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Posted to commits@systemml.apache.org by mb...@apache.org on 2018/04/19 08:17:00 UTC
[2/3] systemml git commit: [SYSTEMML-2256] Exploit native matrix mult
in dense wsigmoid
[SYSTEMML-2256] Exploit native matrix mult in dense wsigmoid
This patch improves the performance of the fused dense wsigmoid on
modern processors with wide SIMD registers and fma by exploiting a
native matrix mult as part of the larger wsigmod computation.
Project: http://git-wip-us.apache.org/repos/asf/systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/systemml/commit/f6e3a91d
Tree: http://git-wip-us.apache.org/repos/asf/systemml/tree/f6e3a91d
Diff: http://git-wip-us.apache.org/repos/asf/systemml/diff/f6e3a91d
Branch: refs/heads/master
Commit: f6e3a91dfe8852839be6b471ba0eabc723f55bf1
Parents: 02e5ba5
Author: Matthias Boehm <mb...@gmail.com>
Authored: Wed Apr 18 21:26:01 2018 -0700
Committer: Matthias Boehm <mb...@gmail.com>
Committed: Wed Apr 18 21:40:17 2018 -0700
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.../runtime/matrix/data/LibMatrixMult.java | 43 +++++++++++++++++++-
.../runtime/matrix/data/LibMatrixNative.java | 2 +-
2 files changed, 43 insertions(+), 2 deletions(-)
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http://git-wip-us.apache.org/repos/asf/systemml/blob/f6e3a91d/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixMult.java
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diff --git a/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixMult.java b/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixMult.java
index d9e741a..3a2c58e 100644
--- a/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixMult.java
+++ b/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixMult.java
@@ -42,6 +42,7 @@ import org.apache.sysml.runtime.functionobjects.ValueFunction;
import org.apache.sysml.runtime.matrix.operators.ReorgOperator;
import org.apache.sysml.runtime.util.CommonThreadPool;
import org.apache.sysml.runtime.util.UtilFunctions;
+import org.apache.sysml.utils.NativeHelper;
/**
* MB: Library for matrix multiplications including MM, MV, VV for all
@@ -557,7 +558,13 @@ public class LibMatrixMult
ret.allocateBlock();
//core weighted square sum mm computation
- if( !mW.sparse && !mU.sparse && !mV.sparse && !mU.isEmptyBlock() && !mV.isEmptyBlock() )
+ boolean allDense = !mW.sparse && !mU.sparse && !mV.sparse
+ && !mU.isEmptyBlock() && !mV.isEmptyBlock();
+ if( NativeHelper.isNativeLibraryLoaded() && allDense && (mW.rlen == 1 || mW.clen == 1)
+ && !LibMatrixNative.isMatMultMemoryBound(mU.rlen, mU.clen, mV.rlen)
+ && mW.getDenseBlock().isContiguous() && mU.getDenseBlock().isContiguous() && mV.getDenseBlock().isContiguous() )
+ matrixMultWSigmoidDenseNative(mW, mU, mV, ret, wt);
+ else if( allDense )
matrixMultWSigmoidDense(mW, mU, mV, ret, wt, 0, mW.rlen);
else if( mW.sparse && !mU.sparse && !mV.sparse && !mU.isEmptyBlock() && !mV.isEmptyBlock())
matrixMultWSigmoidSparseDense(mW, mU, mV, ret, wt, 0, mW.rlen);
@@ -2384,6 +2391,30 @@ public class LibMatrixMult
dotProduct(tmp1.getDenseBlockValues(), tmp2.getDenseBlockValues(), mU.clen*mU.clen)));
}
+ private static void matrixMultWSigmoidDenseNative(MatrixBlock mW, MatrixBlock mU, MatrixBlock mV, MatrixBlock ret, WSigmoidType wt) {
+ double[] w = mW.getDenseBlockValues();
+ double[] c = ret.getDenseBlockValues();
+ final int m = mW.rlen, n = mW.clen;
+ final int cd = mU.clen;
+ boolean flagminus = (wt==WSigmoidType.MINUS || wt==WSigmoidType.LOG_MINUS);
+ boolean flaglog = (wt==WSigmoidType.LOG || wt==WSigmoidType.LOG_MINUS);
+
+ //call native matrix multiplication (only called for single-threaded and matrix-vector
+ //because this ensures that we can deal with the transpose mV without additional transpose)
+ if(!NativeHelper.dmmdd(((m==1)?mV:mU).getDenseBlockValues(),
+ ((m==1)?mU:mV).getDenseBlockValues(), c, (m==1)?n:m, cd, 1, 1) )
+ throw new DMLRuntimeException("Error executing native matrix mult.");
+
+ //compute remaining wsigmoid for all relevant outputs
+ for(int i=0, ix=0; i<m; i++, ix+=n) {
+ for(int j=0; j<n; j++) {
+ double wij = w[ix +j];
+ //if( wij != 0 )
+ c[ix+j] = wsigmoid(wij, c[ix+j], flagminus, flaglog);
+ }
+ }
+ }
+
private static void matrixMultWSigmoidDense(MatrixBlock mW, MatrixBlock mU, MatrixBlock mV, MatrixBlock ret, WSigmoidType wt, int rl, int ru) {
DenseBlock w = mW.getDenseBlock();
DenseBlock c = ret.getDenseBlock();
@@ -3462,6 +3493,16 @@ public class LibMatrixMult
//compute weighted output
return wij * ((flaglog) ? Math.log(cval) : cval);
}
+
+ private static double wsigmoid(final double wij, final double uvij, final boolean flagminus, final boolean flaglog) {
+ //compute core sigmoid function
+ double cval = flagminus ?
+ 1 / (1 + FastMath.exp(uvij)) :
+ 1 / (1 + FastMath.exp(-uvij));
+
+ //compute weighted output
+ return wij * ((flaglog) ? Math.log(cval) : cval);
+ }
private static void wdivmm( final double wij, double[] u, double[] v, double[] c, final int uix, final int vix, final boolean left, final boolean mult, final boolean minus, final int len )
{
http://git-wip-us.apache.org/repos/asf/systemml/blob/f6e3a91d/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixNative.java
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diff --git a/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixNative.java b/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixNative.java
index 1d46927..eade43f 100644
--- a/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixNative.java
+++ b/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixNative.java
@@ -48,7 +48,7 @@ public class LibMatrixNative
// We could encapsulate heuristics in this function
// For now, we only consider matrix-vector operation to be memory bound
- private static boolean isMatMultMemoryBound(int m1Rlen, int m1Clen, int m2Clen) {
+ public static boolean isMatMultMemoryBound(int m1Rlen, int m1Clen, int m2Clen) {
return (m1Rlen == 1 || m1Clen == 1 || m2Clen == 1)
&& (8L*m1Rlen*m1Clen > 16 * LibMatrixMult.L3_CACHESIZE
|| 8L*m1Clen*m2Clen > 16 * LibMatrixMult.L3_CACHESIZE);