
strategy1 = ((1, 2), (2, 1))
|
|
strategy2 = ((8, 2), (2, 1))
|
|
net = Net3(_w, strategy1, strategy2)
|
|
with pytest.raises(RuntimeError):
|
|
compile_net(net)
|