| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://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. |
| */ |
| |
| package org.apache.sysds.runtime.compress.colgroup; |
| |
| import java.io.DataInput; |
| import java.io.DataOutput; |
| import java.io.IOException; |
| import java.util.Arrays; |
| import java.util.HashMap; |
| |
| import org.apache.sysds.runtime.compress.CompressionSettings; |
| import org.apache.sysds.runtime.compress.utils.AbstractBitmap; |
| import org.apache.sysds.runtime.matrix.data.MatrixBlock; |
| import org.apache.sysds.runtime.matrix.operators.ScalarOperator; |
| |
| /** |
| * Class to encapsulate information about a column group that is encoded with dense dictionary encoding (DDC) using 1 |
| * byte codes. |
| */ |
| public class ColGroupDDC1 extends ColGroupDDC { |
| private static final long serialVersionUID = 5204955589230760157L; |
| |
| private byte[] _data; |
| |
| protected ColGroupDDC1() { |
| super(); |
| } |
| |
| protected ColGroupDDC1(int[] colIndices, int numRows, AbstractBitmap ubm, CompressionSettings cs) { |
| super(colIndices, numRows, ubm, cs); |
| |
| int numVals = ubm.getNumValues(); |
| int numCols = ubm.getNumColumns(); |
| |
| _data = new byte[numRows]; |
| |
| // materialize zero values, if necessary |
| if(ubm.getNumOffsets() < (long) numRows * numCols) { |
| int zeroIx = containsAllZeroValue(); |
| if(zeroIx < 0) { |
| zeroIx = numVals; |
| _dict = IDictionary.materializeZeroValue(_dict, numCols); |
| } |
| Arrays.fill(_data, (byte) zeroIx); |
| } |
| |
| // iterate over values and write dictionary codes |
| for(int i = 0; i < numVals; i++) { |
| int[] tmpList = ubm.getOffsetsList(i).extractValues(); |
| int tmpListSize = ubm.getNumOffsets(i); |
| for(int k = 0; k < tmpListSize; k++) |
| _data[tmpList[k]] = (byte) i; |
| } |
| } |
| |
| // Internal Constructor, to be used when copying a DDC Colgroup, and for scalar operations |
| protected ColGroupDDC1(int[] colIndices, int numRows, double[] values, byte[] data) { |
| super(colIndices, numRows, values); |
| _data = data; |
| } |
| |
| @Override |
| protected ColGroupType getColGroupType() { |
| return ColGroupType.DDC1; |
| } |
| |
| /** |
| * Getter method to get the data, contained in The DDC ColGroup. |
| * |
| * Not safe if modifications is made to the byte list. |
| * |
| * @return The contained data |
| */ |
| public byte[] getData() { |
| return _data; |
| } |
| |
| @Override |
| protected int getIndex(int r) { |
| return _data[r] & 0xFF; |
| } |
| |
| @Override |
| protected int getIndex(int r, int colIx) { |
| return _data[r] & 0xFF * getNumCols() + colIx; |
| } |
| |
| @Override |
| protected double getData(int r, double[] dictionary) { |
| return dictionary[_data[r] & 0xFF]; |
| } |
| |
| @Override |
| protected double getData(int r, int colIx, double[] values) { |
| return values[(_data[r] & 0xFF) * getNumCols() + colIx]; |
| } |
| |
| @Override |
| protected void setData(int r, int code) { |
| _data[r] = (byte) code; |
| } |
| |
| @Override |
| protected int getCode(int r) { |
| return(_data[r] & 0xFF); |
| } |
| |
| public void recodeData(HashMap<Double, Integer> map) { |
| // prepare translation table |
| final int numVals = getNumValues(); |
| final double[] values = getValues(); |
| byte[] lookup = new byte[numVals]; |
| for(int k = 0; k < numVals; k++) |
| lookup[k] = map.get(values[k]).byteValue(); |
| |
| // recode the data |
| for(int i = 0; i < _numRows; i++) |
| _data[i] = lookup[_data[i] & 0xFF]; |
| } |
| |
| @Override |
| public void write(DataOutput out) throws IOException { |
| super.write(out); |
| // write data |
| // out.writeInt(_numRows); |
| for(int i = 0; i < _numRows; i++) |
| out.writeByte(_data[i]); |
| } |
| |
| @Override |
| public void readFields(DataInput in) throws IOException { |
| super.readFields(in); |
| // read data |
| _data = new byte[_numRows]; |
| for(int i = 0; i < _numRows; i++) |
| _data[i] = in.readByte(); |
| } |
| |
| @Override |
| public long getExactSizeOnDisk() { |
| long ret = super.getExactSizeOnDisk(); |
| // data |
| ret += _data.length; |
| |
| return ret; |
| } |
| |
| @Override |
| public long estimateInMemorySize() { |
| return ColGroupSizes.estimateInMemorySizeDDC1(getNumCols(), getNumValues(), _data.length, isLossy()); |
| } |
| |
| @Override |
| public void decompressToBlock(MatrixBlock target, int rl, int ru) { |
| int ncol = getNumCols(); |
| double[] values = getValues(); |
| for(int i = rl; i < ru; i++) |
| for(int j = 0; j < ncol; j++) |
| target.appendValue(i, _colIndexes[j], values[(_data[i] & 0xFF) * ncol + j]); |
| // note: append ok because final sort per row |
| } |
| |
| @Override |
| public void decompressToBlock(MatrixBlock target, int colpos) { |
| int nrow = getNumRows(); |
| int ncol = getNumCols(); |
| double[] c = target.getDenseBlockValues(); |
| double[] values = getValues(); |
| int nnz = 0; |
| for(int i = 0; i < nrow; i++) |
| nnz += ((c[i] = values[(_data[i] & 0xFF) * ncol + colpos]) != 0) ? 1 : 0; |
| target.setNonZeros(nnz); |
| } |
| |
| @Override |
| public int[] getCounts(int[] counts) { |
| return getCounts(0, getNumRows(), counts); |
| } |
| |
| @Override |
| public int[] getCounts(int rl, int ru, int[] counts) { |
| final int numVals = getNumValues(); |
| Arrays.fill(counts, 0, numVals, 0); |
| for(int i = rl; i < ru; i++) |
| counts[_data[i] & 0xFF]++; |
| return counts; |
| } |
| |
| @Override |
| public void countNonZerosPerRow(int[] rnnz, int rl, int ru) { |
| final int ncol = getNumCols(); |
| final int numVals = getNumValues(); |
| final double[] values = getValues(); |
| |
| // pre-aggregate nnz per value tuple |
| int[] counts = new int[numVals]; |
| for(int k = 0, valOff = 0; k < numVals; k++, valOff += ncol) |
| for(int j = 0; j < ncol; j++) |
| counts[k] += (values[valOff + j] != 0) ? 1 : 0; |
| |
| // scan data and add counts to output rows |
| for(int i = rl; i < ru; i++) |
| rnnz[i - rl] += counts[_data[i] & 0xFF]; |
| } |
| |
| @Override |
| public void rightMultByVector(MatrixBlock vector, MatrixBlock result, int rl, int ru) { |
| double[] b = ColGroupConverter.getDenseVector(vector); |
| double[] c = result.getDenseBlockValues(); |
| final int numCols = getNumCols(); |
| final int numVals = getNumValues(); |
| |
| // prepare reduced rhs w/ relevant values |
| double[] sb = new double[numCols]; |
| for(int j = 0; j < numCols; j++) { |
| sb[j] = b[_colIndexes[j]]; |
| } |
| |
| // pre-aggregate all distinct values (guaranteed <=255) |
| double[] vals = preaggValues(numVals, sb); |
| |
| // iterative over codes and add to output |
| for(int i = rl; i < ru; i++) { |
| c[i] += vals[_data[i] & 0xFF]; |
| } |
| } |
| |
| public static void rightMultByVector(ColGroupDDC1[] grps, MatrixBlock vector, MatrixBlock result, int rl, int ru) { |
| double[] b = ColGroupConverter.getDenseVector(vector); |
| double[] c = result.getDenseBlockValues(); |
| |
| // prepare distinct values once |
| double[][] vals = new double[grps.length][]; |
| for(int i = 0; i < grps.length; i++) { |
| // prepare reduced rhs w/ relevant values |
| double[] sb = new double[grps[i].getNumCols()]; |
| for(int j = 0; j < sb.length; j++) { |
| sb[j] = b[grps[i]._colIndexes[j]]; |
| } |
| // pre-aggregate all distinct values (guaranteed <=255) |
| vals[i] = grps[i].preaggValues(grps[i].getNumValues(), sb, true); |
| } |
| |
| // cache-conscious matrix-vector multiplication |
| // iterative over codes of all groups and add to output |
| int blksz = 2048; // 16KB |
| for(int bi = rl; bi < ru; bi += blksz) |
| for(int j = 0; j < grps.length; j++) |
| for(int i = bi; i < Math.min(bi + blksz, ru); i++) |
| c[i] += vals[j][grps[j]._data[i] & 0xFF]; |
| } |
| |
| @Override |
| public void leftMultByRowVector(MatrixBlock vector, MatrixBlock result) { |
| double[] a = ColGroupConverter.getDenseVector(vector); |
| double[] c = result.getDenseBlockValues(); |
| // final int nrow = getNumRows(); |
| final int numVals = getNumValues(); |
| |
| // iterative over codes and pre-aggregate inputs per code (guaranteed <=255) |
| // temporary array also avoids false sharing in multi-threaded environments |
| double[] vals = allocDVector(numVals, true); |
| for(int i = 0; i < _numRows; i++) { |
| int index = getIndex(i); |
| vals[index] += a[i]; |
| } |
| |
| // post-scaling of pre-aggregate with distinct values |
| postScaling(vals, c); |
| } |
| |
| // @Override |
| // public void leftMultByRowVector(ColGroupDDC a, MatrixBlock result) { |
| // double[] c = result.getDenseBlockValues(); |
| // final int nrow = getNumRows(); |
| // final int numVals = getNumValues(); |
| // // final double[] dictionary = getValues(); |
| |
| // // iterative over codes and pre-aggregate inputs per code (guaranteed <=255) |
| // // temporary array also avoids false sharing in multi-threaded environments |
| // double[] vals = allocDVector(numVals, true); |
| // double[] aDict = a.getValues(); |
| // for(int i = 0; i < nrow; i++) { |
| // int rowIdA = a.getIndex(i); |
| // int rowIdThis = getIndex(i); |
| // vals[rowIdThis] += aDict[rowIdA]; |
| // } |
| // // vals[_data[i] & 0xFF] += a.getData(i, dictionary); |
| |
| // // post-scaling of pre-aggregate with distinct values |
| // postScaling(vals, c); |
| // } |
| |
| |
| // public static void computeRowSums(ColGroupDDC1[] grps, MatrixBlock result, KahanFunction kplus, int rl, int ru) { |
| // // note: due to corrections the output might be a large dense block |
| // DenseBlock c = result.getDenseBlock(); |
| |
| // if(grps[0]._dict instanceof QDictionary && !(kplus instanceof KahanPlusSq)) { |
| |
| |
| // return; // early return if needed. |
| // } |
| |
| // KahanObject kbuff = new KahanObject(0, 0); |
| // KahanPlus kplus2 = KahanPlus.getKahanPlusFnObject(); |
| |
| // // prepare distinct values once |
| // double[][] vals = new double[grps.length][]; |
| // for(int i = 0; i < grps.length; i++) { |
| // // pre-aggregate all distinct values (guaranteed <=255) |
| // vals[i] = grps[i].sumAllValues(kplus, kbuff); |
| // } |
| |
| // // cache-conscious row sums operations |
| // // iterative over codes of all groups and add to output |
| // // (use kahan plus not general KahanFunction for correctness in case of sqk+) |
| // int blksz = 1024; // 16KB |
| // double[] tmpAgg = new double[blksz]; |
| // for(int bi = rl; bi < ru; bi += blksz) { |
| // Arrays.fill(tmpAgg, 0); |
| // // aggregate all groups |
| // for(int j = 0; j < grps.length; j++) { |
| // double[] valsj = vals[j]; |
| // byte[] dataj = grps[j]._data; |
| // for(int i = bi; i < Math.min(bi + blksz, ru); i++) |
| // tmpAgg[i - bi] += valsj[dataj[i] & 0xFF]; |
| // } |
| // // add partial results of all ddc groups |
| // for(int i = bi; i < Math.min(bi + blksz, ru); i++) { |
| // double[] cvals = c.values(i); |
| // int cix = c.pos(i); |
| // kbuff.set(cvals[cix], cvals[cix + 1]); |
| // kplus2.execute2(kbuff, tmpAgg[i - bi]); |
| // cvals[cix] = kbuff._sum; |
| // cvals[cix + 1] = kbuff._correction; |
| // } |
| // } |
| |
| // } |
| |
| @Override |
| public ColGroup scalarOperation(ScalarOperator op) { |
| // fast path: sparse-safe and -unsafe operations |
| // as zero are represented, it is sufficient to simply apply the scalar op |
| return new ColGroupDDC1(_colIndexes, _numRows, applyScalarOp(op), _data); |
| } |
| |
| @Override |
| public String toString() { |
| StringBuilder sb = new StringBuilder(); |
| sb.append(super.toString()); |
| sb.append(" DataLength: " + this._data.length); |
| return sb.toString(); |
| } |
| } |