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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.
-
- from tests.common.tensorio import compare_tensor
- import numpy as np
- from akg.utils import kernel_exec as utils
- from . import avgpool_run
- from . import avgpool_grad_run
- from tests.common.test_op.avgpool_ad import avgpool_ad
- from tests.common.test_op.avgpool_ad import avgpool_ad_no_custom_diff_manual_schedule
- from tests.common.gen_random import random_gaussian
-
- def avgpool_ad_run(shape, kernel, stride, pad, dtype, polyhedral=False, attrs=None):
- support_list = {"float16": np.float16, "float32": np.float32}
- if attrs is None:
- attrs = {'loop_partition_unroll': True}
- else:
- attrs['loop_partition_unroll'] = True
-
- kernel_name = 'avgpool_ad'
- if polyhedral:
- avgpool = avgpool_ad
- else:
- kernel_name = kernel_name + "_manual_schedule"
- avgpool = avgpool_ad_no_custom_diff_manual_schedule
-
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- input = random_gaussian(shape, miu=1, sigma=0.1).astype(support_list[dtype])
- y = avgpool_run.benchmark(input, kernel, stride, pad)
- mod = utils.op_build_test(avgpool, [y.shape, shape], [dtype, dtype], op_attrs=[kernel, stride, pad],
- kernel_name=kernel_name, attrs=attrs, log_cce=True, dump_code=True, tuning=t)
- if t:
- expect, head, output = gen_data(dtype, input, kernel, pad, stride, support_list, y)
- return mod, expect, (head, input, output)
- else:
- return mod
- else:
- input = random_gaussian(shape, miu=1, sigma=0.1).astype(support_list[dtype])
- y = avgpool_run.benchmark(input, kernel, stride, pad)
- mod = utils.op_build_test(avgpool, [y.shape, shape], [dtype, dtype], op_attrs=[kernel, stride, pad],
- kernel_name=kernel_name, attrs=attrs, log_cce=True, dump_code=True)
- expect, head, output = gen_data(dtype, input, kernel, pad, stride, support_list, y)
- output = utils.mod_launch(mod, [head, input, output], expect=expect)
-
- return [head, input], output, expect, compare_tensor(output, expect, rtol=5e-03, atol=5e-03, equal_nan=True)
-
-
- def gen_data(dtype, input, kernel, pad, stride, support_list, y):
- head = random_gaussian(y.shape, miu=1, sigma=0.1).astype(support_list[dtype])
- expect = avgpool_grad_run.benchmark(dtype, input, y, head, kernel, stride, pad)
- out_shape = expect.shape
- output = np.full(out_shape, 0, dtype)
- return expect, head, output
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