forked from mindspore-Ecosystem/mindspore
475 lines
14 KiB
Python
Executable File
475 lines
14 KiB
Python
Executable File
# 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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""" test math ops """
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import functools
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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
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from mindspore.common import dtype as mstype
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from mindspore.ops import prim_attr_register, PrimitiveWithInfer
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from ..ut_filter import non_graph_engine
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from ....mindspore_test_framework.mindspore_test import mindspore_test
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from ....mindspore_test_framework.pipeline.forward.compile_forward \
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import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
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from ....mindspore_test_framework.pipeline.forward.verify_exception \
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import pipeline_for_verify_exception_for_case_by_case_config
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context.set_context(mode=context.GRAPH_MODE)
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# pylint: disable=W0613
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# pylint: disable=W0231
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# W0613: unused-argument
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# W0231: super-init-not-called
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grad = C.GradOperation()
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def test_multiply():
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""" test_multiply """
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input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]))
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input_y = Tensor(np.array([[0.1, 0.3, -3.6], [0.4, 0.5, -3.2]]))
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mul = P.Mul()
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result = mul(input_x, input_y)
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expect = np.array([[-0.01, 0.09, -12.96], [0.16, 0.25, 10.24]])
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diff = result.asnumpy() - expect
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error = np.ones(shape=[2, 3]) * 1.0e-6
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assert np.all(diff < error)
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assert np.all(-diff < error)
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def test_sub():
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""" test_sub """
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input_x = Tensor(np.ones(shape=[3]))
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input_y = Tensor(np.zeros(shape=[3]))
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sub = P.Sub()
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result = sub(input_x, input_y)
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expect = np.ones(shape=[3])
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assert np.all(result.asnumpy() == expect)
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def test_square():
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""" test_square """
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input_tensor = Tensor(np.array([[1, 2, 3], [4, 5, 6]]))
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square = P.Square()
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result = square(input_tensor)
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expect = np.array([[1, 4, 9], [16, 25, 36]])
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assert np.all(result.asnumpy() == expect)
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def test_sqrt():
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""" test_sqrt """
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input_tensor = Tensor(np.array([[4, 4], [9, 9]]))
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sqrt = P.Sqrt()
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expect = np.array([[2, 2], [3, 3]])
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result = sqrt(input_tensor)
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assert np.all(result.asnumpy() == expect)
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class PowNet(nn.Cell):
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def __init__(self):
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super(PowNet, self).__init__()
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self.pow = P.Pow()
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def construct(self, x, y):
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return self.pow(x, y)
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def test_pow():
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""" test_pow """
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input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
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power = Tensor(np.array(3.0, np.int64))
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power2 = Tensor(np.array(True, np.bool))
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testpow = P.Pow()
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expect = np.array([[8, 8], [27, 27]])
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result = testpow(input_tensor, power)
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assert np.all(result.asnumpy() == expect)
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net = PowNet()
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net(input_tensor, True)
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net(input_tensor, power2)
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def test_exp():
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""" test_exp """
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input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
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testexp = P.Exp()
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result = testexp(input_tensor)
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expect = np.exp(np.array([[2, 2], [3, 3]]))
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assert np.all(result.asnumpy() == expect)
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def test_realdiv():
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""" test_realdiv """
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x = Tensor(2048.0)
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y = Tensor(128.0)
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div = P.RealDiv()
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result = div(x, y)
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x = x.asnumpy()
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y = y.asnumpy()
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expect = x / y
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assert np.all(result.asnumpy() == expect)
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def test_eye():
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""" test_eye """
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x = np.arange(3)
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expect = np.ones_like(x)
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expect = np.diag(expect)
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eye = P.Eye()
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eye_output = eye(3, 3, ms.float32)
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assert np.all(eye_output.asnumpy() == expect)
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class VirtualLossGrad(PrimitiveWithInfer):
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""" VirtualLossGrad definition """
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@prim_attr_register
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def __init__(self):
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"""init VirtualLossGrad"""
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def __call__(self, x, out, dout):
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raise NotImplementedError
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def infer_shape(self, x_shape, out_shape, dout_shape):
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return x_shape
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def infer_dtype(self, x_dtype, out_dtype, dout_dtype):
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return x_dtype
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class VirtualLoss(PrimitiveWithInfer):
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""" VirtualLoss definition """
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@prim_attr_register
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def __init__(self):
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"""init VirtualLoss"""
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def __call__(self, x):
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raise NotImplementedError
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def get_bprop(self):
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loss_grad = VirtualLossGrad()
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def bprop(x, out, dout):
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dx = loss_grad(x, out, dout)
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return (dx,)
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return bprop
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def infer_shape(self, x_shape):
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return [1]
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def infer_dtype(self, x_dtype):
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return x_dtype
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class NetWithLoss(nn.Cell):
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""" NetWithLoss definition """
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def __init__(self, network):
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super(NetWithLoss, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x, y, b):
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predict = self.network(x, y, b)
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return self.loss(predict)
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class GradWrap(nn.Cell):
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""" GradWrap definition """
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x, y, b):
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return grad(self.network)(x, y, b)
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class MatMulNet(nn.Cell):
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""" MatMulNet definition """
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def __init__(self):
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super(MatMulNet, self).__init__()
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self.matmul = P.MatMul()
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self.biasAdd = P.BiasAdd()
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def construct(self, x, y, b):
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return self.biasAdd(self.matmul(x, y), b)
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class NetWithLossSub(nn.Cell):
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""" NetWithLossSub definition """
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def __init__(self, network):
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super(NetWithLossSub, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x, y):
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predict = self.network(x, y)
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return self.loss(predict)
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class GradWrapSub(nn.Cell):
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""" GradWrapSub definition """
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def __init__(self, network):
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super(GradWrapSub, self).__init__()
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self.network = network
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def construct(self, x, y):
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return grad(self.network)(x, y)
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class SubNet(nn.Cell):
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""" SubNet definition """
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def __init__(self):
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super(SubNet, self).__init__()
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self.sub = P.Sub()
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def construct(self, x, y):
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return self.sub(x, y)
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class NpuFloatNet(nn.Cell):
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""" NpuFloat definition """
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def __init__(self):
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super(NpuFloatNet, self).__init__()
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self.mul = P.Mul()
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self.alloc_status = P.NPUAllocFloatStatus()
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self.get_status = P.NPUGetFloatStatus()
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self.clear_status = P.NPUClearFloatStatus()
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self.fill = P.Fill()
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self.shape_op = P.Shape()
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self.select = P.Select()
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self.less = P.Less()
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self.cast = P.Cast()
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self.dtype = P.DType()
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self.reduce_sum = P.ReduceSum(keep_dims=True)
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self.sub = P.Sub()
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self.neg = P.Neg()
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@C.add_flags(has_effect=True)
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def construct(self, x):
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init = self.alloc_status()
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self.clear_status(init)
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res = self.sub(x, self.neg(x))
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self.get_status(init)
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flag_sum = self.reduce_sum(init, (0,))
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base = self.cast(self.fill(self.dtype(res), self.shape_op(res), 0.0), self.dtype(flag_sum))
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cond = self.less(base, flag_sum)
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out = self.select(cond, self.cast(base, self.dtype(res)), res)
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return out
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class DiagNet(nn.Cell):
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""" DiagNet definition """
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def __init__(self):
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super(DiagNet, self).__init__()
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self.fill = P.Fill()
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self.diag = P.Diag()
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def construct(self, x):
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return x - self.diag(self.fill(mstype.float32, (3,), 1.0))
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class NetWithLossCumSum(nn.Cell):
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""" NetWithLossCumSum definition """
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def __init__(self, network):
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super(NetWithLossCumSum, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, input_):
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predict = self.network(input_)
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return self.loss(predict)
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class GradWrapCumSum(nn.Cell):
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""" GradWrap definition """
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def __init__(self, network):
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super(GradWrapCumSum, self).__init__()
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self.network = network
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def construct(self, input_):
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return grad(self.network)(input_)
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class NetCumSum(nn.Cell):
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""" NetCumSum definition """
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def __init__(self):
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super(NetCumSum, self).__init__()
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self.cumsum = P.CumSum()
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self.axis = 1
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def construct(self, input_):
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return self.cumsum(input_, self.axis)
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class SignNet(nn.Cell):
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def __init__(self):
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super(SignNet, self).__init__()
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self.sign = P.Sign()
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def construct(self, x):
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return self.sign(x)
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class AssignAdd(nn.Cell):
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def __init__(self):
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super().__init__()
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self.op = P.AssignAdd()
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self.inputdata = Parameter(initializer(1, [1], ms.float32), name="global_step")
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def construct(self, input_):
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self.inputdata = input_
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return self.op(self.inputdata, input_)
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class FloorNet(nn.Cell):
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def __init__(self):
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super(FloorNet, self).__init__()
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self.floor = P.Floor()
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def construct(self, x):
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return self.floor(x)
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class Log1pNet(nn.Cell):
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def __init__(self):
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super(Log1pNet, self).__init__()
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self.log1p = P.Log1p()
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def construct(self, x):
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return self.log1p(x)
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class ErfcNet(nn.Cell):
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def __init__(self):
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super(ErfcNet, self).__init__()
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self.erfc = P.Erfc()
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def construct(self, x):
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return self.erfc(x)
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test_case_math_ops = [
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('MatMulGrad', {
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'block': GradWrap(NetWithLoss(MatMulNet())),
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'desc_inputs': [Tensor(np.ones([3, 3]).astype(np.int32)),
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Tensor(np.ones([3, 3]).astype(np.int32)),
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Tensor(np.ones([3]).astype(np.int32))],
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'desc_bprop': [Tensor(np.ones([3, 3]).astype(np.int32)),
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Tensor(np.ones([3, 3]).astype(np.int32)),
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Tensor(np.ones([3]).astype(np.int32))],
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'skip': ['backward']}),
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('CumSumGrad', {
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'block': GradWrapCumSum(NetWithLossCumSum(NetCumSum())),
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'desc_inputs': [Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]).astype(np.float16))],
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'desc_bprop': [Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]).astype(np.float16))],
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'skip': ['backward']}),
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('Diag', {
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'block': DiagNet(),
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'desc_inputs': [Tensor(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]], np.float32))],
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'desc_bprop': [Tensor(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]], np.float32))],
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'skip': ['backward']}),
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('SubBroadcast', {
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'block': GradWrapSub(NetWithLossSub(SubNet())),
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'desc_inputs': [Tensor(np.ones([5, 3])), Tensor(np.ones([8, 5, 3]))],
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'desc_bprop': [Tensor(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]], np.float32))],
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'skip': ['backward']}),
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('NpuFloat_NotOverflow', {
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'block': NpuFloatNet(),
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'desc_inputs': [Tensor(np.full((8, 5, 3, 1), 655, dtype=np.float16), dtype=ms.float16)],
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'desc_bprop': [Tensor(np.full((8, 5, 3, 1), 655, dtype=np.float16), dtype=ms.float16)],
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'skip': ['backward']}),
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('NpuFloat_Overflow', {
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'block': NpuFloatNet(),
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'desc_inputs': [Tensor(np.full((8, 5, 3, 1), 65504, dtype=np.float16), dtype=ms.float16)],
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'desc_bprop': [Tensor(np.full((8, 5, 3, 1), 65504, dtype=np.float16), dtype=ms.float16)],
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'skip': ['backward']}),
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('Sign', {
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'block': SignNet(),
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'desc_inputs': [Tensor(np.array([[1., 0., -2.]], np.float32))],
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'desc_bprop': [Tensor(np.array([[1., 0., -2.]], np.float32))],
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'skip': ['backward']}),
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('Floor', {
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'block': FloorNet(),
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'desc_inputs': [Tensor(np.array([[1., 0., -2.]], np.float32))],
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'desc_bprop': [Tensor(np.array([[1., 0., -2.]], np.float32))],
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'skip': ['backward']}),
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('Log1p', {
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'block': Log1pNet(),
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'desc_inputs': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
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'desc_bprop': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
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'skip': ['backward']}),
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('Erfc', {
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'block': ErfcNet(),
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'desc_inputs': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
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'desc_bprop': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))],
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}),
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]
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test_case_lists = [test_case_math_ops]
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test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
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# use -k to select certain testcast
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# pytest tests/python/ops/test_ops.py::test_backward -k LayerNorm
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@non_graph_engine
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@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
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def test_exec():
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context.set_context(mode=context.GRAPH_MODE)
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return test_exec_case
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raise_set = [
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('StridedSlice_1_Error', {
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'block': (lambda x: P.StridedSlice(begin_mask="1"), {'exception': TypeError}),
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'desc_inputs': [0]}),
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('StridedSlice_2_Error', {
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'block': (lambda x: P.StridedSlice(end_mask="1"), {'exception': TypeError}),
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'desc_inputs': [0]}),
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('StridedSlice_3_Error', {
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'block': (lambda x: P.StridedSlice(ellipsis_mask=1.1), {'exception': TypeError}),
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'desc_inputs': [0]}),
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('StridedSlice_4_Error', {
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'block': (lambda x: P.StridedSlice(new_axis_mask="1.1"), {'exception': TypeError}),
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'desc_inputs': [0]}),
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('AssignAdd_Error', {
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'block': (P.AssignAdd(), {'exception': IndexError}),
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'desc_inputs': [[1]]}),
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]
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@mindspore_test(pipeline_for_verify_exception_for_case_by_case_config)
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def test_check_exception():
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return raise_set
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