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org.nd4j.linalg.api.ops.BaseOp Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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.
*
* 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.nd4j.linalg.api.ops;
import lombok.Data;
import lombok.Getter;
import lombok.Setter;
import lombok.val;
import onnx.OnnxProto3;
import org.nd4j.autodiff.functions.DifferentialFunction;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.linalg.factory.Nd4j;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.nio.Buffer;
import java.util.Arrays;
import java.util.Map;
/**
* Base op. An op involves iterating over 2 buffers (x,y) up to n elements
* and applying a transform or accumulating a result.
*
* @author Adam Gibson
*/
@Data
public abstract class BaseOp extends DifferentialFunction implements Op {
protected INDArray x, y, z;
protected long n;
protected long numProcessed;
protected Object[] extraArgs;
protected boolean passThrough;
@Getter @Setter
protected String xVertexId,yVertexId,zVertexId;
// cached instance, for dataType checks
protected DataBuffer extraArgz;
public BaseOp() {
}
public BaseOp(SameDiff sameDiff, boolean inPlace, Object[] extraArgs) {
super(sameDiff, inPlace, extraArgs);
}
public BaseOp(SameDiff sameDiff, Object[] extraArgs) {
super(sameDiff, extraArgs);
}
@Override
public boolean isExecSpecial() {
return false;
}
public static Type getOpType(Op op) {
Type type = null;
if (op instanceof CustomOp) {
return Type.CUSTOM;
} else if (op instanceof ShapeOp) {
return Type.SHAPE;
} else if (op instanceof TransformOp) {
if (op.y() == null) {
if (!op.isExecSpecial())
type = Op.Type.TRANSFORM;
else
type = Op.Type.SPECIAL;
} else {
type = Op.Type.PAIRWISE;
}
} else if (op instanceof Accumulation) {
if (op.y() == null)
type = Op.Type.REDUCE;
else
type = Op.Type.REDUCE3;
} else if (op instanceof ScalarOp) {
type = Op.Type.SCALAR;
} else if (op instanceof BroadcastOp) {
type = Op.Type.BROADCAST;
} else if (op instanceof IndexAccumulation) {
type = Op.Type.INDEXREDUCE;
} else if (op instanceof MetaOp) {
type = Type.META;
} else if (op instanceof GridOp) {
type = Type.GRID;
}
return type;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
}
@Override
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.GraphProto graph) {
}
@Override
public DataBuffer extraArgsDataBuff() {
if (extraArgz != null)
return extraArgz;
if (extraArgs != null) {
DataBuffer.Type dtype = x != null ? x.data().dataType() : Nd4j.dataType();
if (dtype == DataBuffer.Type.FLOAT || dtype == DataBuffer.Type.HALF) {
float extraz[] = new float[extraArgs.length];
for (int i = 0; i < extraArgs.length; i++) {
Number arg = (Number) extraArgs[i];
float val = arg.floatValue();
extraz[i] = val;
}
extraArgz = Nd4j.getConstantHandler().getConstantBuffer(extraz);
return extraArgz;
} else if (dtype == DataBuffer.Type.DOUBLE) {
double extraz[] = new double[extraArgs.length];
for (int i = 0; i < extraArgs.length; i++) {
if (!(extraArgs[i] instanceof Number))
continue;
Number arg = (Number) extraArgs[i];
if (arg == null)
arg = 0.0;
double val = arg.doubleValue();
extraz[i] = val;
}
extraArgz = Nd4j.getConstantHandler().getConstantBuffer(extraz);
return extraArgz;
}
}
return null;
}
@Override
public Buffer extraArgsBuff() {
if (extraArgs != null) {
DataBuffer retBuff;
if (x.data().dataType() == DataBuffer.Type.FLOAT) {
retBuff = Nd4j.createBuffer(new float[extraArgs.length]);
for (int i = 0; i < extraArgs.length; i++) {
Number val = (Number) extraArgs[i];
retBuff.put(i, val.floatValue());
}
return retBuff.asNioFloat();
} else {
retBuff = Nd4j.createBuffer(new double[extraArgs.length]);
for (int i = 0; i < extraArgs.length; i++) {
Number val = (Number) extraArgs[i];
retBuff.put(i, val.doubleValue());
}
return retBuff.asNioDouble();
}
}
return null;
}
@Override
public boolean isPassThrough() {
return passThrough;
}
@Override
public void setX(INDArray x) {
if (x == null) {
if (args() != null && args().length >= 1) {
DifferentialFunction firstArg = args()[0];
if (firstArg instanceof SDVariable) {
SDVariable sdVariable = (SDVariable) firstArg;
if (sdVariable.getArr() != null)
this.x = sdVariable.getArr();
}
} else
throw new ND4JIllegalStateException("Unable to set null array for x. Also unable to infer from differential function arguments");
} else
this.x = x;
numProcessed = 0;
}
@Override
public void setZ(INDArray z) {
if (z == null) {
SDVariable getResult = sameDiff.getVariable(zVertexId);
if (getResult != null) {
if (getResult.getArr() != null)
this.z = getResult.getArr();
else if(sameDiff.getShapeForVarName(getResult.getVarName()) != null) {
val shape = sameDiff.getShapeForVarName(getResult.getVarName());
sameDiff.putArrayForVarName(getResult.getVarName(),getResult.getWeightInitScheme().create(shape));
}
else
throw new ND4JIllegalStateException("Unable to set null array for z. Also unable to infer from differential function arguments");
} else
throw new ND4JIllegalStateException("Unable to set null array for z. Also unable to infer from differential function arguments");
} else
this.z = z;
numProcessed = 0;
}
@Override
public void setY(INDArray y) {
if (y == null) {
if (args() != null && args().length > 1) {
DifferentialFunction firstArg = args()[1];
if (firstArg instanceof SDVariable) {
SDVariable sdVariable = (SDVariable) firstArg;
if (sdVariable.getArr() != null)
this.y = sdVariable.getArr();
}
} else
throw new ND4JIllegalStateException("Unable to set null array for y. Also unable to infer from differential function arguments");
} else
this.y = y;
numProcessed = 0;
}
/**
* Specify an alternative result array
*
* @param x the input
* @param z the output array
*/
public BaseOp(INDArray x, INDArray z) {
this(x, z, x.lengthLong());
}
/**
* Specify an alternative output array
*
* @param x the input
* @param z the output
* @param n the number of elements to iterate on
*/
public BaseOp(INDArray x, INDArray z, long n) {
this(x, null, z, n);
}
public BaseOp(INDArray x, INDArray y, INDArray z, long n) {
init(x, y, z, n);
}
/**
* An op for one ndarray
*
* @param x the ndarray
*/
public BaseOp(INDArray x) {
this(x, null, x, x == null ? 0 : x.lengthLong());
}
@Override
public Object[] extraArgs() {
return extraArgs;
}
@Override
public INDArray x() {
if(x == null) {
if(sameDiff != null && args() != null && args().length > 0) {
this.x = sameDiff.getArrForVarName(args()[0].getVarName());
if(x == null && args()[0].getShape() != null) {
x = args()[0].storeAndAllocateNewArray();
}
}
}
return x;
}
@Override
public INDArray y() {
if(y == null) {
if(sameDiff != null && args() != null && args().length > 1) {
this.y = sameDiff.getArrForVarName(args()[1].getVarName());
if(y == null && args()[1].getShape() != null) {
y = args()[1].storeAndAllocateNewArray();
}
}
}
return y;
}
@Override
public INDArray z() {
if(z == null) {
if(sameDiff != null) {
this.z = outputVariables()[0].getArr();
if(this.z == null) {
val var = outputVariables()[0];
if(var.getShape() != null)
this. z = var.storeAndAllocateNewArray();
}
}
}
else if(zVertexId != null && sameDiff != null && sameDiff.getArrForVarName(zVertexId) == null && z != null) {
sameDiff.putArrayForVarName(zVertexId,z);
}
return z;
}
@Override
public SDVariable[] outputVariables(String baseName) {
if(zVertexId == null) {
val outputNames = sameDiff.getOutputsForFunction(this);
//no need to dynamically create if already exists
if(outputNames != null) {
zVertexId = sameDiff.getVariable(outputNames[0]).getVarName();
return new SDVariable[]{sameDiff.getVariable(outputNames[0])};
}
if(isInPlace()) {
val newVars = sameDiff.generateOutputVariableForOp(this,null);
val inputArr = x();
//in place op
if(inputArr == null) {
return newVars;
}
sameDiff.putArrayForVarName(newVars[0].getVarName(),inputArr);
z = inputArr;
if(sameDiff.getOutputsForFunction(this) == null)
sameDiff.addOutgoingFor(newVars,this);
return newVars;
}
val newVars = sameDiff.generateOutputVariableForOp(this, baseName);
INDArray arr = null;
if(newVars == null || newVars.length < 1 || newVars[0].getShape() == null) {
arr = null;
}
else if(newVars[0].getArr() == null) {
arr = newVars[0].storeAndAllocateNewArray();
}
else
arr = newVars[0].getArr();
if(arr == null) {
val shapes = calculateOutputShape();
if(shapes != null && !shapes.isEmpty() && shapes.get(0) != null) {
sameDiff.putShapeForVarName(newVars[0].getVarName(),shapes.get(0));
arr = newVars[0].storeAndAllocateNewArray();
}
}
if(arr != null) {
setZ(arr);
}
if(sameDiff.getOutputsForFunction(this) == null)
sameDiff.addOutgoingFor(newVars,this);
return newVars;
}
return new SDVariable[]{sameDiff.getVariable(zVertexId)};
}
@Override
public long n() {
if(n == 0) {
if(arg() != null)
this.n = Shape.lengthOf(arg().getShape());
}
return n;
}
@Override
public void init(INDArray x, INDArray y, INDArray z, long n) {
this.x = x;
this.y = y;
this.z = z;
this.n = n;
}
@Override
public void setN(long n) {
this.n = n;
}
@Override
public long numProcessed() {
return numProcessed;
}
@Override
public String toString() {
return opName();
}
@Override
public CustomOp toCustomOp() {
DynamicCustomOp.DynamicCustomOpsBuilder customOpBuilder = DynamicCustomOp.builder(opName());
customOpBuilder.callInplace(x() == z());
if (y() != null)
customOpBuilder.addInputs(x(), y());
else
customOpBuilder.addInputs(x());
customOpBuilder.addOutputs(z());
if (extraArgs != null) {
for (int i = 0; i < extraArgs.length; i++) {
if (extraArgs[i] instanceof Integer) {
customOpBuilder.addIntegerArguments((Integer) extraArgs[i]);
} else if (extraArgs[i] instanceof Double || extraArgs[i] instanceof Float) {
Double num = (Double) extraArgs[i];
customOpBuilder.addFloatingPointArguments(num);
}
}
}
return customOpBuilder.build();
}
@Override
public void exec() {
//no-op
}
@Override
public void exec(int... dimensions) {
//no-op
}
@Override
public boolean equals(Object o) {
if (this == o) return true;
if (o == null || getClass() != o.getClass()) return false;
BaseOp baseOp = (BaseOp) o;
if (n != baseOp.n) return false;
if (numProcessed != baseOp.numProcessed) return false;
if (passThrough != baseOp.passThrough) return false;
if (x != null ? !x.equals(baseOp.x) : baseOp.x != null) return false;
if (y != null ? !y.equals(baseOp.y) : baseOp.y != null) return false;
if (z != null ? !z.equals(baseOp.z) : baseOp.z != null) return false;
// Probably incorrect - comparing Object[] arrays with Arrays.equals
if (!Arrays.equals(extraArgs, baseOp.extraArgs)) return false;
return extraArgz != null ? extraArgz.equals(baseOp.extraArgz) : baseOp.extraArgz == null;
}
@Override
public int hashCode() {
int result = super.hashCode();
result = 31 * result + (x != null ? x.hashCode() : 0);
result = 31 * result + (y != null ? y.hashCode() : 0);
result = 31 * result + (z != null ? z.hashCode() : 0);
result = 31 * result + (int) (n ^ (n >>> 32));
result = 31 * result + (int) (numProcessed ^ (numProcessed >>> 32));
result = 31 * result + Arrays.hashCode(extraArgs);
result = 31 * result + (passThrough ? 1 : 0);
result = 31 * result + (extraArgz != null ? extraArgz.hashCode() : 0);
return result;
}
}
