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140 lines
4.1 KiB
140 lines
4.1 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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__all__ = ['ProbabilityEntry', 'CountFilterEntry']
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class EntryAttr(object):
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"""
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Entry Config for paddle.static.nn.sparse_embedding with Parameter Server.
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Examples:
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.. code-block:: python
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import paddle
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sparse_feature_dim = 1024
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embedding_size = 64
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entry = paddle.distributed.ProbabilityEntry(0.1)
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input = paddle.static.data(name='ins', shape=[1], dtype='int64')
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emb = paddle.static.nn.sparse_embedding((
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input=input,
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size=[sparse_feature_dim, embedding_size],
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is_test=False,
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entry=entry,
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param_attr=paddle.ParamAttr(name="SparseFeatFactors",
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initializer=paddle.nn.initializer.Uniform()))
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"""
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def __init__(self):
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self._name = None
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def _to_attr(self):
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"""
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Returns the attributes of this parameter.
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Returns:
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Parameter attributes(map): The attributes of this parameter.
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"""
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raise NotImplementedError("EntryAttr is base class")
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class ProbabilityEntry(EntryAttr):
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"""
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Examples:
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.. code-block:: python
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import paddle
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sparse_feature_dim = 1024
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embedding_size = 64
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entry = paddle.distributed.ProbabilityEntry(0.1)
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input = paddle.static.data(name='ins', shape=[1], dtype='int64')
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emb = paddle.static.nn.sparse_embedding((
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input=input,
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size=[sparse_feature_dim, embedding_size],
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is_test=False,
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entry=entry,
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param_attr=paddle.ParamAttr(name="SparseFeatFactors",
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initializer=paddle.nn.initializer.Uniform()))
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"""
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def __init__(self, probability):
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super(EntryAttr, self).__init__()
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if not isinstance(probability, float):
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raise ValueError("probability must be a float in (0,1)")
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if probability <= 0 or probability >= 1:
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raise ValueError("probability must be a float in (0,1)")
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self._name = "probability_entry"
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self._probability = probability
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def _to_attr(self):
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return ":".join([self._name, str(self._probability)])
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class CountFilterEntry(EntryAttr):
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"""
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Examples:
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.. code-block:: python
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import paddle
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sparse_feature_dim = 1024
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embedding_size = 64
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entry = paddle.distributed.CountFilterEntry(10)
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input = paddle.static.data(name='ins', shape=[1], dtype='int64')
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emb = paddle.static.nn.sparse_embedding((
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input=input,
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size=[sparse_feature_dim, embedding_size],
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is_test=False,
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entry=entry,
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param_attr=paddle.ParamAttr(name="SparseFeatFactors",
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initializer=paddle.nn.initializer.Uniform()))
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"""
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def __init__(self, count_filter):
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super(EntryAttr, self).__init__()
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if not isinstance(count_filter, int):
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raise ValueError(
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"count_filter must be a valid integer greater than 0")
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if count_filter < 0:
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raise ValueError(
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"count_filter must be a valid integer greater or equal than 0")
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self._name = "count_filter_entry"
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self._count_filter = count_filter
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def _to_attr(self):
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return ":".join([self._name, str(self._count_filter)])
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