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From "Weichen Xu (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (SPARK-10078) Vector-free L-BFGS
Date Thu, 12 Jan 2017 04:58:16 GMT

    [ https://issues.apache.org/jira/browse/SPARK-10078?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15819964#comment-15819964
] 

Weichen Xu edited comment on SPARK-10078 at 1/12/17 4:58 AM:
-------------------------------------------------------------

[~debasish83]  Considering make VF-LBFGS/VF-OWLQN supporting generic distributed vector interface
(move into breeze ?) and make them support multiple distributed platform(not only spark) will
make the optimization against spark platform difficult I think,
because when we implement VF-LBFGS/VF-OWLQN base on spark, we found that many optimizations
need to combine spark features and the optimizer algorithm closely, make a abstract interface
supporting distributed vector (for example, Vector space operator include dot, add, scale,
persist/unpersist operators and so on...) seems not enough.
I give two simple problem to show the complexity when considering general interface:
1. Look this VF-OWLQN implementation based on spark: https://github.com/yanboliang/spark-vlbfgs/blob/master/src/main/scala/org/apache/spark/ml/optim/VectorFreeOWLQN.scala
We know that OWLQN internal will help compute the pseudo-gradient for L1 reg, look the code
function `calculateComponentWithL1`, here when computing pseudo-gradient using RDD, it also
use an accumulator(only spark have) to calculate the adjusted fnValue, so that will the abstract
interface containing something about `accumulator` in spark ?
2. About persist, unpersist, checkpoint problem in spark. Because of spark lazy computation
feature, improper persist/unpersist/checkpoint order may cause serious problem (may cause
RDD recomputation, checkpoint take no effect and so on), about this complexity, we can take
a look into the VF-BFGS implementation on spark:
https://github.com/yanboliang/spark-vlbfgs/blob/master/src/main/scala/org/apache/spark/ml/optim/VectorFreeLBFGS.scala
it use the pattern "persist current step RDDs, then unpersist previous step RDDs" like many
other algos in spark mllib. The complexity is at, spark always do lazy computation, when you
persist RDD, it do not persist immediately, but postponed to RDD.action called. If the internal
code call `unpersist` too early, it will cause the problem that an RDD haven't been computed
and haven't been actually persisted(although the persist API called), but already been unpersisted,
so that such awful situation will cause the whole RDD lineage recomputation.
This feature may be much different than other distributed platform, so that a general interface
can really handle this problem correctly and still keep high efficient in the same time? 

As the detail problems I list above(I only list a small part problems), in my opinion, breeze
can provide the following base class and/or abstract interface:
* FirstOrderMinimizer
* DiffFunction interface
* LineSearch implementation (including StrongWolfeLinsearch and BacktrackingLinesearch)
* DistributedVector abstract interface
*BUT*, the core logic of VF-LBFGS and VF-OWLQN (based on VF-LBFGS) should be implemented in
spark mllib, for better optimization.


was (Author: weichenxu123):
[~debasish83] 
Considering make VF-LBFGS/VF-OWLQN supporting generic distributed vector interface (move into
breeze ?) and make them support multiple distributed platform(not only spark) will make the
optimization against spark platform difficult I think,
because when we implement VF-LBFGS/VF-OWLQN base on spark, we found that many optimizations
need to combine spark features and the optimizer algorithm closely, make a abstract interface
supporting distributed vector (for example, Vector space operator include dot, add, scale,
persist/unpersist operators and so on...) seems not enough.
I give two simple problem to show the complexity when considering general interface:
1. Look this VF-OWLQN implementation based on spark: https://github.com/yanboliang/spark-vlbfgs/blob/master/src/main/scala/org/apache/spark/ml/optim/VectorFreeOWLQN.scala
We know that OWLQN internal will help compute the pseudo-gradient for L1 reg, look the code
function `calculateComponentWithL1`, here when computing pseudo-gradient using RDD, it also
use an accumulator(only spark have) to calculate the adjusted fnValue, so that will the abstract
interface containing something about `accumulator` in spark ?
2. About persist, unpersist, checkpoint problem in spark. Because of spark lazy computation
feature, improper persist/unpersist/checkpoint order may cause serious problem (may cause
RDD recomputation, checkpoint take no effect and so on), about this complexity, we can take
a look into the VF-BFGS implementation on spark:
https://github.com/yanboliang/spark-vlbfgs/blob/master/src/main/scala/org/apache/spark/ml/optim/VectorFreeLBFGS.scala
it use the pattern "persist current step RDDs, then unpersist previous step RDDs" like many
other algos in spark mllib. The complexity is at, spark always do lazy computation, when you
persist RDD, it do not persist immediately, but postponed to RDD.action called. If the internal
code call `unpersist` too early, it will cause the problem that an RDD haven't been computed
and haven't been actually persisted(although the persist API called), but already been unpersisted,
so that such awful situation will cause the whole RDD lineage recomputation.
This feature may be much different than other distributed platform, so that a general interface
can really handle this problem correctly and still keep high efficient in the same time? 

> Vector-free L-BFGS
> ------------------
>
>                 Key: SPARK-10078
>                 URL: https://issues.apache.org/jira/browse/SPARK-10078
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML
>            Reporter: Xiangrui Meng
>            Assignee: Yanbo Liang
>
> This is to implement a scalable version of vector-free L-BFGS (http://papers.nips.cc/paper/5333-large-scale-l-bfgs-using-mapreduce.pdf).
> Design document:
> https://docs.google.com/document/d/1VGKxhg-D-6-vZGUAZ93l3ze2f3LBvTjfHRFVpX68kaw/edit?usp=sharing



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