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Support dataset as the eval
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...park-gpu/src/main/resources/META-INF/services/ml.dmlc.xgboost4j.scala.spark.XGBoostPlugin
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| ml.dmlc.xgboost4j.scala.spark.GPUXGBoostPlugin | ||
| ml.dmlc.xgboost4j.scala.spark.GpuXGBoostPlugin |
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...s/xgboost4j-spark-gpu/src/main/scala/ml/dmlc/xgboost4j/scala/spark/GPUXGBoostPlugin.scala
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...s/xgboost4j-spark-gpu/src/main/scala/ml/dmlc/xgboost4j/scala/spark/GpuXGBoostPlugin.scala
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| /* | ||
| Copyright (c) 2024 by Contributors | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| */ | ||
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| package ml.dmlc.xgboost4j.scala.spark | ||
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| import scala.collection.mutable.ArrayBuffer | ||
| import scala.jdk.CollectionConverters.seqAsJavaListConverter | ||
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| import ai.rapids.cudf.Table | ||
| import com.nvidia.spark.rapids.ColumnarRdd | ||
| import org.apache.spark.ml.param.Param | ||
| import org.apache.spark.rdd.RDD | ||
| import org.apache.spark.sql.{Column, Dataset} | ||
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| import ml.dmlc.xgboost4j.java.{CudfColumnBatch, GpuColumnBatch} | ||
| import ml.dmlc.xgboost4j.scala.QuantileDMatrix | ||
| import ml.dmlc.xgboost4j.scala.spark.params.HasGroupCol | ||
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| /** | ||
| * GpuXGBoostPlugin is the XGBoost plugin which leverage spark-rapids | ||
| * to accelerate the XGBoost from ETL to train. | ||
| */ | ||
| class GpuXGBoostPlugin extends XGBoostPlugin { | ||
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| /** | ||
| * Whether the plugin is enabled or not, if not enabled, fallback | ||
| * to the regular CPU pipeline | ||
| * | ||
| * @param dataset the input dataset | ||
| * @return Boolean | ||
| */ | ||
| override def isEnabled(dataset: Dataset[_]): Boolean = { | ||
| val conf = dataset.sparkSession.conf | ||
| val hasRapidsPlugin = conf.get("spark.sql.extensions", "").split(",").contains( | ||
| "com.nvidia.spark.rapids.SQLExecPlugin") | ||
| val rapidsEnabled = conf.get("spark.rapids.sql.enabled", "false").toBoolean | ||
| hasRapidsPlugin && rapidsEnabled | ||
| } | ||
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| // TODO, support numeric type | ||
| private def preprocess[T <: XGBoostEstimator[T, M], M <: XGBoostModel[M]]( | ||
| estimator: XGBoostEstimator[T, M], dataset: Dataset[_]): Dataset[_] = { | ||
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| // Columns to be selected for XGBoost training | ||
| val selectedCols: ArrayBuffer[Column] = ArrayBuffer.empty | ||
| val schema = dataset.schema | ||
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| def selectCol(c: Param[String]) = { | ||
| // TODO support numeric types | ||
| if (estimator.isDefinedNonEmpty(c)) { | ||
| selectedCols.append(estimator.castToFloatIfNeeded(schema, estimator.getOrDefault(c))) | ||
| } | ||
| } | ||
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| Seq(estimator.labelCol, estimator.weightCol, estimator.baseMarginCol).foreach(selectCol) | ||
| estimator match { | ||
| case p: HasGroupCol => selectCol(p.groupCol) | ||
| case _ => | ||
| } | ||
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| // TODO support array/vector feature | ||
| estimator.getFeaturesCols.foreach { name => | ||
| val col = estimator.castToFloatIfNeeded(dataset.schema, name) | ||
| selectedCols.append(col) | ||
| } | ||
| val input = dataset.select(selectedCols: _*) | ||
| estimator.repartitionIfNeeded(input) | ||
| } | ||
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| private def validate[T <: XGBoostEstimator[T, M], M <: XGBoostModel[M]]( | ||
| estimator: XGBoostEstimator[T, M], | ||
| dataset: Dataset[_]): Unit = { | ||
| require(estimator.getTreeMethod == "gpu_hist" || estimator.getDevice != "cpu", | ||
| "Using Spark-Rapids to accelerate XGBoost must set device=cuda") | ||
| } | ||
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| /** | ||
| * Convert Dataset to RDD[Watches] which will be fed into XGBoost | ||
| * | ||
| * @param estimator which estimator to be handled. | ||
| * @param dataset to be converted. | ||
| * @return RDD[Watches] | ||
| */ | ||
| override def buildRddWatches[T <: XGBoostEstimator[T, M], M <: XGBoostModel[M]]( | ||
| estimator: XGBoostEstimator[T, M], | ||
| dataset: Dataset[_]): RDD[Watches] = { | ||
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| validate(estimator, dataset) | ||
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| val train = preprocess(estimator, dataset) | ||
| val schema = train.schema | ||
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| val indices = estimator.buildColumnIndices(schema) | ||
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| val maxBin = estimator.getMaxBins | ||
| val nthread = estimator.getNthread | ||
| val missing = estimator.getMissing | ||
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| /** build QuantilDMatrix on the executor side */ | ||
| def buildQuantileDMatrix(iter: Iterator[Table]): QuantileDMatrix = { | ||
| val colBatchIter = iter.map { table => | ||
| withResource(new GpuColumnBatch(table, null)) { batch => | ||
| new CudfColumnBatch( | ||
| batch.slice(indices.featureIds.get.map(Integer.valueOf).asJava), | ||
| batch.slice(indices.labelId), | ||
| batch.slice(indices.weightId.getOrElse(-1)), | ||
| batch.slice(indices.marginId.getOrElse(-1))); | ||
| } | ||
| } | ||
| new QuantileDMatrix(colBatchIter, missing, maxBin, nthread) | ||
| } | ||
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| estimator.getEvalDataset().map { evalDs => | ||
| val evalProcessed = preprocess(estimator, evalDs) | ||
| ColumnarRdd(train.toDF()).zipPartitions(ColumnarRdd(evalProcessed.toDF())) { | ||
| (trainIter, evalIter) => | ||
| val trainDM = buildQuantileDMatrix(trainIter) | ||
| val evalDM = buildQuantileDMatrix(evalIter) | ||
| Iterator.single(new Watches(Array(trainDM, evalDM), | ||
| Array(Utils.TRAIN_NAME, Utils.VALIDATION_NAME), None)) | ||
| } | ||
| }.getOrElse( | ||
| ColumnarRdd(train.toDF()).mapPartitions { iter => | ||
| val dm = buildQuantileDMatrix(iter) | ||
| Iterator.single(new Watches(Array(dm), Array(Utils.TRAIN_NAME), None)) | ||
| } | ||
| ) | ||
| } | ||
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| /** Executes the provided code block and then closes the resource */ | ||
| def withResource[T <: AutoCloseable, V](r: T)(block: T => V): V = { | ||
| try { | ||
| block(r) | ||
| } finally { | ||
| r.close() | ||
| } | ||
| } | ||
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| } |
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