Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/6209#discussion_r30473011
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala ---
@@ -473,44 +473,161 @@ 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
+
+ scal(beta, y)
+ 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
--- End diff --
Actually, each element of yValues is looped through here, let's change it to
```
yValues(rowCounterForA) += sum * alpha + beta * yValues(rowCounterForA)
```
and then have the following code before this block
```
if (alpha == 0.0 && beta != 0) {
scal(beta, y)
return
}
```
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