forked from mindspore-Ecosystem/mindspore
79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
# Copyright 2021 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 scatter update """
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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, Model, Parameter
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from mindspore.ops import operations as P
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from mindspore import context
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class Net(nn.Cell):
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"""Net definition"""
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def __init__(self, strategy1=None, strategy2=None):
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super(Net, self).__init__()
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self.inputs = Parameter(Tensor(np.ones([32, 64, 128]).astype(np.float32)), "input")
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self.indices = Tensor(np.ones([4, 8]).astype(np.int32))
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self.updates = Tensor(np.ones([4, 8, 64, 128]).astype(np.float32))
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self.scatter_update = P.ScatterUpdate().shard(strategy1)
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self.add = P.TensorAdd().shard(strategy2)
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self.relu = P.ReLU()
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def construct(self, x):
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out = self.scatter_update(self.inputs, self.indices, self.updates)
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out = self.add(x, out)
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out = self.relu(out)
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return out
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def test_distribute_predict():
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, full_batch=True)
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inputs = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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strategy1 = ((1, 2, 4), (1, 1), (1, 1, 2, 4))
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strategy2 = ((1, 2, 4), (1, 2, 4))
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net = Net(strategy1, strategy2)
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model = Model(net)
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predict_map = model.infer_predict_layout(inputs)
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output = model.predict(inputs)
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context.reset_auto_parallel_context()
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return predict_map, output
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def test_scatter_update_wrong_strategy():
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, full_batch=True)
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inputs = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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strategy1 = ((1, 2, 4), (1, 1), (1, 1, 4, 2))
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strategy2 = ((1, 2, 4), (1, 2, 4))
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net = Net(strategy1, strategy2)
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model = Model(net)
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with pytest.raises(RuntimeError):
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model.predict(inputs)
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context.reset_auto_parallel_context()
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def test_distribute_predict_auto_parallel():
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, full_batch=True)
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inputs = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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net = Net()
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model = Model(net)
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predict_map = model.infer_predict_layout(inputs)
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output = model.predict(inputs)
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context.reset_auto_parallel_context()
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return predict_map, output
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