forked from mindspore-Ecosystem/mindspore
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
# Copyright 2021 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 pytest
|
|
from mindspore import context
|
|
from mindspore import Tensor, nn
|
|
from mindspore.ops import composite as C
|
|
from mindspore.ops import operations as P
|
|
from mindspore.common import dtype as mstype
|
|
|
|
grad_all = C.GradOperation(get_all=True)
|
|
|
|
# Although we don't transform for to while any more, we keep this test case.
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_single_for_01():
|
|
class SingleForNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.mul = P.Mul()
|
|
|
|
def construct(self, x, y, z):
|
|
x = self.add(x, y)
|
|
for _ in range(0, 3):
|
|
z = self.add(z, x)
|
|
y = self.mul(z, y)
|
|
return y
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
|
|
def construct(self, *inputs):
|
|
return grad_all(self.net)(*inputs)
|
|
|
|
x = Tensor([2], mstype.int32)
|
|
y = Tensor([5], mstype.int32)
|
|
z = Tensor([4], mstype.int32)
|
|
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
for_net = SingleForNet()
|
|
net = GradNet(for_net)
|
|
graph_forward_res = for_net(x, y, z)
|
|
graph_backward_res = net(x, y, z)
|
|
|
|
# pynative mode
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
for_net = SingleForNet()
|
|
net = GradNet(for_net)
|
|
pynative_forward_res = for_net(x, y, z)
|
|
pynative_backward_res = net(x, y, z)
|
|
|
|
assert graph_forward_res == pynative_forward_res
|
|
assert graph_backward_res == pynative_backward_res
|