forked from mindspore-Ecosystem/mindspore
127 lines
3.9 KiB
Python
127 lines
3.9 KiB
Python
# Copyright 2022 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.
|
|
# ============================================================================
|
|
|
|
from mindspore.nn import Cell
|
|
from mindspore.common import Tensor
|
|
import mindspore.ops.operations as P
|
|
import mindspore.ops.functional as F
|
|
from mindspore import context
|
|
import numpy as np
|
|
import pytest
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_switch_simplify_avoid_dead_node():
|
|
"""
|
|
Feature: Switch simplify pass.
|
|
Description: If switch simplify pass can't simplify constant tensor condition,
|
|
dead node will exist in backend.
|
|
Expectation: output correct.
|
|
"""
|
|
|
|
class Net(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.op = P.Add()
|
|
|
|
def construct(self, x, y):
|
|
if y != x:
|
|
x = y - 3
|
|
elif x == 4:
|
|
for r in range(2):
|
|
x = 1 / y
|
|
if x > 2:
|
|
y = y + 3
|
|
y = y - y
|
|
y = y * x
|
|
elif y >= x:
|
|
x = x * x
|
|
elif x > y:
|
|
x = y - r
|
|
else:
|
|
y = 2 + x
|
|
for _ in range(2):
|
|
x = x * y
|
|
x = x - 3
|
|
y = y + 2
|
|
if x > 3:
|
|
break
|
|
if x > 2:
|
|
break
|
|
elif x == y:
|
|
if y <= x:
|
|
y = x / 2
|
|
x = 3 + y
|
|
x = x * 2
|
|
elif x == 2:
|
|
x = y * y
|
|
elif x < y:
|
|
y = 2 * y
|
|
elif x != 2:
|
|
y = x * y
|
|
while x != 5:
|
|
break
|
|
return self.op(x, y)
|
|
|
|
x = np.array([4], np.float32)
|
|
y = np.array([4], np.float32)
|
|
net = Net()
|
|
out = net(Tensor(x), Tensor(y))
|
|
grad_net = F.grad(net, grad_position=(0, 1))
|
|
fgrad = grad_net(Tensor(x), Tensor(y))
|
|
sgrad_net = F.grad(grad_net)
|
|
sgrad = sgrad_net(Tensor(x), Tensor(y))
|
|
assert np.allclose(out.asnumpy(), np.array([-19.75], np.float32))
|
|
assert np.allclose(fgrad[0].asnumpy(), np.array([0.], np.float32))
|
|
assert np.allclose(fgrad[1].asnumpy(), np.array([-2.03125], np.float32))
|
|
assert np.allclose(sgrad.asnumpy(), np.array([0.], np.float32))
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_condition():
|
|
"""
|
|
Feature: control flow function.
|
|
Description: Switch condition is tensor determinate condition.
|
|
Expectation: Null.
|
|
"""
|
|
|
|
class Net(Cell):
|
|
def construct(self, x, y):
|
|
if x < 5:
|
|
x = y + 2
|
|
for p in range(1):
|
|
x = p * x
|
|
if x >= y:
|
|
x = 2 * x
|
|
|
|
if x <= 5:
|
|
x = 2 + y
|
|
elif x >= 2:
|
|
x = x * y
|
|
|
|
return x + y
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
x = np.array([3], np.float32)
|
|
y = np.array([1], np.float32)
|
|
net = Net()
|
|
out = net(Tensor(x), Tensor(y))
|
|
assert np.allclose(out.asnumpy(), np.array([4.], np.float32))
|