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1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15import numpy as np
16import pytest
17import mindspore.nn as nn
18from mindspore import Tensor, context
19from mindspore.nn import TrainOneStepCell, WithLossCell
20
21
22context.set_context(enable_sparse=True,
23                    mode=context.GRAPH_MODE)
24
25
26class NetWithEmbeddingLookUp(nn.Cell):
27    def __init__(self, vocab_size, embedding_size, target="CPU"):
28        super(NetWithEmbeddingLookUp, self).__init__()
29        self.embedding_lookup =  \
30                        nn.EmbeddingLookup(vocab_size=vocab_size,
31                                           embedding_size=embedding_size,
32                                           param_init="ones", target=target)
33
34    def construct(self, indices):
35        out = self.embedding_lookup(indices)
36        return out
37
38
39@pytest.mark.level0
40@pytest.mark.platform_arm_ascend_training
41@pytest.mark.platform_x86_ascend_training
42@pytest.mark.platform_x86_gpu_training
43@pytest.mark.env_onecard
44def test_sit_embedding_lookup_net():
45    indices = Tensor(np.array([0, 1, 2]).astype(np.int32))
46    label = Tensor(np.random.randn(3, 8).astype(np.float32))
47
48    net1 = NetWithEmbeddingLookUp(vocab_size=8, embedding_size=8, target="CPU")
49    loss = nn.SoftmaxCrossEntropyWithLogits(reduction="mean")
50    optimizer1 = nn.Adam(params=net1.trainable_params(), learning_rate=0.1)
51    optimizer1.unique = True
52    train_network1 = TrainOneStepCell(WithLossCell(net1, loss), optimizer1)
53    train_network1.set_train()
54    out1 = train_network1(indices, label)
55
56    net2 = NetWithEmbeddingLookUp(vocab_size=8, embedding_size=8, target="CPU")
57    optimizer2 = nn.Adam(params=net2.trainable_params(), learning_rate=0.1)
58    optimizer2.unique = False
59    optimizer2.target = "CPU"
60    train_network2 = TrainOneStepCell(WithLossCell(net2, loss), optimizer2)
61    train_network2.set_train()
62    out2 = train_network2(indices, label)
63
64    assert np.allclose(out1.asnumpy(), out2.asnumpy(), 0.001, 0.001)
65