RNN Example not supported for MXNet Scala

[lanking520]

This model has not been updated since 2017/07, the RNN example for LstmBucketing is no longer runnable.
Please contribute to RNN support on Scala for the current version. If you would like to run this example for now, please revert your MXNet version to v0.12.

In summary, I suspect that some operator changes caused this issue.

• LstmBucketing
• TestCharRnn
• TrainCharRNN

Please contact @lanking520 or @nswamy if you would like to dig in and fix this problem. The following is a way to reproduce the issues I found:

LSTM Bucketing

Setup

Download the required ptb file from here:

wget https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/ptb/ptb.train.txt
wget https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/ptb/ptb.valid.txt

You can use this script to run the model
or manually identify the file path by following this

Additional step to get it work

Please add the follows to IO.scala

if (shapes.toIndexedSeq(0)._2.length == 2) {
   shapes.map { case (k, s) => new DataDesc(k, s, layout = "NT") }.toIndexedSeq
}
else shapes.map { case (k, s) => new DataDesc(k, s) }.toIndexedSeq

Problem that I found

Segmentation fault caused by C++ backend

#
# A fatal error has been detected by the Java Runtime Environment:
#
#  SIGSEGV (0xb) at pc=0x000000012d026fd8, pid=56081, tid=0x0000000000000e03
#
# JRE version: Java(TM) SE Runtime Environment (8.0_171-b11) (build 1.8.0_171-b11)
# Java VM: Java HotSpot(TM) 64-Bit Server VM (25.171-b11 mixed mode bsd-amd64 compressed oops)
# Problematic frame:
# C  [mxnet-scala+0x120afd8]
[error occurred during error reporting (printing problematic frame), id 0xe0000000]

# Failed to write core dump. Core dumps have been disabled. To enable core dumping, try "ulimit -c unlimited" before starting Java again
#
# An error report file with more information is saved as:
# /Users/qingla/Downloads/ITJ-Proj/hs_err_pid56081.log
Compiled method (nm)    2499 1793     n 0       org.apache.mxnet.LibInfo::mxNDArrayGetShape (native)
 total in heap  [0x00000001133e7c50,0x00000001133e7fc0] = 880
 relocation     [0x00000001133e7d78,0x00000001133e7db8] = 64
 main code      [0x00000001133e7dc0,0x00000001133e7fb8] = 504
 oops           [0x00000001133e7fb8,0x00000001133e7fc0] = 8
#
# If you would like to submit a bug report, please visit:
#   http://bugreport.java.com/bugreport/crash.jsp
# The crash happened outside the Java Virtual Machine in native code.
# See problematic frame for where to report the bug.
#

Process finished with exit code 134 (interrupted by signal 6: SIGABRT)

NDArray size mismatch

2018-07-05 14:20:37,771 [main] [org.apache.mxnet.examples.rnn.LstmBucketing] [ERROR] - requirement failed: array size (320) do not match the size of NDArray (1)
java.lang.IllegalArgumentException: requirement failed: array size (320) do not match the size of NDArray (1)
	at scala.Predef$.require(Predef.scala:224)
	at org.apache.mxnet.NDArray.syncCopyfrom(NDArray.scala:623)
	at org.apache.mxnet.NDArray.set(NDArray.scala:755)
	at org.apache.mxnet.examples.rnn.BucketIo$BucketSentenceIter.next(BucketIo.scala:185)
	at org.apache.mxnet.module.BaseModule$$anonfun$fit$1.apply$mcVI$sp(BaseModule.scala:417)
	at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:160)
	at org.apache.mxnet.module.BaseModule.fit(BaseModule.scala:411)
	at org.apache.mxnet.examples.rnn.LstmBucketing$.main(LstmBucketing.scala:118)
	at org.apache.mxnet.examples.rnn.LstmBucketing.main(LstmBucketing.scala)

Data name not the same

2018-07-05 14:41:46,508 [main] [org.apache.mxnet.examples.rnn.LstmBucketing] [ERROR] - Data provided by data_shapes don't match names specified by data_names (DataDesc[data,(32,20),float32,NT], DataDesc[l0_init_c_beta,(32,200),float32,NT], DataDesc[l1_init_c_beta,(32,200),float32,NT], DataDesc[l0_init_h_beta,(32,200),float32,NT], DataDesc[l1_init_h_beta,(32,200),float32,NT] vs. data)
java.lang.IllegalArgumentException: Data provided by data_shapes don't match names specified by data_names (DataDesc[data,(32,20),float32,NT], DataDesc[l0_init_c_beta,(32,200),float32,NT], DataDesc[l1_init_c_beta,(32,200),float32,NT], DataDesc[l0_init_h_beta,(32,200),float32,NT], DataDesc[l1_init_h_beta,(32,200),float32,NT] vs. data)
	at org.apache.mxnet.module.Module._checkNamesMatch(Module.scala:317)
	at org.apache.mxnet.module.Module._parseDataDesc(Module.scala:329)
	at org.apache.mxnet.module.Module.reshape(Module.scala:342)
	at org.apache.mxnet.module.Module.forward(Module.scala:450)
	at org.apache.mxnet.module.BucketingModule.forward(BucketingModule.scala:314)
	at org.apache.mxnet.module.BaseModule.forwardBackward(BaseModule.scala:153)
	at org.apache.mxnet.module.BaseModule$$anonfun$fit$1.apply$mcVI$sp(BaseModule.scala:420)
	at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:160)
	at org.apache.mxnet.module.BaseModule.fit(BaseModule.scala:411)
	at org.apache.mxnet.examples.rnn.LstmBucketing$.main(LstmBucketing.scala:118)
	at org.apache.mxnet.examples.rnn.LstmBucketing.main(LstmBucketing.scala)
2018-07-05 14:41:46,516 [Thread-0] [org.apache.mxnet.util.NativeLibraryLoader] [INFO] - Deleting /var/folders/qy/41_sjvss273_xcdjd_y74rzmzrh5b6/T/mxnet7527817581344011515/mxnet-scala
 --cpus VAL            : the cpus will be used, e.g. '0,1,2,3'
2018-07-05 14:41:46,516 [Thread-0] [org.apache.mxnet.util.NativeLibraryLoader] [INFO] - Deleting /var/folders/qy/41_sjvss273_xcdjd_y74rzmzrh5b6/T/mxnet7527817581344011515

TestCharRNN

Please follow this tutorial

The problem I have

2018-07-05 15:49:03,982 [main] [org.apache.mxnet.examples.rnn.TrainCharRnn] [ERROR] - [15:49:03] src/operator/contrib/../tensor/../elemwise_op_common.h:123: Check failed: assign(&dattr, (*vec)[i]) Incompatible attr in node  at 0-th output: expected [84,256], got [83,256]

Stack trace returned 10 entries:
[bt] (0) 0   mxnet-scala                         0x00000001287b0420 mxnet-scala + 25632
[bt] (1) 1   mxnet-scala                         0x00000001287b01cf mxnet-scala + 25039
[bt] (2) 2   mxnet-scala                         0x00000001288c2093 mxnet-scala + 1147027
[bt] (3) 3   mxnet-scala                         0x00000001288c1d16 mxnet-scala + 1146134
[bt] (4) 4   mxnet-scala                         0x00000001288c023b mxnet-scala + 1139259
[bt] (5) 5   mxnet-scala                         0x0000000129a83a41 mxnet-scala + 19765825
[bt] (6) 6   mxnet-scala                         0x0000000129a82439 mxnet-scala + 19760185
[bt] (7) 7   mxnet-scala                         0x00000001299d2745 mxnet-scala + 19040069
[bt] (8) 8   mxnet-scala                         0x00000001299d3707 mxnet-scala + 19044103
[bt] (9) 9   mxnet-scala                         0x000000012a01ae68 Java_org_apache_mxnet_LibInfo_mxImperativeInvoke + 424


org.apache.mxnet.MXNetError: [15:49:03] src/operator/contrib/../tensor/../elemwise_op_common.h:123: Check failed: assign(&dattr, (*vec)[i]) Incompatible attr in node  at 0-th output: expected [84,256], got [83,256]

Stack trace returned 10 entries:
[bt] (0) 0   mxnet-scala                         0x00000001287b0420 mxnet-scala + 25632
[bt] (1) 1   mxnet-scala                         0x00000001287b01cf mxnet-scala + 25039
[bt] (2) 2   mxnet-scala                         0x00000001288c2093 mxnet-scala + 1147027
[bt] (3) 3   mxnet-scala                         0x00000001288c1d16 mxnet-scala + 1146134
[bt] (4) 4   mxnet-scala                         0x00000001288c023b mxnet-scala + 1139259
[bt] (5) 5   mxnet-scala                         0x0000000129a83a41 mxnet-scala + 19765825
[bt] (6) 6   mxnet-scala                         0x0000000129a82439 mxnet-scala + 19760185
[bt] (7) 7   mxnet-scala                         0x00000001299d2745 mxnet-scala + 19040069
[bt] (8) 8   mxnet-scala                         0x00000001299d3707 mxnet-scala + 19044103
[bt] (9) 9   mxnet-scala                         0x000000012a01ae68 Java_org_apache_mxnet_LibInfo_mxImperativeInvoke + 424


	at org.apache.mxnet.Base$.checkCall(Base.scala:131)
	at org.apache.mxnet.NDArray$.genericNDArrayFunctionInvoke(NDArray.scala:101)
	at org.apache.mxnet.NDArray.copyTo(NDArray.scala:968)
	at org.apache.mxnet.examples.rnn.RnnModel$LSTMInferenceModel$$anonfun$3.apply(RnnModel.scala:44)
	at org.apache.mxnet.examples.rnn.RnnModel$LSTMInferenceModel$$anonfun$3.apply(RnnModel.scala:42)
	at scala.collection.Iterator$class.foreach(Iterator.scala:893)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
	at scala.collection.MapLike$DefaultKeySet.foreach(MapLike.scala:174)
	at org.apache.mxnet.examples.rnn.RnnModel$LSTMInferenceModel.<init>(RnnModel.scala:42)
	at org.apache.mxnet.examples.rnn.TestCharRnn$.main(TestCharRnn.scala:62)
	at org.apache.mxnet.examples.rnn.TestCharRnn.main(TestCharRnn.scala)
2018-07-05 15:49:03,988 [Thread-0] [org.apache.mxnet.util.NativeLibraryLoader] [INFO] - Deleting /var/folders/qy/41_sjvss273_xcdjd_y74rzmzrh5b6/T/mxnet607484524537890023/mxnet-scala
2018-07-05 15:49:03,989 [Thread-0] [org.apache.mxnet.util.NativeLibraryLoader] [INFO] - Deleting /var/folders/qy/41_sjvss273_xcdjd_y74rzmzrh5b6/T/mxnet607484524537890023

[szha]

Note that we're no longer using ptb. Instead we're using Sherlock Holmes dataset.

[lanking520]

@szha , Do you know if there is any operator changes/abandoned related the RNN for the past 1 year? The code we have written 1 year ago cannot work for now. I believe there shouldn't be any changes on Scala side.

[szha]

Not that I'm aware of. Other than the fused RNN kernels in CPU, there hasn't been much change since September 2016. https://github.com/apache/incubator-mxnet/commits/master/src/operator/rnn-inl.h

[lanking520]

I can reproduce the same issue on

• v1.1.0
  However, v1.0.0 works just fine for LSTMBucketing. There has to be something changed from 1.0.0 -> 1.1.0 cause that break. @yzhliu Any major change on Scala happened in the middle?

The TestCharRNN still contains the same issue on both 1.1.0 and 1.0.0

[Update] The changes on Scala package was the garbage collection, see more in here.

[yzhliu]

Can we confirm whether it is caused by Scala frontend changes? e.g., use scala frontend @v1.0.0 but backend @v1.1.0 (or inverse) to see if it also crashes.

[lanking520]

@yzhliu tested on v1.1.0 backend and v1.0.0 frontend and didn't reproduce the issue. So I think it should be Scala side some code changes on dispose that cause the crashes

Files add dispose method:

• module/DataParallelExecutorGroup.scala
• module/BaseModule.scala (reverting this changes, no error thrown)
• io/NDArrayIter.scala
• NDArray.scala (tested, not this one)

@mxnet-jenkins

[lanking520]

The root cause of this issue is this line. The databatch got disposed due to the implementation of DataIter is different. The RNN module define the .next() as a reference copy of NDArray. Dispose that will cause losing the training data

[nswamy]

@lanking520 The code you pointed out is correct, it disposes the NDArrays in the DataBatch created using the Slice operator. Slice operator does not create new NDArrays instead gives a reference to the original NDArray with an offset. The pointers to the NDArrays(both sliced and original) use shared_ptr which only decrements the reference count, so it won't free the original NDArray correspondingly if you don't dispose the Sliced NDArray it won't free the original even if the original NDArray is freed.

I suspect the corruption to be elsewhere.

[lanking520]

@nswamy and @yzhliu , thanks for your recommendation on the changes. There should be no problem when we dispose a dataBatch in the DataIter. The core problem happened in the RNN BucketingIter. The next() method did not brings a copy. Instead, a reference of NDArray created in the Iterator and passed to the DataIter. The C++ side only see one reference in the original data. In that case, we lose information and that cause the crash.

Solution: pass copies to the BucketIter and this will solve the problem, I will send a PR regarding to this and see how well it can perform

[lanking520]

Thanks for all of you for your help, I have added a PR for RNN example here: #11753