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Move HostConcatResultUtil out of unshimmed classes #5614

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merged 3 commits into from
May 25, 2022
Merged

Move HostConcatResultUtil out of unshimmed classes #5614

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cb32e91
Move HostConcatResultUtil out of unshimmed classes
gerashegalov May 24, 2022
dec78f4
Add smoke test
gerashegalov May 24, 2022
c88cd5e
minimize patch
gerashegalov May 24, 2022
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8 changes: 8 additions & 0 deletions jenkins/spark-premerge-build.sh
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Expand Up @@ -73,6 +73,14 @@ mvn_verify() {

# Triggering here until we change the jenkins file
rapids_shuffle_smoke_test

# non-caller classloader smoke test in pseudo-distributed
# standalone cluster
echo "Running test_cartesian_join_special_case_count with spark.rapids.force.caller.classloader=false"
PYSP_TEST_spark_rapids_force_caller_classloader=false \
NUM_LOCAL_EXECS=1 \
TEST_PARALLEL=0 \
./integration_tests/run_pyspark_from_build.sh -k 'test_cartesian_join_special_case_count[100]'
}

rapids_shuffle_smoke_test() {
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6 changes: 3 additions & 3 deletions sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuShuffleCoalesceExec.scala
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Expand Up @@ -18,7 +18,7 @@ package com.nvidia.spark.rapids

import java.util

import ai.rapids.cudf.{HostConcatResultUtil, HostMemoryBuffer, JCudfSerialization, NvtxColor, NvtxRange}
import ai.rapids.cudf.{HostMemoryBuffer, JCudfSerialization, NvtxColor, NvtxRange}
import ai.rapids.cudf.JCudfSerialization.{HostConcatResult, SerializedTableHeader}
import com.nvidia.spark.rapids.shims.ShimUnaryExecNode

Expand Down Expand Up @@ -103,7 +103,7 @@ class HostShuffleCoalesceIterator(
val firstHeader = serializedTables.peekFirst().header
if (firstHeader.getNumColumns == 0) {
(0 until numTablesInBatch).foreach(_ => serializedTables.removeFirst())
HostConcatResultUtil.rowsOnlyHostConcatResult(numRowsInBatch)
cudf_utils.HostConcatResultUtil.rowsOnlyHostConcatResult(numRowsInBatch)
} else {
val headers = new Array[SerializedTableHeader](numTablesInBatch)
withResource(new Array[HostMemoryBuffer](numTablesInBatch)) { buffers =>
Expand Down Expand Up @@ -211,7 +211,7 @@ class GpuShuffleCoalesceIterator(iter: Iterator[HostConcatResult],
// generate GPU data from batches that are empty.
GpuSemaphore.acquireIfNecessary(TaskContext.get(), semWaitTime)
withResource(new MetricRange(opTimeMetric)) { _ =>
val batch = HostConcatResultUtil.getColumnarBatch(hostConcatResult, dataTypes)
val batch = cudf_utils.HostConcatResultUtil.getColumnarBatch(hostConcatResult, dataTypes)
outputBatchesMetric += 1
outputRowsMetric += batch.numRows()
batch
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4 changes: 2 additions & 2 deletions sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuShuffledHashJoinExec.scala
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Expand Up @@ -16,7 +16,7 @@

package com.nvidia.spark.rapids

import ai.rapids.cudf.{HostConcatResultUtil, NvtxColor, NvtxRange}
import ai.rapids.cudf.{NvtxColor, NvtxRange}
import ai.rapids.cudf.JCudfSerialization.HostConcatResult
import com.nvidia.spark.rapids.shims.{GpuHashPartitioning, ShimBinaryExecNode}

Expand Down Expand Up @@ -345,7 +345,7 @@ object GpuShuffledHashJoinExec extends Arm {
// we can bring the build batch to the GPU now
withResource(hostConcatResult) { _ =>
buildTime.ns {
HostConcatResultUtil.getColumnarBatch(hostConcatResult, dataTypes)
cudf_utils.HostConcatResultUtil.getColumnarBatch(hostConcatResult, dataTypes)
}
}
}
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15 changes: 7 additions & 8 deletions ...ai/rapids/cudf/HostConcatResultUtil.scala → ...ids/cudf_utils/HostConcatResultUtil.scala
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Expand Up @@ -14,9 +14,9 @@
* limitations under the License.
*/

package ai.rapids.cudf
package com.nvidia.spark.rapids.cudf_utils

import ai.rapids.cudf.JCudfSerialization.HostConcatResult
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import ai.rapids.cudf
import com.nvidia.spark.rapids.{Arm, GpuColumnVectorFromBuffer}

import org.apache.spark.sql.types.DataType
Expand All @@ -26,11 +26,10 @@ object HostConcatResultUtil extends Arm {
/**
* Create a rows-only `HostConcatResult`.
*/
def rowsOnlyHostConcatResult(numRows: Int): HostConcatResult = {
new HostConcatResult(
new JCudfSerialization.SerializedTableHeader(
Array.empty, numRows, 0L),
HostMemoryBuffer.allocate(0, false))
def rowsOnlyHostConcatResult(numRows: Int): cudf.JCudfSerialization.HostConcatResult = {
new cudf.JCudfSerialization.HostConcatResult(
new cudf.JCudfSerialization.SerializedTableHeader(numRows),
cudf.HostMemoryBuffer.allocate(0, false))
}

/**
Expand All @@ -41,7 +40,7 @@ object HostConcatResultUtil extends Arm {
* callers are responsible for closing the resulting `ColumnarBatch`
*/
def getColumnarBatch(
hostConcatResult: HostConcatResult,
hostConcatResult: cudf.JCudfSerialization.HostConcatResult,
sparkSchema: Array[DataType]): ColumnarBatch = {
if (hostConcatResult.getTableHeader.getNumColumns == 0) {
// We expect the caller to have acquired the GPU unconditionally before calling
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