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org.deeplearning4j.nn.layers.samediff.SameDiffLayer Maven / Gradle / Ivy
/*
* ******************************************************************************
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* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* * License for the specific language governing permissions and limitations
* * under the License.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.deeplearning4j.nn.layers.samediff;
import lombok.val;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.AbstractLayer;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.autodiff.samediff.array.SingleThreadArrayHolder;
import org.nd4j.autodiff.samediff.internal.InferenceSession;
import org.nd4j.autodiff.samediff.internal.SessionMemMgr;
import org.nd4j.autodiff.util.SameDiffUtils;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.memory.conf.WorkspaceConfiguration;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.layers.ExternalErrorsFunction;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.primitives.Pair;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.common.util.ArrayUtil;
import java.util.*;
public class SameDiffLayer extends AbstractLayer {
public static final String INPUT_KEY = "input";
public static final String MASK_KEY = "mask";
protected SameDiff sameDiff;
protected SDVariable outputVar;
protected ExternalErrorsFunction fn;
protected String outputKey;
protected INDArray params;
protected INDArray gradients;
protected Map paramTable;
protected Map gradTable;
public SameDiffLayer(NeuralNetConfiguration conf, DataType dataType){
super(conf, dataType);
}
@Override
public Layer clone() {
throw new UnsupportedOperationException();
}
@Override
public boolean isPretrainLayer() {
return false;
}
@Override
public void clearNoiseWeightParams() {
//TODO - properly support weight noise...
}
@Override
public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(false);
try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
if (sameDiff == null) {
doInit();
}
}
org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer bl = (org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer) layerConf();
bl.validateInput(input);
Map phMap = new HashMap<>();
phMap.put(INPUT_KEY, input);
if(maskArray != null){
phMap.put(MASK_KEY, maskArray);
} else {
phMap.put(MASK_KEY, layerConf().onesMaskForInput(input));
}
//Configure memory management for SameDiff instance - use DL4J workspaces
String wsNameWorking = workspaceMgr.getWorkspaceName(ArrayType.FF_WORKING_MEM);
String wsNameOutput = workspaceMgr.getWorkspaceName(ArrayType.ACTIVATIONS);
WorkspaceConfiguration confWorking = workspaceMgr.getConfiguration(ArrayType.FF_WORKING_MEM);
WorkspaceConfiguration confOutput = workspaceMgr.getConfiguration(ArrayType.ACTIVATIONS);
boolean actScopedOut = workspaceMgr.isScopedOut(ArrayType.ACTIVATIONS);
Preconditions.checkState(actScopedOut || wsNameOutput != null, "Activations must have a workspace or must be scoped out");
SessionMemMgr mmgr = new DL4JSameDiffMemoryMgr(wsNameWorking, wsNameOutput, confWorking, confOutput);
InferenceSession is = sameDiff.getSessions().get(Thread.currentThread().getId());
if(is == null){
is = SameDiff.getInferenceFactory().create(sameDiff);
sameDiff.getSessions().put(Thread.currentThread().getId(), is);
}
is.setMmgr(mmgr);
Map out = sameDiff.output(phMap, outputKey);
INDArray result = out.get(outputKey);
//Edge case - identity activation
//TODO there may be a cleaner way to do this...
if(!actScopedOut && !result.data().getParentWorkspace().getId().equals(wsNameOutput)){
result = workspaceMgr.dup(ArrayType.ACTIVATIONS, result);
} else if(actScopedOut && result.isAttached()){
result = result.detach();
}
//Clear placeholders and op inputs to ensure no out-of-scope arrays are still referenced anywhere
sameDiff.clearPlaceholders(true);
sameDiff.clearOpInputs();
return result;
}
@Override
public Pair backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
assertInputSet(true);
Gradient g = new DefaultGradient();
INDArray dLdIn;
try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
if (sameDiff == null) {
doInit();
}
if (!sameDiff.hasGradientFunction()) {
//Create when scoped out, to ensure any arrays are not in WS
sameDiff.createGradFunction(INPUT_KEY);
}
}
//Configure memory management for SameDiff instance - use DL4J workspaces
Map sessionMap = sameDiff.getFunction("grad").getSessions();
if(!sessionMap.containsKey(Thread.currentThread().getId())){
sessionMap.put(Thread.currentThread().getId(), SameDiff.getInferenceFactory().create(sameDiff.getFunction("grad")));
}
String wsNameWorking = workspaceMgr.getWorkspaceName(ArrayType.BP_WORKING_MEM);
String wsNameActGrad = workspaceMgr.getWorkspaceName(ArrayType.ACTIVATION_GRAD);
WorkspaceConfiguration confWorking = workspaceMgr.getConfiguration(ArrayType.BP_WORKING_MEM);
WorkspaceConfiguration confOutput = workspaceMgr.getConfiguration(ArrayType.ACTIVATION_GRAD);
boolean actGradScopedOut = workspaceMgr.isScopedOut(ArrayType.ACTIVATION_GRAD);
Preconditions.checkState(actGradScopedOut || wsNameActGrad != null, "Activation gradients must have a workspace or be scoped out");
SessionMemMgr mmgr = new DL4JSameDiffMemoryMgr(wsNameWorking, wsNameActGrad, confWorking, confOutput);
sessionMap.get(Thread.currentThread().getId()).setMmgr(mmgr);
org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer bl = (org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer) layerConf();
bl.validateInput(input);
Map phMap = new HashMap<>();
phMap.put(INPUT_KEY, input);
phMap.put(fn.getGradPlaceholderName(), epsilon);
if(maskArray != null){
phMap.put(MASK_KEY, maskArray);
} else {
phMap.put(MASK_KEY, layerConf().onesMaskForInput(input));
}
List requiredGrads = new ArrayList<>(paramTable.size() + 1);
requiredGrads.add(INPUT_KEY);
requiredGrads.addAll(paramTable.keySet());
Map m = sameDiff.calculateGradients(phMap, requiredGrads);
for(String s : paramTable.keySet() ){
INDArray sdGrad = m.get(s);
INDArray dl4jGrad = gradTable.get(s);
dl4jGrad.assign(sdGrad); //TODO OPTIMIZE THIS
g.gradientForVariable().put(s, dl4jGrad);
}
dLdIn = m.get(INPUT_KEY);
//Clear placeholders and op inputs to ensure no out-of-scope arrays are still referenced anywhere
sameDiff.clearPlaceholders(true);
sameDiff.clearOpInputs();
Pair ret = new Pair<>(g, workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, dLdIn)); //TODO OPTIMIZE THIS
return ret;
}
/**Returns the parameters of the neural network as a flattened row vector
* @return the parameters of the neural network
*/
@Override
public INDArray params() {
return params;
}
@Override
public INDArray getParam(String param) {
return paramTable.get(param);
}
@Override
public long numParams(){
return params == null ? 0 : (int)params.length();
}
@Override
public void setParam(String key, INDArray val) {
if(!paramTable.containsKey(key)){
throw new IllegalArgumentException("Cannot set parameter, invalid/unknown parameter key: " + key);
}
INDArray current = paramTable.get(key);
if(!Arrays.equals(current.shape(), val.shape())){
throw new IllegalArgumentException("Cannot set parameter \"" + key + "\", invalid shape: parameter array has shape "
+ Arrays.toString(current.shape()) + ", trying to set parameter of shape " + Arrays.toString(val.shape()));
}
}
@Override
public void setParams(INDArray params) {
if(this.params == null && params == null)
return;
if(this.params == null)
throw new IllegalStateException("Cannot set parameters of length " + params.length() + " to a layer with no parameters");
if(params == null)
throw new IllegalStateException("Cannot set null parameters");
Preconditions.checkState(this.params.length() == params.length(), "Cannot assign parameter vector of length %s to a layer with %s parameters",
params.length(), this.params.length());
this.params.assign(params);
}
protected void setParams(INDArray params, char order) {
setParams(params);
}
@Override
public void setParamsViewArray(INDArray params) {
this.params = params;
}
@Override
public INDArray getGradientsViewArray() {
return gradients;
}
@Override
public void setBackpropGradientsViewArray(INDArray gradients) {
this.gradients = gradients;
this.gradTable = layerConf().initializer().getGradientsFromFlattened(conf(), gradients);
}
@Override
public void setParamTable(Map paramTable) {
if(this.paramTable == null){
this.paramTable = paramTable;
} else {
for (Map.Entry e : paramTable.entrySet()) {
setParam(e.getKey(), e.getValue());
}
}
}
@Override
public Map paramTable() {
return paramTable(false);
}
@Override
public Map paramTable(boolean backpropParamsOnly) {
return paramTable;
}
protected void doInit() {
try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer bl = (org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer) layerConf();
sameDiff = SameDiff.create();
//Use SingleThreadArrayHolder so we can use views (also don't nede multithreading here, DL4J is not thread safe)
sameDiff.setArrayHolders(new SingleThreadArrayHolder(), new SingleThreadArrayHolder(), false);
Map p = paramTable();
long[] inputShape = input.shape().clone();
inputShape[0] = -1;
SDVariable inputVar = sameDiff.placeHolder(INPUT_KEY, dataType, inputShape);
Map paramShapes = layerConf().getLayerParams().getParamShapes();
Map params = new LinkedHashMap<>();
for (String s : paramShapes.keySet()) {
val ps = paramShapes.get(s);
SDVariable v = sameDiff.var(s, dataType, ps);
params.put(s, v);
}
long[] maskShape = ArrayUtil.nTimes((long)inputShape.length, -1);
SDVariable mask = sameDiff.placeHolder(MASK_KEY, dataType, maskShape);
SDVariable layerOutput = bl.defineLayer(sameDiff, inputVar, params, mask);
Preconditions.checkNotNull(layerOutput, "Invalid output: layer output is null");
outputVar = layerOutput;
for (Map.Entry e : p.entrySet()) {
sameDiff.associateArrayWithVariable(e.getValue(), sameDiff.getVariable(e.getKey()));
}
//Define the function for external errors:
fn = SameDiffUtils.externalErrors(sameDiff, null,layerOutput);
fn.outputVariable();
this.outputKey = outputVar.name();
}
}
@Override
public Pair feedForwardMaskArray(INDArray maskArray, MaskState currentMaskState, int minibatchSize) {
org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer bl = (org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer) layerConf();
this.maskArray = maskArray;
this.maskState = currentMaskState;
return bl.feedForwardMaskArray(maskArray, currentMaskState, minibatchSize);
}
}
