diff options
4 files changed, 240 insertions, 33 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala index 87052e1ba8..ec38529cf8 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala @@ -463,7 +463,7 @@ private[spark] object BLAS extends Serializable with Logging { def gemv( alpha: Double, A: Matrix, - x: DenseVector, + x: Vector, beta: Double, y: DenseVector): Unit = { require(A.numCols == x.size, @@ -473,44 +473,169 @@ private[spark] object BLAS extends Serializable with Logging { if (alpha == 0.0) { logDebug("gemv: alpha is equal to 0. Returning y.") } else { - A match { - case sparse: SparseMatrix => - gemv(alpha, sparse, x, beta, y) - case dense: DenseMatrix => - gemv(alpha, dense, x, beta, y) + (A, x) match { + case (smA: SparseMatrix, dvx: DenseVector) => + gemv(alpha, smA, dvx, beta, y) + case (smA: SparseMatrix, svx: SparseVector) => + gemv(alpha, smA, svx, beta, y) + case (dmA: DenseMatrix, dvx: DenseVector) => + gemv(alpha, dmA, dvx, beta, y) + case (dmA: DenseMatrix, svx: SparseVector) => + gemv(alpha, dmA, svx, beta, y) case _ => - throw new IllegalArgumentException(s"gemv doesn't support matrix type ${A.getClass}.") + throw new IllegalArgumentException(s"gemv doesn't support running on matrix type " + + s"${A.getClass} and vector type ${x.getClass}.") } } } /** * y := alpha * A * x + beta * y - * For `DenseMatrix` A. + * For `DenseMatrix` A and `DenseVector` x. */ private def gemv( alpha: Double, A: DenseMatrix, x: DenseVector, beta: Double, - y: DenseVector): Unit = { + y: DenseVector): Unit = { val tStrA = if (A.isTransposed) "T" else "N" val mA = if (!A.isTransposed) A.numRows else A.numCols val nA = if (!A.isTransposed) A.numCols else A.numRows nativeBLAS.dgemv(tStrA, mA, nA, alpha, A.values, mA, x.values, 1, beta, y.values, 1) } + + /** + * y := alpha * A * x + beta * y + * For `DenseMatrix` A and `SparseVector` x. + */ + private def gemv( + alpha: Double, + A: DenseMatrix, + x: SparseVector, + beta: Double, + y: DenseVector): Unit = { + val mA: Int = A.numRows + val nA: Int = A.numCols + + val Avals = A.values + + val xIndices = x.indices + val xNnz = xIndices.length + val xValues = x.values + val yValues = y.values + if (alpha == 0.0) { + scal(beta, y) + return + } + + if (A.isTransposed) { + var rowCounterForA = 0 + while (rowCounterForA < mA) { + var sum = 0.0 + var k = 0 + while (k < xNnz) { + sum += xValues(k) * Avals(xIndices(k) + rowCounterForA * nA) + k += 1 + } + yValues(rowCounterForA) = sum * alpha + beta * yValues(rowCounterForA) + rowCounterForA += 1 + } + } else { + var rowCounterForA = 0 + while (rowCounterForA < mA) { + var sum = 0.0 + var k = 0 + while (k < xNnz) { + sum += xValues(k) * Avals(xIndices(k) * mA + rowCounterForA) + k += 1 + } + yValues(rowCounterForA) = sum * alpha + beta * yValues(rowCounterForA) + rowCounterForA += 1 + } + } + } + /** * y := alpha * A * x + beta * y - * For `SparseMatrix` A. + * For `SparseMatrix` A and `SparseVector` x. + */ + private def gemv( + alpha: Double, + A: SparseMatrix, + x: SparseVector, + beta: Double, + y: DenseVector): Unit = { + val xValues = x.values + val xIndices = x.indices + val xNnz = xIndices.length + + val yValues = y.values + + val mA: Int = A.numRows + val nA: Int = A.numCols + + val Avals = A.values + val Arows = if (!A.isTransposed) A.rowIndices else A.colPtrs + val Acols = if (!A.isTransposed) A.colPtrs else A.rowIndices + + if (alpha == 0.0) { + scal(beta, y) + return + } + + if (A.isTransposed) { + var rowCounter = 0 + while (rowCounter < mA) { + var i = Arows(rowCounter) + val indEnd = Arows(rowCounter + 1) + var sum = 0.0 + var k = 0 + while (k < xNnz && i < indEnd) { + if (xIndices(k) == Acols(i)) { + sum += Avals(i) * xValues(k) + i += 1 + } + k += 1 + } + yValues(rowCounter) = sum * alpha + beta * yValues(rowCounter) + rowCounter += 1 + } + } else { + scal(beta, y) + + var colCounterForA = 0 + var k = 0 + while (colCounterForA < nA && k < xNnz) { + if (xIndices(k) == colCounterForA) { + var i = Acols(colCounterForA) + val indEnd = Acols(colCounterForA + 1) + + val xTemp = xValues(k) * alpha + while (i < indEnd) { + val rowIndex = Arows(i) + yValues(Arows(i)) += Avals(i) * xTemp + i += 1 + } + k += 1 + } + colCounterForA += 1 + } + } + } + + /** + * y := alpha * A * x + beta * y + * For `SparseMatrix` A and `DenseVector` x. */ private def gemv( alpha: Double, A: SparseMatrix, x: DenseVector, beta: Double, - y: DenseVector): Unit = { + y: DenseVector): Unit = { val xValues = x.values val yValues = y.values val mA: Int = A.numRows @@ -534,10 +659,7 @@ private[spark] object BLAS extends Serializable with Logging { rowCounter += 1 } } else { - // Scale vector first if `beta` is not equal to 0.0 - if (beta != 0.0) { - scal(beta, y) - } + scal(beta, y) // Perform matrix-vector multiplication and add to y var colCounterForA = 0 while (colCounterForA < nA) { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala index a609674df6..9584da8e3a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala @@ -77,8 +77,13 @@ sealed trait Matrix extends Serializable { C } - /** Convenience method for `Matrix`-`DenseVector` multiplication. */ + /** Convenience method for `Matrix`-`DenseVector` multiplication. For binary compatibility. */ def multiply(y: DenseVector): DenseVector = { + multiply(y.asInstanceOf[Vector]) + } + + /** Convenience method for `Matrix`-`Vector` multiplication. */ + def multiply(y: Vector): DenseVector = { val output = new DenseVector(new Array[Double](numRows)) BLAS.gemv(1.0, this, y, 0.0, output) output diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala index 002cb25386..64ecd12ea7 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala @@ -256,42 +256,108 @@ class BLASSuite extends FunSuite { val dA = new DenseMatrix(4, 3, Array(0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 3.0)) val sA = new SparseMatrix(4, 3, Array(0, 1, 3, 4), Array(1, 0, 2, 3), Array(1.0, 2.0, 1.0, 3.0)) - - val x = new DenseVector(Array(1.0, 2.0, 3.0)) + + val dA2 = + new DenseMatrix(4, 3, Array(0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 3.0), true) + val sA2 = + new SparseMatrix(4, 3, Array(0, 1, 2, 3, 4), Array(1, 0, 1, 2), Array(2.0, 1.0, 1.0, 3.0), + true) + + val dx = new DenseVector(Array(1.0, 2.0, 3.0)) + val sx = dx.toSparse val expected = new DenseVector(Array(4.0, 1.0, 2.0, 9.0)) - assert(dA.multiply(x) ~== expected absTol 1e-15) - assert(sA.multiply(x) ~== expected absTol 1e-15) - + assert(dA.multiply(dx) ~== expected absTol 1e-15) + assert(sA.multiply(dx) ~== expected absTol 1e-15) + assert(dA.multiply(sx) ~== expected absTol 1e-15) + assert(sA.multiply(sx) ~== expected absTol 1e-15) + val y1 = new DenseVector(Array(1.0, 3.0, 1.0, 0.0)) val y2 = y1.copy val y3 = y1.copy val y4 = y1.copy + val y5 = y1.copy + val y6 = y1.copy + val y7 = y1.copy + val y8 = y1.copy + val y9 = y1.copy + val y10 = y1.copy + val y11 = y1.copy + val y12 = y1.copy + val y13 = y1.copy + val y14 = y1.copy + val y15 = y1.copy + val y16 = y1.copy + val expected2 = new DenseVector(Array(6.0, 7.0, 4.0, 9.0)) val expected3 = new DenseVector(Array(10.0, 8.0, 6.0, 18.0)) - gemv(1.0, dA, x, 2.0, y1) - gemv(1.0, sA, x, 2.0, y2) - gemv(2.0, dA, x, 2.0, y3) - gemv(2.0, sA, x, 2.0, y4) + gemv(1.0, dA, dx, 2.0, y1) + gemv(1.0, sA, dx, 2.0, y2) + gemv(1.0, dA, sx, 2.0, y3) + gemv(1.0, sA, sx, 2.0, y4) + + gemv(1.0, dA2, dx, 2.0, y5) + gemv(1.0, sA2, dx, 2.0, y6) + gemv(1.0, dA2, sx, 2.0, y7) + gemv(1.0, sA2, sx, 2.0, y8) + + gemv(2.0, dA, dx, 2.0, y9) + gemv(2.0, sA, dx, 2.0, y10) + gemv(2.0, dA, sx, 2.0, y11) + gemv(2.0, sA, sx, 2.0, y12) + + gemv(2.0, dA2, dx, 2.0, y13) + gemv(2.0, sA2, dx, 2.0, y14) + gemv(2.0, dA2, sx, 2.0, y15) + gemv(2.0, sA2, sx, 2.0, y16) + assert(y1 ~== expected2 absTol 1e-15) assert(y2 ~== expected2 absTol 1e-15) - assert(y3 ~== expected3 absTol 1e-15) - assert(y4 ~== expected3 absTol 1e-15) + assert(y3 ~== expected2 absTol 1e-15) + assert(y4 ~== expected2 absTol 1e-15) + + assert(y5 ~== expected2 absTol 1e-15) + assert(y6 ~== expected2 absTol 1e-15) + assert(y7 ~== expected2 absTol 1e-15) + assert(y8 ~== expected2 absTol 1e-15) + + assert(y9 ~== expected3 absTol 1e-15) + assert(y10 ~== expected3 absTol 1e-15) + assert(y11 ~== expected3 absTol 1e-15) + assert(y12 ~== expected3 absTol 1e-15) + + assert(y13 ~== expected3 absTol 1e-15) + assert(y14 ~== expected3 absTol 1e-15) + assert(y15 ~== expected3 absTol 1e-15) + assert(y16 ~== expected3 absTol 1e-15) + withClue("columns of A don't match the rows of B") { intercept[Exception] { - gemv(1.0, dA.transpose, x, 2.0, y1) + gemv(1.0, dA.transpose, dx, 2.0, y1) + } + intercept[Exception] { + gemv(1.0, sA.transpose, dx, 2.0, y1) + } + intercept[Exception] { + gemv(1.0, dA.transpose, sx, 2.0, y1) + } + intercept[Exception] { + gemv(1.0, sA.transpose, sx, 2.0, y1) } } + val dAT = new DenseMatrix(3, 4, Array(0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 3.0)) val sAT = new SparseMatrix(3, 4, Array(0, 1, 2, 3, 4), Array(1, 0, 1, 2), Array(2.0, 1.0, 1.0, 3.0)) - + val dATT = dAT.transpose val sATT = sAT.transpose - assert(dATT.multiply(x) ~== expected absTol 1e-15) - assert(sATT.multiply(x) ~== expected absTol 1e-15) + assert(dATT.multiply(dx) ~== expected absTol 1e-15) + assert(sATT.multiply(dx) ~== expected absTol 1e-15) + assert(dATT.multiply(sx) ~== expected absTol 1e-15) + assert(sATT.multiply(sx) ~== expected absTol 1e-15) } } diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 513bbaf98d..f8d0160f64 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -87,7 +87,14 @@ object MimaExcludes { ProblemFilters.exclude[MissingMethodProblem]( "org.apache.spark.mllib.linalg.Vector.toSparse"), ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.mllib.linalg.Vector.numActives") + "org.apache.spark.mllib.linalg.Vector.numActives"), + // SPARK-7681 add SparseVector support for gemv + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.Matrix.multiply"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.DenseMatrix.multiply"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.SparseMatrix.multiply") ) ++ Seq( // Execution should never be included as its always internal. MimaBuild.excludeSparkPackage("sql.execution"), @@ -180,7 +187,14 @@ object MimaExcludes { ProblemFilters.exclude[MissingMethodProblem]( "org.apache.spark.mllib.linalg.Matrix.isTransposed"), ProblemFilters.exclude[MissingMethodProblem]( - "org.apache.spark.mllib.linalg.Matrix.foreachActive") + "org.apache.spark.mllib.linalg.Matrix.foreachActive"), + // SPARK-7681 add SparseVector support for gemv + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.Matrix.multiply"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.DenseMatrix.multiply"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.SparseMatrix.multiply") ) ++ Seq( // SPARK-5540 ProblemFilters.exclude[MissingMethodProblem]( |