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106 lines
3.3 KiB
106 lines
3.3 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import print_function
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import paddle
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paddle.enable_static()
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import unittest
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import paddle.fluid as fluid
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from paddle.distributed import ProbabilityEntry, CountFilterEntry
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class EntryAttrChecks(unittest.TestCase):
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def base(self):
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with self.assertRaises(NotImplementedError):
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from paddle.distributed.entry_attr import EntryAttr
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base = EntryAttr()
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base._to_attr()
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def probability_entry(self):
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prob = ProbabilityEntry(0.5)
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ss = prob._to_attr()
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self.assertEqual("probability_entry:0.5", ss)
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with self.assertRaises(ValueError):
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prob1 = ProbabilityEntry("none")
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with self.assertRaises(ValueError):
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prob2 = ProbabilityEntry(-1)
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def countfilter_entry(self):
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counter = CountFilterEntry(20)
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ss = counter._to_attr()
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self.assertEqual("count_filter_entry:20", ss)
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with self.assertRaises(ValueError):
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counter1 = CountFilterEntry("none")
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with self.assertRaises(ValueError):
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counter2 = CountFilterEntry(-1)
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def spaese_layer(self):
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prog = fluid.Program()
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scope = fluid.core.Scope()
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with fluid.scope_guard(scope):
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with fluid.program_guard(prog):
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input = fluid.layers.data(
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name="dnn_data",
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shape=[-1, 1],
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dtype="int64",
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lod_level=1,
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append_batch_size=False)
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prob = ProbabilityEntry(0.5)
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emb = paddle.static.nn.sparse_embedding(
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input=input,
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size=[100, 10],
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is_test=False,
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entry=prob,
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param_attr=fluid.ParamAttr(name="deep_embedding"))
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pool = fluid.layers.sequence_pool(input=emb, pool_type="sum")
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predict = fluid.layers.fc(input=pool, size=2, act='softmax')
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block = prog.global_block()
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for op in block.ops:
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if op.type == "lookup_table":
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entry = op.attr("entry")
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is_test = op.attr("is_test")
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is_sparse = op.attr("is_sparse")
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is_distributed = op.attr("is_distributed")
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self.assertEqual(entry, "probability_entry:0.5")
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self.assertTrue(is_distributed)
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self.assertTrue(is_sparse)
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self.assertFalse(is_test)
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class TestEntryAttrs(EntryAttrChecks):
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def test_base(self):
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self.base()
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def test_prob(self):
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self.probability_entry()
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def test_counter(self):
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self.countfilter_entry()
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def test_spaese_embedding_layer(self):
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self.spaese_layer()
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if __name__ == '__main__':
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unittest.main()
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