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org.nd4j.linalg.api.ops.impl.reduce.Mmul 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.impl.reduce;
import lombok.EqualsAndHashCode;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.blas.params.MMulTranspose;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.lang.reflect.Field;
import java.util.*;
/**
* Matrix multiplication/dot product
*
* @author Adam Gibson
*/
@EqualsAndHashCode
public class Mmul extends DynamicCustomOp {
protected MMulTranspose mt;
/**
*
* @param sameDiff
* @param i_v1
* @param i_v2
* @param mt
*/
public Mmul(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
MMulTranspose mt) {
super(null,sameDiff,new SDVariable[]{i_v1,i_v2});
this.mt = mt;
addIArgument(ArrayUtil.fromBoolean(mt.isTransposeA()), ArrayUtil.fromBoolean(mt.isTransposeB()), ArrayUtil.fromBoolean(mt.isTransposeResult()));
}
/**
*
* @param sameDiff
* @param i_v1
* @param i_v2
*/
public Mmul(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) {
this(sameDiff,i_v1,i_v2,MMulTranspose.allFalse());
}
/**
*
* @param x
* @param y
* @param z
*/
public Mmul(INDArray x,
INDArray y,
INDArray z,
MMulTranspose mt) {
super(null, new INDArray[]{x, y}, z == null ? null : new INDArray[]{z});
if (mt != null) {
this.mt = mt;
addIArgument(ArrayUtil.fromBoolean(mt.isTransposeA()),
ArrayUtil.fromBoolean(mt.isTransposeB()),
ArrayUtil.fromBoolean(mt.isTransposeResult()));
}
}
public Mmul() {}
@Override
public Object getValue(Field property) {
if (mt == null) {
mt = MMulTranspose.builder().build();
}
return mt.getValue(property);
}
@Override
public Map propertiesForFunction() {
return mt.toProperties();
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName() {
return "mt";
}
public void setPropertiesForFunction(Map properties){
if(mt == null)
mt = MMulTranspose.builder().build();
mt.setProperties(properties);
}
/**
* For a 2D matrix of shape (M, N) we return (N, M).
* For a 3D matrix with leading mini-batch dimension (mb, M, N)
* we return (mb, N, M)
*
* @param shape input shape array
* @return
*/
public long[] transposeShapeArray(long[] shape) {
if (shape.length == 2) {
return ArrayUtil.reverseCopy(shape);
} else if (shape.length == 3) {
return new long[] {shape[0], shape[2], shape[1]};
} else {
throw new IllegalArgumentException("Matrix input has to be of length 2 or 3, got: " + shape.length );
}
}
@Override
public String onnxName() {
return "MatMul";
}
@Override
public String[] tensorflowNames() {
return new String[]{"MatMul", "BatchMatMul", "BatchMatMulV2"};
}
@Override
public String opName() {
return "mmul";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
boolean isTransposeA;
boolean isTransposeB;
if(nodeDef.getOp().equalsIgnoreCase("MatMul")){
isTransposeA = attributesForNode.get("transpose_a").getB();
isTransposeB = attributesForNode.get("transpose_b").getB();
} else {
//BatchMatMul, BatchMatMulV2
//In practice, BatchMatMul seems to use "adj_x" and "adj_y" instead of "transpose_a" and "transpose_b"
if(attributesForNode.containsKey("transpose_a")){
isTransposeA = attributesForNode.get("transpose_a").getB();
} else {
isTransposeA = attributesForNode.get("adj_x").getB();
}
if(attributesForNode.containsKey("transpose_b")){
isTransposeB = attributesForNode.get("transpose_b").getB();
} else {
isTransposeB = attributesForNode.get("adj_y").getB();
}
}
MMulTranspose mMulTranspose = MMulTranspose.builder()
.transposeA(isTransposeA).transposeB(isTransposeB)
.build();
this.mt = mMulTranspose;
iArguments.clear();
addIArgument(ArrayUtil.fromBoolean(mt.isTransposeA()), ArrayUtil.fromBoolean(mt.isTransposeB()));
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
val isTransposeA = !attributesForNode.containsKey("transA") ? false : attributesForNode.get("transA").getI() > 0;
val isTransposeB = !attributesForNode.containsKey("transB") ? false : attributesForNode.get("transB").getI() > 0;
MMulTranspose mMulTranspose = MMulTranspose.builder()
.transposeA(isTransposeA).transposeB(isTransposeB)
.build();
this.mt = mMulTranspose;
}
@Override
public List doDiff(List gradients) {
return sameDiff.f().mmulBp(larg(),rarg(), gradients.get(0), mt);
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val transposeA = PropertyMapping.builder()
.onnxAttrName("transA")
.tfAttrName("transpose_a")
.propertyNames(new String[]{"transposeA"})
.build();
val transposeB = PropertyMapping.builder()
.onnxAttrName("transB")
.tfAttrName("transpose_b")
.propertyNames(new String[]{"transposeB"})
.build();
map.put("transposeA",transposeA);
map.put("transposeB",transposeB);
for(String s : tensorflowNames()){
ret.put(s,map);
}
ret.put(onnxName(),map);
return ret;
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 inputs to mmul op, got %s", dataTypes);
Preconditions.checkState(dataTypes.get(0).isFPType() && dataTypes.get(1).isFPType(), "Inputs to mmul op must both be a floating" +
"point type: got %s", dataTypes);
return Collections.singletonList(dataTypes.get(0));
}
}
