!7884 Pynative support dynamic shape op
Merge pull request !7884 from JoyLvliang/pynative-support-dynamic-shapepull/7884/MERGE
mindspore-ci-bot
4 years ago
committed by
Gitee
commit
57fc8f45ae
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# 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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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.nn import TrainOneStepCell
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from mindspore.nn.optim import FTRL, LazyAdam
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from mindspore.ops import operations as P
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context.set_context(enable_sparse=True,
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mode=context.PYNATIVE_MODE,
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device_target="Ascend")
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class NetWithSparseGatherV2(nn.Cell):
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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([3, 1, 2]).astype(np.float32)), name="weight2")
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self.axis = 1
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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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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_pynative_ftrl_net():
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indices = Tensor(np.array([0, 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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optimizer = FTRL(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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output = train_network(indices, label)
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np.allclose(output.asnumpy(), np.array([[[2, 2]], [[2, 2]], [[2, 2]]]))
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np.allclose(net.weight1.asnumpy(), np.array([[[0.7884067, 0.7884067]],
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[[0.68213105, 0.68213105]],
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[[1.0, 1.0]]]))
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np.allclose(net.weight2.asnumpy(), np.array([[[0.6821311, 0.6821311]],
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[[0.6821311, 0.6821311]],
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[[0.6821311, 0.6821311]]]))
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_pynative_lazy_adam_net():
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indices = Tensor(np.array([0, 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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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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output = train_network(indices, label)
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np.allclose(output.asnumpy(), np.array([[[2, 2]], [[2, 2]], [[2, 2]]]))
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np.allclose(net.weight1.asnumpy(), np.array([[[0.9, 0.9]], [[0.9, 0.9]], [[1.0, 1.0]]]))
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np.allclose(net.weight2.asnumpy(), np.array([[[0.9, 0.9]], [[0.9, 0.9]], [[0.9, 0.9]]]))
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@ -0,0 +1,44 @@
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# 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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import numpy as np
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import pytest
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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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import mindspore.common.dtype as mstype
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from mindspore.ops import operations as P
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.unique = P.Unique()
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def construct(self, x):
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return self.unique(x)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_pynative_unqiue():
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x = Tensor(np.array([1, 1, 2, 2, 3, 3]), mstype.int32)
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unique = Net()
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output = unique(x)
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expect1 = np.array([1, 2, 3])
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expect2 = np.array([0, 0, 1, 1, 2, 2])
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assert (output[0].asnumpy() == expect1).all()
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assert (output[1].asnumpy() == expect2).all()
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