forked from mindspore-Ecosystem/mindspore
86 lines
3.2 KiB
Python
86 lines
3.2 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 pytest
|
|
import numpy as np
|
|
import mindspore.nn as nn
|
|
import mindspore.ops.operations as P
|
|
import mindspore.ops.functional as F
|
|
from mindspore import context, Tensor
|
|
from mindspore.common import dtype as mstype
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
|
|
|
|
|
class NpuFloatNet(nn.Cell):
|
|
""" NpuFloat definition, base on the related code in test_math_ops.py."""
|
|
|
|
def __init__(self):
|
|
super(NpuFloatNet, self).__init__()
|
|
self.mul = P.Mul()
|
|
self.alloc_status = P.NPUAllocFloatStatus()
|
|
self.get_status = P.NPUGetFloatStatus()
|
|
self.clear_status = P.NPUClearFloatStatus()
|
|
self.fill = P.Fill()
|
|
self.shape_op = P.Shape()
|
|
self.select = P.Select()
|
|
self.less = P.Less()
|
|
self.cast = P.Cast()
|
|
self.dtype = P.DType()
|
|
self.reduce_sum = P.ReduceSum(keep_dims=True)
|
|
self.sub = P.Sub()
|
|
self.neg = P.Neg()
|
|
|
|
def construct(self, x):
|
|
init = self.alloc_status()
|
|
clear_status = self.clear_status(init)
|
|
x = F.depend(x, clear_status) # let x depend on clear_status
|
|
res = self.sub(x, self.neg(x))
|
|
init = F.depend(init, res) # let get_status depend on res
|
|
get_status = self.get_status(init)
|
|
# let reduce_sum depend on get_statusk
|
|
init = F.depend(init, get_status)
|
|
flag_sum = self.reduce_sum(init, (0,))
|
|
base = self.cast(self.fill(self.dtype(
|
|
res), self.shape_op(res), 0.0), self.dtype(flag_sum))
|
|
cond = self.less(base, flag_sum)
|
|
out = self.select(cond, self.cast(base, self.dtype(res)), res)
|
|
return out
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_float_not_overflow():
|
|
input_data = Tensor(np.full((8, 5, 3, 1), 655, dtype=np.float16), dtype=mstype.float16)
|
|
net = NpuFloatNet()
|
|
out = net(input_data)
|
|
# not overflow, we should got expected output.
|
|
expect = Tensor(np.full((8, 5, 3, 1), 655 * 2,
|
|
dtype=np.float16), dtype=mstype.float16)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_float_overflow():
|
|
input_data = Tensor(np.full((8, 5, 3, 1), 65504, dtype=np.float16), dtype=mstype.float16)
|
|
net = NpuFloatNet()
|
|
out = net(input_data)
|
|
# all zero if overflowed.
|
|
assert np.all(out.asnumpy() == 0)
|