Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3200#discussion_r23128478
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
---
@@ -0,0 +1,217 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You 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.
+ */
+
+package org.apache.spark.mllib.linalg.distributed
+
+import breeze.linalg.{DenseMatrix => BDM}
+
+import org.apache.spark._
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.rdd.RDDFunctions._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.Utils
+
+/**
+ * A grid partitioner, which stores every block in a separate partition.
+ *
+ * @param numRowBlocks Number of blocks that form the rows of the matrix.
+ * @param numColBlocks Number of blocks that form the columns of the
matrix.
+ * @param rowPerBlock Number of rows that make up each block.
+ * @param colPerBlock Number of columns that make up each block.
+ */
+private[mllib] class GridPartitioner(
+ val numRowBlocks: Int,
+ val numColBlocks: Int,
+ val rowPerBlock: Int,
+ val colPerBlock: Int,
+ override val numPartitions: Int) extends Partitioner {
+
+ /**
+ * Returns the index of the partition the SubMatrix belongs to.
+ *
+ * @param key The key for the SubMatrix. Can be its position in the grid
(its column major index)
+ * or a tuple of three integers that are the final row index
after the multiplication,
+ * the index of the block to multiply with, and the final
column index after the
+ * multiplication.
+ * @return The index of the partition, which the SubMatrix belongs to.
+ */
+ override def getPartition(key: Any): Int = {
+ key match {
+ case ind: (Int, Int) =>
+ Utils.nonNegativeMod(ind._1 + ind._2 * numRowBlocks, numPartitions)
+ case indices: (Int, Int, Int) =>
+ Utils.nonNegativeMod(indices._1 + indices._3 * numRowBlocks,
numPartitions)
+ case _ =>
+ throw new IllegalArgumentException("Unrecognized key")
+ }
+ }
+
+ /** Checks whether the partitioners have the same characteristics */
+ override def equals(obj: Any): Boolean = {
+ obj match {
+ case r: GridPartitioner =>
+ (this.numPartitions == r.numPartitions) && (this.rowPerBlock ==
r.rowPerBlock) &&
+ (this.colPerBlock == r.colPerBlock)
+ case _ =>
+ false
+ }
+ }
+}
+
+/**
+ * Represents a distributed matrix in blocks of local matrices.
+ *
+ * @param numRowBlocks Number of blocks that form the rows of this matrix
+ * @param numColBlocks Number of blocks that form the columns of this
matrix
+ * @param rdd The RDD of SubMatrices (local matrices) that form this matrix
+ */
+class BlockMatrix(
+ val numRowBlocks: Int,
+ val numColBlocks: Int,
+ val rdd: RDD[((Int, Int), Matrix)]) extends DistributedMatrix with
Logging {
+
+ type SubMatrix = ((Int, Int), Matrix) // ((blockRowIndex,
blockColIndex), matrix)
+
+ /**
+ * Alternate constructor for BlockMatrix without the input of a
partitioner. Will use a Grid
+ * Partitioner by default.
+ *
+ * @param numRowBlocks Number of blocks that form the rows of this matrix
+ * @param numColBlocks Number of blocks that form the columns of this
matrix
+ * @param rdd The RDD of SubMatrices (local matrices) that form this
matrix
+ * @param rowPerBlock Number of rows that make up each block.
+ * @param colPerBlock Number of columns that make up each block.
+ */
+ def this(
+ numRowBlocks: Int,
+ numColBlocks: Int,
+ rdd: RDD[((Int, Int), Matrix)],
+ rowPerBlock: Int,
+ colPerBlock: Int) = {
+ this(numRowBlocks, numColBlocks, rdd)
+ val part = new GridPartitioner(numRowBlocks, numColBlocks, rowPerBlock,
+ colPerBlock, rdd.partitions.length)
+ setPartitioner(part)
+ }
+
+ private[mllib] var partitioner: GridPartitioner = {
+ val firstSubMatrix = rdd.first()._2
+ new GridPartitioner(numRowBlocks, numColBlocks,
+ firstSubMatrix.numRows, firstSubMatrix.numCols,
rdd.partitions.length)
+ }
+
+ /**
+ * Set the partitioner for the matrix. For internal use only. Users
should use `repartition`.
+ * @param part A partitioner that specifies how SubMatrices are stored
in the cluster
+ */
+ private def setPartitioner(part: GridPartitioner): Unit = {
+ partitioner = part
+ }
+
+ private lazy val dims: (Long, Long) = getDim
+
+ override def numRows(): Long = dims._1
+ override def numCols(): Long = dims._2
+
+ /** Returns the dimensions of the matrix. */
+ def getDim: (Long, Long) = {
+ // picks the sizes of the matrix with the maximum indices
+ def pickSizeByGreaterIndex(
+ example: (Int, Int, Int, Int),
+ base: (Int, Int, Int, Int)): (Int, Int, Int, Int) = {
+ if (example._1 > base._1 && example._2 > base._2) {
+ (example._1, example._2, example._3, example._4)
+ } else if (example._1 > base._1) {
+ (example._1, base._2, example._3, base._4)
+ } else if (example._2 > base._2) {
+ (base._1, example._2, base._3, example._4)
+ } else {
+ (base._1, base._2, base._3, base._4)
+ }
+ }
+
+ val lastRowCol = rdd.treeAggregate((0, 0, 0, 0))(
+ seqOp = (c, v) => (c, v) match { case (base, ((blockXInd,
blockYInd), mat)) =>
+ pickSizeByGreaterIndex((blockXInd, blockYInd, mat.numRows,
mat.numCols), base)
+ },
+ combOp = (c1, c2) => (c1, c2) match {
+ case (res1, res2) =>
+ pickSizeByGreaterIndex(res1, res2)
+ })
+
+ (lastRowCol._1.toLong * partitioner.rowPerBlock + lastRowCol._3,
+ lastRowCol._2.toLong * partitioner.colPerBlock + lastRowCol._4)
+ }
+
+ /** Returns the Frobenius Norm of the matrix */
+ def normFro(): Double = {
+ math.sqrt(rdd.map { mat => mat._2 match {
+ case sparse: SparseMatrix =>
+ sparse.values.map(x => math.pow(x, 2)).sum
+ case dense: DenseMatrix =>
+ dense.values.map(x => math.pow(x, 2)).sum
+ }
+ }.reduce(_ + _))
+ }
+
+ /** Cache the underlying RDD. */
+ def cache(): DistributedMatrix = {
+ rdd.cache()
+ this
+ }
+
+ /** Set the storage level for the underlying RDD. */
+ def persist(storageLevel: StorageLevel): DistributedMatrix = {
+ rdd.persist(storageLevel)
+ this
+ }
+
+ /** Collect the distributed matrix on the driver as a local matrix. */
+ def toLocalMatrix(): Matrix = {
+ val parts = rdd.collect().sortBy(x => (x._1._2, x._1._1))
+ val nRows = numRows().toInt
+ val nCols = numCols().toInt
+ val values = new Array[Double](nRows * nCols)
+
+ parts.foreach { part =>
+ val rowOffset = part._1._1 * partitioner.rowPerBlock
+ val colOffset = part._1._2 * partitioner.colPerBlock
+ val block = part._2
+ var j = 0
+ while (j < block.numCols) {
+ var i = 0
+ val indStart = (j + colOffset) * nRows + rowOffset
+ val indEnd = block.numRows
+ val matStart = j * block.numRows
+ val mat = block.toArray
+ while (i < indEnd) {
+ values(indStart + i) = mat(matStart + i)
+ i += 1
+ }
+ j += 1
+ }
+ }
+ new DenseMatrix(nRows, nCols, values)
+ }
+
+ /** Collects data and assembles a local dense breeze matrix (for test
only). */
+ private[mllib] def toBreeze(): BDM[Double] = {
--- End diff --
Oh I see. Yeah, I think we should change that later, but later is fine
since it's internal.
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