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109 lines
3.8 KiB
109 lines
3.8 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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""" test lazy adam """
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter, context
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from mindspore.common.api import _executor
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.nn.optim import LazyAdam
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from mindspore.ops import operations as P
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@pytest.fixture(scope="module", autouse=True)
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def setup_teardown():
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context.set_context(enable_sparse=True)
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yield
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context.set_context(enable_sparse=False)
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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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x = self.biasAdd(self.matmul(x, self.weight), self.bias)
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return x
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class NetWithSparseGatherV2(nn.Cell):
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""" NetWithSparseGatherV2 definition """
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def __init__(self):
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super(NetWithSparseGatherV2, self).__init__()
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self.weight1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="weight1")
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self.weight2 = Parameter(Tensor(np.ones([2, 1, 2]).astype((np.float32))), name="weight2")
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self.axis = 0
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self.gather = P.SparseGatherV2()
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def construct(self, indices, label):
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return self.gather(self.weight1, indices, self.axis) + self.weight2
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def test_lazy_adam_compile():
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""" test lazy adam compile """
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inputs = Tensor(np.ones([1, 64]).astype(np.float32))
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label = Tensor(np.zeros([1, 10]).astype(np.float32))
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net = Net()
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net.set_train()
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loss = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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_executor.compile(train_network, inputs, label)
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def test_spares_lazy_adam_compile():
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""" test sparse adam compile """
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indices = Tensor(np.array([0, 1]).astype(np.int32))
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label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
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net = NetWithSparseGatherV2()
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net.set_train()
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optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
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optimizer.target = 'CPU'
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train_network = TrainOneStepCell(net, optimizer)
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_executor.compile(train_network, indices, label)
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def test_spares_lazy_adam():
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""" test sparse adam"""
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indices = Tensor(np.array([0, 1]).astype(np.int32))
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label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
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net = NetWithSparseGatherV2()
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net.set_train()
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optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
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optimizer.target = 'Ascend'
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train_network = TrainOneStepCell(net, optimizer)
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_executor.compile(train_network, indices, label)
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def test_lazy_adam_error():
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net = Net()
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with pytest.raises(ValueError):
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LazyAdam(net.get_parameters(), learning_rate=-0.1)
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with pytest.raises(TypeError):
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LazyAdam(net.get_parameters(), learning_rate=0.1, beta1=2)
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