forked from mindspore-Ecosystem/mindspore
67 lines
1.9 KiB
Python
67 lines
1.9 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.
|
|
# ============================================================================
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
from mindspore import dtype
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
|
|
|
|
class NetCeil(nn.Cell):
|
|
def __init__(self):
|
|
super(NetCeil, self).__init__()
|
|
self.ceil = P.Ceil()
|
|
|
|
def construct(self, x):
|
|
return self.ceil(x)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_ceil_fp32():
|
|
"""
|
|
Feature: Ceil gpu kernel
|
|
Description: test the ceil.
|
|
Expectation: match to np benchmark.
|
|
"""
|
|
ceil = NetCeil()
|
|
x = np.random.rand(3, 8).astype(np.float32)
|
|
output = ceil(Tensor(x, dtype=dtype.float32))
|
|
expect = np.ceil(x)
|
|
assert np.allclose(output.asnumpy(), expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_ceil_fp16():
|
|
"""
|
|
Feature: Ceil gpu kernel
|
|
Description: test the ceil.
|
|
Expectation: match to np benchmark.
|
|
"""
|
|
ceil = NetCeil()
|
|
x = np.random.rand(3, 8).astype(np.float16)
|
|
output = ceil(Tensor(x, dtype=dtype.float16))
|
|
expect = np.ceil(x)
|
|
assert np.allclose(output.asnumpy(), expect)
|