forked from mindspore-Ecosystem/mindspore
99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
# Copyright 2019 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 bprop disorder """
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import functools
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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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.common.parameter import ParameterTuple
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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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class DisOrderTest1(nn.Cell):
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""" DisOrderTest1 definition """
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def __init__(self):
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super(DisOrderTest1, self).__init__()
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weight = Tensor(np.ones([1], np.float32))
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self.s1 = Parameter(weight, name="s1")
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self.s2 = Parameter(weight, name="s2")
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self.s3 = Parameter(weight, name="s3")
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self.s4 = Parameter(weight, name="s4")
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self.mul = P.Mul()
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self.add = P.TensorAdd()
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def construct(self, x):
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return x * (self.s1 * self.s2 + self.s2 * self.s3 + self.s3 * self.s4 + self.s4 * self.s1)
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class DisOrderTest2(nn.Cell):
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""" DisOrderTest2 definition """
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def __init__(self):
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super(DisOrderTest2, self).__init__()
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weight = Tensor(np.ones([1], np.float32))
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self.s1 = Parameter(weight, name="s1")
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self.s2 = Parameter(weight, name="s2")
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self.s3 = Parameter(weight, name="s3")
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self.s4 = Parameter(weight, name="s4")
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self.mul = P.Mul()
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self.add = P.TensorAdd()
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def construct(self, x):
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return self.mul(x, (self.add(self.add(self.add(self.mul(self.s1, self.s2), self.mul(self.s2, self.s3)),
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self.mul(self.s3, self.s4)), self.mul(self.s4, self.s1))))
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class GradNetWrap(nn.Cell):
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""" GradNetWrap definition """
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def __init__(self, net):
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super(GradNetWrap, self).__init__()
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self.net = net
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self.weights = ParameterTuple(net.get_parameters())
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def construct(self, x, sens):
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return C.grad_by_list_with_sens(self.net, self.weights)(x, sens)
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test_case_ops = [
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('DisOrderTest1', {
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'block': GradNetWrap(DisOrderTest1()),
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'desc_inputs': [Tensor(np.ones([1], np.float32)), Tensor(np.ones([1], np.float32))]}),
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('DisOrderTest2', {
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'block': GradNetWrap(DisOrderTest2()),
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'desc_inputs': [Tensor(np.ones([1], np.float32)), Tensor(np.ones([1], np.float32))]}),
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]
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test_case_lists = [test_case_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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import mindspore.context as context
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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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