@Override
public Iterable<Tuple2<MatrixIndexes, MatrixBlock>> call(
Tuple2<MatrixIndexes, MatrixBlock> arg0) throws Exception {
ArrayList<Tuple2<MatrixIndexes, MatrixBlock>> ret =
new ArrayList<Tuple2<MatrixIndexes, MatrixBlock>>();
MatrixIndexes ixIn = arg0._1();
MatrixBlock mb2 = arg0._2();
// get the right hand side matrix
MatrixBlock mb1 = _pmV.getMatrixBlock((int) ixIn.getRowIndex(), 1);
// compute target block indexes
long minPos = UtilFunctions.toLong(mb1.minNonZero());
long maxPos = UtilFunctions.toLong(mb1.max());
long rowIX1 = (minPos - 1) / _brlen + 1;
long rowIX2 = (maxPos - 1) / _brlen + 1;
boolean multipleOuts = (rowIX1 != rowIX2);
if (minPos >= 1) // at least one row selected
{
// output sparsity estimate
double spmb1 = OptimizerUtils.getSparsity(mb1.getNumRows(), 1, mb1.getNonZeros());
long estnnz = (long) (spmb1 * mb2.getNonZeros());
boolean sparse = MatrixBlock.evalSparseFormatInMemory(_brlen, mb2.getNumColumns(), estnnz);
// compute and allocate output blocks
MatrixBlock out1 = new MatrixBlock();
MatrixBlock out2 = multipleOuts ? new MatrixBlock() : null;
out1.reset(_brlen, mb2.getNumColumns(), sparse);
if (out2 != null)
out2.reset(
UtilFunctions.computeBlockSize(_rlen, rowIX2, _brlen), mb2.getNumColumns(), sparse);
// compute core matrix permutation (assumes that out1 has default blocksize,
// hence we do a meta data correction afterwards)
mb1.permutationMatrixMultOperations(mb2, out1, out2);
out1.setNumRows(UtilFunctions.computeBlockSize(_rlen, rowIX1, _brlen));
ret.add(
new Tuple2<MatrixIndexes, MatrixBlock>(
new MatrixIndexes(rowIX1, ixIn.getColumnIndex()), out1));
if (out2 != null)
ret.add(
new Tuple2<MatrixIndexes, MatrixBlock>(
new MatrixIndexes(rowIX2, ixIn.getColumnIndex()), out2));
}
return ret;
}
/**
* @param vector
* @param singleColBlock
* @param dense
* @param unknownDims
*/
private void testDataFrameConversion(
ValueType[] schema, boolean containsID, boolean dense, boolean unknownDims) {
boolean oldConfig = DMLScript.USE_LOCAL_SPARK_CONFIG;
RUNTIME_PLATFORM oldPlatform = DMLScript.rtplatform;
SparkExecutionContext sec = null;
try {
DMLScript.USE_LOCAL_SPARK_CONFIG = true;
DMLScript.rtplatform = RUNTIME_PLATFORM.HYBRID_SPARK;
// generate input data and setup metadata
int cols = schema.length + colsVector - 1;
double sparsity = dense ? sparsity1 : sparsity2;
double[][] A = TestUtils.round(getRandomMatrix(rows1, cols, -10, 1000, sparsity, 2373));
MatrixBlock mbA = DataConverter.convertToMatrixBlock(A);
int blksz = ConfigurationManager.getBlocksize();
MatrixCharacteristics mc1 =
new MatrixCharacteristics(rows1, cols, blksz, blksz, mbA.getNonZeros());
MatrixCharacteristics mc2 =
unknownDims ? new MatrixCharacteristics() : new MatrixCharacteristics(mc1);
// setup spark context
sec = (SparkExecutionContext) ExecutionContextFactory.createContext();
JavaSparkContext sc = sec.getSparkContext();
SQLContext sqlctx = new SQLContext(sc);
// create input data frame
DataFrame df = createDataFrame(sqlctx, mbA, containsID, schema);
// dataframe - frame conversion
JavaPairRDD<Long, FrameBlock> out =
FrameRDDConverterUtils.dataFrameToBinaryBlock(sc, df, mc2, containsID);
// get output frame block
FrameBlock fbB =
SparkExecutionContext.toFrameBlock(
out, UtilFunctions.nCopies(cols, ValueType.DOUBLE), rows1, cols);
// compare frame blocks
MatrixBlock mbB = DataConverter.convertToMatrixBlock(fbB);
double[][] B = DataConverter.convertToDoubleMatrix(mbB);
TestUtils.compareMatrices(A, B, rows1, cols, eps);
} catch (Exception ex) {
throw new RuntimeException(ex);
} finally {
sec.close();
DMLScript.USE_LOCAL_SPARK_CONFIG = oldConfig;
DMLScript.rtplatform = oldPlatform;
}
}
@Override
public MatrixBlock call(MatrixBlock arg0) throws Exception {
_aNnz.add((double) arg0.getNonZeros());
return arg0;
}
@SuppressWarnings("deprecation")
public void flushBuffer(Reporter reporter) throws RuntimeException {
try {
if (_mapBuffer != null) {
MatrixIndexes key = null; // new MatrixIndexes();
MatrixCell value = new MatrixCell();
for (Entry<Byte, CTableMap> ctable : _mapBuffer.entrySet()) {
ArrayList<Integer> resultIDs =
ReduceBase.getOutputIndexes(ctable.getKey(), _resultIndexes);
CTableMap resultMap = ctable.getValue();
// maintain result dims and nonzeros
for (Integer i : resultIDs) {
_resultNonZeros[i] += resultMap.size();
if (_resultDimsUnknown[i] == (byte) 1) {
_resultMaxRowDims[i] = Math.max(resultMap.getMaxRow(), _resultMaxRowDims[i]);
_resultMaxColDims[i] = Math.max(resultMap.getMaxColumn(), _resultMaxColDims[i]);
}
}
// output result data
for (LLDoubleEntry e : resultMap.entrySet()) {
key = new MatrixIndexes(e.key1, e.key2);
value.setValue(e.value);
for (Integer i : resultIDs) {
_collector.collectOutput(key, value, i, reporter);
}
}
}
} else if (_blockBuffer != null) {
MatrixIndexes key = new MatrixIndexes(1, 1);
// DataConverter.writeBinaryBlockMatrixToHDFS(path, job, mat, mc.get_rows(), mc.get_cols(),
// mc.get_rows_per_block(), mc.get_cols_per_block(), replication);
for (Entry<Byte, MatrixBlock> ctable : _blockBuffer.entrySet()) {
ArrayList<Integer> resultIDs =
ReduceBase.getOutputIndexes(ctable.getKey(), _resultIndexes);
MatrixBlock outBlock = ctable.getValue();
outBlock.recomputeNonZeros();
// TODO: change hard coding of 1000
int brlen = 1000, bclen = 1000;
int rlen = outBlock.getNumRows();
int clen = outBlock.getNumColumns();
// final output matrix is smaller than a single block
if (rlen <= brlen && clen <= brlen) {
key = new MatrixIndexes(1, 1);
for (Integer i : resultIDs) {
_collector.collectOutput(key, outBlock, i, reporter);
_resultNonZeros[i] += outBlock.getNonZeros();
}
} else {
// Following code is similar to that in
// DataConverter.DataConverter.writeBinaryBlockMatrixToHDFS
// initialize blocks for reuse (at most 4 different blocks required)
MatrixBlock[] blocks =
MatrixWriter.createMatrixBlocksForReuse(
rlen, clen, brlen, bclen, true, outBlock.getNonZeros());
// create and write subblocks of matrix
for (int blockRow = 0; blockRow < (int) Math.ceil(rlen / (double) brlen); blockRow++) {
for (int blockCol = 0;
blockCol < (int) Math.ceil(clen / (double) bclen);
blockCol++) {
int maxRow = (blockRow * brlen + brlen < rlen) ? brlen : rlen - blockRow * brlen;
int maxCol = (blockCol * bclen + bclen < clen) ? bclen : clen - blockCol * bclen;
int row_offset = blockRow * brlen;
int col_offset = blockCol * bclen;
// get reuse matrix block
MatrixBlock block =
MatrixWriter.getMatrixBlockForReuse(blocks, maxRow, maxCol, brlen, bclen);
// copy submatrix to block
outBlock.sliceOperations(
row_offset,
row_offset + maxRow - 1,
col_offset,
col_offset + maxCol - 1,
block);
// TODO: skip empty "block"
// append block to sequence file
key.setIndexes(blockRow + 1, blockCol + 1);
for (Integer i : resultIDs) {
_collector.collectOutput(key, block, i, reporter);
_resultNonZeros[i] += block.getNonZeros();
}
// reset block for later reuse
block.reset();
}
}
}
}
} else {
throw new DMLRuntimeException("Unexpected.. both ctable buffers are empty.");
}
} catch (Exception ex) {
throw new RuntimeException("Failed to flush ctable buffer.", ex);
}
// remove existing partial ctables
if (_mapBuffer != null) _mapBuffer.clear();
else _blockBuffer.clear();
}
