walterddr commented on a change in pull request #9732: [FLINK-14153][ml] Add to
BLAS a method that performs DenseMatrix and SparseVector multiplication.
URL: https://github.com/apache/flink/pull/9732#discussion_r337844336
##########
File path:
flink-ml-parent/flink-ml-lib/src/main/java/org/apache/flink/ml/common/linalg/BLAS.java
##########
@@ -131,19 +172,56 @@ public static void gemm(double alpha, DenseMatrix matA,
boolean transA, DenseMat
}
/**
- * y := alpha * A * x + beta * y .
+ * Check the compatibility of matrix and vector sizes in
<code>gemv</code>.
*/
- public static void gemv(double alpha, DenseMatrix matA, boolean transA,
- DenseVector x, double
beta, DenseVector y) {
+ private static void gemvDimensionCheck(DenseMatrix matA, boolean
transA, Vector x, Vector y) {
if (transA) {
- assert (matA.numCols() == y.size() && matA.numRows() ==
x.size()) : "Matrix and vector size mismatched.";
+ Preconditions.checkArgument(matA.numCols() == y.size()
&& matA.numRows() == x.size(),
+ "Matrix and vector size mismatched.");
} else {
- assert (matA.numRows() == y.size() && matA.numCols() ==
x.size()) : "Matrix and vector size mismatched.";
+ Preconditions.checkArgument(matA.numRows() == y.size()
&& matA.numCols() == x.size(),
+ "Matrix and vector size mismatched.");
}
+ }
+
+ /**
+ * y := alpha * A * x + beta * y .
+ */
+ public static void gemv(double alpha, DenseMatrix matA, boolean transA,
+ DenseVector x, double
beta, DenseVector y) {
+ gemvDimensionCheck(matA, transA, x, y);
final int m = matA.numRows();
final int n = matA.numCols();
final int lda = matA.numRows();
final String ta = transA ? "T" : "N";
NATIVE_BLAS.dgemv(ta, m, n, alpha, matA.getData(), lda,
x.getData(), 1, beta, y.getData(), 1);
}
+
+ /**
+ * y := alpha * A * x + beta * y .
+ */
+ public static void gemv(double alpha, DenseMatrix matA, boolean transA,
+ SparseVector x, double
beta, DenseVector y) {
+ gemvDimensionCheck(matA, transA, x, y);
+ final int m = matA.numRows();
+ final int n = matA.numCols();
+ if (transA) {
+ int start = 0;
+ for (int i = 0; i < n; i++) {
+ double s = 0.;
+ for (int j = 0; j < x.indices.length; j++) {
+ s += x.values[j] * matA.data[start +
x.indices[j]];
+ }
+ y.data[i] = beta * y.data[i] + alpha * s;
+ start += m;
+ }
+ } else {
+ scal(beta, y);
+ for (int i = 0; i < x.indices.length; i++) {
+ int index = x.indices[i];
+ double value = alpha * x.values[i];
+ F2J_BLAS.daxpy(m, value, matA.data, index * m,
1, y.data, 0, 1);
Review comment:
could you add the explanation you added in this PR comment into the actual
code comments? I think it helps others to understand this code in the future.
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