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package org.deeplearning4j.nn.layers.recurrent;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.common.primitives.Pair;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import java.util.Map;
@Slf4j
public class GravesBidirectionalLSTM
extends BaseRecurrentLayer {
protected FwdPassReturn cachedPassForward;
protected FwdPassReturn cachedPassBackward;
public GravesBidirectionalLSTM(NeuralNetConfiguration conf, DataType dataType) {
super(conf, dataType);
}
@Override
public Gradient gradient() {
throw new UnsupportedOperationException("Not supported " + layerId());
}
@Override
public Pair backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
return backpropGradientHelper(epsilon, false, -1, workspaceMgr);
}
@Override
public Pair tbpttBackpropGradient(INDArray epsilon, int tbpttBackwardLength, LayerWorkspaceMgr workspaceMgr) {
return backpropGradientHelper(epsilon, true, tbpttBackwardLength, workspaceMgr);
}
private Pair backpropGradientHelper(final INDArray epsilon, final boolean truncatedBPTT,
final int tbpttBackwardLength, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(true);
if (truncatedBPTT) {
throw new UnsupportedOperationException(
"Time step for bidirectional RNN not supported: it has to run on a batch of data all at once "
+ layerId());
}
final FwdPassReturn fwdPass = activateHelperDirectional(true, null, null, true, true, workspaceMgr);
fwdPass.fwdPassOutput = permuteIfNWC(fwdPass.fwdPassOutput);
final Pair forwardsGradient = LSTMHelpers.backpropGradientHelper(this,
this.conf,
this.layerConf().getGateActivationFn(), permuteIfNWC(this.input),
getParam(GravesBidirectionalLSTMParamInitializer.RECURRENT_WEIGHT_KEY_FORWARDS),
getParam(GravesBidirectionalLSTMParamInitializer.INPUT_WEIGHT_KEY_FORWARDS), permuteIfNWC(epsilon),
truncatedBPTT, tbpttBackwardLength, fwdPass, true,
GravesBidirectionalLSTMParamInitializer.INPUT_WEIGHT_KEY_FORWARDS,
GravesBidirectionalLSTMParamInitializer.RECURRENT_WEIGHT_KEY_FORWARDS,
GravesBidirectionalLSTMParamInitializer.BIAS_KEY_FORWARDS, gradientViews, maskArray, true,
null, workspaceMgr, layerConf().isHelperAllowFallback());
final FwdPassReturn backPass = activateHelperDirectional(true, null, null, true, false, workspaceMgr);
final Pair backwardsGradient = LSTMHelpers.backpropGradientHelper(this,
this.conf,
this.layerConf().getGateActivationFn(), permuteIfNWC(this.input),
getParam(GravesBidirectionalLSTMParamInitializer.RECURRENT_WEIGHT_KEY_BACKWARDS),
getParam(GravesBidirectionalLSTMParamInitializer.INPUT_WEIGHT_KEY_BACKWARDS), permuteIfNWC(epsilon),
truncatedBPTT, tbpttBackwardLength, backPass, false,
GravesBidirectionalLSTMParamInitializer.INPUT_WEIGHT_KEY_BACKWARDS,
GravesBidirectionalLSTMParamInitializer.RECURRENT_WEIGHT_KEY_BACKWARDS,
GravesBidirectionalLSTMParamInitializer.BIAS_KEY_BACKWARDS, gradientViews, maskArray, true,
null, workspaceMgr, layerConf().isHelperAllowFallback());
forwardsGradient.setSecond(permuteIfNWC(forwardsGradient.getSecond()));
backwardsGradient.setSecond(permuteIfNWC(backwardsGradient.getSecond()));
//merge the gradient, which is key value pair of String,INDArray
//the keys for forwards and backwards should be different
final Gradient combinedGradient = new DefaultGradient();
for (Map.Entry entry : forwardsGradient.getFirst().gradientForVariable().entrySet()) {
combinedGradient.setGradientFor(entry.getKey(), entry.getValue());
}
for (Map.Entry entry : backwardsGradient.getFirst().gradientForVariable().entrySet()) {
combinedGradient.setGradientFor(entry.getKey(), entry.getValue());
}
final Gradient correctOrderedGradient = new DefaultGradient();
for (final String key : params.keySet()) {
correctOrderedGradient.setGradientFor(key, combinedGradient.getGradientFor(key));
}
final INDArray forwardEpsilon = forwardsGradient.getSecond();
final INDArray backwardsEpsilon = backwardsGradient.getSecond();
final INDArray combinedEpsilon = forwardEpsilon.addi(backwardsEpsilon);
//sum the errors that were back-propagated
return new Pair<>(correctOrderedGradient, combinedEpsilon);
}
@Override
public INDArray activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr) {
setInput(input, workspaceMgr);
return activateOutput(training, false, workspaceMgr);
}
@Override
public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
return activateOutput(training, false, workspaceMgr);
}
private INDArray activateOutput(final boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(false);
final FwdPassReturn forwardsEval;
final FwdPassReturn backwardsEval;
if (cacheMode != CacheMode.NONE && cachedPassForward != null && cachedPassBackward != null) {
// restore from cache. but this coll will probably never happen
forwardsEval = cachedPassForward;
backwardsEval = cachedPassBackward;
cachedPassBackward = null;
cachedPassForward = null;
} else {
forwardsEval = LSTMHelpers.activateHelper(this, this.conf, this.layerConf().getGateActivationFn(),
permuteIfNWC(this.input), getParam(GravesBidirectionalLSTMParamInitializer.RECURRENT_WEIGHT_KEY_FORWARDS),
getParam(GravesBidirectionalLSTMParamInitializer.INPUT_WEIGHT_KEY_FORWARDS),
getParam(GravesBidirectionalLSTMParamInitializer.BIAS_KEY_FORWARDS), training, null, null,
forBackprop || (cacheMode != CacheMode.NONE && training), true,
GravesBidirectionalLSTMParamInitializer.INPUT_WEIGHT_KEY_FORWARDS, maskArray, true, null,
forBackprop ? cacheMode : CacheMode.NONE, workspaceMgr, layerConf().isHelperAllowFallback());
backwardsEval = LSTMHelpers.activateHelper(this, this.conf, this.layerConf().getGateActivationFn(),
permuteIfNWC(this.input),
getParam(GravesBidirectionalLSTMParamInitializer.RECURRENT_WEIGHT_KEY_BACKWARDS),
getParam(GravesBidirectionalLSTMParamInitializer.INPUT_WEIGHT_KEY_BACKWARDS),
getParam(GravesBidirectionalLSTMParamInitializer.BIAS_KEY_BACKWARDS), training, null, null,
forBackprop || (cacheMode != CacheMode.NONE && training), false,
GravesBidirectionalLSTMParamInitializer.INPUT_WEIGHT_KEY_BACKWARDS, maskArray, true, null,
forBackprop ? cacheMode : CacheMode.NONE, workspaceMgr, layerConf().isHelperAllowFallback());
forwardsEval.fwdPassOutput = permuteIfNWC(forwardsEval.fwdPassOutput);
backwardsEval.fwdPassOutput = permuteIfNWC(backwardsEval.fwdPassOutput);
cachedPassForward = forwardsEval;
cachedPassBackward = backwardsEval;
}
//sum outputs
final INDArray fwdOutput = forwardsEval.fwdPassOutput;
final INDArray backOutput = backwardsEval.fwdPassOutput;
// if we're on ff pass & cache enabled - we should not modify fwdOutput, and for backprop pass - we don't care
final INDArray totalOutput = training && cacheMode != CacheMode.NONE && !forBackprop ? fwdOutput.add(backOutput)
: fwdOutput.addi(backOutput);
return totalOutput;
}
private FwdPassReturn activateHelperDirectional(final boolean training, final INDArray prevOutputActivations,
final INDArray prevMemCellState, boolean forBackprop, boolean forwards, LayerWorkspaceMgr workspaceMgr) {
if (cacheMode == null)
cacheMode = CacheMode.NONE;
if (cacheMode != CacheMode.NONE && forwards && forBackprop && cachedPassForward != null) {
FwdPassReturn ret = cachedPassForward;
cachedPassForward = null;
return ret;
} else if (cacheMode != CacheMode.NONE && !forwards && forBackprop) {
FwdPassReturn ret = cachedPassBackward;
cachedPassBackward = null;
return ret;
} else {
String recurrentKey = GravesBidirectionalLSTMParamInitializer.RECURRENT_WEIGHT_KEY_FORWARDS;
String inputKey = GravesBidirectionalLSTMParamInitializer.INPUT_WEIGHT_KEY_FORWARDS;
String biasKey = GravesBidirectionalLSTMParamInitializer.BIAS_KEY_FORWARDS;
if (!forwards) {
recurrentKey = GravesBidirectionalLSTMParamInitializer.RECURRENT_WEIGHT_KEY_BACKWARDS;
inputKey = GravesBidirectionalLSTMParamInitializer.INPUT_WEIGHT_KEY_BACKWARDS;
biasKey = GravesBidirectionalLSTMParamInitializer.BIAS_KEY_BACKWARDS;
}
FwdPassReturn ret = LSTMHelpers.activateHelper(this, this.conf, this.layerConf().getGateActivationFn(), permuteIfNWC(this.input),
getParam(recurrentKey), getParam(inputKey), getParam(biasKey), training,
prevOutputActivations, prevMemCellState, forBackprop, forwards, inputKey, maskArray, true,
null, forBackprop ? cacheMode : CacheMode.NONE, workspaceMgr, layerConf().isHelperAllowFallback());
ret.fwdPassOutput = permuteIfNWC(ret.fwdPassOutput);
return ret;
}
}
@Override
public Type type() {
return Type.RECURRENT;
}
@Override
public boolean isPretrainLayer() {
return false;
}
@Override
public INDArray rnnTimeStep(INDArray input, LayerWorkspaceMgr workspaceMgr) {
throw new UnsupportedOperationException(
"you can not time step a bidirectional RNN, it has to run on a batch of data all at once "
+ layerId());
}
@Override
public INDArray rnnActivateUsingStoredState(INDArray input, boolean training, boolean storeLastForTBPTT, LayerWorkspaceMgr workspaceMgr) {
throw new UnsupportedOperationException(
"Cannot set stored state: bidirectional RNNs don't have stored state " + layerId());
}
@Override
public Pair feedForwardMaskArray(INDArray maskArray, MaskState currentMaskState,
int minibatchSize) {
//Bidirectional RNNs operate differently to standard RNNs from a masking perspective
//Specifically, the masks are applied regardless of the mask state
//For example, input -> RNN -> Bidirectional-RNN: we should still mask the activations and errors in the bi-RNN
// even though the normal RNN has marked the current mask state as 'passthrough'
//Consequently, the mask is marked as active again
this.maskArray = maskArray;
this.maskState = currentMaskState;
return new Pair<>(maskArray, MaskState.Active);
}
}
