ok that was very helpful - I just pushed two additional fixes which
should resolve these issues. The underlying cause was an incorrect
sparse row preallocation (to reduce GC overhead), which resulted in
resizing issues for initial sizes of zero. These two patches fix the
underlying issues, make both MCSR and COO more robust for such
ultra-sparse cases, and improve the performance for converting
ultra-sparse matrices. Thanks again for your help Anthony.
As a side note: our default block size is 1000 but converting to 1024 is
fine if you also set 'sysml.defaultblocksize' to 1024; otherwise there
will be an unnecessary reblock (with shuffle) from block size 1024 to
1000 on the first access of this input.
Regards,
Matthias
On 12/25/2017 3:07 AM, Anthony Thomas wrote:
Thanks Matthias - unfortunately I'm still running into an
ArrayIndexOutOfBounds exception both in reading the file as IJV and when
calling dataFrametoBinaryBlock. Just to confirm: I downloaded and compiled
the latest version using:
git clone https://github.com/apache/systemml
cd systemml
mvn clean package
mvn -version
Apache Maven 3.3.9
Maven home: /usr/share/maven
Java version: 1.8.0_151, vendor: Oracle Corporation
Java home: /usr/lib/jvm/java-8-oracle/jre
Default locale: en_US, platform encoding: UTF-8
OS name: "linux", version: "4.4.0-103-generic", arch: "amd64", family: "unix"
I have a simple driver script written in Scala which calls the API methods.
I compile the script using SBT (version 1.0.4) and submit using
spark-submit (spark version 2.2.0). Here's how I'm calling the methods:
val x = spark.read.parquet(inputPath).select(featureNames)
val mc = new MatrixCharacteristics(199563535L, 71403L, 1024, 1024,
2444225947L) // as far as I know 1024x1024 is default block size in sysml?
println("Reading Direct")
val xrdd = RDDConverterUtils.dataFrameToBinaryBlock(jsc, x, mc,
false, true)
xrdd.count
here is the stacktrace from calling dataFrameToBinaryBlock:
java.lang.ArrayIndexOutOfBoundsException: 0
at
org.apache.sysml.runtime.matrix.data.SparseRowVector.append(SparseRowVector.java:196)
at
org.apache.sysml.runtime.matrix.data.SparseBlockMCSR.append(SparseBlockMCSR.java:267)
at
org.apache.sysml.runtime.matrix.data.MatrixBlock.appendValue(MatrixBlock.java:685)
at
org.apache.sysml.runtime.instructions.spark.utils.RDDConverterUtils$DataFrameToBinaryBlockFunction.call(RDDConverterUtils.java:1067)
at
org.apache.sysml.runtime.instructions.spark.utils.RDDConverterUtils$DataFrameToBinaryBlockFunction.call(RDDConverterUtils.java:999)
at
org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
at
org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:797)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:797)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
and here is the stacktrace from calling "read()" directly:
java.lang.ArrayIndexOutOfBoundsException: 2
at
org.apache.sysml.runtime.matrix.data.SparseBlockCOO.sort(SparseBlockCOO.java:399)
at
org.apache.sysml.runtime.matrix.data.MatrixBlock.mergeIntoSparse(MatrixBlock.java:1784)
at
org.apache.sysml.runtime.matrix.data.MatrixBlock.merge(MatrixBlock.java:1687)
at
org.apache.sysml.runtime.instructions.spark.utils.RDDAggregateUtils$MergeBlocksFunction.call(RDDAggregateUtils.java:627)
at
org.apache.sysml.runtime.instructions.spark.utils.RDDAggregateUtils$MergeBlocksFunction.call(RDDAggregateUtils.java:596)
at
org.apache.spark.api.java.JavaPairRDD$$anonfun$toScalaFunction2$1.apply(JavaPairRDD.scala:1037)
at
org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:189)
at
org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:188)
at
org.apache.spark.util.collection.AppendOnlyMap.changeValue(AppendOnlyMap.scala:150)
at
org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.changeValue(SizeTrackingAppendOnlyMap.scala:32)
at
org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:194)
at
org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:63)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Best,
Anthony
On Sun, Dec 24, 2017 at 3:14 AM, Matthias Boehm <mboe...@gmail.com> wrote:
Thanks again for catching this issue Anthony - this IJV reblock issue with
large ultra-sparse matrices is now fixed in master. It likely did not show
up on the 1% sample because the data was small enough to read it directly
into the driver.
However, the dataFrameToBinaryBlock might be another issue that I could
not reproduce yet, so it would be very helpful if you could give it another
try. Thanks.
Regards,
Matthias
On 12/24/2017 9:57 AM, Matthias Boehm wrote:
Hi Anthony,
thanks for helping to debug this issue. There are no limits other than
the dimensions and number of non-zeros being of type long. It sounds
more like an issues of converting special cases of ultra-sparse
matrices. I'll try to reproduce this issue and give an update as soon as
I know more. In the meantime, could you please (a) also provide the
stacktrace of calling dataFrameToBinaryBlock with SystemML 1.0, and (b)
try calling your IJV conversion script via spark submit to exclude that
this issue is API-related? Thanks.
Regards,
Matthias
On 12/24/2017 1:40 AM, Anthony Thomas wrote:
Okay thanks for the suggestions - I upgraded to 1.0 and tried providing
dimensions and blocksizes to dataFrameToBinaryBlock both without
success. I
additionally wrote out the matrix to hdfs in IJV format and am still
getting the same error when calling "read()" directly in the DML.
However,
I created a 1% sample of the original data in IJV format and SystemML was
able to read the smaller file without any issue. This would seem to
suggest
that either there is some corruption in the full file or I'm running into
some limit. The matrix is on the larger side: 1.9e8 rows by 7e4 cols with
2.4e9 nonzero values, but this seems like it should be well within the
limits of what SystemML/Spark can handle. I also checked for obvious data
errors (file is not 1 indexed or contains blank lines). In case it's
helpful, the stacktrace from reading the data from hdfs in IJV format is
attached. Thanks again for your help - I really appreciate it.
00:24:18 WARN TaskSetManager: Lost task 30.0 in stage 0.0 (TID 126,
10.11.10.13, executor 0): java.lang.ArrayIndexOutOfBoundsException
at java.lang.System.arraycopy(Native Method)
at
org.apache.sysml.runtime.matrix.data.SparseBlockCOO.shiftRig
htByN(SparseBlockCOO.java:594)
at
org.apache.sysml.runtime.matrix.data.SparseBlockCOO.set(
SparseBlockCOO.java:323)
at
org.apache.sysml.runtime.matrix.data.MatrixBlock.mergeIntoSp
arse(MatrixBlock.java:1790)
at
org.apache.sysml.runtime.matrix.data.MatrixBlock.merge(Matri
xBlock.java:1736)
at
org.apache.sysml.runtime.instructions.spark.utils.RDDAggrega
teUtils$MergeBlocksFunction.call(RDDAggregateUtils.java:627)
at
org.apache.sysml.runtime.instructions.spark.utils.RDDAggrega
teUtils$MergeBlocksFunction.call(RDDAggregateUtils.java:596)
at
org.apache.spark.api.java.JavaPairRDD$$anonfun$toScalaFuncti
on2$1.apply(JavaPairRDD.scala:1037)
at
org.apache.spark.util.collection.ExternalSorter$$anonfun$5.
apply(ExternalSorter.scala:189)
at
org.apache.spark.util.collection.ExternalSorter$$anonfun$5.
apply(ExternalSorter.scala:188)
at
org.apache.spark.util.collection.AppendOnlyMap.changeValue(
AppendOnlyMap.scala:150)
at
org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.c
hangeValue(SizeTrackingAppendOnlyMap.scala:32)
at
org.apache.spark.util.collection.ExternalSorter.insertAll(
ExternalSorter.scala:194)
at
org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortSh
uffleWriter.scala:63)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMap
Task.scala:96)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMap
Task.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPool
Executor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoo
lExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Anthony
On Sat, Dec 23, 2017 at 4:27 AM, Matthias Boehm <mboe...@gmail.com>
wrote:
Given the line numbers from the stacktrace, it seems that you use a
rather
old version of SystemML. Hence, I would recommend to upgrade to SystemML
1.0 or at least 0.15 first.
If the error persists or you're not able to upgrade, please try to call
dataFrameToBinaryBlock with provided matrix characteristics of
dimensions
and blocksizes. The issue you've shown usually originates from incorrect
meta data (e.g., negative number of columns or block sizes), which
prevents
the sparse rows from growing to the necessary sizes.
Regards,
Matthias
On 12/22/2017 10:42 PM, Anthony Thomas wrote:
Hi Matthias,
Thanks for the help! In response to your questions:
1. Sorry - this was a typo: the correct schema is: [y: int,
features:
vector] - the column "features" was created using Spark's
VectorAssembler
and the underlying type is an
org.apache.spark.ml.linalg.SparseVector.
Calling x.schema results in: org.apache.spark.sql.types.StructType
=
StructType(StructField(features,org.apache.spark.ml.
linalg.VectorUDT@3bfc3ba7,true)
2. "y" converts fine - it appears the only issue is with X. The
script
still crashes when running "print(sum(X))". The full stack trace is
attached at the end of the message.
3. Unfortunately, the error persists when calling
RDDConverterUtils.dataFrameToBinaryBlock directly.
4. Also just in case this matters: I'm packaging the script into
a jar
using SBT assembly and submitting via spark-submit.
Here's an updated script:
val input_df = spark.read.parquet(inputPath)
val x = input_df.select(featureNames)
val y = input_df.select("y")
val meta_x = new MatrixMetadata(DF_VECTOR)
val meta_y = new MatrixMetadata(DF_DOUBLES)
val script_x = dml("print(sum(X))").in("X", x, meta_x)
println("Reading X")
val res_x = ml.execute(script_x)
Here is the output of the runtime plan generated by SystemML:
# EXPLAIN (RUNTIME):
# Memory Budget local/remote = 76459MB/?MB/?MB/?MB
# Degree of Parallelism (vcores) local/remote = 24/?
PROGRAM ( size CP/SP = 3/0 )
--MAIN PROGRAM
----GENERIC (lines 1-2) [recompile=false]
------CP uak+ X.MATRIX.DOUBLE _Var0.SCALAR.STRING 24
------CP print _Var0.SCALAR.STRING.false _Var1.SCALAR.STRING
------CP rmvar _Var0 _Var1
And the resulting stack trace:
7/12/22 21:27:20 WARN TaskSetManager: Lost task 3.0 in stage 7.0
(TID 205,
10.11.10.12, executor 3): java.lang.ArrayIndexOutOfBoundsException: 0
at org.apache.sysml.runtime.matrix.data.SparseRow.append(
SparseRow.java:215)
at org.apache.sysml.runtime.matrix.data.SparseBlockMCSR.
append(SparseBlockMCSR.java:253)
at org.apache.sysml.runtime.matrix.data.MatrixBlock.
appendValue(MatrixBlock.java:663)
at org.apache.sysml.runtime.instructions.spark.utils.RDDConvert
erUtils$
DataFrameToBinaryBlockFunction.call(RDDConverterUtils.java:1076)
at org.apache.sysml.runtime.instructions.spark.utils.RDDConvert
erUtils$
DataFrameToBinaryBlockFunction.call(RDDConverterUtils.java:1008)
at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.
apply(JavaRDDLike.scala:186)
at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.
apply(JavaRDDLike.scala:186)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$
anonfun$apply$23.apply(RDD.scala:797)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$
anonfun$apply$23.apply(RDD.scala:797)
at org.apache.spark.rdd.MapPartitionsRDD.compute(
MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(
ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(
ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.
scala:335)
at java.util.concurrent.ThreadPoolExecutor.runWorker(
ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(
ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
17/12/22 21:27:21 ERROR TaskSetManager: Task 19 in stage 7.0 failed 4
times; aborting job
Exception in thread "main" org.apache.sysml.api.mlcontext
.MLContextException:
Exception when executing script
at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.
java:311)
at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.
java:280)
at SystemMLMLAlgorithms$$anonfun$main$1.apply$mcVI$sp(systemml_
ml_algorithms.scala:63)
at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:
160)
at SystemMLMLAlgorithms$.main(systemml_ml_algorithms.scala:60)
at SystemMLMLAlgorithms.main(systemml_ml_algorithms.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(
NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(
DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$
deploy$SparkSubmit$$runMain(SparkSubmit.scala:755)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(
SparkSubmit.scala:180)
at
org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:
119)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: org.apache.sysml.api.mlcontext.MLContextException:
Exception
occurred while executing runtime program
at org.apache.sysml.api.mlcontext.ScriptExecutor.executeRuntime
Program(
ScriptExecutor.java:390)
at org.apache.sysml.api.mlcontext.ScriptExecutor.
execute(ScriptExecutor.java:298)
at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.
java:303)
... 14 more
Caused by: org.apache.sysml.runtime.DMLRuntimeException:
org.apache.sysml.runtime.DMLRuntimeException: ERROR: Runtime error in
program block generated from statement block between lines 1 and 2 --
Error
evaluating instruction: CP°uak+°X·MATRIX·DOUBLE°_Var0·SCALAR·STRING°24
at org.apache.sysml.runtime.controlprogram.Program.
execute(Program.java:130)
at org.apache.sysml.api.mlcontext.ScriptExecutor.executeRuntime
Program(
ScriptExecutor.java:388)
... 16 more
...
On Fri, Dec 22, 2017 at 5:48 AM, Matthias Boehm <mboe...@gmail.com>
wrote:
well, let's do the following to figure this out:
1) If the schema is indeed [label: Integer, features: SparseVector],
please change the third line to val y = input_data.select("label").
2) For debugging, I would recommend to use a simple script like
"print(sum(X));" and try converting X and y separately to isolate the
problem.
3) If it's still failing, it would be helpful to known (a) if it's an
issue of converting X, y, or both, as well as (b) the full stacktrace.
4) As a workaround you might also call our internal converter directly
via:
RDDConverterUtils.dataFrameToBinaryBlock(jsc, df, mc, containsID,
isVector),
where jsc is the java spark context, df is the dataset, mc are matrix
characteristics (if unknown, simply use new MatrixCharacteristics()),
containsID indicates if the dataset contains a column "__INDEX"
with the
row indexes, and isVector indicates if the passed datasets contains
vectors
or basic types such as double.
Regards,
Matthias
On 12/22/2017 12:00 AM, Anthony Thomas wrote:
Hi SystemML folks,
I'm trying to pass some data from Spark to a DML script via the
MLContext
API. The data is derived from a parquet file containing a
dataframe with
the schema: [label: Integer, features: SparseVector]. I am doing the
following:
val input_data = spark.read.parquet(inputPath)
val x = input_data.select("features")
val y = input_data.select("y")
val x_meta = new MatrixMetadata(DF_VECTOR)
val y_meta = new MatrixMetadata(DF_DOUBLES)
val script = dmlFromFile(s"${script_path}/script.dml").
in("X", x, x_meta).
in("Y", y, y_meta)
...
However, this results in an error from SystemML:
java.lang.ArrayIndexOutOfBoundsException: 0
I'm guessing this has something to do with SparkML being zero indexed
and
SystemML being 1 indexed. Is there something I should be doing
differently
here? Note that I also tried converting the dataframe to a
CoordinateMatrix
and then creating an RDD[String] in IJV format. That too resulted in
"ArrayIndexOutOfBoundsExceptions." I'm guessing there's something
simple
I'm doing wrong here, but I haven't been able to figure out exactly
what.
Please let me know if you need more information (I can send along the
full
error stacktrace if that would be helpful)!
Thanks,
Anthony
