forked from mindspore-Ecosystem/mindspore
139 lines
4.5 KiB
Python
139 lines
4.5 KiB
Python
# 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_momentum """
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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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import mindspore.context as context
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from mindspore import Parameter, ParameterTuple, Tensor
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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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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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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# pylint: disable=W0613
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# W0613: unused-argument
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run_opt = C.MultitypeFuncGraph("run_opt")
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grad_by_list = C.GradOperation(get_by_list=True)
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@run_opt.register("Function", "Tensor", "Tensor", "Tensor",
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"Tensor", "Tensor",
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"Tensor")
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def tensor_run_opt(opt, iters, learning_rate, momentum,
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gradient, variable, moment):
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""" tensor_run_opt """
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success = True
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new_weight = opt(variable, moment, learning_rate, gradient, momentum)[0]
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success = F.depend(success, F.assign(variable, new_weight))
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return success
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class OptimizerByMomentum(nn.Cell):
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""" OptimizerByMomentum definition """
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def __init__(self, weights):
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super(OptimizerByMomentum, self).__init__()
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self.learning_rate = Parameter(0.1, name="learning_rate")
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self.momentum = Parameter(0.05, name="momentum")
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self.iter = Parameter(0, name="iter")
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self.weights = weights
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self.moments = weights.clone(prefix="moments", init='zeros')
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self.hyper_map = C.HyperMap()
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self.opt = P.ApplyMomentum()
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def construct(self, grads):
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success = True
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weights = self.weights
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moments = self.moments
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success = self.hyper_map(F.partial(run_opt, self.opt, self.iter,
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self.learning_rate, self.momentum),
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grads, weights, moments)
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return success
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class TrainStepWrap(nn.Cell):
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""" TrainStepWrap definition """
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def __init__(self, network):
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super(TrainStepWrap, self).__init__()
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self.network = network
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self.weights = ParameterTuple(network.get_parameters())
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self.optimizer = OptimizerByMomentum(self.weights)
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self.hyper_map = C.HyperMap()
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def construct(self, x, label):
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weights = self.weights
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grads = grad_by_list(self.network, weights)(x, label)
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return self.optimizer(grads)
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class NetWithLossClass(nn.Cell):
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""" NetWithLossClass definition """
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def __init__(self, network):
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super(NetWithLossClass, self).__init__(auto_prefix=False)
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self.loss = nn.SoftmaxCrossEntropyWithLogits()
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self.network = network
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def construct(self, x, label):
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predict = self.network(x)
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return self.loss(predict, label)
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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.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")
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self.bias = Parameter(Tensor(np.ones([10]).astype(np.float32)), name="bias")
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self.matmul = P.MatMul()
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self.biasAdd = P.BiasAdd()
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def construct(self, x):
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return self.biasAdd(self.matmul(x, self.weight), self.bias)
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test_case_ops = [
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('Momentum', {
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'block': TrainStepWrap(NetWithLossClass(Net())),
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'desc_inputs': [Tensor(np.ones([1, 64]).astype(np.float32)),
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Tensor(np.zeros([1, 10]).astype(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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@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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