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Small build fix, after last PR (#177) 

Signed-off-by: Alex Black <blacka101@gmail.com>
master
Alex Black 2019-08-27 19:46:26 +10:00 committed by GitHub
parent efbfafe3f7
commit fd22a8ecc7
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 11 additions and 14 deletions
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4
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/BaseLayer.java
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@ -93,7 +93,7 @@ public abstract class BaseLayer<LayerConfT extends org.deeplearning4j.nn.conf.la
INDArray g = getParam(DefaultParamInitializer.GAIN_KEY);
INDArray dldg = gradientViews.get(DefaultParamInitializer.GAIN_KEY);
Nd4j.getExecutioner().exec(new LayerNormBp(preNorm, g, delta, delta, dldg, 1));
Nd4j.getExecutioner().exec(new LayerNormBp(preNorm, g, delta, delta, dldg, true, 1));
ret.gradientForVariable().put(DefaultParamInitializer.GAIN_KEY, dldg);
}
@ -318,7 +318,7 @@ public abstract class BaseLayer<LayerConfT extends org.deeplearning4j.nn.conf.la
INDArray preNorm = ret;
if(hasLayerNorm()){
preNorm = (forBackprop ? ret.dup(ret.ordering()) : ret);
Nd4j.getExecutioner().exec(new LayerNorm(preNorm, g, ret, 1));
Nd4j.getExecutioner().exec(new LayerNorm(preNorm, g, ret, true, 1));
}
if(hasBias()){

8
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/SimpleRnn.java
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@ -158,7 +158,7 @@ public class SimpleRnn extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.lay
dldnCurrent = workspaceMgr.createUninitialized(ArrayType.BP_WORKING_MEM, dldzCurrent.dataType(), dldzCurrent.shape());
INDArray ggCur = workspaceMgr.createUninitialized(ArrayType.BP_WORKING_MEM, gg.dataType(), gxg.shape());
INDArray bgCur = workspaceMgr.createUninitialized(ArrayType.BP_WORKING_MEM, bg.dataType(), bg.shape());
Nd4j.getExecutioner().exec(new LayerNormBp(nCurrent, gx, b, dldzCurrent, dldnCurrent, ggCur, bgCur, 1));
Nd4j.getExecutioner().exec(new LayerNormBp(nCurrent, gx, b, dldzCurrent, dldnCurrent, ggCur, bgCur, true, 1));
gxg.addi(ggCur);
bg.addi(bgCur);
}else{
@ -177,7 +177,7 @@ public class SimpleRnn extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.lay
if(hasLayerNorm() && i > end){
dldzNext = workspaceMgr.createUninitialized(ArrayType.BP_WORKING_MEM, dldzCurrent.dataType(), dldzCurrent.shape());
INDArray ggCur = workspaceMgr.createUninitialized(ArrayType.BP_WORKING_MEM, gg.dataType(), grg.shape());
Nd4j.getExecutioner().exec(new LayerNormBp(rCurrent, gr, dldzCurrent, dldzNext, ggCur, 1));
Nd4j.getExecutioner().exec(new LayerNormBp(rCurrent, gr, dldzCurrent, dldzNext, ggCur, true, 1));
grg.addi(ggCur);
}else{
dldzNext = dldzCurrent;
@ -256,7 +256,7 @@ public class SimpleRnn extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.lay
if(hasLayerNorm()){
INDArray currOutPreNorm = (forBackprop ? outPreNorm : out).get(all(), all(), point(i));
Nd4j.gemm(currIn, w, currOutPreNorm, false, false, 1.0, 0.0);
Nd4j.getExecutioner().exec(new LayerNorm(currOutPreNorm, gx, b, currOut, 1));
Nd4j.getExecutioner().exec(new LayerNorm(currOutPreNorm, gx, b, currOut, true, 1));
}else{
Nd4j.gemm(currIn, w, currOut, false, false, 1.0, 1.0); //beta = 1.0 to keep previous contents (bias)
}
@ -266,7 +266,7 @@ public class SimpleRnn extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.lay
INDArray currRecPreNorm = forBackprop ? recPreNorm.get(all(), all(), point(i)) : workspaceMgr.createUninitialized(ArrayType.FF_WORKING_MEM, currOut.dataType(), currOut.shape(), 'f');;
Nd4j.gemm(prevStepOut, rw, currRecPreNorm, false, false, 1.0, 0.0);
INDArray recNorm = workspaceMgr.createUninitialized(ArrayType.FF_WORKING_MEM, currOut.dataType(), currOut.shape(), 'f');
Nd4j.getExecutioner().exec(new LayerNorm(currRecPreNorm, gr, recNorm, 1));
Nd4j.getExecutioner().exec(new LayerNorm(currRecPreNorm, gr, recNorm, true, 1));
currOut.addi(recNorm);
}else {
Nd4j.gemm(prevStepOut, rw, currOut, false, false, 1.0, 1.0); //beta = 1.0 to keep previous contents

13
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/LayerNorm.java
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@ -54,17 +54,14 @@ public class LayerNorm extends DynamicCustomOp {
this(sameDiff, input, gain, null, channelsFirst, dimensions);
}
public LayerNorm(INDArray input, INDArray gain, INDArray bias, INDArray result, int... dimensions) {
super("layer_norm", new INDArray[]{input, gain, bias}, new INDArray[]{result});
Preconditions.checkArgument(bias != null, "LayerNorm: Use different constructor if bias is null.");
public LayerNorm(INDArray input, INDArray gain, INDArray bias, INDArray result, boolean channelsFirst, int... dimensions) {
super("layer_norm", wrapFilterNull(input, gain, bias), wrapOrNull(result));
this.channelsFirst = channelsFirst;
setDimensions(dimensions);
}
public LayerNorm(INDArray input, INDArray gain, INDArray result, int... dimensions) {
super("layer_norm", new INDArray[]{input, gain}, new INDArray[]{result});
noBias = true;
setDimensions(dimensions);
public LayerNorm(INDArray input, INDArray gain, INDArray result, boolean channelsFirst, int... dimensions) {
this(input, gain, null, result, channelsFirst, dimensions);
}
@Override