Class MultiHeadDotProductAttention
- java.lang.Object
-
- org.nd4j.autodiff.functions.DifferentialFunction
-
- org.nd4j.linalg.api.ops.DynamicCustomOp
-
- org.nd4j.linalg.api.ops.impl.transforms.custom.MultiHeadDotProductAttention
-
- All Implemented Interfaces:
CustomOp
public class MultiHeadDotProductAttention extends DynamicCustomOp
-
-
Nested Class Summary
-
Nested classes/interfaces inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp
DynamicCustomOp.DynamicCustomOpsBuilder
-
-
Field Summary
-
Fields inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp
axis, bArguments, dArguments, iArguments, inplaceCall, inputArguments, outputArguments, outputVariables, sArguments, tArguments
-
Fields inherited from class org.nd4j.autodiff.functions.DifferentialFunction
dimensions, extraArgs, inPlace, ownName, ownNameSetWithDefault, sameDiff, scalarValue
-
-
Constructor Summary
Constructors Constructor Description MultiHeadDotProductAttention(@NonNull INDArray queries, @NonNull INDArray keys, @NonNull INDArray values, @NonNull INDArray Wq, @NonNull INDArray Wk, @NonNull INDArray Wv, @NonNull INDArray Wo, INDArray mask, boolean scaled)
MultiHeadDotProductAttention(@NonNull INDArray queries, @NonNull INDArray keys, @NonNull INDArray values, @NonNull INDArray Wq, @NonNull INDArray Wk, @NonNull INDArray Wv, @NonNull INDArray Wo, INDArray mask, boolean scaled, boolean withWeights)
MultiHeadDotProductAttention(SameDiff sameDiff, SDVariable queries, SDVariable keys, SDVariable values, SDVariable Wq, SDVariable Wk, SDVariable Wv, SDVariable Wo, SDVariable mask, boolean scaled, boolean withWeights)
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description List<DataType>
calculateOutputDataTypes(List<DataType> dataTypes)
Calculate the data types for the output arrays.List<SDVariable>
doDiff(List<SDVariable> gradient)
The actual implementation for automatic differentiation.int
getNumOutputs()
String
opName()
This method returns op opName as string-
Methods inherited from class org.nd4j.linalg.api.ops.DynamicCustomOp
addBArgument, addDArgument, addIArgument, addIArgument, addInputArgument, addOutputArgument, addOutputsToOp, addSArgument, addTArgument, assertValidForExecution, bArgs, builder, calculateOutputShape, calculateOutputShape, clearArrays, computeArrays, configureFromArguments, dArgs, generateFake, generateFake, getBArgument, getDescriptor, getIArgument, getInputArgument, getOutputArgument, getSArgument, getTArgument, getValue, iArgs, initFromOnnx, initFromTensorFlow, inputArguments, mappingsForFunction, numBArguments, numDArguments, numIArguments, numInputArguments, numOutputArguments, numSArguments, numTArguments, onnxName, opHash, opNum, opType, outputArguments, outputVariables, outputVariables, propertiesForFunction, removeIArgument, removeInputArgument, removeOutputArgument, removeSArgument, removeTArgument, sArgs, setInputArgument, setInputArguments, setOutputArgument, setPropertiesForFunction, setValueFor, tArgs, tensorflowName, toString, wrapFilterNull, wrapOrNull, wrapOrNull
-
Methods inherited from class org.nd4j.autodiff.functions.DifferentialFunction
arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, configureWithSameDiff, diff, dup, equals, getBooleanFromProperty, getDoubleValueFromProperty, getIntValueFromProperty, getLongValueFromProperty, getStringFromProperty, hashCode, isConfigProperties, larg, onnxNames, outputs, outputVariable, outputVariablesNames, rarg, replaceArg, setInstanceId, tensorflowNames
-
Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
-
Methods inherited from interface org.nd4j.linalg.api.ops.CustomOp
isInplaceCall
-
-
-
-
Constructor Detail
-
MultiHeadDotProductAttention
public MultiHeadDotProductAttention(SameDiff sameDiff, SDVariable queries, SDVariable keys, SDVariable values, SDVariable Wq, SDVariable Wk, SDVariable Wv, SDVariable Wo, SDVariable mask, boolean scaled, boolean withWeights)
-
MultiHeadDotProductAttention
public MultiHeadDotProductAttention(@NonNull @NonNull INDArray queries, @NonNull @NonNull INDArray keys, @NonNull @NonNull INDArray values, @NonNull @NonNull INDArray Wq, @NonNull @NonNull INDArray Wk, @NonNull @NonNull INDArray Wv, @NonNull @NonNull INDArray Wo, INDArray mask, boolean scaled)
-
MultiHeadDotProductAttention
public MultiHeadDotProductAttention(@NonNull @NonNull INDArray queries, @NonNull @NonNull INDArray keys, @NonNull @NonNull INDArray values, @NonNull @NonNull INDArray Wq, @NonNull @NonNull INDArray Wk, @NonNull @NonNull INDArray Wv, @NonNull @NonNull INDArray Wo, INDArray mask, boolean scaled, boolean withWeights)
-
-
Method Detail
-
opName
public String opName()
Description copied from class:DynamicCustomOp
This method returns op opName as string- Specified by:
opName
in interfaceCustomOp
- Overrides:
opName
in classDynamicCustomOp
- Returns:
-
doDiff
public List<SDVariable> doDiff(List<SDVariable> gradient)
Description copied from class:DifferentialFunction
The actual implementation for automatic differentiation.- Overrides:
doDiff
in classDynamicCustomOp
- Returns:
-
calculateOutputDataTypes
public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes)
Description copied from class:DifferentialFunction
Calculate the data types for the output arrays. Though datatypes can also be inferred fromDifferentialFunction.calculateOutputShape()
, this method differs in that it does not require the input arrays to be populated. This is important as it allows us to do greedy datatype inference for the entire net - even if arrays are not available.- Overrides:
calculateOutputDataTypes
in classDifferentialFunction
- Parameters:
dataTypes
- The data types of the inputs- Returns:
- The data types of the outputs
-
getNumOutputs
public int getNumOutputs()
- Overrides:
getNumOutputs
in classDifferentialFunction
-
-