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44 lines
1.5 KiB
44 lines
1.5 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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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.common.dtype as mstype
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from mindspore import Tensor
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE,
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device_target="Ascend")
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class Net(nn.Cell):
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def __init__(self, offset):
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super(Net, self).__init__()
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self.embedding = P.EmbeddingLookup()
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self.offset = offset
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def construct(self, param, index):
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return self.embedding(param, index, self.offset)
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def test_embedding_lookup_sparse():
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params = Tensor(np.array([[8, 9], [10, 11], [12, 13], [14, 15]]), mstype.int32)
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indices = Tensor(np.array([[5, 2], [8, 5]]), mstype.int32)
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offset = 4
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embedding = Net(offset)
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out = embedding(params, indices)
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assert(out.asnumpy() == [[[10, 11], [0, 0]], [[0, 0], [10, 11]]]).all()
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