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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.
-
- from akg.utils import kernel_exec as utils
- from tests.common.tensorio import compare_tensor
- import numpy as np
- from tests.common.gen_random import random_gaussian
- from tests.common.test_op.sub_ad import sub_ad
-
-
- def sub_ad_run(ashape, bshape, dtype, kernel_name, attrs):
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- a, b, out = gen_input_data(ashape, bshape, dtype)
- mod = utils.op_build_test(sub_ad, [out.shape, ashape, bshape], [dtype, dtype, dtype], kernel_name=kernel_name,
- attrs=attrs, tuning=t)
- if t:
- expect, head_np, output = gen_data(dtype, out)
- return mod, expect, (head_np, a, b, output)
- else:
- return mod
- else:
- a, b, out = gen_input_data(ashape, bshape, dtype)
- expect, head_np, output = gen_data(dtype, out)
- mod = utils.op_build_test(sub_ad, [out.shape, ashape, bshape], [dtype, dtype, dtype], kernel_name=kernel_name,
- attrs=attrs)
- output = utils.mod_launch(mod, (head_np, a, b, output), expect=expect)
- return (head_np, a, b), output, expect, compare_tensor(output, expect, atol=0.1)
-
-
- def gen_data(dtype, out):
- head_np = np.random.uniform(low=-1.0, high=1.0, size=out.shape).astype(out.dtype)
- expect = head_np
- output = np.full(expect.shape, np.nan, dtype)
- return expect, head_np, output
-
-
- def gen_input_data(ashape, bshape, dtype):
- a = random_gaussian(ashape, miu=1, sigma=0.1).astype(dtype)
- b = random_gaussian(bshape, miu=1, sigma=0.1).astype(dtype)
- out = np.subtract(a, b)
- return a, b, out
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