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62 lines
1.9 KiB
62 lines
1.9 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops.operations import _grad_ops as G
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.sqrt_grad = G.SqrtGrad()
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def construct(self, x, dout):
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return self.sqrt_grad(x, dout)
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def get_output(x, dout, enable_graph_kernel=False):
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if enable_graph_kernel:
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context.set_context(enable_graph_kernel=True)
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net = Net()
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output = net(x, dout)
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return output
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def test_sqrt_grad(shape_x, shape_dout, dtype):
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x = Tensor(np.random.normal(0, 1, shape_x).astype(dtype))
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dout = Tensor(np.random.normal(0, 1, shape_dout).astype(dtype))
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expect = get_output(x, dout, False)
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output = get_output(x, dout, True)
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expect_np = expect.asnumpy().copy()
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output_np = output.asnumpy().copy()
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rtol = 0.0001
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atol = 0.0001
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if dtype == np.float16:
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rtol = 0.001
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atol = 0.001
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assert np.allclose(expect_np, output_np, rtol, atol)
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def test_sqrt_grad_ascend():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_sqrt_grad((16, 16), (16, 16), np.float16)
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test_sqrt_grad((16, 16), (16, 16), np.float32)
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