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92 lines
3.5 KiB
92 lines
3.5 KiB
# Copyright 2021 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 pytest
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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 NetReciprocalGrad(nn.Cell):
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def __init__(self):
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super(NetReciprocalGrad, self).__init__()
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self.grad = G.ReciprocalGrad()
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def construct(self, y, dy):
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return self.grad(y, dy)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_reciprocal_grad_float32():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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y = Tensor(np.array([[[[-1, 1, 12],
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[5, 34, 6],
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[10, 2, -1]]]]).astype(np.float32))
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dy = Tensor(np.array([[[[29, 1, 55],
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[2.2, 63, 2],
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[3, 3, 12]]]]).astype(np.float32))
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expect = np.array([[[[-29, -1, -7920],
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[-55, -72828, -72],
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[-300, -12, -12]]]]).astype(np.float32)
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net = NetReciprocalGrad()
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output = net(y, dy)
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np.testing.assert_array_almost_equal(output.asnumpy(), expect)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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y = Tensor(np.array([[[[-1, 1, 12],
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[5, 34, 6],
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[10, 2, -1]]]]).astype(np.float32))
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dy = Tensor(np.array([[[[29, 1, 55],
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[2.2, 63, 2],
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[3, 3, 12]]]]).astype(np.float32))
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expect = np.array([[[[-29, -1, -7920],
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[-55, -72828, -72],
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[-300, -12, -12]]]]).astype(np.float32)
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net = NetReciprocalGrad()
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output = net(y, dy)
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np.testing.assert_array_almost_equal(output.asnumpy(), expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_reciprocal_grad_float16():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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y = Tensor(np.array([[0.01, 0.2, 0.22],
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[10.002, 2, -1]]).astype(np.float16))
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dy = Tensor(np.array([[34, 1, 55],
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[3, 3, 63]]).astype(np.float16))
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expect = np.array([[-0.0034, -0.03998, -2.662],
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[-300, -12, -63]]).astype(np.float16)
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net = NetReciprocalGrad()
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output = net(y, dy)
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np.testing.assert_array_almost_equal(output.asnumpy(), expect)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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y = Tensor(np.array([[0.01, 0.2, 0.22],
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[10.002, 2, -1]]).astype(np.float16))
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dy = Tensor(np.array([[34, 1, 55],
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[3, 3, 63]]).astype(np.float16))
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expect = np.array([[-0.0034, -0.03998, -2.662],
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[-300, -12, -63]]).astype(np.float16)
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net = NetReciprocalGrad()
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output = net(y, dy)
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np.testing.assert_array_almost_equal(output.asnumpy(), expect)
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