use of com.tencent.angel.ml.math2.storage.LongLongSortedVectorStorage in project angel by Tencent.
the class HashRouterUtils method splitLongLongVector.
public static void splitLongLongVector(KeyHash hasher, MatrixMeta matrixMeta, LongLongVector vector, KeyValuePart[] dataParts) {
int dataPartNum = dataParts.length;
int dataPartNumMinus1 = dataPartNum - 1;
if (isPow2(dataPartNum)) {
LongLongVectorStorage storage = vector.getStorage();
if (storage.isSparse()) {
// Use iterator
LongLongSparseVectorStorage sparseStorage = (LongLongSparseVectorStorage) storage;
ObjectIterator<Long2LongMap.Entry> iter = sparseStorage.entryIterator();
while (iter.hasNext()) {
Long2LongMap.Entry keyValue = iter.next();
int partId = computeHashCode(hasher, keyValue.getLongKey()) & dataPartNumMinus1;
((HashLongKeysLongValuesPart) dataParts[partId]).add(keyValue.getLongKey(), keyValue.getLongValue());
}
} else {
// Key and value array pair
LongLongSortedVectorStorage sortStorage = (LongLongSortedVectorStorage) storage;
long[] keys = sortStorage.getIndices();
long[] values = sortStorage.getValues();
for (int i = 0; i < keys.length; i++) {
int partId = computeHashCode(hasher, keys[i]) & dataPartNumMinus1;
((HashLongKeysLongValuesPart) dataParts[partId]).add(keys[i], values[i]);
}
}
} else {
LongLongVectorStorage storage = vector.getStorage();
if (storage.isSparse()) {
// Use iterator
LongLongSparseVectorStorage sparseStorage = (LongLongSparseVectorStorage) storage;
ObjectIterator<Long2LongMap.Entry> iter = sparseStorage.entryIterator();
while (iter.hasNext()) {
Long2LongMap.Entry keyValue = iter.next();
int partId = computeHashCode(hasher, keyValue.getLongKey()) % dataPartNum;
((HashLongKeysLongValuesPart) dataParts[partId]).add(keyValue.getLongKey(), keyValue.getLongValue());
}
} else {
// Key and value array pair
LongLongSortedVectorStorage sortStorage = (LongLongSortedVectorStorage) storage;
long[] keys = sortStorage.getIndices();
long[] values = sortStorage.getValues();
for (int i = 0; i < keys.length; i++) {
int partId = computeHashCode(hasher, keys[i]) % dataPartNum;
((HashLongKeysLongValuesPart) dataParts[partId]).add(keys[i], values[i]);
}
}
}
}
use of com.tencent.angel.ml.math2.storage.LongLongSortedVectorStorage in project angel by Tencent.
the class MixedBinaryInAllExecutor method apply.
private static Vector apply(CompLongLongVector v1, LongDummyVector v2, Binary op) {
LongLongVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof LongLongSortedVectorStorage) {
resParts[i] = new LongLongSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
for (int i = 0; i < v1.getDim(); i++) {
int pidx = (int) (i / subDim);
long subidx = i % subDim;
((LongLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
}
LongLongVector[] res = new LongLongVector[parts.length];
int i = 0;
for (LongLongVector part : parts) {
res[i] = new LongLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (LongLongVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
use of com.tencent.angel.ml.math2.storage.LongLongSortedVectorStorage in project angel by Tencent.
the class MixedBinaryInAllExecutor method apply.
private static Vector apply(CompLongLongVector v1, LongLongVector v2, Binary op) {
LongLongVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof LongLongSortedVectorStorage) {
resParts[i] = new LongLongSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
for (int i = 0; i < v1.getDim(); i++) {
int pidx = (int) (i / subDim);
long subidx = i % subDim;
if (v2.getStorage().hasKey(i)) {
((LongLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2.get(i)));
} else {
((LongLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 0));
}
}
LongLongVector[] res = new LongLongVector[parts.length];
int i = 0;
for (LongLongVector part : parts) {
res[i] = new LongLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (LongLongVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
use of com.tencent.angel.ml.math2.storage.LongLongSortedVectorStorage in project angel by Tencent.
the class MixedBinaryOutNonZAExecutor method apply.
private static Vector apply(CompLongLongVector v1, LongLongVector v2, Binary op) {
LongLongVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (v2.isSparse()) {
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof LongLongSortedVectorStorage) {
resParts[i] = new LongLongSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
ObjectIterator<Long2LongMap.Entry> iter = v2.getStorage().entryIterator();
while (iter.hasNext()) {
Long2LongMap.Entry entry = iter.next();
long gidx = entry.getLongKey();
int pidx = (int) (gidx / subDim);
long subidx = gidx % subDim;
((LongLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), entry.getLongValue()));
}
} else {
// sorted
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof LongLongSortedVectorStorage) {
resParts[i] = new LongLongSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
long[] v2Indices = v2.getStorage().getIndices();
long[] v2Values = v2.getStorage().getValues();
for (int i = 0; i < v2Indices.length; i++) {
long gidx = v2Indices[i];
int pidx = (int) (gidx / subDim);
long subidx = gidx % subDim;
((LongLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), v2Values[i]));
}
}
LongLongVector[] res = new LongLongVector[parts.length];
int i = 0;
for (LongLongVector part : parts) {
res[i] = new LongLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (LongLongVectorStorage) resParts[i]);
i++;
}
return new CompLongLongVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), res, v1.getSubDim());
}
use of com.tencent.angel.ml.math2.storage.LongLongSortedVectorStorage in project angel by Tencent.
the class MixedBinaryInNonZAExecutor method apply.
private static Vector apply(CompLongLongVector v1, LongDummyVector v2, Binary op) {
LongLongVector[] parts = v1.getPartitions();
Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
if (!op.isKeepStorage()) {
for (int i = 0; i < parts.length; i++) {
if (parts[i].getStorage() instanceof LongLongSortedVectorStorage) {
resParts[i] = new LongLongSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
}
}
}
long subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
long[] v2Indices = v2.getIndices();
for (int i = 0; i < v2Indices.length; i++) {
long gidx = v2Indices[i];
int pidx = (int) (gidx / subDim);
long subidx = gidx % subDim;
((LongLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 1));
}
LongLongVector[] res = new LongLongVector[parts.length];
int i = 0;
for (LongLongVector part : parts) {
res[i] = new LongLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (LongLongVectorStorage) resParts[i]);
i++;
}
v1.setPartitions(res);
return v1;
}
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