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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.
-
- """run function for xdivy_grad"""
-
- 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 xdivy_grad
- from tests.common.gen_random import random_gaussian
- from tests.common.base import get_rtol_atol
- from akg.utils.dsl_create import produce_shapes
-
-
- def xdivy_grad_run(shape1, shape2, dtype, attrs):
- _, _, grad_shape = produce_shapes(shape1, shape2)
- mod = utils.op_build_test(xdivy_grad.xdivy_grad,
- [shape1, shape2, grad_shape],
- [dtype, dtype, dtype],
- kernel_name="xdivy_grad", attrs=attrs)
- expects, inputs, outputs = gen_data(shape1, shape2, dtype)
- reses = utils.mod_launch(
- mod, (*inputs, *outputs), expect=expects,
- outputs=(-2, -1))
-
- rtol, atol = get_rtol_atol("xdivy_grad", dtype)
- TestCase_Results = list(map(lambda x, y: compare_tensor(
- x, y, rtol=rtol, atol=atol, equal_nan=False), reses, expects))
- return inputs, reses, expects, all(TestCase_Results)
-
-
- def gen_data(shape1, shape2, dtype):
- x1 = random_gaussian(shape1, miu=1, sigma=0.3).astype(dtype)
- x2 = random_gaussian(shape2, miu=1, sigma=0.3).astype(dtype)
- shape1, shape2, fout_shape = produce_shapes(shape1, shape2)
- dy = random_gaussian(fout_shape, miu=1, sigma=0.3).astype(dtype)
- rx, ry = xdivy_grad.broadcast_gradient_args(shape1, shape2)
- dx1_bc = np.where(np.equal(x1, 0.),
- np.zeros_like(np.multiply(x1, x2)),
- np.divide(1., x2)) * dy
- dx2_bc = np.where(np.equal(x1, 0.),
- np.zeros_like(np.multiply(x1, x2)),
- -1. * np.divide(x1, np.square(x2))) * dy
- dx1 = (np.sum(
- dx1_bc.astype("float64"),
- axis=tuple(rx),
- keepdims=True) if len(rx) > 0 else dx1_bc).astype(dtype)
- dx2 = (np.sum(
- dx2_bc.astype("float64"),
- axis=tuple(ry),
- keepdims=True) if len(ry) > 0 else dx2_bc).astype(dtype)
- output1 = np.full(shape1, np.nan, dtype)
- output2 = np.full(shape2, np.nan, dtype)
- return (dx1, dx2), (x1, x2, dy), (output1, output2)
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