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Merged
davidrohr merged 1 commit into from
Jul 1, 2025
Merged

NN clusterizer improve kernel speed #14457

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4 changes: 2 additions & 2 deletions GPU/GPUTracking/Definitions/GPUDefParametersDefaults.h
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Expand Up @@ -488,8 +488,8 @@
#define GPUCA_LB_GPUTPCCFNoiseSuppression_updatePeaks GPUCA_LB_GPUTPCCFNoiseSuppression

#define GPUCA_LB_GPUTPCNNClusterizerKernels_runCfClusterizer GPUCA_LB_GPUTPCNNClusterizerKernels
#define GPUCA_LB_GPUTPCNNClusterizerKernels_fillInputNN GPUCA_LB_GPUTPCNNClusterizerKernels
#define GPUCA_LB_GPUTPCNNClusterizerKernels_fillInputNNSingleElement GPUCA_LB_GPUTPCNNClusterizerKernels
#define GPUCA_LB_GPUTPCNNClusterizerKernels_fillInputNNCPU GPUCA_LB_GPUTPCNNClusterizerKernels
#define GPUCA_LB_GPUTPCNNClusterizerKernels_fillInputNNGPU GPUCA_LB_GPUTPCNNClusterizerKernels
#define GPUCA_LB_GPUTPCNNClusterizerKernels_determineClass1Labels GPUCA_LB_GPUTPCNNClusterizerKernels
#define GPUCA_LB_GPUTPCNNClusterizerKernels_determineClass2Labels GPUCA_LB_GPUTPCNNClusterizerKernels
#define GPUCA_LB_GPUTPCNNClusterizerKernels_publishClass1Regression GPUCA_LB_GPUTPCNNClusterizerKernels
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16 changes: 11 additions & 5 deletions GPU/GPUTracking/Global/GPUChainTrackingClusterizer.cxx
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Expand Up @@ -990,11 +990,17 @@ int32_t GPUChainTracking::RunTPCClusterizer(bool synchronizeOutput)
uint batchStart = batch * clustererNNShadow.mNnClusterizerBatchedMode;
size_t iSize = CAMath::Min((uint)clustererNNShadow.mNnClusterizerBatchedMode, (uint)(clusterer.mPmemory->counters.nClusters - batchStart));

// auto start0 = std::chrono::high_resolution_clock::now();
runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::fillInputNNSingleElement>({GetGrid(iSize * clustererNNShadow.mNnClusterizerElementSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceInputDType, propagateMCLabels, batchStart); // Filling the data
// Filling the data
if (mRec->IsGPU()) {
// Fills element by element of each input matrix -> better parallelizability, but worse on CPU due to unnecessary computations
runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::fillInputNNGPU>({GetGrid(iSize * clustererNNShadow.mNnClusterizerElementSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceInputDType, propagateMCLabels, batchStart);
} else {
// Fills the whole input matrix at once -> better performance on CPU, but worse parallelizability
runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::fillInputNNCPU>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceInputDType, propagateMCLabels, batchStart);
}

if (clustererNNShadow.mNnClusterizerSetDeconvolutionFlags) {
runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::publishDeconvolutionFlags>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceInputDType, propagateMCLabels, batchStart); // Filling the regression data
runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::publishDeconvolutionFlags>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceInputDType, propagateMCLabels, batchStart); // Publishing the deconvolution flags
}

// NN evaluations
Expand Down Expand Up @@ -1042,7 +1048,7 @@ int32_t GPUChainTracking::RunTPCClusterizer(bool synchronizeOutput)
}
}

// Publishing kernels
// Publishing kernels for class labels and regression results
if (nnApplication.mModelClass.getNumOutputNodes()[0][1] == 1) {
runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::determineClass1Labels>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceOutputDType, propagateMCLabels, batchStart); // Assigning class labels
} else {
Expand All @@ -1057,7 +1063,7 @@ int32_t GPUChainTracking::RunTPCClusterizer(bool synchronizeOutput)
}

if (clustererNNShadow.mNnClusterizerUseCfRegression) {
if(!nn_settings.nnClusterizerApplyCfDeconvolution) {
if(!nn_settings.nnClusterizerApplyCfDeconvolution) { // If it is already applied don't do it twice, otherwise apply now
runKernel<GPUTPCCFDeconvolution>({GetGrid(clusterer.mPmemory->counters.nPositions, lane), {iSector}}, true);
}
DoDebugAndDump(RecoStep::TPCClusterFinding, GPUChainTrackingDebugFlags::TPCClustererChargeMap, clusterer, &GPUTPCClusterFinder::DumpChargeMap, *mDebugFile, "Split Charges");
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20 changes: 14 additions & 6 deletions GPU/GPUTracking/TPCClusterFinder/GPUTPCNNClusterizerKernels.cxx
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Expand Up @@ -51,7 +51,7 @@ GPUdii() void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::run
}

template <>
GPUdii() void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::fillInputNN>(int32_t nBlocks, int32_t nThreads, int32_t iBlock, int32_t iThread, GPUSharedMemory& smem, processorType& processors, uint8_t sector, int8_t dtype, int8_t withMC, uint32_t batchStart)
GPUdii() void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::fillInputNNCPU>(int32_t nBlocks, int32_t nThreads, int32_t iBlock, int32_t iThread, GPUSharedMemory& smem, processorType& processors, uint8_t sector, int8_t dtype, int8_t withMC, uint32_t batchStart)
{
uint32_t glo_idx = get_global_id(0);
auto& clusterer = processors.tpcClusterer[sector];
Expand All @@ -65,16 +65,14 @@ GPUdii() void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::fil
float central_charge = static_cast<float>(chargeMap[peak].unpack());
int32_t row_offset = GPUTPCNNClusterizerKernels::rowOffset(row, clustererNN.mNnClusterizerSizeInputRow);

#ifndef GPUCA_GPUCODE
GPUCA_UNROLL(U(), U());
#endif
for (int32_t r = -clustererNN.mNnClusterizerSizeInputRow; r <= clustererNN.mNnClusterizerSizeInputRow; r++) {
bool is_row_boundary = ((row + r) > (o2::tpc::constants::MAXGLOBALPADROW - 1)) || ((row + r) < 0);
int32_t pad_offset = is_row_boundary ? 0 : GPUTPCNNClusterizerKernels::padOffset(row, row + r);
for (int32_t p = -clustererNN.mNnClusterizerSizeInputPad + pad_offset; p <= clustererNN.mNnClusterizerSizeInputPad + pad_offset; p++) {
bool is_boundary = is_row_boundary || GPUTPCNNClusterizerKernels::isBoundary(row + r + row_offset, pad + p, clustererNN.mNnClusterizerSizeInputRow);
for (int32_t t = -clustererNN.mNnClusterizerSizeInputTime; t <= clustererNN.mNnClusterizerSizeInputTime; t++) {
if (!is_boundary) {
int32_t time_pos = time + t;
if (!is_boundary && (time_pos >= 0) && (time_pos < TPC_MAX_FRAGMENT_LEN_GPU)) {
CfChargePos tmp_pos(row + r, pad + p, time + t);
if (r == 0 && !clustererNN.mClusterFlags[2 * glo_idx] && CAMath::Abs(p) < 3 && CAMath::Abs(t) < 3 && p != 0 && t != 0) { // ordering is done for short circuit optimization
clustererNN.mClusterFlags[2 * glo_idx] += CfUtils::isPeak(isPeakMap[tmp_pos]);
Expand Down Expand Up @@ -108,10 +106,20 @@ GPUdii() void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::fil
clustererNN.mInputData_32[write_idx + 2] = static_cast<float>(pad) / GPUTPCGeometry::NPads(row);
}
}
if (!clustererNN.mNnClusterizerSetDeconvolutionFlags) {
clustererNN.mClusterFlags[2 * glo_idx] = 0;
clustererNN.mClusterFlags[2 * glo_idx + 1] = 0;
for (uint16_t i = 0; i < 8; i++) {
Delta2 d = cfconsts::InnerNeighbors[i];
CfChargePos tmp_pos = peak.delta(d);
clustererNN.mClusterFlags[2 * glo_idx] += CfUtils::isPeak(isPeakMap[tmp_pos]);
}
clustererNN.mClusterFlags[2 * glo_idx + 1] = clustererNN.mClusterFlags[2 * glo_idx];
}
}

template <>
GPUdii() void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::fillInputNNSingleElement>(int32_t nBlocks, int32_t nThreads, int32_t iBlock, int32_t iThread, GPUSharedMemory& smem, processorType& processors, uint8_t sector, int8_t dtype, int8_t withMC, uint32_t batchStart)
GPUdii() void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::fillInputNNGPU>(int32_t nBlocks, int32_t nThreads, int32_t iBlock, int32_t iThread, GPUSharedMemory& smem, processorType& processors, uint8_t sector, int8_t dtype, int8_t withMC, uint32_t batchStart)
{
uint32_t glo_idx = get_global_id(0);
auto& clusterer = processors.tpcClusterer[sector];
Expand Down
8 changes: 4 additions & 4 deletions GPU/GPUTracking/TPCClusterFinder/GPUTPCNNClusterizerKernels.h
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Expand Up @@ -38,8 +38,8 @@ class GPUTPCNNClusterizerKernels : public GPUKernelTemplate
{
public:
// Must all have same number of threads, since they use a common SCRATCH_PAD_WORK_GROUP_SIZE below
static_assert(GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_fillInputNN) == GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_runCfClusterizer));
static_assert(GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_fillInputNNSingleElement) == GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_runCfClusterizer));
static_assert(GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_fillInputNNCPU) == GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_runCfClusterizer));
static_assert(GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_fillInputNNGPU) == GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_runCfClusterizer));
static_assert(GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_determineClass1Labels) == GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_runCfClusterizer));
static_assert(GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_determineClass2Labels) == GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_runCfClusterizer));
static_assert(GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_publishClass1Regression) == GPUCA_GET_THREAD_COUNT(GPUCA_LB_GPUTPCNNClusterizerKernels_runCfClusterizer));
Expand All @@ -59,8 +59,8 @@ class GPUTPCNNClusterizerKernels : public GPUKernelTemplate

enum K : int32_t {
runCfClusterizer = 0,
fillInputNN = 1,
fillInputNNSingleElement = 2,
fillInputNNCPU = 1,
fillInputNNGPU = 2,
determineClass1Labels = 3,
determineClass2Labels = 4,
publishClass1Regression = 5,
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4 changes: 2 additions & 2 deletions GPU/GPUTracking/kernels.cmake
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Expand Up @@ -127,8 +127,8 @@ o2_gpu_add_kernel("GPUTrackingRefitKernel, mode0asGPU" "= GLO
o2_gpu_add_kernel("GPUTrackingRefitKernel, mode1asTrackParCov" "= GLOBALREFIT " LB)
if(onnxruntime_FOUND)
o2_gpu_add_kernel("GPUTPCNNClusterizerKernels, runCfClusterizer" "= TPCNNCLUSTERFINDER" LB uint8_t sector int8_t dtype int8_t withMC uint32_t batchStart)
o2_gpu_add_kernel("GPUTPCNNClusterizerKernels, fillInputNN" "= TPCNNCLUSTERFINDER" LB uint8_t sector int8_t dtype int8_t withMC uint32_t batchStart)
o2_gpu_add_kernel("GPUTPCNNClusterizerKernels, fillInputNNSingleElement" "= TPCNNCLUSTERFINDER" LB uint8_t sector int8_t dtype int8_t withMC uint32_t batchStart)
o2_gpu_add_kernel("GPUTPCNNClusterizerKernels, fillInputNNCPU" "= TPCNNCLUSTERFINDER" LB uint8_t sector int8_t dtype int8_t withMC uint32_t batchStart)
o2_gpu_add_kernel("GPUTPCNNClusterizerKernels, fillInputNNGPU" "= TPCNNCLUSTERFINDER" LB uint8_t sector int8_t dtype int8_t withMC uint32_t batchStart)
o2_gpu_add_kernel("GPUTPCNNClusterizerKernels, determineClass1Labels" "= TPCNNCLUSTERFINDER" LB uint8_t sector int8_t dtype int8_t withMC uint32_t batchStart)
o2_gpu_add_kernel("GPUTPCNNClusterizerKernels, determineClass2Labels" "= TPCNNCLUSTERFINDER" LB uint8_t sector int8_t dtype int8_t withMC uint32_t batchStart)
o2_gpu_add_kernel("GPUTPCNNClusterizerKernels, publishClass1Regression" "= TPCNNCLUSTERFINDER" LB uint8_t sector int8_t dtype int8_t withMC uint32_t batchStart)
Expand Down