You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@systemml.apache.org by ni...@apache.org on 2019/03/04 21:32:46 UTC
[systemml] branch master updated (0cabde0 -> 792da5d)
This is an automated email from the ASF dual-hosted git repository.
niketanpansare pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/systemml.git.
from 0cabde0 [SYSTEMML-540] Improve the performance of lstm builtin function
new adff5ee added profiling info
new 792da5d [SYSTEMML-540] Improved the performance of lstm builtin function for sparse inputs
The 2 revisions listed above as "new" are entirely new to this
repository and will be described in separate emails. The revisions
listed as "add" were already present in the repository and have only
been added to this reference.
Summary of changes:
.../sysml/runtime/matrix/data/LibMatrixDNN.java | 85 ++++++++++++++--------
1 file changed, 55 insertions(+), 30 deletions(-)
[systemml] 01/02: added profiling info
Posted by ni...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
niketanpansare pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/systemml.git
commit adff5ee743992fdcfed9923ee876791e01532220
Author: Niketan Pansare <np...@us.ibm.com>
AuthorDate: Thu Feb 28 09:29:56 2019 -0800
added profiling info
---
.../sysml/runtime/matrix/data/LibMatrixDNN.java | 37 ++++++++++++++++++++--
1 file changed, 35 insertions(+), 2 deletions(-)
diff --git a/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixDNN.java b/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixDNN.java
index 0f932ba..e2742d8 100644
--- a/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixDNN.java
+++ b/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixDNN.java
@@ -333,10 +333,25 @@ public class LibMatrixDNN {
MatrixBlock W2 = W.slice(D, D+M-1);
MatrixBlock c_t = null;
MatrixBlock out_t = null;
+
+ boolean profile = true;
+ long t1 = 0, t2 = 0, t3 = 0, t4 = 0, t5 = 0;
for(int t = 1; t <= T; t++) {
+ long s = profile ? System.nanoTime() : 0;
MatrixBlock X_t = X.slice(0, N-1, (t-1)*D, t*D-1, new MatrixBlock());
+ if(profile) {
+ long e = System.nanoTime();
+ t1 += e - s;
+ }
+
+ s = profile ? System.nanoTime() : 0;
MatrixBlock ifog_raw = add(add(matmult(X_t, W1, numThreads), matmult(out_prev, W2, numThreads), true), b, true);
+ if(profile) {
+ long e = System.nanoTime();
+ t2 += e - s;
+ }
+ s = profile ? System.nanoTime() : 0;
MatrixBlock ifo = ifog_raw.slice(0, N-1, 0, 3*M-1, new MatrixBlock());
ifo = sigmoid(ifo, numThreads, true);
MatrixBlock i = ifo.slice(0, N-1, 0, M-1, new MatrixBlock());
@@ -345,16 +360,30 @@ public class LibMatrixDNN {
MatrixBlock g = ifog_raw.slice(0, N-1, 3*M, 4*M-1, new MatrixBlock());
g = tanh(g, numThreads, true);
+ if(profile) {
+ long e = System.nanoTime();
+ t3 += e - s;
+ }
+ s = profile ? System.nanoTime() : 0;
// c_t = f*c_prev + i*g
c_t = plusMultiply(multiply(f, c_prev, true), i, g);
-
// out_t = o*tanh(c)
out_t = multiply(o, tanh(c_t, numThreads, false), true);
+ if(profile) {
+ long e = System.nanoTime();
+ t4 += e - s;
+ }
+ s = profile ? System.nanoTime() : 0;
if(return_seq) {
out = out.leftIndexingOperations(out_t, 0, N-1, (t-1)*M, t*M-1, new MatrixBlock(), UpdateType.INPLACE);
}
+ if(profile) {
+ long e = System.nanoTime();
+ t5 += e - s;
+ }
+
out_prev = out_t;
c_prev = c_t;
@@ -369,7 +398,11 @@ public class LibMatrixDNN {
c.copy(c_t);
else
c.copy(c0);
-
+ System.out.println("Time taken in lstm forward call: [X_t indexing:" + String.format("%.3f", t1*1e-9) +
+ ", ifog_raw computation:" + String.format("%.3f", t2*1e-9) +
+ ", lstm_squash computation:" + String.format("%.3f", t3*1e-9) +
+ ", c_t/out_t computation:" + String.format("%.3f", t4*1e-9) +
+ ", out leftIndexing computation:" + String.format("%.3f", t5*1e-9));
}
/**
[systemml] 02/02: [SYSTEMML-540] Improved the performance of lstm
builtin function for sparse inputs
Posted by ni...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
niketanpansare pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/systemml.git
commit 792da5d0aa2abd6e650a3a17f243795d0f9a4b35
Author: Niketan Pansare <np...@us.ibm.com>
AuthorDate: Mon Mar 4 13:32:35 2019 -0800
[SYSTEMML-540] Improved the performance of lstm builtin function for sparse inputs
This commits allows matrix multiplication operator to exploit sparsity by separating lstm into three cases:
1. If W is sparse, perform cbind(X_t, out_prev) %*% W
2. If X_t is sparse, perform X_t %*% W1 + out_prev %*% W2
3. If none of the case is applicable, perform cbind(X_t, out_prev) %*% W to maximize parallelism within matrix multiplication operator
---
.../sysml/runtime/matrix/data/LibMatrixDNN.java | 114 ++++++++++-----------
1 file changed, 53 insertions(+), 61 deletions(-)
diff --git a/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixDNN.java b/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixDNN.java
index e2742d8..365d7a2 100644
--- a/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixDNN.java
+++ b/src/main/java/org/apache/sysml/runtime/matrix/data/LibMatrixDNN.java
@@ -284,26 +284,34 @@ public class LibMatrixDNN {
private static MatrixBlock add(MatrixBlock matBlock1, MatrixBlock matBlock2, boolean inplace) {
BinaryOperator bop = new BinaryOperator(Plus.getPlusFnObject());
-// if(inplace) {
-// matBlock1.binaryOperationsInPlace(bop, matBlock2);
-// return matBlock1;
-// }
-// else {
+ if(inplace && matBlock1.isInSparseFormat() == matBlock2.isInSparseFormat() &&
+ matBlock1.getNumRows() == matBlock2.getNumRows() && matBlock1.getNumColumns() == matBlock2.getNumColumns()) {
+ matBlock1.binaryOperationsInPlace(bop, matBlock2);
+ return matBlock1;
+ }
+ else {
return (MatrixBlock) matBlock1.binaryOperations(bop, matBlock2, new MatrixBlock());
-// }
+ }
+ }
+ private static MatrixBlock plusMultiply(MatrixBlock matBlock1, MatrixBlock matBlock2, MatrixBlock matBlock3) {
+ return matBlock1.ternaryOperations(new TernaryOperator(PlusMultiply.getFnObject()),
+ matBlock2, matBlock3, new MatrixBlock());
}
+
private static MatrixBlock multiply(MatrixBlock matBlock1, MatrixBlock matBlock2, boolean inplace) {
BinaryOperator bop = new BinaryOperator(Multiply.getMultiplyFnObject());
-// if(inplace) {
-// matBlock1.binaryOperationsInPlace(bop, matBlock2);
-// return matBlock1;
-// }
-// else {
+ if(inplace && matBlock1.isInSparseFormat() == matBlock2.isInSparseFormat() &&
+ matBlock1.getNumRows() == matBlock2.getNumRows() && matBlock1.getNumColumns() == matBlock2.getNumColumns()) {
+ matBlock1.binaryOperationsInPlace(bop, matBlock2);
+ return matBlock1;
+ }
+ else {
return (MatrixBlock) matBlock1.binaryOperations(bop, matBlock2, new MatrixBlock());
-// }
+ }
}
+
// sigmoid(0)*c_prev + sigmoid(0)*tanh(0);
private static Builtin sigmoidOp = Builtin.getBuiltinFnObject(BuiltinCode.SIGMOID);
@@ -311,16 +319,10 @@ public class LibMatrixDNN {
private static MatrixBlock sigmoid(MatrixBlock in, int numThreads, boolean inPlace) {
return (MatrixBlock) in.unaryOperations(new UnaryOperator(sigmoidOp, numThreads, inPlace), new MatrixBlock());
}
-
private static MatrixBlock tanh(MatrixBlock in, int numThreads, boolean inPlace) {
return (MatrixBlock) in.unaryOperations(new UnaryOperator(tanhOp, numThreads, inPlace), new MatrixBlock());
}
- private static MatrixBlock plusMultiply(MatrixBlock matBlock1, MatrixBlock matBlock2, MatrixBlock matBlock3) {
- return matBlock1.ternaryOperations(new TernaryOperator(PlusMultiply.getFnObject()),
- matBlock2, matBlock3, new MatrixBlock());
- }
-
public static void lstm(MatrixBlock X, MatrixBlock W, MatrixBlock b, MatrixBlock out0, MatrixBlock c0,
boolean return_seq, int N, int T, int D, int M,
MatrixBlock out, MatrixBlock c, // output
@@ -329,61 +331,56 @@ public class LibMatrixDNN {
MatrixBlock out_prev = out0;
MatrixBlock c_prev = c0;
- MatrixBlock W1 = W.slice(0, D-1);
- MatrixBlock W2 = W.slice(D, D+M-1);
+ MatrixBlock W1 = null;
+ MatrixBlock W2 = null;
MatrixBlock c_t = null;
MatrixBlock out_t = null;
- boolean profile = true;
- long t1 = 0, t2 = 0, t3 = 0, t4 = 0, t5 = 0;
+ MatrixBlock input = null;
for(int t = 1; t <= T; t++) {
- long s = profile ? System.nanoTime() : 0;
- MatrixBlock X_t = X.slice(0, N-1, (t-1)*D, t*D-1, new MatrixBlock());
- if(profile) {
- long e = System.nanoTime();
- t1 += e - s;
- }
-
- s = profile ? System.nanoTime() : 0;
- MatrixBlock ifog_raw = add(add(matmult(X_t, W1, numThreads), matmult(out_prev, W2, numThreads), true), b, true);
- if(profile) {
- long e = System.nanoTime();
- t2 += e - s;
+ final MatrixBlock X_t = (T == 1) ? X : X.slice(0, N-1, (t-1)*D, t*D-1, new MatrixBlock());
+ MatrixBlock ifog_raw = null;
+ // Logic: Exploit sparse matrix multiplication whenever possible:
+ // 1. If W is sparse, perform cbind(X_t, out_prev) %*% W
+ // 2. Else if X_t is sparse, perform X_t %*% W1 + out_prev %*% W2
+ // 3. If none of the case is applicable, perform cbind(X_t, out_prev) %*% W
+ boolean isCase1 = W.isInSparseFormat();
+ boolean isCase2 = !isCase1 && X_t.isInSparseFormat();
+ if(isCase2) {
+ // Perform X_t %*% W1 + out_prev %*% W2
+ if(W1 == null) {
+ // Lazy slicing: applicable only when atleast one X_t is sparse.
+ W1 = W.slice(0, D-1);
+ W2 = W.slice(D, D+M-1);
+ }
+ ifog_raw = add(matmult(X_t, W1, numThreads), matmult(out_prev, W2, numThreads), true);
+ ifog_raw = add(ifog_raw, b, true);
+ }
+ else {
+ // Case 1 and 3:
+ // Perform input %*% W, where input = cbind(X_t, out_prev)
+ if(input == null) {
+ input = new MatrixBlock(N, D+M, false);
+ input.allocateDenseBlock();
+ }
+ input = X_t.append(out_prev, input);
+ ifog_raw = add(matmult(input, W, numThreads), b, true);
}
- s = profile ? System.nanoTime() : 0;
MatrixBlock ifo = ifog_raw.slice(0, N-1, 0, 3*M-1, new MatrixBlock());
ifo = sigmoid(ifo, numThreads, true);
MatrixBlock i = ifo.slice(0, N-1, 0, M-1, new MatrixBlock());
MatrixBlock f = ifo.slice(0, N-1, M, 2*M-1, new MatrixBlock());
MatrixBlock o = ifo.slice(0, N-1, 2*M, 3*M-1, new MatrixBlock());
-
- MatrixBlock g = ifog_raw.slice(0, N-1, 3*M, 4*M-1, new MatrixBlock());
- g = tanh(g, numThreads, true);
- if(profile) {
- long e = System.nanoTime();
- t3 += e - s;
- }
-
- s = profile ? System.nanoTime() : 0;
+ MatrixBlock g = tanh(ifog_raw.slice(0, N-1, 3*M, 4*M-1, new MatrixBlock()), numThreads, true);
+
// c_t = f*c_prev + i*g
c_t = plusMultiply(multiply(f, c_prev, true), i, g);
// out_t = o*tanh(c)
out_t = multiply(o, tanh(c_t, numThreads, false), true);
- if(profile) {
- long e = System.nanoTime();
- t4 += e - s;
- }
-
- s = profile ? System.nanoTime() : 0;
if(return_seq) {
out = out.leftIndexingOperations(out_t, 0, N-1, (t-1)*M, t*M-1, new MatrixBlock(), UpdateType.INPLACE);
}
- if(profile) {
- long e = System.nanoTime();
- t5 += e - s;
- }
-
out_prev = out_t;
c_prev = c_t;
@@ -398,11 +395,6 @@ public class LibMatrixDNN {
c.copy(c_t);
else
c.copy(c0);
- System.out.println("Time taken in lstm forward call: [X_t indexing:" + String.format("%.3f", t1*1e-9) +
- ", ifog_raw computation:" + String.format("%.3f", t2*1e-9) +
- ", lstm_squash computation:" + String.format("%.3f", t3*1e-9) +
- ", c_t/out_t computation:" + String.format("%.3f", t4*1e-9) +
- ", out leftIndexing computation:" + String.format("%.3f", t5*1e-9));
}
/**
@@ -1009,4 +1001,4 @@ public class LibMatrixDNN {
params.end_indexes_w[q] = Math.min(ix+params.S, params.W);
}
}
-}
+}
\ No newline at end of file