Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3200#discussion_r23582382
--- 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 = {
--- End diff --
If we modify `base` in-place, we should put `base` as the first parameter.
This is the convention used in Spark aggregation.
We should simply this code block. Essentially we want to find the largest
row index and col index:
~~~
val (rows, cols) = rdd.map { case (blockRowIndex, blockColIndex, mat) =>
(blockRowIndex * rowsPerBlock + mat.numRows, blockColIndex * colsPerBlock
+ mat.numCols)
}.reduce((x0, x1) => (math.max(x0._1, x1._1), math.max(x0._2, x1._2)))
(math.max(rows, nRows), math.max(cols, nCols))
~~~
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