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Merge b3c74d0 into sapling-pr-archive-ktf
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‎Common/ML/include/ML/3rdparty/GPUORTFloat16.h‎

Lines changed: 72 additions & 54 deletions
Large diffs are not rendered by default.

‎Common/ML/include/ML/OrtInterface.h‎

Lines changed: 7 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -41,6 +41,7 @@ class OrtModel
4141
OrtModel(std::unordered_map<std::string, std::string> optionsMap) { reset(optionsMap); }
4242
void init(std::unordered_map<std::string, std::string> optionsMap) { reset(optionsMap); }
4343
void reset(std::unordered_map<std::string, std::string>);
44+
bool isInitialized() { return mInitialized; }
4445

4546
virtual ~OrtModel() = default;
4647

@@ -55,6 +56,9 @@ class OrtModel
5556
template <class I, class O> // class I is the input data type, e.g. float, class O is the output data type, e.g. O2::gpu::OrtDataType::Float16_t from O2/GPU/GPUTracking/ML/convert_float16.h
5657
std::vector<O> inference(std::vector<std::vector<I>>&);
5758

59+
template <class I, class O> // class I is the input data type, e.g. float, class O is the output data type, e.g. OrtDataType::Float16_t from O2/Common/ML/include/ML/GPUORTFloat16.h
60+
void inference(I*, size_t, O*);
61+
5862
// template<class I, class T, class O> // class I is the input data type, e.g. float, class T the throughput data type and class O is the output data type
5963
// std::vector<O> inference(std::vector<I>&);
6064

@@ -79,8 +83,9 @@ class OrtModel
7983
std::vector<std::vector<int64_t>> mInputShapes, mOutputShapes;
8084

8185
// Environment settings
82-
std::string modelPath, device = "cpu", dtype = "float"; // device options should be cpu, rocm, migraphx, cuda
83-
int intraOpNumThreads = 0, deviceId = 0, enableProfiling = 0, loggingLevel = 0, allocateDeviceMemory = 0, enableOptimizations = 0;
86+
bool mInitialized = false;
87+
std::string modelPath, device = "cpu", dtype = "float", thread_affinity = ""; // device options should be cpu, rocm, migraphx, cuda
88+
int intraOpNumThreads = 1, interOpNumThreads = 1, deviceId = 0, enableProfiling = 0, loggingLevel = 0, allocateDeviceMemory = 0, enableOptimizations = 0;
8489

8590
std::string printShape(const std::vector<int64_t>&);
8691
};

‎Common/ML/src/OrtInterface.cxx‎

Lines changed: 65 additions & 99 deletions
Original file line numberDiff line numberDiff line change
@@ -44,17 +44,20 @@ void OrtModel::reset(std::unordered_map<std::string, std::string> optionsMap)
4444
if (!optionsMap.contains("model-path")) {
4545
LOG(fatal) << "(ORT) Model path cannot be empty!";
4646
}
47-
modelPath = optionsMap["model-path"];
48-
device = (optionsMap.contains("device") ? optionsMap["device"] : "CPU");
49-
dtype = (optionsMap.contains("dtype") ? optionsMap["dtype"] : "float");
50-
deviceId = (optionsMap.contains("device-id") ? std::stoi(optionsMap["device-id"]) : 0);
51-
allocateDeviceMemory = (optionsMap.contains("allocate-device-memory") ? std::stoi(optionsMap["allocate-device-memory"]) : 0);
52-
intraOpNumThreads = (optionsMap.contains("intra-op-num-threads") ? std::stoi(optionsMap["intra-op-num-threads"]) : 0);
53-
loggingLevel = (optionsMap.contains("logging-level") ? std::stoi(optionsMap["logging-level"]) : 2);
54-
enableProfiling = (optionsMap.contains("enable-profiling") ? std::stoi(optionsMap["enable-profiling"]) : 0);
55-
enableOptimizations = (optionsMap.contains("enable-optimizations") ? std::stoi(optionsMap["enable-optimizations"]) : 0);
56-
57-
std::string dev_mem_str = "Hip";
47+
48+
if (!optionsMap["model-path"].empty()) {
49+
modelPath = optionsMap["model-path"];
50+
device = (optionsMap.contains("device") ? optionsMap["device"] : "CPU");
51+
dtype = (optionsMap.contains("dtype") ? optionsMap["dtype"] : "float");
52+
deviceId = (optionsMap.contains("device-id") ? std::stoi(optionsMap["device-id"]) : 0);
53+
allocateDeviceMemory = (optionsMap.contains("allocate-device-memory") ? std::stoi(optionsMap["allocate-device-memory"]) : 0);
54+
intraOpNumThreads = (optionsMap.contains("intra-op-num-threads") ? std::stoi(optionsMap["intra-op-num-threads"]) : 0);
55+
interOpNumThreads = (optionsMap.contains("inter-op-num-threads") ? std::stoi(optionsMap["inter-op-num-threads"]) : 0);
56+
loggingLevel = (optionsMap.contains("logging-level") ? std::stoi(optionsMap["logging-level"]) : 0);
57+
enableProfiling = (optionsMap.contains("enable-profiling") ? std::stoi(optionsMap["enable-profiling"]) : 0);
58+
enableOptimizations = (optionsMap.contains("enable-optimizations") ? std::stoi(optionsMap["enable-optimizations"]) : 0);
59+
60+
std::string dev_mem_str = "Hip";
5861
#if defined(ORT_ROCM_BUILD)
5962
#if ORT_ROCM_BUILD == 1
6063
if (device == "ROCM") {
@@ -88,12 +91,15 @@ void OrtModel::reset(std::unordered_map<std::string, std::string> optionsMap)
8891

8992
if (device == "CPU") {
9093
(pImplOrt->sessionOptions).SetIntraOpNumThreads(intraOpNumThreads);
91-
if (intraOpNumThreads > 1) {
94+
(pImplOrt->sessionOptions).SetInterOpNumThreads(interOpNumThreads);
95+
if (intraOpNumThreads > 1 || interOpNumThreads > 1) {
9296
(pImplOrt->sessionOptions).SetExecutionMode(ExecutionMode::ORT_PARALLEL);
9397
} else if (intraOpNumThreads == 1) {
9498
(pImplOrt->sessionOptions).SetExecutionMode(ExecutionMode::ORT_SEQUENTIAL);
9599
}
96-
LOG(info) << "(ORT) CPU execution provider set with " << intraOpNumThreads << " threads";
100+
if (loggingLevel < 2) {
101+
LOG(info) << "(ORT) CPU execution provider set with " << intraOpNumThreads << " (intraOpNumThreads) and " << interOpNumThreads << " (interOpNumThreads) threads";
102+
}
97103
}
98104

99105
(pImplOrt->sessionOptions).DisableMemPattern();
@@ -109,6 +115,9 @@ void OrtModel::reset(std::unordered_map<std::string, std::string> optionsMap)
109115
} else {
110116
(pImplOrt->sessionOptions).DisableProfiling();
111117
}
118+
119+
mInitialized = true;
120+
112121
(pImplOrt->sessionOptions).SetGraphOptimizationLevel(GraphOptimizationLevel(enableOptimizations));
113122
(pImplOrt->sessionOptions).SetLogSeverityLevel(OrtLoggingLevel(loggingLevel));
114123

@@ -154,16 +163,9 @@ void OrtModel::reset(std::unordered_map<std::string, std::string> optionsMap)
154163
outputNamesChar.resize(mOutputNames.size(), nullptr);
155164
std::transform(std::begin(mOutputNames), std::end(mOutputNames), std::begin(outputNamesChar),
156165
[&](const std::string& str) { return str.c_str(); });
157-
158-
// Print names
159-
LOG(info) << "\tInput Nodes:";
160-
for (size_t i = 0; i < mInputNames.size(); i++) {
161-
LOG(info) << "\t\t" << mInputNames[i] << " : " << printShape(mInputShapes[i]);
162166
}
163-
164-
LOG(info) << "\tOutput Nodes:";
165-
for (size_t i = 0; i < mOutputNames.size(); i++) {
166-
LOG(info) << "\t\t" << mOutputNames[i] << " : " << printShape(mOutputShapes[i]);
167+
if (loggingLevel < 2) {
168+
LOG(info) << "(ORT) Model loaded successfully! (input: " << printShape(mInputShapes[0]) << ", output: " << printShape(mOutputShapes[0]) << ")";
167169
}
168170
}
169171

@@ -187,36 +189,6 @@ std::vector<O> OrtModel::v2v(std::vector<I>& input, bool clearInput)
187189
}
188190
}
189191

190-
template <class I, class O> // class I is the input data type, e.g. float, class O is the output data type, e.g. O2::gpu::OrtDataType::Float16_t from O2/GPU/GPUTracking/ML/convert_float16.h
191-
std::vector<O> OrtModel::inference(std::vector<I>& input)
192-
{
193-
std::vector<int64_t> inputShape{(int64_t)(input.size() / mInputShapes[0][1]), (int64_t)mInputShapes[0][1]};
194-
std::vector<Ort::Value> inputTensor;
195-
inputTensor.emplace_back(Ort::Value::CreateTensor<O>(pImplOrt->memoryInfo, reinterpret_cast<O*>(input.data()), input.size(), inputShape.data(), inputShape.size()));
196-
// input.clear();
197-
auto outputTensors = (pImplOrt->session)->Run(pImplOrt->runOptions, inputNamesChar.data(), inputTensor.data(), inputTensor.size(), outputNamesChar.data(), outputNamesChar.size());
198-
O* outputValues = reinterpret_cast<O*>(outputTensors[0].template GetTensorMutableData<O>());
199-
std::vector<O> outputValuesVec{outputValues, outputValues + inputShape[0] * mOutputShapes[0][1]};
200-
outputTensors.clear();
201-
return outputValuesVec;
202-
}
203-
204-
template <class I, class O> // class I is the input data type, e.g. float, class O is the output data type, e.g. O2::gpu::OrtDataType::Float16_t from O2/GPU/GPUTracking/ML/convert_float16.h
205-
std::vector<O> OrtModel::inference(std::vector<std::vector<I>>& input)
206-
{
207-
std::vector<Ort::Value> inputTensor;
208-
for (auto i : input) {
209-
std::vector<int64_t> inputShape{(int64_t)(i.size() / mInputShapes[0][1]), (int64_t)mInputShapes[0][1]};
210-
inputTensor.emplace_back(Ort::Value::CreateTensor<O>(pImplOrt->memoryInfo, reinterpret_cast<O*>(i.data()), i.size(), inputShape.data(), inputShape.size()));
211-
}
212-
// input.clear();
213-
auto outputTensors = (pImplOrt->session)->Run(pImplOrt->runOptions, inputNamesChar.data(), inputTensor.data(), inputTensor.size(), outputNamesChar.data(), outputNamesChar.size());
214-
O* outputValues = reinterpret_cast<O*>(outputTensors[0].template GetTensorMutableData<O>());
215-
std::vector<O> outputValuesVec{outputValues, outputValues + inputTensor.size() / mInputShapes[0][1] * mOutputShapes[0][1]};
216-
outputTensors.clear();
217-
return outputValuesVec;
218-
}
219-
220192
std::string OrtModel::printShape(const std::vector<int64_t>& v)
221193
{
222194
std::stringstream ss("");
@@ -227,74 +199,68 @@ std::string OrtModel::printShape(const std::vector<int64_t>& v)
227199
return ss.str();
228200
}
229201

230-
template <>
231-
std::vector<float> OrtModel::inference<float, float>(std::vector<float>& input)
202+
template <class I, class O>
203+
std::vector<O> OrtModel::inference(std::vector<I>& input)
232204
{
233205
std::vector<int64_t> inputShape{(int64_t)(input.size() / mInputShapes[0][1]), (int64_t)mInputShapes[0][1]};
234206
std::vector<Ort::Value> inputTensor;
235-
inputTensor.emplace_back(Ort::Value::CreateTensor<float>(pImplOrt->memoryInfo, input.data(), input.size(), inputShape.data(), inputShape.size()));
207+
if constexpr (std::is_same_v<I, OrtDataType::Float16_t>) {
208+
inputTensor.emplace_back(Ort::Value::CreateTensor<Ort::Float16_t>(pImplOrt->memoryInfo, reinterpret_cast<Ort::Float16_t*>(input.data()), input.size(), inputShape.data(), inputShape.size()));
209+
} else {
210+
inputTensor.emplace_back(Ort::Value::CreateTensor<I>(pImplOrt->memoryInfo, input.data(), input.size(), inputShape.data(), inputShape.size()));
211+
}
236212
// input.clear();
237213
auto outputTensors = (pImplOrt->session)->Run(pImplOrt->runOptions, inputNamesChar.data(), inputTensor.data(), inputTensor.size(), outputNamesChar.data(), outputNamesChar.size());
238-
float* outputValues = outputTensors[0].template GetTensorMutableData<float>();
239-
std::vector<float> outputValuesVec{outputValues, outputValues + inputShape[0] * mOutputShapes[0][1]};
214+
O* outputValues = outputTensors[0].template GetTensorMutableData<O>();
215+
std::vector<O> outputValuesVec{outputValues, outputValues + inputShape[0] * mOutputShapes[0][1]};
240216
outputTensors.clear();
241217
return outputValuesVec;
242218
}
243219

244-
template <>
245-
std::vector<float> OrtModel::inference<OrtDataType::Float16_t, float>(std::vector<OrtDataType::Float16_t>& input)
246-
{
247-
std::vector<int64_t> inputShape{(int64_t)(input.size() / mInputShapes[0][1]), (int64_t)mInputShapes[0][1]};
248-
std::vector<Ort::Value> inputTensor;
249-
inputTensor.emplace_back(Ort::Value::CreateTensor<Ort::Float16_t>(pImplOrt->memoryInfo, reinterpret_cast<Ort::Float16_t*>(input.data()), input.size(), inputShape.data(), inputShape.size()));
250-
// input.clear();
251-
auto outputTensors = (pImplOrt->session)->Run(pImplOrt->runOptions, inputNamesChar.data(), inputTensor.data(), inputTensor.size(), outputNamesChar.data(), outputNamesChar.size());
252-
float* outputValues = outputTensors[0].template GetTensorMutableData<float>();
253-
std::vector<float> outputValuesVec{outputValues, outputValues + inputShape[0] * mOutputShapes[0][1]};
254-
outputTensors.clear();
255-
return outputValuesVec;
256-
}
220+
template std::vector<float> OrtModel::inference<float, float>(std::vector<float>&);
257221

258-
template <>
259-
std::vector<OrtDataType::Float16_t> OrtModel::inference<OrtDataType::Float16_t, OrtDataType::Float16_t>(std::vector<OrtDataType::Float16_t>& input)
260-
{
261-
std::vector<int64_t> inputShape{(int64_t)(input.size() / mInputShapes[0][1]), (int64_t)mInputShapes[0][1]};
262-
std::vector<Ort::Value> inputTensor;
263-
inputTensor.emplace_back(Ort::Value::CreateTensor<Ort::Float16_t>(pImplOrt->memoryInfo, reinterpret_cast<Ort::Float16_t*>(input.data()), input.size(), inputShape.data(), inputShape.size()));
264-
// input.clear();
265-
auto outputTensors = (pImplOrt->session)->Run(pImplOrt->runOptions, inputNamesChar.data(), inputTensor.data(), inputTensor.size(), outputNamesChar.data(), outputNamesChar.size());
266-
OrtDataType::Float16_t* outputValues = reinterpret_cast<OrtDataType::Float16_t*>(outputTensors[0].template GetTensorMutableData<Ort::Float16_t>());
267-
std::vector<OrtDataType::Float16_t> outputValuesVec{outputValues, outputValues + inputShape[0] * mOutputShapes[0][1]};
268-
outputTensors.clear();
269-
return outputValuesVec;
270-
}
222+
template std::vector<float> OrtModel::inference<OrtDataType::Float16_t, float>(std::vector<OrtDataType::Float16_t>&);
271223

272-
template <>
273-
std::vector<OrtDataType::Float16_t> OrtModel::inference<float, OrtDataType::Float16_t>(std::vector<float>& input)
224+
template std::vector<OrtDataType::Float16_t> OrtModel::inference<OrtDataType::Float16_t, OrtDataType::Float16_t>(std::vector<OrtDataType::Float16_t>&);
225+
226+
template <class I, class O>
227+
void OrtModel::inference(I* input, size_t input_size, O* output)
274228
{
275-
std::vector<int64_t> inputShape{(int64_t)(input.size() / mInputShapes[0][1]), (int64_t)mInputShapes[0][1]};
276-
std::vector<Ort::Value> inputTensor;
277-
inputTensor.emplace_back(Ort::Value::CreateTensor<Ort::Float16_t>(pImplOrt->memoryInfo, reinterpret_cast<Ort::Float16_t*>(input.data()), input.size(), inputShape.data(), inputShape.size()));
278-
// input.clear();
279-
auto outputTensors = (pImplOrt->session)->Run(pImplOrt->runOptions, inputNamesChar.data(), inputTensor.data(), inputTensor.size(), outputNamesChar.data(), outputNamesChar.size());
280-
OrtDataType::Float16_t* outputValues = reinterpret_cast<OrtDataType::Float16_t*>(outputTensors[0].template GetTensorMutableData<Ort::Float16_t>());
281-
std::vector<OrtDataType::Float16_t> outputValuesVec{outputValues, outputValues + inputShape[0] * mOutputShapes[0][1]};
282-
outputTensors.clear();
283-
return outputValuesVec;
229+
std::vector<int64_t> inputShape{(int64_t)(input_size / mInputShapes[0][1]), (int64_t)mInputShapes[0][1]};
230+
Ort::Value inputTensor = Ort::Value(nullptr);
231+
if constexpr (std::is_same_v<I, OrtDataType::Float16_t>) {
232+
inputTensor = Ort::Value::CreateTensor<Ort::Float16_t>(pImplOrt->memoryInfo, reinterpret_cast<Ort::Float16_t*>(input), input_size, inputShape.data(), inputShape.size());
233+
} else {
234+
inputTensor = Ort::Value::CreateTensor<I>(pImplOrt->memoryInfo, input, input_size, inputShape.data(), inputShape.size());
235+
}
236+
237+
std::vector<int64_t> outputShape{inputShape[0], mOutputShapes[0][1]};
238+
size_t outputSize = (int64_t)(input_size * mOutputShapes[0][1] / mInputShapes[0][1]);
239+
Ort::Value outputTensor = Ort::Value::CreateTensor<O>(pImplOrt->memoryInfo, output, outputSize, outputShape.data(), outputShape.size());
240+
241+
(pImplOrt->session)->Run(pImplOrt->runOptions, inputNamesChar.data(), &inputTensor, 1, outputNamesChar.data(), &outputTensor, outputNamesChar.size()); // TODO: Not sure if 1 is correct here
284242
}
285243

286-
template <>
287-
std::vector<OrtDataType::Float16_t> OrtModel::inference<OrtDataType::Float16_t, OrtDataType::Float16_t>(std::vector<std::vector<OrtDataType::Float16_t>>& input)
244+
template void OrtModel::inference<OrtDataType::Float16_t, float>(OrtDataType::Float16_t*, size_t, float*);
245+
246+
template void OrtModel::inference<float, float>(float*, size_t, float*);
247+
248+
template <class I, class O>
249+
std::vector<O> OrtModel::inference(std::vector<std::vector<I>>& input)
288250
{
289251
std::vector<Ort::Value> inputTensor;
290252
for (auto i : input) {
291253
std::vector<int64_t> inputShape{(int64_t)(i.size() / mInputShapes[0][1]), (int64_t)mInputShapes[0][1]};
292-
inputTensor.emplace_back(Ort::Value::CreateTensor<Ort::Float16_t>(pImplOrt->memoryInfo, reinterpret_cast<Ort::Float16_t*>(i.data()), i.size(), inputShape.data(), inputShape.size()));
254+
if constexpr (std::is_same_v<I, OrtDataType::Float16_t>) {
255+
inputTensor.emplace_back(Ort::Value::CreateTensor<Ort::Float16_t>(pImplOrt->memoryInfo, reinterpret_cast<Ort::Float16_t*>(i.data()), i.size(), inputShape.data(), inputShape.size()));
256+
} else {
257+
inputTensor.emplace_back(Ort::Value::CreateTensor<I>(pImplOrt->memoryInfo, i.data(), i.size(), inputShape.data(), inputShape.size()));
258+
}
293259
}
294260
// input.clear();
295261
auto outputTensors = (pImplOrt->session)->Run(pImplOrt->runOptions, inputNamesChar.data(), inputTensor.data(), inputTensor.size(), outputNamesChar.data(), outputNamesChar.size());
296-
OrtDataType::Float16_t* outputValues = reinterpret_cast<OrtDataType::Float16_t*>(outputTensors[0].template GetTensorMutableData<Ort::Float16_t>());
297-
std::vector<OrtDataType::Float16_t> outputValuesVec{outputValues, outputValues + inputTensor.size() / mInputShapes[0][1] * mOutputShapes[0][1]};
262+
O* outputValues = reinterpret_cast<O*>(outputTensors[0].template GetTensorMutableData<O>());
263+
std::vector<O> outputValuesVec{outputValues, outputValues + inputTensor.size() / mInputShapes[0][1] * mOutputShapes[0][1]};
298264
outputTensors.clear();
299265
return outputValuesVec;
300266
}

‎Detectors/CTP/workflowScalers/include/CTPWorkflowScalers/RunManager.h‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -47,7 +47,7 @@ class CTPRunManager : public ctpCCDBManager
4747
CTPRunManager() = default;
4848
void init();
4949
int loadRun(const std::string& cfg);
50-
int startRun(const std::string& cfg);
50+
int setRunConfigBK(uint32_t runNumber, const std::string& cfg);
5151
int stopRun(uint32_t irun, long timeStamp);
5252
int addScalers(uint32_t irun, std::time_t time, bool start = 0);
5353
int processMessage(std::string& topic, const std::string& message);

‎Detectors/CTP/workflowScalers/include/CTPWorkflowScalers/ctpCCDBManager.h‎

Lines changed: 7 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -27,12 +27,11 @@ class ctpCCDBManager
2727
int saveRunScalersToCCDB(CTPRunScalers& scalers, long timeStart, long timeStop);
2828
int saveRunScalersToQCDB(CTPRunScalers& scalers, long timeStart, long timeStop);
2929
int saveRunConfigToCCDB(CTPConfiguration* cfg, long timeStart);
30+
int saveSoxOrbit(uint32_t runNumber, uint32_t soxOrbit, long timeStart);
31+
int saveOrbitReset(long timeStamp);
3032
static CTPConfiguration getConfigFromCCDB(long timestamp, std::string run, bool& ok);
3133
static CTPConfiguration getConfigFromCCDB(long timestamp, std::string run);
3234
CTPRunScalers getScalersFromCCDB(long timestamp, std::string, bool& ok);
33-
void setCCDBPathConfig(std::string path) { mCCDBPathCTPConfig = path; };
34-
void setCCDBPathScalers(std::string path) { mCCDBPathCTPScalers = path; };
35-
void setQCDBPathScalers(std::string path) { mQCDBPathCTPScalers = path; };
3635
static void setCCDBHost(std::string host) { mCCDBHost = host; };
3736
static void setQCDBHost(std::string host) { mQCDBHost = host; };
3837

@@ -42,9 +41,11 @@ class ctpCCDBManager
4241
// std::string mQCDBHost = "http://ali-qcdb.cern.ch:8083";
4342
static std::string mCCDBHost;
4443
static std::string mQCDBHost;
45-
std::string mCCDBPathCTPScalers = "CTP/Calib/Scalers";
46-
std::string mCCDBPathCTPConfig = "CTP/Config/Config";
47-
std::string mQCDBPathCTPScalers = "qc/CTP/Scalers";
44+
const std::string mCCDBPathCTPScalers = "CTP/Calib/Scalers";
45+
// std::string mCCDBPathCTPConfig = "CTP/Config/Config"; - in Configuration.h
46+
const std::string mQCDBPathCTPScalers = "qc/CTP/Scalers";
47+
const std::string mCCDBPathSoxOrbit = "CTP/Calib/FirstRunOrbit";
48+
const std::string mCCDBPathOrbitReset = "CTP/Calib/OrbitReset";
4849
ClassDefNV(ctpCCDBManager, 1);
4950
};
5051
} // namespace ctp

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