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- # Copyright 2020 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.
- # ============================================================================
- import numpy as np
- import pytest
- import mindspore.nn as nn
- from mindspore import Tensor, context
- from mindspore.nn import TrainOneStepCell, WithLossCell
-
-
- context.set_context(enable_sparse=True,
- mode=context.GRAPH_MODE)
-
-
- class NetWithEmbeddingLookUp(nn.Cell):
- def __init__(self, vocab_size, embedding_size, target="CPU"):
- super(NetWithEmbeddingLookUp, self).__init__()
- self.embedding_lookup = \
- nn.EmbeddingLookup(vocab_size=vocab_size,
- embedding_size=embedding_size,
- param_init="ones", target=target)
-
- def construct(self, indices):
- out = self.embedding_lookup(indices)
- return out
-
-
- @pytest.mark.level0
- @pytest.mark.platform_arm_ascend_training
- @pytest.mark.platform_x86_ascend_training
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_sit_embedding_lookup_net():
- indices = Tensor(np.array([0, 1, 2]).astype(np.int32))
- label = Tensor(np.random.randn(3, 8).astype(np.float32))
-
- net1 = NetWithEmbeddingLookUp(vocab_size=8, embedding_size=8, target="CPU")
- loss = nn.SoftmaxCrossEntropyWithLogits(reduction="mean")
- optimizer1 = nn.Adam(params=net1.trainable_params(), learning_rate=0.1)
- optimizer1.unique = True
- train_network1 = TrainOneStepCell(WithLossCell(net1, loss), optimizer1)
- train_network1.set_train()
- out1 = train_network1(indices, label)
-
- net2 = NetWithEmbeddingLookUp(vocab_size=8, embedding_size=8, target="CPU")
- optimizer2 = nn.Adam(params=net2.trainable_params(), learning_rate=0.1)
- optimizer2.unique = False
- optimizer2.target = "CPU"
- train_network2 = TrainOneStepCell(WithLossCell(net2, loss), optimizer2)
- train_network2.set_train()
- out2 = train_network2(indices, label)
-
- assert np.allclose(out1.asnumpy(), out2.asnumpy(), 0.001, 0.001)
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