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- # 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 numpy as np
- from akg.utils import kernel_exec as utils
- from tests.common.test_op import eltwise
- from tests.common.tensorio import compare_tensor
- from tests.common.base import get_rtol_atol
- from tests.common.gen_random import random_gaussian
-
- def eltwise_execute(shape, dtype, n, mod, coeff, attrs):
- # Result_eltwise
- module = eltwise_compile(shape, dtype, n, mod, coeff, attrs)
- exp_output, inputs, args = gen_data(shape, dtype, n, mod, coeff)
- acu_output = utils.mod_launch(module, tuple(args), expect=exp_output)
- # compare result
- rtol, atol = get_rtol_atol("eltwise", dtype)
- TestCase_Result = compare_tensor(acu_output, exp_output, rtol=rtol, atol=atol, equal_nan=True)
- return inputs, acu_output, exp_output, TestCase_Result
-
-
- def gen_data(shape, dtype, n, mod, coeff):
- inputs = []
- for i in range(n):
- input = random_gaussian(shape, miu=1, sigma=0.1).astype(dtype)
- inputs.append(input)
- if mod == 0:
- exp_output = inputs[0]
- for i in range(1, n):
- exp_output = np.multiply(exp_output, inputs[i])
- if mod == 1 and len(coeff) == 0:
- exp_output = np.sum(inputs, axis=0)
- if mod == 1 and len(coeff) == n:
- exp_output = inputs[0] * coeff[0]
- for i in range(1, n):
- exp_output = exp_output + inputs[i] * coeff[i]
- if mod == 2:
- exp_output = inputs[0]
- for i in range(1, n):
- exp_output = np.maximum(exp_output, inputs[i])
- # inputs and output to hold the data
- output = np.full(shape, np.nan, dtype)
- args = inputs + [output]
- return exp_output, inputs, args
-
- def eltwise_compile(shape, dtype, n, mod, coeff, attrs, kernel_name="eltwise", tuning=False):
- shapes = []
- for i in range(n):
- shapes.append(shape)
- return utils.op_build_test(eltwise.eltwise, [shapes], [dtype], op_attrs=[mod, coeff], kernel_name=kernel_name, attrs=attrs, tuning=tuning)
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