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Posted to issues@spark.apache.org by "Apache Spark (Jira)" <ji...@apache.org> on 2023/01/31 03:58:00 UTC
[jira] [Assigned] (SPARK-42250) predict_batch_udf with float fails when the batch size consists of single value
[ https://issues.apache.org/jira/browse/SPARK-42250?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-42250:
------------------------------------
Assignee: Apache Spark
> predict_batch_udf with float fails when the batch size consists of single value
> -------------------------------------------------------------------------------
>
> Key: SPARK-42250
> URL: https://issues.apache.org/jira/browse/SPARK-42250
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
> Affects Versions: 3.4.0
> Reporter: Hyukjin Kwon
> Assignee: Apache Spark
> Priority: Major
>
> {code}
> import numpy as np
> import pandas as pd
> from pyspark.ml.functions import predict_batch_udf
> from pyspark.sql.types import ArrayType, FloatType, StructType, StructField
> from typing import Mapping
> df = spark.createDataFrame([[[0.0, 1.0, 2.0, 3.0], [0.0, 1.0, 2.0]], [[4.0, 5.0, 6.0, 7.0], [4.0, 5.0, 6.0]]], schema=["t1", "t2"])
> def make_multi_sum_fn():
> def predict(x1: np.ndarray, x2: np.ndarray) -> np.ndarray:
> return np.sum(x1, axis=1) + np.sum(x2, axis=1)
> return predict
> multi_sum_udf = predict_batch_udf(
> make_multi_sum_fn,
> return_type=FloatType(),
> batch_size=1,
> input_tensor_shapes=[[4], [3]],
> )
> df.select(multi_sum_udf("t1", "t2")).collect()
> {code}
> fails as below:
> {code}
> File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 829, in main
> process()
> File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 821, in process
> serializer.dump_stream(out_iter, outfile)
> File "/.../spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 345, in dump_stream
> return ArrowStreamSerializer.dump_stream(self, init_stream_yield_batches(), stream)
> File "/.../spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 86, in dump_stream
> for batch in iterator:
> File "/.../spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 339, in init_stream_yield_batches
> batch = self._create_batch(series)
> File "/.../spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 275, in _create_batch
> arrs.append(create_array(s, t))
> File "/.../spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 245, in create_array
> raise e
> File "/.../spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 233, in create_array
> array = pa.Array.from_pandas(s, mask=mask, type=t, safe=self._safecheck)
> File "pyarrow/array.pxi", line 1044, in pyarrow.lib.Array.from_pandas
> File "pyarrow/array.pxi", line 316, in pyarrow.lib.array
> File "pyarrow/array.pxi", line 83, in pyarrow.lib._ndarray_to_array
> File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
> pyarrow.lib.ArrowInvalid: Could not convert array(569.) with type numpy.ndarray: tried to convert to float32
> at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:554)
> at org.apache.spark.sql.execution.python.PythonArrowOutput$$anon$1.read(PythonArrowOutput.scala:118)
> at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:507)
> at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:491)
> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:760)
> at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:391)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:888)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:888)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
> at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
> at org.apache.spark.scheduler.Task.run(Task.scala:139)
> at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1520)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> {code}
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