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LRN op context
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@AlexDBlack
AlexDBlack committed Mar 9, 2019
1 parent 82c5a28 commit 455466f
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Showing 4 changed files with 40 additions and 39 deletions.
7 changes: 4 additions & 3 deletions ...ing4j/deeplearning4j-core/src/test/java/org/deeplearning4j/LayerHelperValidationUtil.java
View file
Expand Up @@ -127,7 +127,7 @@ public static void validateMLN(MultiLayerNetwork netOrig, TestCase t){
double d2 = arr2.dup('c').getDouble(idx);
System.out.println("Different values at index " + idx + ": " + d1 + ", " + d2 + " - RE = " + maxRE);
}
assertTrue(s + layerName + " - max RE: " + maxRE, maxRE < t.getMaxRelError());
assertTrue(s + layerName + "activations - max RE: " + maxRE, maxRE < t.getMaxRelError());
log.info("Forward pass, max relative error: " + layerName + " - " + maxRE);
}

Expand Down Expand Up @@ -190,7 +190,8 @@ public static void validateMLN(MultiLayerNetwork netOrig, TestCase t){
} else {
System.out.println("OK: " + p);
}
assertTrue("Gradients are not equal: " + p, maxRE < t.getMaxRelError());
assertTrue("Gradients are not equal: " + p + " - highest relative error = " + maxRE + " > max relative error = " + t.getMaxRelError(),
maxRE < t.getMaxRelError());
}
}

Expand Down Expand Up @@ -261,7 +262,7 @@ private static void removeHelpers(Layer[] layers, List<Class<?>> keepHelpersFor)
try {
if (keepAndAssertPresent) {
Object o = f.get(l);
assertNotNull(o);
assertNotNull("Expect helper to be present for layer: " + l.getClass(), o);
} else {
f.set(l, null);
Integer i = map.get(l.getClass());
Expand Down
13 changes: 7 additions & 6 deletions ...ning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/mkldnn/ValidateMKLDNN.java
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Expand Up @@ -158,7 +158,7 @@ public void validateBatchNorm() {
netWithout.init();

LayerHelperValidationUtil.TestCase tc = LayerHelperValidationUtil.TestCase.builder()
.allowHelpersForClasses(Arrays.<Class<?>>asList(org.deeplearning4j.nn.layers.normalization.BatchNormalization.class))
.allowHelpersForClasses(Collections.<Class<?>>singletonList(org.deeplearning4j.nn.layers.normalization.BatchNormalization.class))
.testForward(true)
.testScore(true)
.testBackward(true)
Expand All @@ -172,9 +172,8 @@ public void validateBatchNorm() {
}
}

@Test @Ignore
@Test
public void validateLRN() {
//2019-02-14 AB - Ignored: LRN backprop is broken: https://github.com/deeplearning4j/deeplearning4j/issues/6958 issue 20

//Only run test if using nd4j-native backend
assumeTrue(Nd4j.getBackend().getClass().getName().toLowerCase().contains("native"));
Expand All @@ -188,7 +187,7 @@ public void validateLRN() {
double[] a = new double[]{1e-4, 1e-4, 1e-3, 1e-3};
double[] b = new double[]{0.75, 0.9, 0.6, 0.75};
double[] n = new double[]{5, 3, 2, 4};
double[] k = new double[]{2, 3, 1.5, 2};
double[] k = new double[]{2, 2.5, 1.5, 2};

for (int minibatch : new int[]{1, 3}) {
for( int i=0; i<a.length; i++ ) {
Expand Down Expand Up @@ -236,11 +235,13 @@ public void validateLRN() {
.labels(l)
.data(new SingletonDataSetIterator(new DataSet(f, l)))
//Very infrequent minor differences - as far as I can tell, just numerical precision issues...
.minAbsError(1e-4)
.maxRelError(2e-4)
.minAbsError(1e-3)
.maxRelError(1e-2)
.build();

LayerHelperValidationUtil.validateMLN(netWith, tc);

System.out.println("/////////////////////////////////////////////////////////////////////////////");
}
}
}
Expand Down
56 changes: 28 additions & 28 deletions ...main/java/org/deeplearning4j/nn/layers/mkldnn/MKLDNNLocalResponseNormalizationHelper.java
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Expand Up @@ -22,6 +22,7 @@
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.OpContext;
import org.nd4j.linalg.api.ops.impl.layers.convolution.LocalResponseNormalization;
import org.nd4j.linalg.api.ops.impl.layers.convolution.LocalResponseNormalizationDerivative;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.LocalResponseNormalizationConfig;
Expand All @@ -37,6 +38,9 @@
* @author Alex Black
*/
public class MKLDNNLocalResponseNormalizationHelper extends BaseMKLDNNHelper implements LocalResponseNormalizationHelper {

protected OpContext context;

@Override
public boolean checkSupported(double k, double n, double alpha, double beta) {
return BaseMKLDNNHelper.mklDnnEnabled();
Expand All @@ -45,22 +49,20 @@ public boolean checkSupported(double k, double n, double alpha, double beta) {
@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilon, double k, double n, double alpha, double beta, LayerWorkspaceMgr workspaceMgr) {
INDArray gradAtInput = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, input.dataType(), input.shape());
gradAtInput.assign(0);

LocalResponseNormalizationConfig conf = LocalResponseNormalizationConfig.builder()
.alpha(alpha)
.beta(beta)
.bias(k)
.depth((int)n) //Adjacent kernel maps
.build();

LocalResponseNormalizationDerivative op = LocalResponseNormalizationDerivative.derivativeBuilder()
.config(conf)
.inputs(new INDArray[]{input, epsilon})
.outputs(new INDArray[]{gradAtInput})
.build();

Nd4j.exec(op);

if(context == null){
context = Nd4j.getExecutioner().buildContext();
context.setTArguments(k, alpha, beta);
context.setIArguments((int)n);
}

LocalResponseNormalization op = new LocalResponseNormalization();

context.setInputArray(0, input);
context.setInputArray(0, epsilon);
context.setOutputArray(0, gradAtInput);

Nd4j.exec(op, context);
Gradient g = new DefaultGradient();
return new Pair<>(g, gradAtInput);
}
Expand All @@ -69,20 +71,18 @@ public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilo
public INDArray activate(INDArray x, boolean training, double k, double n, double alpha, double beta, LayerWorkspaceMgr workspaceMgr) {
INDArray out = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, x.dataType(), x.shape());

LocalResponseNormalizationConfig conf = LocalResponseNormalizationConfig.builder()
.alpha(alpha)
.beta(beta)
.bias(k)
.depth((int)n) //Adjacent kernel maps
.build();
if(context == null){
context = Nd4j.getExecutioner().buildContext();
context.setTArguments(k, alpha, beta);
context.setIArguments((int)n);
}

context.setInputArray(0, x);
context.setOutputArray(0, out);

LocalResponseNormalization op = LocalResponseNormalization.builder()
.config(conf)
.inputs(new INDArray[]{x})
.outputs(new INDArray[]{out})
.build();
LocalResponseNormalization op = new LocalResponseNormalization();

Nd4j.exec(op);
Nd4j.exec(op, context);
return out;
}

Expand Down
3 changes: 1 addition & 2 deletions .../src/main/java/org/deeplearning4j/nn/layers/normalization/LocalResponseNormalization.java
View file
Expand Up @@ -105,12 +105,11 @@ void initializeHelper() {
}
}
}
/*
//2019-02-14 AB - MKL-DNN helper disabled: LRN backprop is broken: https://github.com/deeplearning4j/deeplearning4j/issues/6958 issue 20
else if("CPU".equalsIgnoreCase(backend)){
helper = new MKLDNNLocalResponseNormalizationHelper();
log.debug("Created MKLDNNLocalResponseNormalizationHelper");
}*/
}
if (helper != null && !helper.checkSupported(layerConf().getK(), layerConf().getN(), layerConf().getAlpha(), layerConf().getBeta())) {
log.debug("Removed helper {} as not supported (k={}, n={}, alpha={}, beta={})", helper.getClass(), layerConf().getK(), layerConf().getN(), layerConf().getAlpha(), layerConf().getBeta());
helper = null;
Expand Down

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