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Merged
davidrohr merged 4 commits into from
Apr 24, 2025
Merged

Fixing member variable naming #14217

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7de6492
Fixing member variable naming
ChSonnabend Apr 23, 2025
0cd665b
Please consider the following formatting changes
alibuild Apr 23, 2025
df00152
Merge pull request #24 from alibuild/alibot-cleanup-14217
ChSonnabend Apr 23, 2025
6d0ccb2
Changing to mPImplOrt
ChSonnabend Apr 23, 2025
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30 changes: 15 additions & 15 deletions Common/ML/include/ML/OrtInterface.h
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Original file line number Diff line number Diff line change
Expand Up @@ -70,23 +70,23 @@ class OrtModel
Ort::SessionOptions* getSessionOptions();
Ort::MemoryInfo* getMemoryInfo();
Ort::Env* getEnv();
int32_t getIntraOpNumThreads() const { return intraOpNumThreads; }
int32_t getInterOpNumThreads() const { return interOpNumThreads; }
int32_t getIntraOpNumThreads() const { return mIntraOpNumThreads; }
int32_t getInterOpNumThreads() const { return mInterOpNumThreads; }

// Setters
void setDeviceId(int32_t id) { deviceId = id; }
void setDeviceId(int32_t id) { mDeviceId = id; }
void setIO();
void setActiveThreads(int threads) { intraOpNumThreads = threads; }
void setActiveThreads(int threads) { mIntraOpNumThreads = threads; }
void setIntraOpNumThreads(int threads)
{
if (deviceType == "CPU") {
intraOpNumThreads = threads;
if (mDeviceType == "CPU") {
mIntraOpNumThreads = threads;
}
}
void setInterOpNumThreads(int threads)
{
if (deviceType == "CPU") {
interOpNumThreads = threads;
if (mDeviceType == "CPU") {
mInterOpNumThreads = threads;
}
}
void setEnv(Ort::Env*);
Expand All @@ -113,19 +113,19 @@ class OrtModel
private:
// ORT variables -> need to be hidden as pImpl
struct OrtVariables;
OrtVariables* pImplOrt;
OrtVariables* mPImplOrt;

// Input & Output specifications of the loaded network
std::vector<const char*> inputNamesChar, outputNamesChar;
std::vector<const char*> mInputNamesChar, mOutputNamesChar;
std::vector<std::string> mInputNames, mOutputNames;
std::vector<std::vector<int64_t>> mInputShapes, mOutputShapes, inputShapesCopy, outputShapesCopy; // Input shapes
std::vector<int64_t> inputSizePerNode, outputSizePerNode; // Output shapes
int32_t mInputsTotal = 0, mOutputsTotal = 0; // Total number of inputs and outputs
std::vector<std::vector<int64_t>> mInputShapes, mOutputShapes, mInputShapesCopy, mOutputShapesCopy; // Input shapes
std::vector<int64_t> mInputSizePerNode, mOutputSizePerNode; // Output shapes
int32_t mInputsTotal = 0, mOutputsTotal = 0; // Total number of inputs and outputs

// Environment settings
bool mInitialized = false;
std::string modelPath, envName = "", deviceType = "CPU", thread_affinity = ""; // device options should be cpu, rocm, migraphx, cuda
int32_t intraOpNumThreads = 1, interOpNumThreads = 1, deviceId = -1, enableProfiling = 0, loggingLevel = 0, allocateDeviceMemory = 0, enableOptimizations = 0;
std::string mModelPath, mEnvName = "", mDeviceType = "CPU", mThreadAffinity = ""; // device options should be cpu, rocm, migraphx, cuda
int32_t mIntraOpNumThreads = 1, mInterOpNumThreads = 1, mDeviceId = -1, mEnableProfiling = 0, mLoggingLevel = 0, mAllocateDeviceMemory = 0, mEnableOptimizations = 0;

std::string printShape(const std::vector<int64_t>&);
std::string printShape(const std::vector<std::vector<int64_t>>&, std::vector<std::string>&);
Expand Down
250 changes: 125 additions & 125 deletions Common/ML/src/OrtInterface.cxx
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172 changes: 86 additions & 86 deletions GPU/GPUTracking/Global/GPUChainTrackingClusterizer.cxx
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78 changes: 39 additions & 39 deletions GPU/GPUTracking/TPCClusterFinder/GPUTPCNNClusterizer.cxx
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Original file line number Diff line number Diff line change
Expand Up @@ -25,69 +25,69 @@ void GPUTPCNNClusterizer::SetMaxData(const GPUTrackingInOutPointers& io) {}

void* GPUTPCNNClusterizer::setIOPointers(void* mem)
{
if (nnClusterizerBatchedMode > 0) {
if (nnInferenceInputDType == 0 && nnClusterizerElementSize > 0) {
computePointerWithAlignment(mem, inputData_16, nnClusterizerBatchedMode * nnClusterizerElementSize);
} else if (nnInferenceInputDType == 1 && nnClusterizerElementSize > 0) {
computePointerWithAlignment(mem, inputData_32, nnClusterizerBatchedMode * nnClusterizerElementSize);
if (mNnClusterizerBatchedMode > 0) {
if (mNnInferenceInputDType == 0 && mNnClusterizerElementSize > 0) {
computePointerWithAlignment(mem, mInputData_16, mNnClusterizerBatchedMode * mNnClusterizerElementSize);
} else if (mNnInferenceInputDType == 1 && mNnClusterizerElementSize > 0) {
computePointerWithAlignment(mem, mInputData_32, mNnClusterizerBatchedMode * mNnClusterizerElementSize);
}
computePointerWithAlignment(mem, clusterFlags, 2 * nnClusterizerBatchedMode);
computePointerWithAlignment(mem, mClusterFlags, 2 * mNnClusterizerBatchedMode);

if (nnInferenceOutputDType == 0 && nnClusterizerElementSize > 0) {
if (nnClusterizerModelClassNumOutputNodes > 0) {
computePointerWithAlignment(mem, modelProbabilities_16, nnClusterizerBatchedMode * nnClusterizerModelClassNumOutputNodes);
if (mNnInferenceOutputDType == 0 && mNnClusterizerElementSize > 0) {
if (mNnClusterizerModelClassNumOutputNodes > 0) {
computePointerWithAlignment(mem, mModelProbabilities_16, mNnClusterizerBatchedMode * mNnClusterizerModelClassNumOutputNodes);
}
if (!nnClusterizerUseCfRegression) {
if (nnClusterizerModelReg1NumOutputNodes > 0) {
computePointerWithAlignment(mem, outputDataReg1_16, nnClusterizerBatchedMode * nnClusterizerModelReg1NumOutputNodes);
if (!mNnClusterizerUseCfRegression) {
if (mNnClusterizerModelReg1NumOutputNodes > 0) {
computePointerWithAlignment(mem, mOutputDataReg1_16, mNnClusterizerBatchedMode * mNnClusterizerModelReg1NumOutputNodes);
}
if (nnClusterizerModelReg2NumOutputNodes > 0) {
computePointerWithAlignment(mem, outputDataReg2_16, nnClusterizerBatchedMode * nnClusterizerModelReg2NumOutputNodes);
if (mNnClusterizerModelReg2NumOutputNodes > 0) {
computePointerWithAlignment(mem, mOutputDataReg2_16, mNnClusterizerBatchedMode * mNnClusterizerModelReg2NumOutputNodes);
}
}
} else if (nnInferenceOutputDType == 1 && nnClusterizerElementSize > 0) {
if (nnClusterizerModelClassNumOutputNodes > 0) {
computePointerWithAlignment(mem, modelProbabilities_32, nnClusterizerBatchedMode * nnClusterizerModelClassNumOutputNodes);
} else if (mNnInferenceOutputDType == 1 && mNnClusterizerElementSize > 0) {
if (mNnClusterizerModelClassNumOutputNodes > 0) {
computePointerWithAlignment(mem, mModelProbabilities_32, mNnClusterizerBatchedMode * mNnClusterizerModelClassNumOutputNodes);
}
if (!nnClusterizerUseCfRegression) {
if (nnClusterizerModelReg1NumOutputNodes > 0) {
computePointerWithAlignment(mem, outputDataReg1_32, nnClusterizerBatchedMode * nnClusterizerModelReg1NumOutputNodes);
if (!mNnClusterizerUseCfRegression) {
if (mNnClusterizerModelReg1NumOutputNodes > 0) {
computePointerWithAlignment(mem, mOutputDataReg1_32, mNnClusterizerBatchedMode * mNnClusterizerModelReg1NumOutputNodes);
}
if (nnClusterizerModelReg2NumOutputNodes > 0) {
computePointerWithAlignment(mem, outputDataReg2_32, nnClusterizerBatchedMode * nnClusterizerModelReg2NumOutputNodes);
if (mNnClusterizerModelReg2NumOutputNodes > 0) {
computePointerWithAlignment(mem, mOutputDataReg2_32, mNnClusterizerBatchedMode * mNnClusterizerModelReg2NumOutputNodes);
}
}
}
}
if (nnClusterizerTotalClusters > 0) {
computePointerWithAlignment(mem, outputDataClass, nnClusterizerTotalClusters);
if (mNnClusterizerTotalClusters > 0) {
computePointerWithAlignment(mem, mOutputDataClass, mNnClusterizerTotalClusters);
}
return mem;
}

// std::vector<int32_t> GPUTPCNNClusterizer::pointerSizes() {
// std::vector<int32_t> sizes(7, -1);
// if (nnClusterizerBatchedMode > 0) {
// if (nnInferenceInputDType == 0 && nnClusterizerElementSize > 0) {
// sizes[0] = nnClusterizerBatchedMode * nnClusterizerElementSize; // inputData16
// } else if (nnInferenceInputDType == 1 && nnClusterizerElementSize > 0) {
// sizes[1] = nnClusterizerBatchedMode * nnClusterizerElementSize; // inputData32
// if (mNnClusterizerBatchedMode > 0) {
// if (mNnInferenceInputDType == 0 && mNnClusterizerElementSize > 0) {
// sizes[0] = mNnClusterizerBatchedMode * mNnClusterizerElementSize; // inputData16
// } else if (mNnInferenceInputDType == 1 && mNnClusterizerElementSize > 0) {
// sizes[1] = mNnClusterizerBatchedMode * mNnClusterizerElementSize; // inputData32
// }
// sizes[2] = 2 * nnClusterizerBatchedMode; // clusterFlags
// if (nnClusterizerModelClassNumOutputNodes > 0) {
// sizes[3] = nnClusterizerBatchedMode * nnClusterizerModelClassNumOutputNodes; // modelProbabilities
// sizes[2] = 2 * mNnClusterizerBatchedMode; // mClusterFlags
// if (mNnClusterizerModelClassNumOutputNodes > 0) {
// sizes[3] = mNnClusterizerBatchedMode * mNnClusterizerModelClassNumOutputNodes; // modelProbabilities
// }
// if (!nnClusterizerUseCfRegression) {
// if (nnClusterizerModelReg1NumOutputNodes > 0) {
// sizes[4] = nnClusterizerBatchedMode * nnClusterizerModelReg1NumOutputNodes; // outputDataReg1
// if (!mNnClusterizerUseCfRegression) {
// if (mNnClusterizerModelReg1NumOutputNodes > 0) {
// sizes[4] = mNnClusterizerBatchedMode * mNnClusterizerModelReg1NumOutputNodes; // outputDataReg1
// }
// if (nnClusterizerModelReg2NumOutputNodes > 0) {
// sizes[5] = nnClusterizerBatchedMode * nnClusterizerModelReg2NumOutputNodes; // outputDataReg2
// if (mNnClusterizerModelReg2NumOutputNodes > 0) {
// sizes[5] = mNnClusterizerBatchedMode * mNnClusterizerModelReg2NumOutputNodes; // outputDataReg2
// }
// }
// }
// if (nnClusterizerTotalClusters > 0) {
// sizes[6] = nnClusterizerTotalClusters; // outputDataClass
// if (mNnClusterizerTotalClusters > 0) {
// sizes[6] = mNnClusterizerTotalClusters; // mOutputDataClass
// }
// return sizes;
// }
Expand Down
56 changes: 28 additions & 28 deletions GPU/GPUTracking/TPCClusterFinder/GPUTPCNNClusterizer.h
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Expand Up @@ -37,42 +37,42 @@ class GPUTPCNNClusterizer : public GPUProcessor

// Neural network clusterization

int nnClusterizerSizeInputRow = 3;
int nnClusterizerSizeInputPad = 3;
int nnClusterizerSizeInputTime = 3;
int nnClusterizerElementSize = -1;
bool nnClusterizerAddIndexData = true;
float nnClassThreshold = 0.01;
bool nnSigmoidTrafoClassThreshold = 1;
int nnClusterizerUseCfRegression = 0;
int nnClusterizerBatchedMode = 1;
int nnClusterizerTotalClusters = 1;
int nnClusterizerVerbosity = 0;
int nnClusterizerBoundaryFillValue = -1;
int nnClusterizerModelClassNumOutputNodes = -1;
int nnClusterizerModelReg1NumOutputNodes = -1;
int nnClusterizerModelReg2NumOutputNodes = -1;
int nnInferenceInputDType = 0; // 0: float16, 1: float32
int nnInferenceOutputDType = 0; // 0: float16, 1: float32
int mNnClusterizerSizeInputRow = 3;
int mNnClusterizerSizeInputPad = 3;
int mNnClusterizerSizeInputTime = 3;
int mNnClusterizerElementSize = -1;
bool mNnClusterizerAddIndexData = true;
float mNnClassThreshold = 0.01;
bool mNnSigmoidTrafoClassThreshold = 1;
int mNnClusterizerUseCfRegression = 0;
int mNnClusterizerBatchedMode = 1;
int mNnClusterizerTotalClusters = 1;
int mNnClusterizerVerbosity = 0;
int mNnClusterizerBoundaryFillValue = -1;
int mNnClusterizerModelClassNumOutputNodes = -1;
int mNnClusterizerModelReg1NumOutputNodes = -1;
int mNnClusterizerModelReg2NumOutputNodes = -1;
int mNnInferenceInputDType = 0; // 0: float16, 1: float32
int mNnInferenceOutputDType = 0; // 0: float16, 1: float32
int mISector = -1;
int deviceId = -1;
int mDeviceId = -1;

// Memory allocation for neural network

bool* clusterFlags = nullptr; // mSplitInTime, mSplitInPad. Techincally both flags are set in the same way -> ClusterAccumulator.cx=nullptr
int* outputDataClass = nullptr;
bool* mClusterFlags = nullptr; // mSplitInTime, mSplitInPad. Techincally both flags are set in the same way -> ClusterAccumulator.cx=nullptr
int* mOutputDataClass = nullptr;

// FP32
float* inputData_32 = nullptr;
float* modelProbabilities_32 = nullptr;
float* outputDataReg1_32 = nullptr;
float* outputDataReg2_32 = nullptr;
float* mInputData_32 = nullptr;
float* mModelProbabilities_32 = nullptr;
float* mOutputDataReg1_32 = nullptr;
float* mOutputDataReg2_32 = nullptr;

// FP16
OrtDataType::Float16_t* inputData_16 = nullptr;
OrtDataType::Float16_t* modelProbabilities_16 = nullptr;
OrtDataType::Float16_t* outputDataReg1_16 = nullptr;
OrtDataType::Float16_t* outputDataReg2_16 = nullptr;
OrtDataType::Float16_t* mInputData_16 = nullptr;
OrtDataType::Float16_t* mModelProbabilities_16 = nullptr;
OrtDataType::Float16_t* mOutputDataReg1_16 = nullptr;
OrtDataType::Float16_t* mOutputDataReg2_16 = nullptr;

int16_t mMemoryId = -1;
}; // class GPUTPCNNClusterizer
Expand Down
82 changes: 41 additions & 41 deletions GPU/GPUTracking/TPCClusterFinder/GPUTPCNNClusterizerHost.cxx
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Expand Up @@ -45,7 +45,7 @@ void GPUTPCNNClusterizerHost::init(const GPUSettingsProcessingNNclusterizer& set
}
}

OrtOptions = {
mOrtOptions = {
{"model-path", class_model_path},
{"device-type", settings.nnInferenceDevice},
{"allocate-device-memory", std::to_string(settings.nnInferenceAllocateDevMem)},
Expand All @@ -57,60 +57,60 @@ void GPUTPCNNClusterizerHost::init(const GPUSettingsProcessingNNclusterizer& set
{"logging-level", std::to_string(settings.nnInferenceVerbosity)},
{"onnx-environment-name", "c1"}};

model_class.initOptions(OrtOptions);
modelsUsed[0] = true;
mModelClass.initOptions(mOrtOptions);
mModelsUsed[0] = true;

reg_model_paths_local = o2::utils::Str::tokenize(reg_model_path, ':');

if (!settings.nnClusterizerUseCfRegression) {
if (reg_model_paths_local.size() == 1) {
OrtOptions["model-path"] = reg_model_paths_local[0];
OrtOptions["onnx-environment-name"] = "r1";
model_reg_1.initOptions(OrtOptions);
modelsUsed[1] = true;
mOrtOptions["model-path"] = reg_model_paths_local[0];
mOrtOptions["onnx-environment-name"] = "r1";
mModelReg1.initOptions(mOrtOptions);
mModelsUsed[1] = true;
} else {
OrtOptions["model-path"] = reg_model_paths_local[0];
OrtOptions["onnx-environment-name"] = "r1";
model_reg_1.initOptions(OrtOptions);
modelsUsed[1] = true;
OrtOptions["model-path"] = reg_model_paths_local[1];
OrtOptions["onnx-environment-name"] = "r2";
model_reg_2.initOptions(OrtOptions);
modelsUsed[2] = true;
mOrtOptions["model-path"] = reg_model_paths_local[0];
mOrtOptions["onnx-environment-name"] = "r1";
mModelReg1.initOptions(mOrtOptions);
mModelsUsed[1] = true;
mOrtOptions["model-path"] = reg_model_paths_local[1];
mOrtOptions["onnx-environment-name"] = "r2";
mModelReg2.initOptions(mOrtOptions);
mModelsUsed[2] = true;
}
}
}

void GPUTPCNNClusterizerHost::initClusterizer(const GPUSettingsProcessingNNclusterizer& settings, GPUTPCNNClusterizer& clustererNN)
{
clustererNN.nnClusterizerUseCfRegression = settings.nnClusterizerUseCfRegression;
clustererNN.nnClusterizerSizeInputRow = settings.nnClusterizerSizeInputRow;
clustererNN.nnClusterizerSizeInputPad = settings.nnClusterizerSizeInputPad;
clustererNN.nnClusterizerSizeInputTime = settings.nnClusterizerSizeInputTime;
clustererNN.nnClusterizerAddIndexData = settings.nnClusterizerAddIndexData;
clustererNN.nnClusterizerElementSize = ((2 * settings.nnClusterizerSizeInputRow + 1) * (2 * settings.nnClusterizerSizeInputPad + 1) * (2 * settings.nnClusterizerSizeInputTime + 1)) + (settings.nnClusterizerAddIndexData ? 3 : 0);
clustererNN.nnClusterizerBatchedMode = settings.nnClusterizerBatchedMode;
clustererNN.nnClusterizerBoundaryFillValue = settings.nnClusterizerBoundaryFillValue;
clustererNN.nnSigmoidTrafoClassThreshold = settings.nnSigmoidTrafoClassThreshold;
if (clustererNN.nnSigmoidTrafoClassThreshold) {
clustererNN.nnClassThreshold = (float)std::log(settings.nnClassThreshold / (1.f - settings.nnClassThreshold));
clustererNN.mNnClusterizerUseCfRegression = settings.nnClusterizerUseCfRegression;
clustererNN.mNnClusterizerSizeInputRow = settings.nnClusterizerSizeInputRow;
clustererNN.mNnClusterizerSizeInputPad = settings.nnClusterizerSizeInputPad;
clustererNN.mNnClusterizerSizeInputTime = settings.nnClusterizerSizeInputTime;
clustererNN.mNnClusterizerAddIndexData = settings.nnClusterizerAddIndexData;
clustererNN.mNnClusterizerElementSize = ((2 * settings.nnClusterizerSizeInputRow + 1) * (2 * settings.nnClusterizerSizeInputPad + 1) * (2 * settings.nnClusterizerSizeInputTime + 1)) + (settings.nnClusterizerAddIndexData ? 3 : 0);
clustererNN.mNnClusterizerBatchedMode = settings.nnClusterizerBatchedMode;
clustererNN.mNnClusterizerBoundaryFillValue = settings.nnClusterizerBoundaryFillValue;
clustererNN.mNnSigmoidTrafoClassThreshold = settings.nnSigmoidTrafoClassThreshold;
if (clustererNN.mNnSigmoidTrafoClassThreshold) {
clustererNN.mNnClassThreshold = (float)std::log(settings.nnClassThreshold / (1.f - settings.nnClassThreshold));
} else {
clustererNN.nnClassThreshold = settings.nnClassThreshold;
clustererNN.mNnClassThreshold = settings.nnClassThreshold;
}
if (settings.nnClusterizerVerbosity < 0) {
clustererNN.nnClusterizerVerbosity = settings.nnInferenceVerbosity;
clustererNN.mNnClusterizerVerbosity = settings.nnInferenceVerbosity;
} else {
clustererNN.nnClusterizerVerbosity = settings.nnClusterizerVerbosity;
clustererNN.mNnClusterizerVerbosity = settings.nnClusterizerVerbosity;
}
clustererNN.nnInferenceInputDType = settings.nnInferenceInputDType.find("32") != std::string::npos;
clustererNN.nnInferenceOutputDType = settings.nnInferenceOutputDType.find("32") != std::string::npos;
clustererNN.nnClusterizerModelClassNumOutputNodes = model_class.getNumOutputNodes()[0][1];
clustererNN.mNnInferenceInputDType = settings.nnInferenceInputDType.find("32") != std::string::npos;
clustererNN.mNnInferenceOutputDType = settings.nnInferenceOutputDType.find("32") != std::string::npos;
clustererNN.mNnClusterizerModelClassNumOutputNodes = mModelClass.getNumOutputNodes()[0][1];
if (!settings.nnClusterizerUseCfRegression) {
if (model_class.getNumOutputNodes()[0][1] == 1 || !model_reg_2.isInitialized()) {
clustererNN.nnClusterizerModelReg1NumOutputNodes = model_reg_1.getNumOutputNodes()[0][1];
if (mModelClass.getNumOutputNodes()[0][1] == 1 || !mModelReg2.isInitialized()) {
clustererNN.mNnClusterizerModelReg1NumOutputNodes = mModelReg1.getNumOutputNodes()[0][1];
} else {
clustererNN.nnClusterizerModelReg1NumOutputNodes = model_reg_1.getNumOutputNodes()[0][1];
clustererNN.nnClusterizerModelReg2NumOutputNodes = model_reg_2.getNumOutputNodes()[0][1];
clustererNN.mNnClusterizerModelReg1NumOutputNodes = mModelReg1.getNumOutputNodes()[0][1];
clustererNN.mNnClusterizerModelReg2NumOutputNodes = mModelReg2.getNumOutputNodes()[0][1];
}
}
}
Expand Down Expand Up @@ -199,20 +199,20 @@ void MockedOrtAllocator::LeakCheck()

void GPUTPCNNClusterizerHost::volatileOrtAllocator(Ort::Env* env, Ort::MemoryInfo* memInfo, GPUReconstruction* rec, bool recreate)
{
mockedAlloc = std::make_shared<MockedOrtAllocator>(rec, (OrtMemoryInfo*)(*memInfo));
mMockedAlloc = std::make_shared<MockedOrtAllocator>(rec, (OrtMemoryInfo*)(*memInfo));
if (recreate) {
Ort::ThrowOnError(Ort::GetApi().UnregisterAllocator((OrtEnv*)(*env), (OrtMemoryInfo*)(*memInfo)));
}
Ort::ThrowOnError(Ort::GetApi().RegisterAllocator((OrtEnv*)(*env), mockedAlloc.get()));
memInfo = (Ort::MemoryInfo*)mockedAlloc->Info();
Ort::ThrowOnError(Ort::GetApi().RegisterAllocator((OrtEnv*)(*env), mMockedAlloc.get()));
memInfo = (Ort::MemoryInfo*)mMockedAlloc->Info();
}

const OrtMemoryInfo* GPUTPCNNClusterizerHost::getMockedMemoryInfo()
{
return mockedAlloc->Info();
return mMockedAlloc->Info();
}

MockedOrtAllocator* GPUTPCNNClusterizerHost::getMockedAllocator()
{
return mockedAlloc.get();
return mMockedAlloc.get();
}
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