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47 lines
1.7 KiB
47 lines
1.7 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 pytest
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import mindspore.context as context
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from mindspore import Tensor
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import mindspore.ops.operations._grad_ops as P
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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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_asingrad_fp32():
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error = np.ones(4) * 1.0e-7
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x_np = np.array([0, -0.25, 0.5, 0.3]).astype(np.float32)
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dout_np = np.array([1, 1, 1, 1]).astype(np.float32)
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output_ms = P.AsinGrad()(Tensor(x_np), Tensor(dout_np))
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expect = np.array([1, 1.0327955, 1.1547005, 1.0482849])
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diff = output_ms.asnumpy() - expect
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assert np.all(diff < error)
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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_asingrad_fp16():
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error = np.ones(4) * 1.0e-3
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x_np = np.array([0, -0.25, 0.5, 0.3]).astype(np.float16)
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dout_np = np.array([1, 1, 1, 1]).astype(np.float16)
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output_ms = P.AsinGrad()(Tensor(x_np), Tensor(dout_np))
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expect = np.array([1, 1.033, 1.154, 1.048])
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diff = output_ms.asnumpy() - expect
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assert np.all(diff < error)
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