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- # Copyright 2019 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
- from akg.utils import kernel_exec as utils
- from akg.ops.math import tanh
- from tests.common.tensorio import compare_tensor
- from tests.common.base import get_rtol_atol
- from tests.common.gen_random import random_gaussian
-
- def tanh_execute(shape, dtype, attrs=None):
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = tanh_compile(shape, dtype, attrs, kernel_name=kernel_name, tuning=t)
- if t:
- expect, input, output = gen_data(dtype, shape)
- return mod, expect, (input, output)
- else:
- return mod
- else:
- mod = tanh_compile(shape, dtype, attrs)
- expect, input, output = gen_data(dtype, shape)
- output = utils.mod_launch(mod, (input, output), expect=expect) # unified launch
- rtol, atol = get_rtol_atol("tanh", dtype)
- return input, output, expect, compare_tensor(output, expect, rtol=rtol, atol=atol, equal_nan=True)
-
-
- def gen_data(dtype, shape):
- input = random_gaussian(shape, miu=1, sigma=0.1).astype(dtype)
- expect = np.tanh(input)
- output = np.full(expect.shape, np.nan, dtype)
- return expect, input, output
-
-
- def tanh_compile(shape, dtype, attrs, kernel_name='tanh', tuning=False):
- return utils.op_build_test(tanh.tanh, [shape], [dtype], kernel_name=kernel_name, attrs=attrs, tuning=tuning)
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