forked from mindspore-Ecosystem/mindspore
146 lines
5.5 KiB
Python
146 lines
5.5 KiB
Python
# 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 pytest
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore.common.api import _executor
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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from mindspore import Tensor, context
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from mindspore.nn import TrainOneStepCell, Adam
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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grad_all = C.GradOperation(get_all=True)
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@pytest.fixture(name="test_context")
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def _test_context():
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context.set_context(enable_sparse=True)
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yield
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context.set_context(enable_sparse=False)
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context.reset_auto_parallel_context()
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x, y, z):
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return grad_all(self.network)(x, y, z)
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class NetWithLoss(nn.Cell):
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def __init__(self, network):
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super(NetWithLoss, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x, y, z):
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predict = self.network(x, y, z)
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return self.loss(predict)
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class Net(nn.Cell):
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def __init__(self, shape, field_size=10, slice_mode=nn.EmbeddingLookup.BATCH_SLICE, target="Device",
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operator='SUM'):
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super().__init__()
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self.embedding = nn.MultiFieldEmbeddingLookup(vocab_size=32, embedding_size=64, target=target,
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field_size=field_size, slice_mode=slice_mode, operator=operator)
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self.reshape = P.Reshape()
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self.batch_size = shape[0]
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def construct(self, x, y, z):
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out = self.embedding(x, y, z)
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out = self.reshape(out, (self.batch_size, -1))
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return out
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def compile_net(net, shape):
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x = Tensor(np.ones(shape), dtype=ms.int32)
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y = Tensor(np.ones(shape), dtype=ms.float32)
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z = Tensor(np.ones(shape), dtype=ms.int32)
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optimizer = Adam(net.trainable_params(), learning_rate=0.1)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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train_net.set_train()
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_executor.compile(train_net, x, y, z)
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context.reset_auto_parallel_context()
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def test_embeddinglookup_batch_parallel_sum(test_context):
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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shape = [64, 64]
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net = NetWithLoss(Net(shape, field_size=10, target='DEVICE'))
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compile_net(net, shape)
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def test_embeddinglookup_row_parallel_sum(test_context):
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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shape = [64, 64]
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net = NetWithLoss(Net(shape, field_size=9, slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE, target='DEVICE'))
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compile_net(net, shape)
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def test_embeddinglookup_column_parallel_sum(test_context):
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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shape = [64, 64]
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net = NetWithLoss(Net(shape, field_size=10, slice_mode=nn.EmbeddingLookup.TABLE_COLUMN_SLICE, target='DEVICE'))
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compile_net(net, shape)
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def test_embeddinglookup_batch_parallel_mean(test_context):
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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shape = [64, 64]
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net = NetWithLoss(Net(shape, field_size=1, target='DEVICE', operator='MEAN'))
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compile_net(net, shape)
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def test_embeddinglookup_column_parallel_mean(test_context):
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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shape = [64, 64]
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net = NetWithLoss(Net(shape, target='DEVICE', slice_mode=nn.EmbeddingLookup.TABLE_COLUMN_SLICE, operator='MEAN'))
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compile_net(net, shape)
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def test_embeddinglookup_row_parallel_mean(test_context):
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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shape = [64, 64]
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net = NetWithLoss(Net(shape, target='DEVICE', slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE, operator='MEAN'))
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compile_net(net, shape)
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def test_embeddinglookup_batch_parallel_max(test_context):
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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shape = [64, 64]
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net = NetWithLoss(Net(shape, target='DEVICE', operator='MAX'))
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compile_net(net, shape)
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def test_embeddinglookup_column_parallel_max(test_context):
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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shape = [64, 64]
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net = NetWithLoss(Net(shape, target='DEVICE', slice_mode=nn.EmbeddingLookup.TABLE_COLUMN_SLICE, operator='MAX'))
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compile_net(net, shape)
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def test_embeddinglookup_row_parallel_max(test_context):
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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shape = [64, 64]
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net = NetWithLoss(Net(shape, target='DEVICE', slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE, operator='MAX'))
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compile_net(net, shape)
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