Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3200#discussion_r23582389
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
---
@@ -0,0 +1,242 @@
+/*
+ * 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.{Logging, Partitioner}
+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
+
+/**
+ * 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.
+ */
+private[mllib] class GridPartitioner(
+ val numRowBlocks: Int,
+ val numColBlocks: Int,
+ val numParts: Int) extends Partitioner {
+ // Having the number of partitions greater than the number of sub
matrices does not help
+ override val numPartitions = math.min(numParts, numRowBlocks *
numColBlocks)
+
+ /**
+ * Returns the index of the partition the SubMatrix belongs to. Tries to
achieve block wise
+ * partitioning.
+ *
+ * @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 (blockRowIndex: Int, blockColIndex: Int) =>
+ getBlockId(blockRowIndex, blockColIndex)
+ case (blockRowIndex: Int, innerIndex: Int, blockColIndex: Int) =>
+ getBlockId(blockRowIndex, blockColIndex)
+ case _ =>
+ throw new IllegalArgumentException(s"Unrecognized key. key: $key")
+ }
+ }
+
+ /** Partitions sub-matrices as blocks with neighboring sub-matrices. */
+ private def getBlockId(blockRowIndex: Int, blockColIndex: Int): Int = {
+ val totalBlocks = numRowBlocks * numColBlocks
+ // Gives the number of blocks that need to be in each partition
+ val partitionRatio = math.ceil(totalBlocks * 1.0 / numPartitions).toInt
+ // Number of neighboring blocks to take in each row
+ val subBlocksPerRow = math.ceil(numRowBlocks * 1.0 /
partitionRatio).toInt
+ // Number of neighboring blocks to take in each column
+ val subBlocksPerCol = math.ceil(numColBlocks * 1.0 /
partitionRatio).toInt
+ // Coordinates of the block
+ val i = blockRowIndex / subBlocksPerRow
+ val j = blockColIndex / subBlocksPerCol
+ val blocksPerRow = math.ceil(numRowBlocks * 1.0 /
subBlocksPerRow).toInt
+ j * blocksPerRow + i
+ }
+
+ /** Checks whether the partitioners have the same characteristics */
+ override def equals(obj: Any): Boolean = {
+ obj match {
+ case r: GridPartitioner =>
+ (this.numRowBlocks == r.numRowBlocks) && (this.numColBlocks ==
r.numColBlocks) &&
+ (this.numPartitions == r.numPartitions)
+ case _ =>
+ false
+ }
+ }
+}
+
+/**
+ * Represents a distributed matrix in blocks of local matrices.
+ *
+ * @param rdd The RDD of SubMatrices (local matrices) that form this matrix
+ * @param nRows Number of rows of this matrix
+ * @param nCols Number of columns of this matrix
+ * @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 rowsPerBlock Number of rows that make up each block. The blocks
forming the final
+ * rows are not required to have the given number of
rows
+ * @param colsPerBlock Number of columns that make up each block. The
blocks forming the final
+ * columns are not required to have the given number
of columns
+ */
+class BlockMatrix(
+ val rdd: RDD[((Int, Int), Matrix)],
+ private var nRows: Long,
+ private var nCols: Long,
+ val numRowBlocks: Int,
+ val numColBlocks: Int,
+ val rowsPerBlock: Int,
+ val colsPerBlock: Int) extends DistributedMatrix with Logging {
+
+ private type SubMatrix = ((Int, Int), Matrix) // ((blockRowIndex,
blockColIndex), matrix)
+
+ /**
+ * Alternate constructor for BlockMatrix without the input of the number
of rows and columns.
+ *
+ * @param rdd The RDD of SubMatrices (local matrices) that form this
matrix
+ * @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 rowsPerBlock Number of rows that make up each block. The
blocks forming the final
+ * rows are not required to have the given number of
rows
+ * @param colsPerBlock Number of columns that make up each block. The
blocks forming the final
+ * columns are not required to have the given number
of columns
+ */
+ def this(
+ rdd: RDD[((Int, Int), Matrix)],
+ numRowBlocks: Int,
+ numColBlocks: Int,
+ rowsPerBlock: Int,
+ colsPerBlock: Int) = {
+ this(rdd, 0L, 0L, numRowBlocks, numColBlocks, rowsPerBlock,
colsPerBlock)
+ }
+
+ private[mllib] var partitioner: GridPartitioner =
+ new GridPartitioner(numRowBlocks, numColBlocks, rdd.partitions.length)
+
+ private lazy val dims: (Long, Long) = getDim
+
+ override def numRows(): Long = {
+ if (nRows <= 0L) nRows = dims._1
+ nRows
+ }
+
+ override def numCols(): Long = {
+ if (nCols <= 0L) nCols = dims._2
+ nCols
+ }
+
+ /** Returns the dimensions of the matrix. */
+ private def getDim: (Long, Long) = {
+ case class MatrixMetaData(var rowIndex: Int, var colIndex: Int,
+ var numRows: Int, var numCols: Int)
+ // picks the sizes of the matrix with the maximum indices
+ def pickSizeByGreaterIndex(example: MatrixMetaData, base:
MatrixMetaData): MatrixMetaData = {
+ if (example.rowIndex > base.rowIndex) {
+ base.rowIndex = example.rowIndex
+ base.numRows = example.numRows
+ }
+ if (example.colIndex > base.colIndex) {
+ base.colIndex = example.colIndex
+ base.numCols = example.numCols
+ }
+ base
+ }
+
+ // Aggregate will return an error if the rdd is empty
+ val lastRowCol = rdd.treeAggregate(new MatrixMetaData(0, 0, 0, 0))(
+ seqOp = (c, v) => (c, v) match { case (base, ((blockXInd,
blockYInd), mat)) =>
+ pickSizeByGreaterIndex(
+ new MatrixMetaData(blockXInd, blockYInd, mat.numRows,
mat.numCols), base)
+ },
+ combOp = (c1, c2) => (c1, c2) match {
+ case (res1, res2) =>
+ pickSizeByGreaterIndex(res1, res2)
+ })
+ // We add the size of the edge matrices, because they can be less than
the specified
+ // rowsPerBlock or colsPerBlock.
+ (lastRowCol.rowIndex.toLong * rowsPerBlock + lastRowCol.numRows,
+ lastRowCol.colIndex.toLong * colsPerBlock + lastRowCol.numCols)
+ }
+
+ /** 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(): BlockMatrix = {
+ rdd.cache()
+ this
+ }
+
+ /** Set the storage level for the underlying RDD. */
+ def persist(storageLevel: StorageLevel): BlockMatrix = {
+ rdd.persist(storageLevel)
+ this
+ }
+
+ /** Collect the distributed matrix on the driver as a `DenseMatrix`. */
+ def toLocalMatrix(): Matrix = {
+ require(numRows() < Int.MaxValue, "The number of rows of this matrix
should be less than " +
+ s"Int.MaxValue. Currently numRows: ${numRows()}")
+ require(numCols() < Int.MaxValue, "The number of columns of this
matrix should be less than " +
+ s"Int.MaxValue. Currently numCols: ${numCols()}")
+ val nRows = numRows().toInt
+ val nCols = numCols().toInt
+ val mem = nRows * nCols * 8 / 1000000
+ if (mem > 500) logWarning(s"Storing this matrix will require $mem MB
of memory!")
+
+ val parts = rdd.collect().sortBy(x => (x._1._2, x._1._1))
+ val values = new Array[Double](nRows * nCols)
+ parts.foreach { case ((rowIndex, colIndex), block) =>
--- End diff --
rowBlockIndex and colBlockIndex
---
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