Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $
You can buy this project and download/modify it how often you want.
org.nd4j.linalg.api.ops.impl.reduce.TensorMmul Maven / Gradle / Ivy
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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 org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.shade.guava.primitives.Ints;
import org.nd4j.shade.guava.primitives.Longs;
import lombok.NoArgsConstructor;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
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.ops.Op;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.common.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
import static org.nd4j.common.util.ArrayUtil.*;
/**
* TensorMmul
* @author Adam Gibson
*/
@NoArgsConstructor
public class TensorMmul extends DynamicCustomOp {
private int[][] axes;
protected boolean addedEdges;
protected MMulTranspose mMulTranspose;
public TensorMmul(INDArray x, INDArray y, int[][] axes) {
this(x,y,axes[0], axes[1], false, false, false);
}
/**
* Initialize with the given
* input, pairwise transform, result, and number
* of elements
*
* @param x the input
* @param y the pairwise transform
* @param z the result
*/
public TensorMmul(INDArray x, INDArray y, INDArray z, int[][] axes) {
this(x, y, axes[0], axes[1], false, false, false);
}
public TensorMmul(INDArray x, INDArray y, int[] dimensionsX, int[] dimensionsY,
boolean transposeX, boolean transposeY, boolean transposeZ) {
super(null,new INDArray[]{x, y},null);
this.axes = new int[][]{dimensionsX, dimensionsY};
addIArgument(dimensionsX.length);
addIArgument(dimensionsX);
addIArgument(dimensionsY.length);
addIArgument(dimensionsY);
addBArgument(transposeX, transposeY, transposeZ);
}
public TensorMmul(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[][] dimensions) {
this(sameDiff,i_v1,i_v2,dimensions,MMulTranspose.allFalse());
}
public TensorMmul(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[][] dimensions,
MMulTranspose mMulTranspose) {
super(null, sameDiff, new SDVariable[]{i_v1,i_v2});
this.sameDiff = sameDiff;
this.mMulTranspose = mMulTranspose;
this.axes = dimensions;
if(!addedEdges && sameDiff.getOutputsForOp(this) == null) {
addedEdges = true;
}
addIArgument(dimensions[0].length);
addIArgument(dimensions[0]);
addIArgument(dimensions[1].length);
addIArgument(dimensions[1]);
}
public TensorMmul(SameDiff sameDiff, SDVariable x, SDVariable y, int[] dimensionsX,
int[] dimensionsY, boolean transposeX, boolean transposeY, boolean transposeZ) {
super(null, sameDiff, new SDVariable[]{x,y});
this.sameDiff = sameDiff;
this.axes = new int[][]{dimensionsX, dimensionsY};
addIArgument(dimensionsX.length);
addIArgument(dimensionsX[0]);
addIArgument(dimensionsY.length);
addIArgument(dimensionsY[0]);
addBArgument(transposeX, transposeY, transposeZ);
}
@Override
public List doDiff(List gradients) {
return Arrays.asList(new TensorMmulBp(sameDiff, larg(), rarg(), gradients.get(0), axes ).outputVariables());
}
@Override
public String opName() {
return "tensordot";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
/**
* name: "MatMul"
op: "MatMul"
input: "input"
input: "Variable/read"
attr {
key: "transpose_b"
value {
b: false
}
}
attr {
key: "transpose_a"
value {
b: false
}
}
attr {
key: "T"
value {
type: DT_FLOAT
}
}
*/
val isTransposeA = attributesForNode.get("transpose_a").getB();
val isTransposeB = attributesForNode.get("transpose_b").getB();
MMulTranspose mMulTranspose = MMulTranspose.builder()
.transposeA(isTransposeA).transposeB(isTransposeB)
.build();
this.mMulTranspose = mMulTranspose;
val args = args();
}
@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.mMulTranspose = mMulTranspose;
}
@Override
public boolean equals(Object o) {
if (this == o) return true;
if (o == null || getClass() != o.getClass()) return false;
TensorMmul that = (TensorMmul) o;
if (addedEdges != that.addedEdges) return false;
if (!Arrays.deepEquals(axes, that.axes)) return false;
return mMulTranspose != null ? mMulTranspose.equals(that.mMulTranspose) : that.mMulTranspose == null;
}
@Override
public int hashCode() {
int result = super.hashCode();
result = 31 * result + Arrays.deepHashCode(axes);
result = 31 * result + (addedEdges ? 1 : 0);
result = 31 * result + (mMulTranspose != null ? mMulTranspose.hashCode() : 0);
return result;
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
@Override
public String onnxName() {
return "Gemm";
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 2, "Expected exactly 2 input data types for %s, got %s", getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0));
}
}
