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Implement efficient data layout for modelbuilders and gbt trees #3343

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6aef6df
Add leftChildIndexes array, use it instead of idx * 2 furing prediction
avolkov-intel Jul 25, 2025
820530d
Draft modelbuilders layout update
avolkov-intel Aug 28, 2025
4f32567
Add comments to places where fix might be needed
avolkov-intel Sep 1, 2025
ccb7d32
Comment redundant functions, remove prints
avolkov-intel Sep 1, 2025
badc3fd
Combine leftIds and split points into one array, partially remove usa…
avolkov-intel Oct 8, 2025
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3 changes: 3 additions & 0 deletions ...lgorithms/dtrees/gbt/classification/gbt_classification_predict_dense_default_batch_impl.i
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Original file line number Diff line number Diff line change
Expand Up @@ -417,6 +417,7 @@ services::Status PredictBinaryClassificationTask<algorithmFPType, cpu>::run(cons
services::HostAppIface * pHostApp, bool predShapContributions,
bool predShapInteractions)
{
// std::cerr << "PredictBinaryClassificationTask" << std::endl;
// assert we're not requesting both contributions and interactions
DAAL_ASSERT(!(predShapContributions && predShapInteractions));

Expand Down Expand Up @@ -468,6 +469,7 @@ services::Status PredictBinaryClassificationTask<algorithmFPType, cpu>::run(cons
PRAGMA_VECTOR_ALWAYS
for (size_t iRow = startRow; iRow < finishRow; ++iRow)
{
// TODO: fix indicies - probaly this is 0/1 probability no fix is required
res[iRow] = label[services::internal::SignBit<algorithmFPType, cpu>::get(res[iRow])];
prob_pred[2 * iRow + 1] = expVal[iRow] / (algorithmFPType(1.) + expVal[iRow]);
prob_pred[2 * iRow] = algorithmFPType(1.) - prob_pred[2 * iRow + 1];
Expand Down Expand Up @@ -496,6 +498,7 @@ services::Status PredictBinaryClassificationTask<algorithmFPType, cpu>::run(cons
daal::internal::MathInst<algorithmFPType, cpu>::vExp(finishRow - startRow, expVal + startRow, expVal + startRow);
for (size_t iRow = startRow; iRow < finishRow; ++iRow)
{
// TODO: fix - probaly this is 0/1 probability no fix is required
prob_pred[2 * iRow + 1] = expVal[iRow] / (algorithmFPType(1.) + expVal[iRow]);
prob_pred[2 * iRow] = algorithmFPType(1.) - prob_pred[2 * iRow + 1];
}
Expand Down
5 changes: 5 additions & 0 deletions ...al/src/algorithms/dtrees/gbt/classification/gbt_classification_train_dense_default_impl.i
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Expand Up @@ -101,6 +101,8 @@ public:
for (size_t i = start; i < end; i++)
{
const algorithmFPType sigm = algorithmFPType(1.0) / (algorithmFPType(1.0) + exp[i]);
// TODO: fix
// UPD: probably these are just pairs (gradient/hessian) that are stored in one array
gh[2 * sampleInd[i]] = sigm - y[sampleInd[i]]; //gradient
gh[2 * sampleInd[i] + 1] = sigm * (algorithmFPType(1.0) - sigm); //hessian
}
Expand All @@ -111,6 +113,7 @@ public:
PRAGMA_VECTOR_ALWAYS
for (size_t i = start; i < end; i++)
{
// TODO: fix
const auto sigm = algorithmFPType(1.0) / (algorithmFPType(1.0) + exp[i]);
gh[2 * i] = sigm - y[i]; //gradient
gh[2 * i + 1] = sigm * (algorithmFPType(1.0) - sigm); //hessian
Expand Down Expand Up @@ -145,6 +148,8 @@ public:
{
const algorithmFPType pk = p[k];
const algorithmFPType h = algorithmFPType(2.) * pk * (algorithmFPType(1.) - pk);
// TODO: fix here
// UPD: probably these are just pairs (gradient/hessian) that are stored in one array
algorithmFPType * gh_ik = gh + 2 * (k * nRows + iSample);
gh_ik[1] = h;
if (size_t(y[iSample]) == k)
Expand Down
95 changes: 63 additions & 32 deletions cpp/daal/src/algorithms/dtrees/gbt/gbt_model.cpp
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Expand Up @@ -23,6 +23,9 @@

#include "services/daal_defines.h"
#include "src/algorithms/dtrees/gbt/gbt_model_impl.h"
#include <cassert>
#include <iostream>
#include <utility>

using namespace daal::data_management;
using namespace daal::services;
Expand Down Expand Up @@ -58,6 +61,7 @@ size_t ModelImpl::numberOfTrees() const

void ModelImpl::traverseDF(size_t iTree, algorithms::regression::TreeNodeVisitor & visitor) const
{
// std::cerr << "traverseDF" << std::endl;
if (iTree >= size()) return;

const GbtDecisionTree & gbtTree = *at(iTree);
Expand All @@ -78,6 +82,7 @@ void ModelImpl::traverseDF(size_t iTree, algorithms::regression::TreeNodeVisitor

void ModelImpl::traverseBF(size_t iTree, algorithms::regression::TreeNodeVisitor & visitor) const
{
// std::cerr << "traverseBF" << std::endl;
if (iTree >= size()) return;

const GbtDecisionTree & gbtTree = *at(iTree);
Expand All @@ -103,6 +108,7 @@ void ModelImpl::traverseBF(size_t iTree, algorithms::regression::TreeNodeVisitor

void ModelImpl::traverseBFS(size_t iTree, tree_utils::regression::TreeNodeVisitor & visitor) const
{
// std::cerr << "traverseBFS" << std::endl;
if (iTree >= size()) return;

const GbtDecisionTree & gbtTree = *at(iTree);
Expand Down Expand Up @@ -143,6 +149,7 @@ void ModelImpl::traverseBFS(size_t iTree, tree_utils::regression::TreeNodeVisito

void ModelImpl::traverseDFS(size_t iTree, tree_utils::regression::TreeNodeVisitor & visitor) const
{
// std::cerr << "traverseDFS" << std::endl;
if (iTree >= size()) return;

const GbtDecisionTree & gbtTree = *at(iTree);
Expand Down Expand Up @@ -225,39 +232,49 @@ void ModelImpl::destroy()
super::destroy();
}

bool ModelImpl::nodeIsDummyLeaf(size_t nodeIndex, const GbtDecisionTree & gbtTree)
{
const size_t childArrayIndex = nodeIndex - 1;
const ModelFPType * splitPoints = gbtTree.getSplitPoints();
const FeatureIndexType * splitFeatures = gbtTree.getFeatureIndexesForSplit();

if (childArrayIndex)
{
// check if child node has same split feature and split value as parent
const size_t parent = getIdxOfParent(nodeIndex);
const size_t parentArrayIndex = parent - 1;
return splitPoints[parentArrayIndex] == splitPoints[childArrayIndex] && splitFeatures[parentArrayIndex] == splitFeatures[childArrayIndex];
}
return false;
}
// bool ModelImpl::nodeIsDummyLeaf(size_t nodeIndex, const GbtDecisionTree & gbtTree)
// {
// std::cerr << "This function should not be called" << std::endl;
// assert(0);
// // TODO fix - function is redundant
// const size_t childArrayIndex = nodeIndex - 1;
// const ModelFPType * splitPoints = gbtTree.getSplitPoints();
// const FeatureIndexType * splitFeatures = gbtTree.getFeatureIndexesForSplit();

// if (childArrayIndex)
// {
// // check if child node has same split feature and split value as parent
// const size_t parent = getIdxOfParent(nodeIndex);
// const size_t parentArrayIndex = parent - 1;
// return splitPoints[parentArrayIndex] == splitPoints[childArrayIndex] && splitFeatures[parentArrayIndex] == splitFeatures[childArrayIndex];
// }
// return false;
// }

bool ModelImpl::nodeIsLeaf(size_t idx, const GbtDecisionTree & gbtTree, const size_t lvl)
{
if (lvl == gbtTree.getMaxLvl())
{
return true;
}
else if (nodeIsDummyLeaf(2 * idx, gbtTree)) // check, that left son is dummy
{
return true;
}
return false;
// TODO: fix
const size_t * leftChildIdx = gbtTree.getLeftChildIndexes();
// std::cerr << "nodeIsLeaf: " << idx - 1 << " " << leftChildIdx[idx - 1] << std::endl;
return leftChildIdx[idx - 1] == idx;
// if (lvl == gbtTree.getMaxLvl())
// {
// return true;
// }
// else if (nodeIsDummyLeaf(2 * idx, gbtTree)) // check, that left son is dummy
// {
// return true;
// }
// return false;
}

size_t ModelImpl::getIdxOfParent(const size_t childIdx)
{
return childIdx / 2;
}
// size_t ModelImpl::getIdxOfParent(const size_t childIdx)
// {
// // TODO fix
// std::cerr << "This function should not be called" << std::endl;
// assert(0);
// return childIdx / 2;
// }

void ModelImpl::decisionTreeToGbtTree(const DecisionTreeTable & tree, GbtDecisionTree & newTree)
{
Expand All @@ -273,6 +290,8 @@ void ModelImpl::decisionTreeToGbtTree(const DecisionTreeTable & tree, GbtDecisio
NodeType * parents = parentsArr.data();

ModelFPType * const splitPoints = newTree.getSplitPoints();
size_t * const leftChildIndexes = newTree.getLeftChildIndexes();
SplitLeftIdPair* const SplitAndLeftIds = newTree.getSplitsAndLeftIds();
FeatureIndexType * const featureIndexes = newTree.getFeatureIndexesForSplit();
ModelFPType * const nodeCoverValues = newTree.getNodeCoverValues();
int * const defaultLeft = newTree.getDefaultLeftForSplit();
Expand All @@ -286,6 +305,7 @@ void ModelImpl::decisionTreeToGbtTree(const DecisionTreeTable & tree, GbtDecisio
size_t nParents = 1;
parents[0] = arr;
size_t idxInTable = 0;
size_t idxChild = 2;

for (size_t lvl = 0; lvl < nLvls + 1; ++lvl)
{
Expand All @@ -302,18 +322,29 @@ void ModelImpl::decisionTreeToGbtTree(const DecisionTreeTable & tree, GbtDecisio
nodeCoverValues[idxInTable] = p->cover;
defaultLeft[idxInTable] = p->defaultLeft;
DAAL_ASSERT(featureIndexes[idxInTable] >= 0);
splitPoints[idxInTable] = p->featureValueOrResponse;
splitPoints[idxInTable] = p->featureValueOrResponse;
leftChildIndexes[idxInTable] = idxChild;
SplitAndLeftIds[idxInTable].leftId = idxChild;
SplitAndLeftIds[idxInTable].splitPoint = p->featureValueOrResponse;
idxChild += 2;
}
else
{
sons[nSons++] = p;
sons[nSons++] = p;
//sons[nSons++] = p;
//sons[nSons++] = p;
featureIndexes[idxInTable] = 0;
nodeCoverValues[idxInTable] = p->cover;
defaultLeft[idxInTable] = 0;
splitPoints[idxInTable] = p->featureValueOrResponse;
}

//leftChildIndexes[idxInTable] = idxChild;
//idxChild += 2;
leftChildIndexes[idxInTable] = idxInTable + 1;

SplitAndLeftIds[idxInTable].leftId = idxInTable + 1;
SplitAndLeftIds[idxInTable].splitPoint = p->featureValueOrResponse;
}
// leftChildIndexes[idxInTable] = (idxInTable + 1) * 2;
idxInTable++;
}
swap(parents, sons);
Expand Down
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