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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 tests.common.test_op.erf_ad import erf_ad
- from tests.common.tensorio import compare_tensor
- from tests.common.gen_random import random_gaussian
-
- def erf_diff(x):
- """compute gradient of x by `2/sqrt(pi) * e^{-2x^2}`"""
- return 1.12837916709551257 * np.exp(-np.square(x))
-
- def erf_ad_run(shape, 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(erf_ad, [shape, shape], [dtype, dtype], kernel_name=kernel_name, attrs=attrs, tuning=t)
- if t:
- expect, head_np, input_np, output = gen_data(dtype, shape)
- return mod, expect, (input_np, head_np, output)
- else:
- return mod
- else:
- mod = utils.op_build_test(erf_ad, [shape, shape], [dtype, dtype], kernel_name="erf", attrs=attrs)
- expect, head_np, input_np, output = gen_data(dtype, shape)
- output = utils.mod_launch(mod, (head_np, input_np, output), expect=expect)
- return input_np, output, expect, compare_tensor(output, expect, rtol=5e-02, atol=0.05)
-
-
- def gen_data(dtype, shape):
- support_list = {"float16": np.float16, "float32": np.float32}
- input_np = random_gaussian(shape, miu=0, sigma=0.1).astype(support_list[dtype])
- idx = np.where(np.logical_and(input_np < 0.5, input_np >= 0))
- input_np[idx] = input_np[idx] + 0.5
- idx = np.where(np.logical_and(input_np > -0.5, input_np < 0))
- input_np[idx] = input_np[idx] - 0.5
- expect = erf_diff(input_np)
- head_np = random_gaussian(shape, miu=0, sigma=0.1).astype(support_list[dtype])
- expect = expect * head_np
- output = np.full(shape, 1, support_list[dtype])
- return expect, head_np, input_np, output
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