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Posted to reviews@spark.apache.org by VinceShieh <gi...@git.apache.org> on 2017/05/08 02:18:19 UTC
[GitHub] spark pull request #17894: [SPARK-17134][ML] Use level 2 BLAS operations in ...
GitHub user VinceShieh opened a pull request:
https://github.com/apache/spark/pull/17894
[SPARK-17134][ML] Use level 2 BLAS operations in LogisticAggregator
## What changes were proposed in this pull request?
Multinomial logistic regression uses LogisticAggregator class for gradient updates.
This PR refactors MLOR to use level 2 BLAS operations for the updates
## How was this patch tested?
Existing test would do
Signed-off-by: VinceShieh <vi...@intel.com>
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/VinceShieh/spark SPARK-17134
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/17894.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #17894
----
commit b4fd733a708feb63254728f3af1fd20ee892d085
Author: VinceShieh <vi...@intel.com>
Date: 2017-05-08T01:54:30Z
[SPARK-17134][ML] Use level 2 BLAS operations in LogisticAggregator
Multinomial logistic regression uses LogisticAggregator class for gradient updates.
This PR refactors MLOR to use level 2 BLAS operations for the updates.
Signed-off-by: VinceShieh <vi...@intel.com>
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[GitHub] spark pull request #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operation...
Posted by WeichenXu123 <gi...@git.apache.org>.
Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17894#discussion_r132069046
--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala ---
@@ -1722,25 +1723,22 @@ private class LogisticAggregator(
var maxMargin = Double.NegativeInfinity
val margins = new Array[Double](numClasses)
+ val featureStdArray = new Array[Double](features.size)
features.foreachActive { (index, value) =>
- val stdValue = value / localFeaturesStd(index)
- var j = 0
- while (j < numClasses) {
- margins(j) += localCoefficients(index * numClasses + j) * stdValue
- j += 1
- }
+ featureStdArray(index) = value / localFeaturesStd(index)
--- End diff --
Here why don't handle the case `localFeaturesStd(index) == 0.0` ?
I remember other place it handle such case.
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[GitHub] spark issue #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operations in Lo...
Posted by VinceShieh <gi...@git.apache.org>.
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/17894
sorry for late update!
we tested on this PR against the current implementation with both dense and sparse(0.95 sparsity):
The test on single machine was run on 100 samples on each feature set scale, we can get performance gain (less training time) on both dense and sparse dataset, on distributed case, we can also achieve a good performance with fine tuning (num_cores, data partitions, etc..), but this change inevitably put more constraint on memory and will bring up GC problem if no enough memory is available on worker node, for sparse dataset on distributed cluster, we are still unable to get a good result, so maybe we should bypass this change for sparse case, but before making such change, I
d like to hear your thoughts on current test result we have, maybe we can make it a better PR with your input :)
Thanks.
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[GitHub] spark pull request #17894: [SPARK-17134][ML] Use level 2 BLAS operations in ...
Posted by VinceShieh <gi...@git.apache.org>.
Github user VinceShieh commented on a diff in the pull request:
https://github.com/apache/spark/pull/17894#discussion_r115415823
--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala ---
@@ -1722,25 +1723,22 @@ private class LogisticAggregator(
var maxMargin = Double.NegativeInfinity
val margins = new Array[Double](numClasses)
+ val featureStdArray = new Array[Double](features.size)
--- End diff --
Agree. Still, we will try benchmark on the sparse dataset, if such change hurt the performance for sparse data, we will bypass this change for it.
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[GitHub] spark issue #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operations in Lo...
Posted by VinceShieh <gi...@git.apache.org>.
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/17894
@sethah Sorry for the late response. Setting as WIP. We have performance data for dense features, data for the sparse feature will be ready soon. thanks.
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[GitHub] spark pull request #17894: [SPARK-17134][ML] Use level 2 BLAS operations in ...
Posted by VinceShieh <gi...@git.apache.org>.
Github user VinceShieh commented on a diff in the pull request:
https://github.com/apache/spark/pull/17894#discussion_r115415580
--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala ---
@@ -23,6 +23,7 @@ import scala.collection.mutable
import breeze.linalg.{DenseVector => BDV}
import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, LBFGSB => BreezeLBFGSB, OWLQN => BreezeOWLQN}
+import com.github.fommil.netlib.BLAS.{getInstance => blas}
--- End diff --
MLLib BLAS doesnt have ger support, we might, of course, add an API support in MLLib Blas for this issue
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[GitHub] spark issue #17894: [SPARK-17134][ML] Use level 2 BLAS operations in Logisti...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17894
**[Test build #76558 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/76558/testReport)** for PR 17894 at commit [`b4fd733`](https://github.com/apache/spark/commit/b4fd733a708feb63254728f3af1fd20ee892d085).
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[GitHub] spark issue #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operations in Lo...
Posted by VinceShieh <gi...@git.apache.org>.
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/17894
Forgot to mention, we observed a nearly 2x performance gain with the help of nativeBLAS- MKL, without a fine tuning, so if we can also make F2J version run faster in distributed cluster than the current design, it would truly be a good PR for community. :)
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[GitHub] spark pull request #17894: [SPARK-17134][ML] Use level 2 BLAS operations in ...
Posted by dbtsai <gi...@git.apache.org>.
Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/17894#discussion_r115423264
--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala ---
@@ -1722,25 +1723,22 @@ private class LogisticAggregator(
var maxMargin = Double.NegativeInfinity
val margins = new Array[Double](numClasses)
+ val featureStdArray = new Array[Double](features.size)
--- End diff --
In my company, we have use-case of handing very sparse input with around 20 non-zero features with millions of total feature space. This implementation will break in this scenario.
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[GitHub] spark pull request #17894: [SPARK-17134][ML] Use level 2 BLAS operations in ...
Posted by dbtsai <gi...@git.apache.org>.
Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/17894#discussion_r115313579
--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala ---
@@ -23,6 +23,7 @@ import scala.collection.mutable
import breeze.linalg.{DenseVector => BDV}
import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, LBFGSB => BreezeLBFGSB, OWLQN => BreezeOWLQN}
+import com.github.fommil.netlib.BLAS.{getInstance => blas}
--- End diff --
We have blas interface in https://github.com/apache/spark/blob/master/mllib-local/src/main/scala/org/apache/spark/ml/linalg/BLAS.scala
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[GitHub] spark pull request #17894: [SPARK-17134][ML] Use level 2 BLAS operations in ...
Posted by dbtsai <gi...@git.apache.org>.
Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/17894#discussion_r115313235
--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala ---
@@ -1722,25 +1723,22 @@ private class LogisticAggregator(
var maxMargin = Double.NegativeInfinity
val margins = new Array[Double](numClasses)
+ val featureStdArray = new Array[Double](features.size)
--- End diff --
This will densify the sparse features. We should handle them differently.
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[GitHub] spark pull request #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operation...
Posted by WeichenXu123 <gi...@git.apache.org>.
Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17894#discussion_r132249512
--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala ---
@@ -1722,25 +1723,22 @@ private class LogisticAggregator(
var maxMargin = Double.NegativeInfinity
val margins = new Array[Double](numClasses)
+ val featureStdArray = new Array[Double](features.size)
features.foreachActive { (index, value) =>
- val stdValue = value / localFeaturesStd(index)
- var j = 0
- while (j < numClasses) {
- margins(j) += localCoefficients(index * numClasses + j) * stdValue
- j += 1
- }
+ featureStdArray(index) = value / localFeaturesStd(index)
--- End diff --
it seems to be a bug, I send a PR to fix this #18896
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[GitHub] spark issue #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operations in Lo...
Posted by VinceShieh <gi...@git.apache.org>.
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/17894
@sethah yes, we only take 100 samples and trained with 3 iterations, numClasss is 20 of our test dataset for single node testing.
Yeah, I also believe it'd have a better result if it's possible to use level3 BLAS, please let me know what I can help with that! but some constraint will still emerge such as memory shortage bringing up GC issue.
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[GitHub] spark issue #17894: [SPARK-17134][ML] Use level 2 BLAS operations in Logisti...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17894
**[Test build #76558 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/76558/testReport)** for PR 17894 at commit [`b4fd733`](https://github.com/apache/spark/commit/b4fd733a708feb63254728f3af1fd20ee892d085).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark issue #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operations in Lo...
Posted by sethah <gi...@git.apache.org>.
Github user sethah commented on the issue:
https://github.com/apache/spark/pull/17894
@VinceShieh Thanks for posting your results. You tested these on datasets with only 100 samples correct? That's probably not a representative use case of a normal workload... Also, how many classes (i.e. `numClasses`) did you use?
I've actually been looking at using level 3 BLAS operations in the logistic aggregator, and initial results showed close to 10x speedups in some cases. I am holding off submitting any code because it would require a fairly significant refactoring of the code, which will be made much easier after https://github.com/apache/spark/pull/17094 is merged. Using level 2 BLAS is a less invasive change, but the test results you show provide rather small speedups.
My preference is to wait a bit and submit a change that incorporates level 3 BLAS in logistic regression. We should get @dbtsai's opinion too.
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[GitHub] spark issue #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operations in Lo...
Posted by WeichenXu123 <gi...@git.apache.org>.
Github user WeichenXu123 commented on the issue:
https://github.com/apache/spark/pull/17894
I am also interested in implementation by level-3 BLAS. Can you post a design doc first?
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[GitHub] spark issue #17894: [SPARK-17134][ML] Use level 2 BLAS operations in Logisti...
Posted by VinceShieh <gi...@git.apache.org>.
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/17894
@hhbyyh performance testing is ongoing, thanks!
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[GitHub] spark issue #17894: [SPARK-17134][ML] Use level 2 BLAS operations in Logisti...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17894
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[GitHub] spark issue #17894: [SPARK-17134][ML] Use level 2 BLAS operations in Logisti...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17894
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[GitHub] spark issue #17894: [SPARK-17134][ML] Use level 2 BLAS operations in Logisti...
Posted by hhbyyh <gi...@git.apache.org>.
Github user hhbyyh commented on the issue:
https://github.com/apache/spark/pull/17894
I'm not sure how much acceleration we can get from Level 2 BLAS. For benchmark, we also would need to evaluate the performance for sparse data.
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[GitHub] spark issue #17894: [SPARK-17134][ML] Use level 2 BLAS operations in Logisti...
Posted by sethah <gi...@git.apache.org>.
Github user sethah commented on the issue:
https://github.com/apache/spark/pull/17894
Would you mind adding `[WIP]` to the title? Without even a benchmark for dense features, this is definitely a work-in-progress.
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[GitHub] spark pull request #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operation...
Posted by WeichenXu123 <gi...@git.apache.org>.
Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17894#discussion_r132068663
--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala ---
@@ -1722,25 +1723,22 @@ private class LogisticAggregator(
var maxMargin = Double.NegativeInfinity
val margins = new Array[Double](numClasses)
+ val featureStdArray = new Array[Double](features.size)
--- End diff --
I suggest change the `featureStdArray` as Aggregator class member, so that avoid each update allocate a new temporary array.
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[GitHub] spark pull request #17894: [SPARK-17134][ML] Use level 2 BLAS operations in ...
Posted by dbtsai <gi...@git.apache.org>.
Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/17894#discussion_r115423381
--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala ---
@@ -23,6 +23,7 @@ import scala.collection.mutable
import breeze.linalg.{DenseVector => BDV}
import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, LBFGSB => BreezeLBFGSB, OWLQN => BreezeOWLQN}
+import com.github.fommil.netlib.BLAS.{getInstance => blas}
--- End diff --
Can you add it in spark ml? Thanks.
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[GitHub] spark issue #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operations in Lo...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17894
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[GitHub] spark pull request #17894: [SPARK-17134][ML] Use level 2 BLAS operations in ...
Posted by viirya <gi...@git.apache.org>.
Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/17894#discussion_r115182882
--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala ---
@@ -23,6 +23,7 @@ import scala.collection.mutable
import breeze.linalg.{DenseVector => BDV}
import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, LBFGSB => BreezeLBFGSB, OWLQN => BreezeOWLQN}
+import com.github.fommil.netlib.BLAS.{getInstance => blas}
--- End diff --
Is it better to use MLlib BLAS interface?
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[GitHub] spark pull request #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operation...
Posted by asfgit <gi...@git.apache.org>.
Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/17894
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[GitHub] spark issue #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operations in Lo...
Posted by HyukjinKwon <gi...@git.apache.org>.
Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/17894
gentle ping @VinceShieh for @WeichenXu123's comment.
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[GitHub] spark issue #17894: [WIP][SPARK-17134][ML] Use level 2 BLAS operations in Lo...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17894
Build finished. Test PASSed.
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