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103 lines
3.0 KiB
103 lines
3.0 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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"""
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test assign add
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"""
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import numpy as np
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import mindspore as ms
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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, Parameter
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from mindspore.common.initializer import initializer
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from mindspore.ops import operations as P
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from ..ut_filter import non_graph_engine
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context.set_context(mode=context.GRAPH_MODE)
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class Net(nn.Cell):
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"""Net definition"""
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def __init__(self):
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super(Net, self).__init__()
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self.AssignAdd = P.AssignAdd()
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self.inputdata = Parameter(initializer(1, [1], ms.int64), name="global_step")
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print("inputdata: ", self.inputdata)
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def construct(self, x):
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out = self.AssignAdd(self.inputdata, x)
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return out
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@non_graph_engine
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def test_AssignAdd_1():
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"""test AssignAdd 1"""
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context.set_context(mode=context.GRAPH_MODE)
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net = Net()
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x = Tensor(np.ones([1]).astype(np.int64) * 100)
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print("MyPrintResult dataX:", x)
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result = net(x)
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print("MyPrintResult data::", result)
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expect = np.ones([1]).astype(np.int64) * 101
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diff = result.asnumpy() - expect
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print("MyPrintExpect:", expect)
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print("MyPrintDiff:", diff)
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error = np.ones(shape=[1]) * 1.0e-3
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assert np.all(diff < error)
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@non_graph_engine
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def test_AssignAdd_2():
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"""test AssignAdd 2"""
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context.set_context(mode=context.GRAPH_MODE)
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net = Net()
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x = Tensor(np.ones([1]).astype(np.int64) * 102)
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print("MyPrintResult dataX:", x)
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result = net(x)
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print("MyPrintResult data::", result.asnumpy())
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expect = np.ones([1]).astype(np.int64) * 103
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diff = result.asnumpy() - expect
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print("MyPrintExpect:", expect)
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print("MyPrintDiff:", diff)
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error = np.ones(shape=[1]) * 1.0e-3
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assert np.all(diff < error)
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class AssignAddNet(nn.Cell):
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"""Net definition"""
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def __init__(self):
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super(AssignAddNet, self).__init__()
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self.AssignAdd = P.AssignAdd()
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self.inputdata = Parameter(initializer(1, [1], ms.float16), name="KIND_AUTOCAST_SCALAR_TO_TENSOR")
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self.one = 1
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def construct(self, ixt):
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z1 = self.AssignAdd(self.inputdata, self.one)
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return z1
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@non_graph_engine
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def test_assignadd_scalar_cast():
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net = AssignAddNet()
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x = Tensor(np.ones([1]).astype(np.int64) * 102)
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# _executor.compile(net, 1)
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_ = net(x)
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