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[RF] Avoid expensive double loop in setData() for likelihooods #19529

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Merged
guitargeek merged 1 commit into from
Aug 5, 2025
Merged

[RF] Avoid expensive double loop in setData() for likelihooods #19529

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3 changes: 2 additions & 1 deletion roofit/roofitcore/inc/RooFit/Evaluator.h
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Original file line number Diff line number Diff line change
Expand Up @@ -64,7 +64,8 @@ class Evaluator {
bool _needToUpdateOutputSizes = false;
RooFit::EvalContext _evalContextCPU;
RooFit::EvalContext _evalContextCUDA;
std::vector<NodeInfo> _nodes; // the ordered computation graph
std::vector<NodeInfo> _nodes; // the ordered computation graph
std::unordered_map<TNamed const *, NodeInfo *> _nodesMap; // for quick lookup of nodes
std::stack<std::unique_ptr<ChangeOperModeRAII>> _changeOperModeRAIIs;
};

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85 changes: 45 additions & 40 deletions roofit/roofitcore/src/RooFit/Evaluator.cxx
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Original file line number Diff line number Diff line change
Expand Up @@ -177,6 +177,8 @@ Evaluator::Evaluator(const RooAbsReal &absReal, bool useGPU)

_nodes.emplace_back();
auto &nodeInfo = _nodes.back();
_nodesMap[arg->namePtr()] = &nodeInfo;

nodeInfo.absArg = arg;
nodeInfo.originalOperMode = arg->operMode();
nodeInfo.iNode = iNode;
Expand Down Expand Up @@ -244,49 +246,51 @@ void Evaluator::setInput(std::string const &name, std::span<const double> inputA
throw std::runtime_error("Evaluator can only take device array as input in CUDA mode!");
}

auto namePtr = RooNameReg::ptr(name.c_str());
// Check if "name" is used in the computation graph. If yes, add the span to
// the data map and set the node info accordingly.

// Iterate over the given data spans and add them to the data map. Check if
// they are used in the computation graph. If yes, add the span to the data
// map and set the node info accordingly.
std::size_t iNode = 0;
for (auto &info : _nodes) {
const bool fromArrayInput = info.absArg->namePtr() == namePtr;
if (fromArrayInput) {
info.fromArrayInput = true;
info.absArg->setDataToken(iNode);
info.outputSize = inputArray.size();
if (_useGPU && info.outputSize <= 1) {
// Empty or scalar observables from the data don't need to be
// copied to the GPU.
_evalContextCPU.set(info.absArg, inputArray);
_evalContextCUDA.set(info.absArg, inputArray);
} else if (_useGPU && info.outputSize > 1) {
// For simplicity, we put the data on both host and device for
// now. This could be optimized by inspecting the clients of the
// variable.
if (isOnDevice) {
_evalContextCUDA.set(info.absArg, inputArray);
auto gpuSpan = _evalContextCUDA.at(info.absArg);
info.buffer = _bufferManager->makeCpuBuffer(gpuSpan.size());
info.buffer->assignFromDevice(gpuSpan);
_evalContextCPU.set(info.absArg, {info.buffer->hostReadPtr(), gpuSpan.size()});
} else {
_evalContextCPU.set(info.absArg, inputArray);
auto cpuSpan = _evalContextCPU.at(info.absArg);
info.buffer = _bufferManager->makeGpuBuffer(cpuSpan.size());
info.buffer->assignFromHost(cpuSpan);
_evalContextCUDA.set(info.absArg, {info.buffer->deviceReadPtr(), cpuSpan.size()});
}
} else {
_evalContextCPU.set(info.absArg, inputArray);
}
}
info.isDirty = !info.fromArrayInput;
++iNode;
}
auto found = _nodesMap.find(RooNameReg::ptr(name.c_str()));

if (found == _nodesMap.end())
return;

_needToUpdateOutputSizes = true;

NodeInfo &info = *found->second;

info.fromArrayInput = true;
info.absArg->setDataToken(info.iNode);
info.outputSize = inputArray.size();

if (!_useGPU) {
_evalContextCPU.set(info.absArg, inputArray);
return;
}

if (info.outputSize <= 1) {
// Empty or scalar observables from the data don't need to be
// copied to the GPU.
_evalContextCPU.set(info.absArg, inputArray);
_evalContextCUDA.set(info.absArg, inputArray);
return;
}

// For simplicity, we put the data on both host and device for
// now. This could be optimized by inspecting the clients of the
// variable.
if (isOnDevice) {
_evalContextCUDA.set(info.absArg, inputArray);
auto gpuSpan = _evalContextCUDA.at(info.absArg);
info.buffer = _bufferManager->makeCpuBuffer(gpuSpan.size());
info.buffer->assignFromDevice(gpuSpan);
_evalContextCPU.set(info.absArg, {info.buffer->hostReadPtr(), gpuSpan.size()});
} else {
_evalContextCPU.set(info.absArg, inputArray);
auto cpuSpan = _evalContextCPU.at(info.absArg);
info.buffer = _bufferManager->makeGpuBuffer(cpuSpan.size());
info.buffer->assignFromHost(cpuSpan);
_evalContextCUDA.set(info.absArg, {info.buffer->deviceReadPtr(), cpuSpan.size()});
}
}

void Evaluator::updateOutputSizes()
Expand All @@ -309,6 +313,7 @@ void Evaluator::updateOutputSizes()

for (auto &info : _nodes) {
info.outputSize = outputSizeMap.at(info.absArg);
info.isDirty = true;

// In principle we don't need dirty flag propagation because the driver
// takes care of deciding which node needs to be re-evaluated. However,
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