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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """ test scatter update """
- import numpy as np
- import mindspore.nn as nn
- from mindspore import Tensor, Model, Parameter
- from mindspore.ops import operations as P
- from mindspore import context
-
-
- class Net(nn.Cell):
- """Net definition"""
- def __init__(self):
- super(Net, self).__init__()
- self.inputs = Parameter(Tensor(np.ones([32, 128]).astype(np.float32)), "input")
- self.indices = Tensor(np.ones([4]).astype(np.int32))
- self.updates = Tensor(np.ones([4, 128]).astype(np.float32))
- self.scatter_update = P.ScatterUpdate().shard(((1, 8), (1,), (1, 8)))
- self.add = P.TensorAdd().shard(((8, 1), (8, 1)))
- self.relu = P.ReLU()
-
- def construct(self, x):
- out = self.scatter_update(self.inputs, self.indices, self.updates)
- out = self.add(x, out)
- out = self.relu(out)
- return out
-
-
- def test_distribute_predict():
- context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, full_batch=True)
- inputs = Tensor(np.ones([32, 128]).astype(np.float32))
- net = Net()
- model = Model(net)
- predict_map = model.infer_predict_layout(inputs)
- output = model.predict(inputs)
- context.reset_auto_parallel_context()
- return predict_map, output
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