Loading Check_spelling_dl Pretrained Pipeline crashes in Spark NLP 2.7.2 +

[C-K-Loan]

Since Spark-NLP 2.7.2 + loading Check_spelling_dl crashes Building a pipeline with ContextSpellCheckerModel.pretrained() works fine

Successfully tested in the following versions of Spark NLP

• 2.6.2
• 2.7.0
• 2.7.1

Crashes on the following versions Spark NLP

• 2.7.2
• 2.7.3

This runs fine

import sparknlp
from sparknlp.annotator import * 
from sparknlp.common import *
from sparknlp.base import *
from pyspark.ml import Pipeline
from sparknlp.pretrained import PretrainedPipeline, LightPipeline

spark = sparknlp.start()
document_assembler = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")

tokenizer = Tokenizer()\
    .setInputCols(["document"]) \
    .setOutputCol("token")

spell = ContextSpellCheckerModel.pretrained()\
  .setInputCols(["token"]) \
  .setOutputCol("spell")

nlp_pipeline = Pipeline(stages=[document_assembler, tokenizer, spell])
data = [ {"text": 'Some text hello world'}, ]
df = spark.createDataFrame(data)
nlp_pipeline.fit(df).transform(df).show()

Based on this snippet

import sparknlp
from sparknlp.annotator import *
from sparknlp.common import *
from sparknlp.base import *
from pyspark.ml import Pipeline
from sparknlp.pretrained import PretrainedPipeline, LightPipeline


spark = sparknlp.start()
pipeline = PretrainedPipeline('check_spelling_dl', lang='en')
data = [ {"text": 'Some text hello world'}, ]
df = spark.createDataFrame(data)
pipeline.transform(df).show()

Colab link for reproduction

https://colab.research.google.com/drive/1QpV7RYj65DXJQm2xxB1s2o_6J88yB8-n?usp=sharing

Results in the following Error message :

check_spelling_dl download started this may take some time.
Approx size to download 112.1 MB
[OK!]
---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-2-f7b1aa24d037> in <module>()
     10 
     11 spark = sparknlp.start()
---> 12 pipeline = PretrainedPipeline('check_spelling_dl', lang='en')
     13 data = [ {"text": 'Some text hello world'}, ]
     14 df = spark.createDataFrame(data)

8 frames
/usr/local/lib/python3.6/dist-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling z:com.johnsnowlabs.nlp.pretrained.PythonResourceDownloader.downloadPipeline.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 12.0 failed 1 times, most recent failure: Lost task 1.0 in stage 12.0 (TID 23, localhost, executor driver): java.lang.ArrayStoreException: java.lang.Byte
	at scala.runtime.ScalaRunTime$.array_update(ScalaRunTime.scala:90)
	at scala.Array$.slowcopy(Array.scala:81)
	at scala.Array$.copy(Array.scala:107)
	at scala.collection.mutable.ResizableArray$class.copyToArray(ResizableArray.scala:77)
	at scala.collection.mutable.ArrayBuffer.copyToArray(ArrayBuffer.scala:48)
	at scala.collection.TraversableOnce$class.copyToArray(TraversableOnce.scala:278)
	at scala.collection.AbstractTraversable.copyToArray(Traversable.scala:104)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:286)
	at scala.collection.AbstractTraversable.toArray(Traversable.scala:104)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
	at scala.collection.AbstractIterator.toArray(Iterator.scala:1334)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:123)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
	at scala.Option.foreach(Option.scala:257)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:945)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
	at org.apache.spark.rdd.RDD.collect(RDD.scala:944)
	at com.johnsnowlabs.nlp.serialization.TransducerFeature.deserializeObject(Feature.scala:281)
	at com.johnsnowlabs.nlp.serialization.Feature.deserialize(Feature.scala:47)
	at com.johnsnowlabs.nlp.FeaturesReader$$anonfun$load$1.apply(ParamsAndFeaturesReadable.scala:15)
	at com.johnsnowlabs.nlp.FeaturesReader$$anonfun$load$1.apply(ParamsAndFeaturesReadable.scala:14)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
	at com.johnsnowlabs.nlp.FeaturesReader.load(ParamsAndFeaturesReadable.scala:14)
	at com.johnsnowlabs.nlp.FeaturesReader.load(ParamsAndFeaturesReadable.scala:8)
	at org.apache.spark.ml.util.DefaultParamsReader$.loadParamsInstance(ReadWrite.scala:652)
	at org.apache.spark.ml.Pipeline$SharedReadWrite$$anonfun$4.apply(Pipeline.scala:274)
	at org.apache.spark.ml.Pipeline$SharedReadWrite$$anonfun$4.apply(Pipeline.scala:272)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
	at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
	at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
	at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
	at org.apache.spark.ml.Pipeline$SharedReadWrite$.load(Pipeline.scala:272)
	at org.apache.spark.ml.PipelineModel$PipelineModelReader.load(Pipeline.scala:348)
	at org.apache.spark.ml.PipelineModel$PipelineModelReader.load(Pipeline.scala:342)
	at com.johnsnowlabs.nlp.pretrained.ResourceDownloader$.downloadPipeline(ResourceDownloader.scala:379)
	at com.johnsnowlabs.nlp.pretrained.ResourceDownloader$.downloadPipeline(ResourceDownloader.scala:373)
	at com.johnsnowlabs.nlp.pretrained.PythonResourceDownloader$.downloadPipeline(ResourceDownloader.scala:479)
	at com.johnsnowlabs.nlp.pretrained.PythonResourceDownloader.downloadPipeline(ResourceDownloader.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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.ArrayStoreException: java.lang.Byte
	at scala.runtime.ScalaRunTime$.array_update(ScalaRunTime.scala:90)
	at scala.Array$.slowcopy(Array.scala:81)
	at scala.Array$.copy(Array.scala:107)
	at scala.collection.mutable.ResizableArray$class.copyToArray(ResizableArray.scala:77)
	at scala.collection.mutable.ArrayBuffer.copyToArray(ArrayBuffer.scala:48)
	at scala.collection.TraversableOnce$class.copyToArray(TraversableOnce.scala:278)
	at scala.collection.AbstractTraversable.copyToArray(Traversable.scala:104)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:286)
	at scala.collection.AbstractTraversable.toArray(Traversable.scala:104)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
	at scala.collection.AbstractIterator.toArray(Iterator.scala:1334)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:123)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	... 1 more

[maziyarpanahi]

I think @albertoandreottiATgmail was training new models based on the new graph introduced in 2.7.2, but may have not been published yet.