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246 lines
7.3 KiB
246 lines
7.3 KiB
# Copyright 2019-2021 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.common.api import ms_function
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _inner_ops as inner
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class AddNet(nn.Cell):
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def __init__(self, nptype):
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super(AddNet, self).__init__()
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self.add = P.Add()
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np.random.seed(0)
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self.x = Parameter(initializer(
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Tensor(np.random.randn(2, 0).astype(nptype)), [2, 0]), name='x')
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self.y = Parameter(initializer(
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Tensor(np.random.randn(2, 1).astype(nptype)), [2, 1]), name='y')
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self.x1 = Parameter(initializer(
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Tensor(np.arange(3).reshape(3).astype(nptype)), [3]), name='x1')
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self.y1 = Parameter(initializer(
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Tensor(np.array([2]).astype(nptype)), [1]), name='y1')
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self.x2 = Parameter(initializer(
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Tensor(np.arange(3 * 3 * 3 * 3).reshape(3, 3, 3, 3).astype(nptype)), [3, 3, 3, 3]), name='x2')
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self.y2 = Parameter(initializer(
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Tensor(np.arange(3 * 3 * 3 * 3).reshape(3, 3, 3, 3).astype(nptype)), [3, 3, 3, 3]), name='y2')
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self.x3 = Parameter(initializer(
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Tensor(np.arange(1 * 1 * 3 * 3).reshape(1, 1, 3, 3).astype(nptype)), [1, 1, 3, 3]), name='x3')
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self.y3 = Parameter(initializer(
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Tensor(np.arange(3 * 3 * 3 * 3).reshape(3, 3, 3, 3).astype(nptype)), [3, 3, 3, 3]), name='y3')
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@ms_function
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def construct(self):
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return (
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self.add(self.x, self.y), self.add(self.x1, self.y1), self.add(self.x2, self.y2),
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self.add(self.x3, self.y3))
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def add(nptype):
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context.set_context(device_target='GPU')
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add_net = AddNet(nptype)
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output = add_net()
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expect0 = np.array([])
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expect1 = np.array([2, 3, 4]).astype(nptype)
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expect2 = np.array(
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[[[[0., 2., 4.],
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[6., 8., 10.],
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[12., 14., 16.]],
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[[18., 20., 22.],
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[24., 26., 28.],
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[30., 32., 34.]],
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[[36., 38., 40.],
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[42., 44., 46.],
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[48., 50., 52.]]],
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[[[54., 56., 58.],
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[60., 62., 64.],
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[66., 68., 70.]],
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[[72., 74., 76.],
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[78., 80., 82.],
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[84., 86., 88.]],
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[[90., 92., 94.],
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[96., 98., 100.],
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[102., 104., 106.]]],
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[[[108., 110., 112.],
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[114., 116., 118.],
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[120., 122., 124.]],
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[[126., 128., 130.],
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[132., 134., 136.],
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[138., 140., 142.]],
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[[144., 146., 148.],
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[150., 152., 154.],
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[156., 158., 160.]]]]).astype(nptype)
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expect3 = np.array(
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[[[[0., 2., 4.],
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[6., 8., 10.],
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[12., 14., 16.]],
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[[9., 11., 13.],
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[15., 17., 19.],
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[21., 23., 25.]],
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[[18., 20., 22.],
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[24., 26., 28.],
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[30., 32., 34.]]],
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[[[27., 29., 31.],
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[33., 35., 37.],
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[39., 41., 43.]],
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[[36., 38., 40.],
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[42., 44., 46.],
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[48., 50., 52.]],
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[[45., 47., 49.],
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[51., 53., 55.],
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[57., 59., 61.]]],
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[[[54., 56., 58.],
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[60., 62., 64.],
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[66., 68., 70.]],
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[[63., 65., 67.],
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[69., 71., 73.],
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[75., 77., 79.]],
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[[72., 74., 76.],
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[78., 80., 82.],
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[84., 86., 88.]]]]).astype(nptype)
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assert (output[0].asnumpy() == expect0).all()
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assert (output[1].asnumpy() == expect1).all()
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assert (output[2].asnumpy() == expect2).all()
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assert (output[3].asnumpy() == expect3).all()
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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_add_float64():
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add(np.float64)
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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_add_float32():
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add(np.float32)
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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_add_float16():
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add(np.float16)
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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_add_int64():
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add(np.int64)
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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_add_int32():
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add(np.int32)
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class Tensoradd_d(nn.Cell):
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def __init__(self):
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super(Tensoradd_d, self).__init__()
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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self.add = P.Add()
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def construct(self, x, y):
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x = self.test_dynamic(x)
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y = self.test_dynamic(y)
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return self.add(x, y)
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def add_dynamic(nptype):
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context.set_context(device_target='GPU', mode=context.GRAPH_MODE)
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net = Tensoradd_d()
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x1 = Tensor(np.arange(3).reshape(3).astype(nptype))
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y1 = Tensor(np.array([2]).astype(nptype))
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x2 = Tensor(np.arange(3 * 3 * 3 * 3).reshape(3, 3, 3, 3).astype(nptype))
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y2 = Tensor(np.arange(3 * 3 * 3 * 3).reshape(3, 3, 3, 3).astype(nptype))
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expect1 = np.array([2, 3, 4])
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expect2 = np.array(
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[[[[0., 2., 4.],
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[6., 8., 10.],
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[12., 14., 16.]],
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[[18., 20., 22.],
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[24., 26., 28.],
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[30., 32., 34.]],
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[[36., 38., 40.],
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[42., 44., 46.],
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[48., 50., 52.]]],
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[[[54., 56., 58.],
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[60., 62., 64.],
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[66., 68., 70.]],
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[[72., 74., 76.],
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[78., 80., 82.],
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[84., 86., 88.]],
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[[90., 92., 94.],
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[96., 98., 100.],
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[102., 104., 106.]]],
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[[[108., 110., 112.],
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[114., 116., 118.],
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[120., 122., 124.]],
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[[126., 128., 130.],
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[132., 134., 136.],
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[138., 140., 142.]],
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[[144., 146., 148.],
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[150., 152., 154.],
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[156., 158., 160.]]]])
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output1 = net(x1, y1)
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output2 = net(x2, y2)
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assert (output1.asnumpy() == expect1).all()
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assert (output2.asnumpy() == expect2).all()
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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_add_dynamic_float64():
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add_dynamic(np.float64)
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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_add_dynamic_float32():
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add_dynamic(np.float32)
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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_add_dynamic_float16():
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add_dynamic(np.float16)
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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_add_dynamic_int64():
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add_dynamic(np.int64)
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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_add_dynamic_int32():
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add_dynamic(np.int32)
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