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104 lines
2.5 KiB
104 lines
2.5 KiB
/* Copyright (c) 2016 Baidu, Inc. 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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#pragma once
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#include <string>
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namespace paddle {
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namespace enumeration_wrapper {
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enum PassType {
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PASS_TRAIN, // Train pass
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PASS_TEST, // Test pass
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PASS_GC, // Gradient Check pass
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PASS_METRIC, // pass for generate template output with no drop rate.
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// pass for metric learning training with metric learning error, only used
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// when we are doing KNN evaluation.
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PASS_METRIC_TRAIN,
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PASS_METRIC_TRAIN_WITH_NOERROR, // Pass for metric learning training
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// with no evaluation.
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};
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enum ParameterType {
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PARAMETER_VALUE = 0,
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PARAMETER_GRADIENT,
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PARAMETER_MOMENTUM,
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// Used by ParameterAverager
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PARAMETER_SUM1,
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PARAMETER_SUM2,
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PARAMETER_SUM3,
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// also used by AdagradParameterUpdater/AdadeltaParameterUpdater
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PARAMETER_LEARNING_RATE,
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// Used by Sparse SGD update
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PARAMETER_UPDATE_TIME,
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// Used by async_sgd
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// Change of the parameter since last remote update
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PARAMETER_DELTA,
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// Used by BatchRemoteParameterUpdater
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PARAMETER_GRADIENT_SUM,
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// Used by AdagradParameterUpdater/AdadeltaParameterUpdater
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PARAMETER_GRADIENT_SQURESUM,
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PARAMETER_GRADIENT_SQURESUM1,
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// Used by SparseConnected layer
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PARAMETER_ROWS,
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PARAMETER_COLS,
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// Used by Adam Optimizer.
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PARAMETER_SECOND_MOMENTUM,
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// Used By AdaMax Optimizer.
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PARAMETER_WEIGHTED_INFINITY_NORM,
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// Used by remote parameter average
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PARAMETER_APPLY,
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// Used by sparse momentum
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PARAMETER_MOMENTUM_UT,
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PARAMETER_MOMENTUM_VT,
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NUM_PARAMETER_TYPES,
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};
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} // namespace enumeration_wrapper
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//! explicit import enum into paddle namespace.
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using namespace enumeration_wrapper; // NOLINT
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class TrainAlgorithm {
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public:
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static const std::string SGD;
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static const std::string AsyncSGD;
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static const std::string OWLQN;
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static inline bool isValid(const std::string& algo) {
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return algo == SGD || algo == AsyncSGD || algo == OWLQN;
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}
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};
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#ifdef __AVX__
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const int ALIGN_HINT = 32;
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#else
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const int ALIGN_HINT = 16;
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#endif
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} // namespace paddle
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