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org.nd4j.linalg.api.ops.impl.transforms.custom.MultiHeadDotProductAttention Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2019 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.impl.transforms.custom;
import lombok.NoArgsConstructor;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
/**
* (optionally scaled) multi head dot product attention
*
* See also "Attention is all you need" (https://arxiv.org/abs/1706.03762, pp. 4,5, "3.2.2 Multi-Head Attention")
*
* @author Paul Dubs
*/
@NoArgsConstructor
public class MultiHeadDotProductAttention extends DynamicCustomOp {
private boolean withWeights;
private boolean scaled;
public MultiHeadDotProductAttention(SameDiff sameDiff, SDVariable queries, SDVariable keys, SDVariable values,
SDVariable Wq, SDVariable Wk, SDVariable Wv, SDVariable Wo,
SDVariable mask,
boolean scaled, boolean withWeights) {
super(null, sameDiff,
mask == null ? new SDVariable[] {queries, keys, values, Wq, Wk, Wv, Wo}
: new SDVariable[] {queries, keys, values, Wq, Wk, Wv, Wo, mask},
false);
this.scaled = scaled;
this.withWeights = withWeights;
addIArgument(scaled ? 1 : 0);
addIArgument(withWeights ? 1 : 0);
}
public 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) {
this(queries, keys, values, Wq, Wk, Wv, Wo, mask, scaled, false);
}
public 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) {
super(wrapFilterNull(queries, keys, values, Wq, Wk, Wv, Wo, mask), null);
this.scaled = scaled;
this.withWeights = withWeights;
addIArgument(scaled ? 1 : 0);
addIArgument(withWeights ? 1 : 0);
}
@Override
public String opName() {
return "multi_head_dot_product_attention";
}
@Override
public List doDiff(List gradient) {
return sameDiff.f().multiHeadDotProductAttentionBp(arg(0), arg(1), arg(2), arg(3), arg(4), arg(5), arg(6), gradient.get(0), args().length > 7 ? arg(7) : null, scaled);
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && (dataTypes.size() == 7 || dataTypes.size() == 8), "Expected 7 or 8 input datatypes, got %s", dataTypes);
DataType first = dataTypes.get(0);
for( int i=0; i 0){
Preconditions.checkState(first == dataTypes.get(i), "All datatypes must be same type, got input datatypes %s", dataTypes);
}
}
if(withWeights){
return Arrays.asList(first, first);
}else{
return Collections.singletonList(first);
}
}
@Override
public int getNumOutputs() {
if(withWeights){
return 2;
}else{
return 1;
}
}
}