|
- # 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.
-
- """axpy_run"""
- import numpy as np
- from tests.common.tensorio import compare_tensor
- from akg.utils import kernel_exec as utils
- from tests.common.test_op import axpy
- from tests.common.gen_random import random_gaussian
-
- def axpy_run(shape1, shape2, alpha, dtype, attrs):
- """axpy_run implementation"""
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = utils.op_build_test(axpy.axpy, [shape1, shape2], [dtype, dtype], kernel_name=kernel_name,
- op_attrs=[alpha], attrs=attrs, tuning=t)
- if t:
- args, exp_output, inputs1, inputs2 = gen_data(alpha, dtype, shape1, shape2)
- return mod, exp_output, args
- else:
- return mod
- else:
- mod = utils.op_build_test(axpy.axpy, [shape1, shape2], [dtype, dtype], kernel_name='axpy', op_attrs=[alpha],
- attrs=attrs)
- args, exp_output, inputs1, inputs2 = gen_data(alpha, dtype, shape1, shape2)
- # result_tvm
- acu_output = utils.mod_launch(mod, args, expect=exp_output)
- # compare result
- testcase_result = compare_tensor(acu_output, exp_output, rtol=5e-03, atol=5e-03, equal_nan=True)
-
- return [inputs1, inputs2], acu_output, exp_output, testcase_result
- # return [inputs1,inputs2],acu_output,exp_output,False
-
-
- def gen_data(alpha, dtype, shape1, shape2):
- # result_numpy
- inputs1 = random_gaussian(shape1, miu=1, sigma=10.0).astype(dtype)
- inputs2 = random_gaussian(shape2, miu=1, sigma=10.0).astype(dtype)
- exp_output = np.add(np.dot(inputs1, alpha), inputs2)
- # inputs and output to hold the data
- output = np.full(shape1, np.nan, dtype)
- args = [inputs1, inputs2, output]
- return args, exp_output, inputs1, inputs2
|
