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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.
- # ============================================================================
- import numpy as np
- import mindspore.context as context
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore.ops.operations import _grad_ops as G
-
-
- class Net(nn.Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.sqrt_grad = G.SqrtGrad()
-
- def construct(self, x, dout):
- return self.sqrt_grad(x, dout)
-
-
- def get_output(x, dout, enable_graph_kernel=False):
- if enable_graph_kernel:
- context.set_context(enable_graph_kernel=True)
- net = Net()
- output = net(x, dout)
- return output
-
-
- def test_sqrt_grad(shape_x, shape_dout, dtype):
- x = Tensor(np.random.normal(0, 1, shape_x).astype(dtype))
- dout = Tensor(np.random.normal(0, 1, shape_dout).astype(dtype))
-
- expect = get_output(x, dout, False)
- output = get_output(x, dout, True)
-
- expect_np = expect.asnumpy().copy()
- output_np = output.asnumpy().copy()
-
- rtol = 0.0001
- atol = 0.0001
- if dtype == np.float16:
- rtol = 0.001
- atol = 0.001
-
- assert np.allclose(expect_np, output_np, rtol, atol)
-
-
- def test_sqrt_grad_ascend():
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
- test_sqrt_grad((16, 16), (16, 16), np.float16)
- test_sqrt_grad((16, 16), (16, 16), np.float32)
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