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| # Copyright (c) 2021, NVIDIA CORPORATION. | ||
| # | ||
| # 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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| import pytest | ||
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| from asserts import assert_gpu_and_cpu_are_equal_collect | ||
| from data_gen import * | ||
| from pyspark.sql.types import * | ||
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| @pytest.mark.parametrize('data_gen', all_gen, ids=idfn) | ||
| @pytest.mark.parametrize('size_of_null', ['true', 'false'], ids=idfn) | ||
| def test_size_of_array(data_gen, size_of_null): | ||
| gen = ArrayGen(data_gen) | ||
| assert_gpu_and_cpu_are_equal_collect( | ||
| lambda spark: unary_op_df(spark, gen).selectExpr('size(a)'), | ||
| conf={'spark.sql.legacy.sizeOfNull': size_of_null}) | ||
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| @pytest.mark.parametrize('data_gen', map_gens_sample, ids=idfn) | ||
| @pytest.mark.parametrize('size_of_null', ['true', 'false'], ids=idfn) | ||
| def test_size_of_map(data_gen, size_of_null): | ||
| assert_gpu_and_cpu_are_equal_collect( | ||
| lambda spark: unary_op_df(spark, data_gen).selectExpr('size(a)'), | ||
| conf={'spark.sql.legacy.sizeOfNull': size_of_null}) |
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sql-plugin/src/main/scala/com/nvidia/spark/rapids/collectionOperations.scala
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| /* | ||
| * Copyright (c) 2021, NVIDIA CORPORATION. | ||
| * | ||
| * 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 com.nvidia.spark.rapids | ||
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| import ai.rapids.cudf.ColumnVector | ||
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| import org.apache.spark.sql.catalyst.expressions.Expression | ||
| import org.apache.spark.sql.types._ | ||
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| case class GpuSize(child: Expression, legacySizeOfNull: Boolean) | ||
| extends GpuUnaryExpression { | ||
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| require(child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType], | ||
| s"The size function doesn't support the operand type ${child.dataType}") | ||
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| override def dataType: DataType = IntegerType | ||
| override def nullable: Boolean = if (legacySizeOfNull) false else super.nullable | ||
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| override protected def doColumnar(input: GpuColumnVector): ColumnVector = { | ||
| val inputBase = input.getBase | ||
| if (inputBase.getRowCount == 0) { | ||
| return GpuColumnVector.from(GpuScalar.from(0), 0, IntegerType).getBase | ||
| } | ||
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| // Compute sizes of cuDF.ListType to get sizes of each ArrayData or MapData, considering | ||
| // MapData is represented as List of Struct in terms of cuDF. | ||
| // We compute list size via subtracting the offset of next element(row) to the current offset. | ||
| val collectionSize = { | ||
| // Here is a hack: using index -1 to fetch the offset column of list. | ||
| // In terms of cuDF native, the offset is the first (index 0) child of list_column_view. | ||
| // In JNI layer, we add 1 to the child index when fetching child column of ListType to keep | ||
| // alignment. | ||
| // So, in JVM layer, we have to use -1 as index to fetch the real first child of list_column. | ||
| withResource(inputBase.getChildColumnView(-1)) { offset => | ||
| withResource(offset.subVector(1)) { upBound => | ||
| withResource(offset.subVector(0, offset.getRowCount.toInt - 1)) { lowBound => | ||
| upBound.sub(lowBound) | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| val nullScalar = if (legacySizeOfNull) { | ||
| GpuScalar.from(-1) | ||
| } else { | ||
| GpuScalar.from(null, IntegerType) | ||
| } | ||
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| withResource(collectionSize) { collectionSize => | ||
| withResource(nullScalar) { nullScalar => | ||
| withResource(inputBase.isNull) { inputIsNull => | ||
| inputIsNull.ifElse(nullScalar, collectionSize) | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } |