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org.nd4j.linalg.api.ops.impl.accum.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.accum;
import lombok.EqualsAndHashCode;
import lombok.val;
import onnx.OnnxProto3;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.linalg.api.blas.params.MMulTranspose;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.linalg.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
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() {}
/**
* 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 List calculateOutputShape() {
if(mt == null)
mt = MMulTranspose.allFalse();
long[] aShape = larg().getShape();
long[] bShape = rarg().getShape();
if(Shape.isPlaceholderShape(aShape) || Shape.isPlaceholderShape(bShape))
return Collections.emptyList();
aShape = mt.isTransposeA() ? transposeShapeArray(aShape) : aShape;
bShape = mt.isTransposeB() ? transposeShapeArray(bShape) : bShape;
long[] shape = Shape.getMatrixMultiplyShape(aShape,bShape);
if(mt.isTransposeResult())
shape = transposeShapeArray(shape);
for(int i = 0; i < shape.length; i++) {
if(shape[i] < 1)
throw new ND4JIllegalStateException("Invalid shape computed at index " + i + ": shape " + Arrays.toString(shape));
}
return Collections.singletonList(shape);
}
@Override
public String onnxName() {
return "MatMul";
}
@Override
public String tensorflowName() {
return "MatMul";
}
@Override
public String opName() {
return "mmul";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
val isTransposeA = attributesForNode.get("transpose_a").getB();
val isTransposeB = attributesForNode.get("transpose_b").getB();
MMulTranspose mMulTranspose = MMulTranspose.builder()
.transposeA(isTransposeA).transposeB(isTransposeB)
.build();
this.mt = mMulTranspose;
val args = args();
for(val arg : args) {
if(sameDiff.isPlaceHolder(arg.getVarName()) || arg.getShape() == null) {
sameDiff.addPropertyToResolve(this,arg.getVarName());
}
}
}
@Override
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.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 i_v1) {
// List ret = new ArrayList<>();
// SDVariable dLdOut = i_v1.get(0);
//
// SDVariable dLdx = sameDiff.mmul(dLdOut, rarg(), MMulTranspose.builder()
// .transposeB(true)
// .build());
//
// SDVariable dLdy = sameDiff.mmul(larg(), dLdOut, MMulTranspose.builder()
// .transposeA(true)
// .build());
//
// ret.add(dLdx);
// ret.add(dLdy);
// return ret;
List ret = new ArrayList<>();
SDVariable dLdOut = i_v1.get(0);
/*
In: x=[a,b], y=[b,c]
tX tY tZ x y z dz dLdx dLdy
F F F [a,b] [b,c] [a,c] [a,c] [a,c]*[b,c]T = [a,b] x*yT [a,b]T*[a,c] = [b,c] xT*y
T F F [b,a] [b,c] [a,c] [a,c] ([a,c]*[b,c]T)T = [b,a] (x*yT)T [b,a]*[a,c] = [b,c] x*y
F T F [a,b] [c,b] [a,c] [a,c] ([a,c]*[c,b]) = [a,b] x*y [a,b]T*[a,c] = [b,c] ->T xT*y
T T F [b,a] [c,b] [a,c] [a,c] ([a,c]*[c,b])T = [b,a] (x*y)T [b,a]*[a,c] = [b,c] ->T x*y
F F T [a,b] [b,c] [c,a] [c,a]
*/
//If x=[a,b] and y=[b,c] then x*y=[a,c] - no transpose case
SDVariable dLdx = sameDiff.mmul(dLdOut, rarg(), MMulTranspose.builder() //No transpose: [a,c]*[b,c]^T = [a,b]
.transposeA(mt.isTransposeResult()) //Transpose gradient if fwd result was transposed
.transposeB(!mt.isTransposeB())
.transposeResult(mt.isTransposeA())
.build());
SDVariable dLdy = sameDiff.mmul(larg(), dLdOut, MMulTranspose.builder() //No transpose: [a,b]^T * [a,c] = [b,c]
.transposeA(!mt.isTransposeA())
.transposeB(mt.isTransposeResult()) //Transpose gradient if fwd result was transposed
.transposeResult(mt.isTransposeB())
.build());
ret.add(dLdx);
ret.add(dLdy);
return ret;
}
@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);
ret.put(tensorflowName(),map);
ret.put(onnxName(),map);
return ret;
}
}
