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ImporterContext.hpp
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ImporterContext.hpp
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/*
* SPDX-License-Identifier: Apache-2.0
*/
#pragma once
#include "onnx2trt.hpp"
#include "onnx2trt_utils.hpp"
#include "onnxErrorRecorder.hpp"
#include "onnx/common/stl_backports.h"
#include <list>
#include <unordered_map>
namespace onnx2trt
{
class ErrorRecorderWrapper
{
public:
ErrorRecorderWrapper(nvinfer1::INetworkDefinition* network, nvinfer1::ILogger* logger)
: mNetwork(network)
, mLogger(logger)
{
if (mNetwork)
{
mUserErrorRecorder = mNetwork->getErrorRecorder();
mOnnxErrorRecorder = ONNXParserErrorRecorder::create(logger, mUserErrorRecorder);
if (mOnnxErrorRecorder)
{
if (mUserErrorRecorder)
{
mUserErrorRecorder->incRefCount();
}
mNetwork->setErrorRecorder(mOnnxErrorRecorder);
}
}
}
~ErrorRecorderWrapper()
{
if (mNetwork && mOnnxErrorRecorder)
{
if (mUserErrorRecorder)
{
mNetwork->setErrorRecorder(mUserErrorRecorder);
mUserErrorRecorder->decRefCount();
}
ONNXParserErrorRecorder::destroy(mOnnxErrorRecorder);
}
}
bool hasError() const
{
return mOnnxErrorRecorder != nullptr && mOnnxErrorRecorder->getNbErrors() != 0;
}
//! Return recorder used by hasError().
nvinfer1::IErrorRecorder* getErrorRecorder() const
{
return mOnnxErrorRecorder ? mOnnxErrorRecorder : nullptr;
}
private:
nvinfer1::INetworkDefinition* mNetwork{nullptr};
nvinfer1::ILogger* mLogger{nullptr};
ONNXParserErrorRecorder* mOnnxErrorRecorder{nullptr};
nvinfer1::IErrorRecorder* mUserErrorRecorder{nullptr};
};
class ImporterContext final : public IImporterContext
{
nvinfer1::INetworkDefinition* mNetwork;
nvinfer1::ILogger* mLogger;
std::list<std::vector<uint8_t>> mTempBufs;
StringMap<nvinfer1::ITensor*> mUserInputs;
StringMap<nvinfer1::ITensor**> mUserOutputs;
StringMap<int64_t> mOpsets;
StringMap<TensorOrWeights> mTensors; // All tensors in the graph mapped to their names.
StringMap<nvinfer1::TensorLocation> mTensorLocations;
StringMap<float> mTensorRangeMins;
StringMap<float> mTensorRangeMaxes;
StringMap<nvinfer1::DataType> mLayerPrecisions;
std::set<std::string> mTensorNames; // Keep track of how many times a tensor name shows up, to avoid duplicate naming in TRT.
std::set<std::string> mLayerNames; // Keep track of how many times a tensor name shows up, to avoid duplicate naming in TRT.
int64_t mSuffixCounter{0}; // increasing suffix counter used to uniquify layer names.
std::unordered_set<std::string> mUnsupportedShapeTensors; // Container to hold output tensor names of layers that produce shape tensor outputs but do not natively support them.
StringMap<std::string> mLoopTensors; // Container to map subgraph tensors to their original outer graph names.
std::string mOnnxFileLocation; // Keep track of the directory of the parsed ONNX file
std::unique_ptr<ErrorRecorderWrapper> mErrorWrapper; // error recorder to control TRT errors
public:
ImporterContext(nvinfer1::INetworkDefinition* network, nvinfer1::ILogger* logger)
: mNetwork(network)
, mLogger(logger)
, mErrorWrapper(ONNX_NAMESPACE::make_unique<ErrorRecorderWrapper>(mNetwork, logger))
{
}
nvinfer1::INetworkDefinition* network() override
{
return mNetwork;
}
StringMap<TensorOrWeights>& tensors() override
{
return mTensors;
}
StringMap<nvinfer1::TensorLocation>& tensorLocations() override
{
return mTensorLocations;
}
StringMap<float>& tensorRangeMins() override
{
return mTensorRangeMins;
}
StringMap<float>& tensorRangeMaxes() override
{
return mTensorRangeMaxes;
}
StringMap<nvinfer1::DataType>& layerPrecisions() override
{
return mLayerPrecisions;
}
std::unordered_set<std::string>& unsupportedShapeTensors() override
{
return mUnsupportedShapeTensors;
}
StringMap<std::string>& loopTensors() override
{
return mLoopTensors;
}
void setOnnxFileLocation(std::string location) override
{
mOnnxFileLocation = location;
}
std::string getOnnxFileLocation() override
{
return mOnnxFileLocation;
}
// This actually handles weights as well, but is named this way to be consistent with the tensors()
void registerTensor(TensorOrWeights tensor, const std::string& basename) override
{
// TRT requires unique tensor names.
const std::string uniqueName = generateUniqueName(mTensorNames, basename);
if (tensor)
{
auto* ctx = this; // To enable logging.
if (tensor.is_tensor())
{
tensor.tensor().setName(uniqueName.c_str());
LOG_VERBOSE("Registering tensor: " << uniqueName << " for ONNX tensor: " << basename);
}
else if (tensor.is_weights())
{
const auto& weights = tensor.weights();
if (tensor.weights().type == ::ONNX_NAMESPACE::TensorProto::INT64)
{
tensor = ShapedWeights{::ONNX_NAMESPACE::TensorProto::INT32,
convertINT64(reinterpret_cast<int64_t*>(weights.values), weights.shape, ctx), weights.shape};
}
tensor.weights().setName(basename.c_str());
}
}
// Overwrite previous tensors registered with the same name (this only happens when there are subgraphs,
// and in that case, overwriting is the desired behavior).
this->tensors()[basename] = std::move(tensor);
}
void registerLayer(nvinfer1::ILayer* layer, const std::string& basename) override
{
// No layer will be added for Constant nodes in ONNX.
if (layer)
{
const std::string name = basename.empty() ? layer->getName() : basename;
const std::string uniqueName = generateUniqueName(mLayerNames, name);
auto* ctx = this; // To enable logging.
LOG_VERBOSE("Registering layer: " << uniqueName << " for ONNX node: " << basename);
layer->setName(uniqueName.c_str());
}
}
nvinfer1::ILogger& logger() override
{
return *mLogger;
}
ShapedWeights createTempWeights(ShapedWeights::DataType type, nvinfer1::Dims shape, uint8_t value = 0) override
{
ShapedWeights weights(type, nullptr, shape);
// Need special logic for handling scalars.
if (shape.nbDims == 0)
{
mTempBufs.push_back(std::vector<uint8_t>(getDtypeSize(type), value));
}
else
{
mTempBufs.push_back(std::vector<uint8_t>(weights.size_bytes(), value));
}
weights.values = mTempBufs.back().data();
return weights;
}
bool setUserInput(const char* name, nvinfer1::ITensor* input)
{
mUserInputs[name] = input;
return true;
}
bool setUserOutput(const char* name, nvinfer1::ITensor** output)
{
mUserOutputs[name] = output;
return true;
}
nvinfer1::ITensor* getUserInput(const char* name)
{
if (!mUserInputs.count(name))
{
return nullptr;
}
else
{
return mUserInputs.at(name);
}
}
nvinfer1::ITensor** getUserOutput(const char* name)
{
if (!mUserOutputs.count(name))
{
return nullptr;
}
else
{
return mUserOutputs.at(name);
}
}
StringMap<nvinfer1::ITensor**> const& getUserOutputs() const
{
return mUserOutputs;
}
void clearOpsets()
{
mOpsets.clear();
}
void addOpset(std::string domain, int64_t version)
{
mOpsets.emplace(domain, version);
}
int64_t getOpsetVersion(const char* domain = "") const override
{
if (mOpsets.empty())
{
return 1;
}
else if (mOpsets.size() == 1)
{
return mOpsets.begin()->second;
}
else
{
assert(mOpsets.count(domain));
return mOpsets.at(domain);
}
}
bool hasError() const noexcept override
{
return mErrorWrapper != nullptr && mErrorWrapper->hasError();
}
nvinfer1::IErrorRecorder* getErrorRecorder() const noexcept override
{
return mErrorWrapper ? mErrorWrapper->getErrorRecorder() : nullptr;
}
private:
std::string generateUniqueName(std::set<std::string>& namesSet, const std::string& basename)
{
std::string candidate = basename;
while (namesSet.find(candidate) != namesSet.end())
{
candidate = basename + "_" + std::to_string(mSuffixCounter);
++mSuffixCounter;
}
namesSet.insert(candidate);
return candidate;
}
};
} // namespace onnx2trt