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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 tests.common.base import get_rtol_atol
- from akg.topi.util import get_const_tuple
- from akg.ops.math.exp_ad import exp_ad
- from akg.utils import kernel_exec as utils
- from tests.common.tensorio import compare_tensor
-
- def exp_ad_run(shape, dtype, attrs):
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = utils.op_build_test(exp_ad, [shape, shape], [dtype, dtype], kernel_name=kernel_name, attrs=attrs, tuning=t)
- expect, head_np, input_np = gen_data(dtype, shape)
- if t:
- output = np.full(expect.shape, np.nan, dtype)
- return mod, expect, (head_np, input_np, output)
- else:
- return mod
- else:
- mod = utils.op_build_test(exp_ad, [shape, shape], [dtype, dtype], kernel_name='exp_ad', attrs=attrs)
- expect, head_np, input_np = gen_data(dtype, shape)
- output = np.full(expect.shape, np.nan, dtype)
- output = utils.mod_launch(mod, (head_np, input_np, output), expect=expect)
- rtol, atol = get_rtol_atol("exp", dtype)
- return (head_np, input_np), output, expect, compare_tensor(output, expect, rtol=rtol, atol = atol)
-
-
- def gen_data(dtype, shape):
- input_np = np.random.uniform(low=-1.0, high=1.0, size=get_const_tuple(shape)).astype(dtype)
- head_np = np.random.uniform(low=-1.0, high=1.0, size=shape).astype(dtype)
- expect = np.exp(input_np) * head_np
-
- return expect, head_np, input_np
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