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@nprouvost
nprouvost committed Jan 25, 2024
1 parent 53c25d5 commit 4a85e12
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123 changes: 62 additions & 61 deletions cmssw/MLProf/ONNXRuntimeModule/plugins/ONNXPluginRuntime.cpp
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Original file line number Diff line number Diff line change
@@ -1,38 +1,39 @@
/*
* Example plugin to demonstrate the direct multi-threaded inference with ONNX Runtime.
* Example plugin to demonstrate the direct multi-threaded inference with ONNX
* Runtime.
*/

#include <chrono>
#include <fstream>
#include <iostream>
#include <list>
#include <memory>
#include <random>
#include <stdexcept>
#include <iostream>

#include "FWCore/Framework/interface/Event.h"
#include "FWCore/Framework/interface/Frameworkfwd.h"
#include "FWCore/Framework/interface/MakerMacros.h"
#include "FWCore/Framework/interface/stream/EDAnalyzer.h"
#include "FWCore/ParameterSet/interface/ParameterSet.h"

#include "PhysicsTools/ONNXRuntime/interface/ONNXRuntime.h"

#include "MLProf/Utils/interface/utils.h"
#include "PhysicsTools/ONNXRuntime/interface/ONNXRuntime.h"

using namespace cms::Ort;

class ONNXRuntimePlugin : public edm::stream::EDAnalyzer<edm::GlobalCache<ONNXRuntime>> {
public:
class ONNXRuntimePlugin
: public edm::stream::EDAnalyzer<edm::GlobalCache<ONNXRuntime>> {
public:
explicit ONNXRuntimePlugin(const edm::ParameterSet &, const ONNXRuntime *);
static void fillDescriptions(edm::ConfigurationDescriptions&);
static void fillDescriptions(edm::ConfigurationDescriptions &);

static std::unique_ptr<ONNXRuntime> initializeGlobalCache(const edm::ParameterSet &);
static std::unique_ptr<ONNXRuntime> initializeGlobalCache(
const edm::ParameterSet &);
static void globalEndJob(const ONNXRuntime *);

private:
private:
void beginJob();
void analyze(const edm::Event&, const edm::EventSetup&);
void analyze(const edm::Event &, const edm::EventSetup &);
void endJob();

inline float drawNormal() { return normalPdf_(rndGen_); }
Expand All @@ -56,12 +57,13 @@ class ONNXRuntimePlugin : public edm::stream::EDAnalyzer<edm::GlobalCache<ONNXRu
std::normal_distribution<float> normalPdf_;

std::vector<std::vector<int64_t>> input_shapes_;
FloatArrays data_; // each stream hosts its own data
FloatArrays inputArrays_; // each stream hosts its own data
};


void ONNXRuntimePlugin::fillDescriptions(edm::ConfigurationDescriptions& descriptions) {
// defining this function will lead to a *_cfi file being generated when compiling
void ONNXRuntimePlugin::fillDescriptions(
edm::ConfigurationDescriptions &descriptions) {
// defining this function will lead to a *_cfi file being generated when
// compiling
edm::ParameterSetDescription desc;
// the path to the file containing the graph
desc.add<std::string>("graphPath");
Expand All @@ -76,22 +78,23 @@ void ONNXRuntimePlugin::fillDescriptions(edm::ConfigurationDescriptions& descrip
// the rank (number of dimensions) of each input tensor
desc.add<std::vector<int>>("inputRanks");
// flat list of sizes of each dimension of each input tensor
// (for a graph with a 1D and a 2D input tensor, this would be a vector of three values)
// (for a graph with a 1D and a 2D input tensor, this would be a vector of
// three values)
desc.add<std::vector<int>>("flatInputSizes");
// batch sizes to test
desc.add<int>("batchSize");
// the number of calls to the graph to measure the runtime
desc.add<int>("nCalls");

// desc.add<edm::FileInPath>("model_path", edm::FileInPath("MLProf/ONNXRuntimeModule/data/model.onnx"));
// desc.add<std::vector<std::string>>("input_names", std::vector<std::string>({"my_input"}));
descriptions.addWithDefaultLabel(desc);
}


ONNXRuntimePlugin::ONNXRuntimePlugin(const edm::ParameterSet &iConfig, const ONNXRuntime *cache)
: inputTensorNames_(iConfig.getParameter<std::vector<std::string>>("inputTensorNames")),
outputTensorNames_(iConfig.getParameter<std::vector<std::string>>("outputTensorNames")),
ONNXRuntimePlugin::ONNXRuntimePlugin(const edm::ParameterSet &iConfig,
const ONNXRuntime *cache)
: inputTensorNames_(
iConfig.getParameter<std::vector<std::string>>("inputTensorNames")),
outputTensorNames_(
iConfig.getParameter<std::vector<std::string>>("outputTensorNames")),
outputFile_(iConfig.getParameter<std::string>("outputFile")),
inputTypeStr_(iConfig.getParameter<std::string>("inputType")),
inputRanks_(iConfig.getParameter<std::vector<int>>("inputRanks")),
Expand All @@ -101,32 +104,37 @@ ONNXRuntimePlugin::ONNXRuntimePlugin(const edm::ParameterSet &iConfig, const ONN
nInputs_(inputTensorNames_.size()),
nPreCalls_(10),
rndGen_(rnd_()),
normalPdf_(0.0, 1.0)
{
normalPdf_(0.0, 1.0) {
// the number of input ranks must match the number of input tensors
if ((int)inputRanks_.size() != nInputs_) {
throw cms::Exception("InvalidInputRanks") << "number of input ranks must match number of input tensors";
throw cms::Exception("InvalidInputRanks")
<< "number of input ranks must match number of input tensors";
}
// the input must be at least 1 dimensional
for (auto rank : inputRanks_) {
if (rank < 1) {
throw cms::Exception("InvalidRank") << "only ranks above 0 are supported, got " << rank;
throw cms::Exception("InvalidRank")
<< "only ranks above 0 are supported, got " << rank;
}
}
// the sum of ranks must match the number of flat input sizes
if (std::accumulate(inputRanks_.begin(), inputRanks_.end(), 0) != (int)flatInputSizes_.size()) {
if (std::accumulate(inputRanks_.begin(), inputRanks_.end(), 0) !=
(int)flatInputSizes_.size()) {
throw cms::Exception("InvalidFlatInputSizes")
<< "sum of input ranks must match number of flat input sizes, got " << flatInputSizes_.size();
<< "sum of input ranks must match number of flat input sizes, got "
<< flatInputSizes_.size();
}
// batch size must be positive
if (batchSize_ < 1) {
throw cms::Exception("InvalidBatchSize") << "batch sizes must be positive, got " << batchSize_;
throw cms::Exception("InvalidBatchSize")
<< "batch sizes must be positive, got " << batchSize_;
}

// input sizes must be positive
for (auto size : flatInputSizes_) {
if (size < 1) {
throw cms::Exception("InvalidInputSize") << "input sizes must be positive, got " << size;
throw cms::Exception("InvalidInputSize")
<< "input sizes must be positive, got " << size;
}
}
// check the input type
Expand All @@ -138,39 +146,47 @@ ONNXRuntimePlugin::ONNXRuntimePlugin(const edm::ParameterSet &iConfig, const ONN
inputType_ = mlprof::InputType::Zeros;
} else {
throw cms::Exception("InvalidInputType")
<< "input type must be either 'incremental', 'zeros' or 'random', got " << inputTypeStr_;
<< "input type must be either 'incremental', 'zeros' or 'random', got "
<< inputTypeStr_;
}

// initialize the input_shapes array with inputRanks_ and flatInputSizes_
int i = 0;
for (auto rank : inputRanks_) {
std::vector<int64_t> input_shape(flatInputSizes_.begin() + i, flatInputSizes_.begin() + i + rank);
std::vector<int64_t> input_shape(flatInputSizes_.begin() + i,
flatInputSizes_.begin() + i + rank);
input_shape.insert(input_shape.begin(), batchSize_);
input_shapes_.push_back(input_shape);
i += rank;
}
// initialize the input data arrays
// note there is only one element in the FloatArrays type (i.e. vector<vector<float>>) variable
// note there is only one element in the FloatArrays type (i.e.
// vector<vector<float>>) variable
for (int i = 0; i < nInputs_; i++) {
data_.emplace_back(flatInputSizes_[i] * batchSize_, 0);
inputArrays_.emplace_back(batchSize_ * flatInputSizes_[i], 0);
}
}


std::unique_ptr<ONNXRuntime> ONNXRuntimePlugin::initializeGlobalCache(const edm::ParameterSet &iConfig) {
return std::make_unique<ONNXRuntime>(edm::FileInPath(iConfig.getParameter<std::string>("graphPath")).fullPath());
std::unique_ptr<ONNXRuntime> ONNXRuntimePlugin::initializeGlobalCache(
const edm::ParameterSet &iConfig) {
return std::make_unique<ONNXRuntime>(
edm::FileInPath(iConfig.getParameter<std::string>("graphPath"))
.fullPath());
}

void ONNXRuntimePlugin::globalEndJob(const ONNXRuntime *cache) {}

void ONNXRuntimePlugin::analyze(const edm::Event &iEvent, const edm::EventSetup &iSetup) {
void ONNXRuntimePlugin::analyze(const edm::Event &iEvent,
const edm::EventSetup &iSetup) {
for (int i = 0; i < nInputs_; i++) {
std::vector<float> &group_data = data_[i];
std::vector<float> &group_data = inputArrays_[i];
// fill the input
for (int i = 0; i < (int)group_data.size(); i++) {
group_data[i] = inputType_ == mlprof::InputType::Incremental ? float(i) :
inputType_ == mlprof::InputType::Zeros ? float(0) :
drawNormal();
group_data[i] =
inputType_ == mlprof::InputType::Incremental
? float(i)
: (inputType_ == mlprof::InputType::Zeros ? float(0)
: drawNormal());
}
}

Expand All @@ -179,38 +195,23 @@ void ONNXRuntimePlugin::analyze(const edm::Event &iEvent, const edm::EventSetup

// pre calls to "warm up"
for (int r = 0; r < nPreCalls_; r++) {
outputs = globalCache()->run(inputTensorNames_, data_, input_shapes_, outputTensorNames_, batchSize_);
// std::cout << "nprerun" << r << std::endl;
outputs = globalCache()->run(inputTensorNames_, inputArrays_, input_shapes_,
outputTensorNames_, batchSize_);
}

// actual calls to measure runtimes
std::vector<float> runtimes;
for (int r = 0; r < nCalls_; r++) {
auto start = std::chrono::high_resolution_clock::now();
outputs = globalCache()->run(inputTensorNames_, data_, input_shapes_, outputTensorNames_, batchSize_);
outputs = globalCache()->run(inputTensorNames_, inputArrays_, input_shapes_,
outputTensorNames_, batchSize_);
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration<float> runtime_in_seconds = (end - start);
// std::cout << "nrun" << r << std::endl;
// std::cout << "runtime in seconds" << runtime_in_seconds.count() << std::endl;
runtimes.push_back(runtime_in_seconds.count() * 1000);
}

// // print the input and output data
// std::cout << "input data -> ";
// for ( const auto &input_tensor : data_ ){
// for ( const auto &value : input_tensor ) std::cout << value << ' ';
// std::cout << std::endl;
// }
// std::cout << std::endl << "output data -> ";
// for (auto &output_tensor: outputs) {
// for ( const auto &value : output_tensor ) std::cout << value << ' ';
// std::cout << std::endl;
// }
// std::cout << std::endl;

// save them
mlprof::writeRuntimes(outputFile_, batchSize_, runtimes);
}


DEFINE_FWK_MODULE(ONNXRuntimePlugin);
4 changes: 2 additions & 2 deletions mlprof/tasks/parameters.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -185,8 +185,8 @@ def store_parts(self):
# )
# model_labels = luigi.Parameter(
# default=law.NO_STR,
# description="when set, use this label in plots; when empty, the 'network_name' field in the model json data is "
# "used when existing, and full_model_name otherwise; default: empty",
# description="when set, use this label in plots; when empty, the 'network_name' field in the "
# "model json data is used when existing, and full_model_name otherwise; default: empty",
# )

# def __init__(self, *args, **kwargs):
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