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84 lines
3.3 KiB
84 lines
3.3 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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syntax = "proto2";
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package paddle;
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/**
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* Configuration structure for parameter
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*/
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enum ParameterInitStrategy {
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PARAMETER_INIT_NORMAL = 0;
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PARAMETER_INIT_UNIFORM = 1;
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}
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message ParameterUpdaterHookConfig {
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// hook type such as 'pruning'
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required string type = 1;
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// this represents the ratio of zero element to be set by the Parameter
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optional double sparsity_ratio = 2 [ default = 0.6 ];
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}
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message ParameterConfig {
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required string name = 1;
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required uint64 size = 2;
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optional double learning_rate = 3 [ default = 1.0 ];
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optional double momentum = 4 [ default = 0.0 ];
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optional double initial_mean = 5 [ default = 0.0 ];
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optional double initial_std = 6 [ default = 0.01 ];
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// use L2-regularization if decay_rate set and decay_rate_l1 not set
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optional double decay_rate = 7 [ default = 0.0 ];
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// use L1-regularization if decay_rate_l1 set
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optional double decay_rate_l1 = 8 [ default = 0.0 ];
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// dims of Parameter, e.g. dims[0] as height, dims[1] as width..
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repeated uint64 dims = 9;
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// the gpu device which the parameter in.
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// Only used by ParallelNeuralNetork. Ignored otherwise.
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optional int32 device = 10 [ default = -1 ];
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// how to init the parameter: 0 -> normal, 1 -> uniform
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// 0: treat initial_mean as mean, intial_std as standard deviation
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// 1: range is (initial_mean - initial_std) to (initial_mean + initial_std)
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optional int32 initial_strategy = 11 [ default = 0 ];
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// define the variance when init the parameter, by height of the Matrix
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optional bool initial_smart = 12 [ default = false ];
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// apply regularization every # batches
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optional int32 num_batches_regularization = 13 [ default = 1 ];
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// if is_sparse is true, para is sparse, else para is dense
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optional bool is_sparse = 14 [ default = false ];
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// if para is sparse, format should be "csc" or "csr", empty means is not
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// sparse
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optional string format = 15 [ default = "" ];
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// sparse remote update or not
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optional bool sparse_remote_update = 16 [ default = false ];
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// gradient clipping threshold, no clipping by default
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optional double gradient_clipping_threshold = 17 [ default = 0.0 ];
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// static parameters are fixed when training
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optional bool is_static = 18 [ default = false ];
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// para_id should NOT be set by config_parser. It is for
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// internal use.
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optional uint64 para_id = 19;
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repeated ParameterUpdaterHookConfig update_hooks = 20;
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// setup load mat -> csr
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optional bool need_compact = 21 [ default = false ];
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// whether to do sparse update for this parameter
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optional bool sparse_update = 22 [ default = false ];
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// whether this parameter is shared or not.
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optional bool is_shared = 23 [ default = false ];
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// parameter block size
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optional uint64 parameter_block_size = 24 [ default = 0 ];
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}
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