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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.tensorio import compare_tensor
- from akg.utils import kernel_exec as utils
- from akg.ops.math import realdiv
- from akg.utils.dsl_create import produce_shapes
- from tests.common.gen_random import random_gaussian
-
- def gen_expect(input1, input2):
- a, b, out_shape = produce_shapes(input1.shape, input2.shape)
- n_input1 = np.broadcast_to(input1, out_shape)
- n_input2 = np.broadcast_to(input2, out_shape)
-
- sign2 = np.sign(n_input2)
- input2 = np.add(np.abs(n_input2), 1)
- input2 = np.multiply(n_input2, sign2)
- expect = np.divide(n_input1, n_input2)
- return expect
-
-
- def realdiv_run(shape, shape2, dtype, kernel_name, attrs, cce_path="./"):
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = utils.op_build_test(realdiv.realdiv, [shape, shape2], [dtype, dtype], kernel_name=kernel_name,
- attrs=attrs, tuning=t)
- if t:
- expect, input1, input2, output = gen_data(dtype, shape, shape2)
- return mod, expect, (input1, input2, output)
- else:
- return mod
- else:
- expect, input1, input2, output = gen_data(dtype, shape, shape2)
- mod = utils.op_build_test(realdiv.realdiv, [shape, shape2], [dtype, dtype], kernel_name=kernel_name,
- attrs=attrs)
- output = utils.mod_launch(mod, (input1, input2, output), expect=expect)
-
- return (input1, input2), output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
-
-
- def gen_data(dtype, shape, shape2):
- support_list = {"float16": np.float16, "float32": np.float32}
- input1 = random_gaussian(shape, miu=1, sigma=0.1).astype(support_list[dtype])
- input2 = random_gaussian(shape2, miu=1, sigma=0.1).astype(support_list[dtype])
- expect = gen_expect(input1, input2)
- output = np.full(expect.shape, np.nan, dtype)
- return expect, input1, input2, output
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