Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/6209#discussion_r30473300
--- 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
+ 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
+ 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
+
+ scal(beta, y)
--- End diff --
```
if (alpha == 0.0 && beta != 0.0) {
scal(beta, y)
return
}
```
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
