added MLP with proper documentation (#874)
* [MLP]: create using core TensortRT python APIs * [MLP]: README.md added with chart * [MLP]: C++ TensorRT APIs added * [MLP]: Updated Docs * [MLP]: basic .wts added with APIs * [MLP]: Updated minor details * [MLP]: fix
This commit is contained in:
Subtain Malik
GitHub
parent
e6b22917e6
commit
f2ac76df5a
@ -53,6 +53,7 @@ Following models are implemented.
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|Name | Description |
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|-|-|
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|[mlp](./mlp) | the very basic model for starters, properly documented |
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|[lenet](./lenet) | the simplest, as a "hello world" of this project |
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|[alexnet](./alexnet)| easy to implement, all layers are supported in tensorrt |
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|[googlenet](./googlenet)| GoogLeNet (Inception v1) |
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24
mlp/CMakeLists.txt
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24
mlp/CMakeLists.txt
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@ -0,0 +1,24 @@
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cmake_minimum_required(VERSION 3.14) # change the version, if asked by compiler
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project(mlp)
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set(CMAKE_CXX_STANDARD 14)
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# include and link dirs of tensorrt, you need adapt them if yours are different
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include_directories(/usr/include/x86_64-linux-gnu/)
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link_directories(/usr/lib/x86_64-linux-gnu/)
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# include and link dirs of cuda for inference
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include_directories(/usr/local/cuda/include)
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link_directories(/usr/local/cuda/lib64)
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# create link for executable files
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add_executable(mlp mlp.cpp)
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# perform linking with nvinfer libraries
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target_link_libraries(mlp nvinfer)
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# link with cuda libraries for Inference
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target_link_libraries(mlp cudart)
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add_definitions(-O2 -pthread)
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57
mlp/README.md
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57
mlp/README.md
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@ -0,0 +1,57 @@
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# MLP
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MLP is the most basic net in this tensorrtx project for starters. You can learn the basic procedures of building
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TensorRT app from the provided APIs. The process of building a TensorRT engine explained in the chart below.
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## Helper Files
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`logging.h` : A logger file for using NVIDIA TRT API (mostly same for all models)
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`mlp.wts` : Converted weight file (simple file, you can open and check it)
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## TensorRT C++ API
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```
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// 1. generate mlp.wts from https://github.com/wang-xinyu/pytorchx/tree/master/mlp -- or use the given .wts file
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// 2. put mlp.wts into tensorrtx/mlp (if using the generated weights)
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// 3. build and run
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cd tensorrtx/mlp
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mkdir build
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cd build
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cmake ..
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make
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sudo ./mlp -s // serialize model to plan file i.e. 'mlp.engine'
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sudo ./mlp -d // deserialize plan file and run inference
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```
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## TensorRT Python API
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```
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# 1. Generate mlp.wts from https://github.com/wang-xinyu/pytorchx/tree/master/mlp -- or use the given .wts file
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# 2. Put mlp.wts into tensorrtx/mlp (if using the generated weights)
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# 3. Install Python dependencies (tensorrt/pycuda/numpy)
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# 4. Run
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cd tensorrtx/mlp
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python mlp.py -s # serialize model to plan file, i.e. 'mlp.engine'
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python mlp.py -d # deserialize plan file and run inference
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```
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## Note
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It also supports the latest CUDA-11.4 and TensorRT-8.2.x
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503
mlp/logging.h
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503
mlp/logging.h
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@ -0,0 +1,503 @@
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/*
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* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef TENSORRT_LOGGING_H
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#define TENSORRT_LOGGING_H
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#include "NvInferRuntimeCommon.h"
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#include <cassert>
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#include <ctime>
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#include <iomanip>
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#include <iostream>
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#include <ostream>
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#include <sstream>
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#include <string>
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using Severity = nvinfer1::ILogger::Severity;
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class LogStreamConsumerBuffer : public std::stringbuf
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{
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public:
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LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
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: mOutput(stream)
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, mPrefix(prefix)
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, mShouldLog(shouldLog)
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{
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}
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LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
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: mOutput(other.mOutput)
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{
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}
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~LogStreamConsumerBuffer()
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{
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// std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
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// std::streambuf::pptr() gives a pointer to the current position of the output sequence
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// if the pointer to the beginning is not equal to the pointer to the current position,
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// call putOutput() to log the output to the stream
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if (pbase() != pptr())
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{
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putOutput();
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}
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}
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// synchronizes the stream buffer and returns 0 on success
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// synchronizing the stream buffer consists of inserting the buffer contents into the stream,
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// resetting the buffer and flushing the stream
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virtual int sync()
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{
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putOutput();
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return 0;
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}
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void putOutput()
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{
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if (mShouldLog)
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{
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// prepend timestamp
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std::time_t timestamp = std::time(nullptr);
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tm* tm_local = std::localtime(×tamp);
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std::cout << "[";
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std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/";
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std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
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std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
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std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
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std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
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std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
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// std::stringbuf::str() gets the string contents of the buffer
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// insert the buffer contents pre-appended by the appropriate prefix into the stream
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mOutput << mPrefix << str();
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// set the buffer to empty
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str("");
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// flush the stream
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mOutput.flush();
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}
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}
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void setShouldLog(bool shouldLog)
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{
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mShouldLog = shouldLog;
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}
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private:
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std::ostream& mOutput;
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std::string mPrefix;
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bool mShouldLog;
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};
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//!
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//! \class LogStreamConsumerBase
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//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
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//!
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class LogStreamConsumerBase
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{
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public:
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LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
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: mBuffer(stream, prefix, shouldLog)
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{
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}
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protected:
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LogStreamConsumerBuffer mBuffer;
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};
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//!
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//! \class LogStreamConsumer
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//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
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//! Order of base classes is LogStreamConsumerBase and then std::ostream.
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//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
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//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
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//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
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//! Please do not change the order of the parent classes.
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//!
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class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
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{
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public:
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//! \brief Creates a LogStreamConsumer which logs messages with level severity.
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//! Reportable severity determines if the messages are severe enough to be logged.
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LogStreamConsumer(Severity reportableSeverity, Severity severity)
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: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
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, std::ostream(&mBuffer) // links the stream buffer with the stream
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, mShouldLog(severity <= reportableSeverity)
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, mSeverity(severity)
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{
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}
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LogStreamConsumer(LogStreamConsumer&& other)
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: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
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, std::ostream(&mBuffer) // links the stream buffer with the stream
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, mShouldLog(other.mShouldLog)
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, mSeverity(other.mSeverity)
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{
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}
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void setReportableSeverity(Severity reportableSeverity)
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{
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mShouldLog = mSeverity <= reportableSeverity;
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mBuffer.setShouldLog(mShouldLog);
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}
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private:
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static std::ostream& severityOstream(Severity severity)
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{
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return severity >= Severity::kINFO ? std::cout : std::cerr;
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}
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static std::string severityPrefix(Severity severity)
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{
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switch (severity)
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{
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case Severity::kINTERNAL_ERROR: return "[F] ";
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case Severity::kERROR: return "[E] ";
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case Severity::kWARNING: return "[W] ";
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case Severity::kINFO: return "[I] ";
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case Severity::kVERBOSE: return "[V] ";
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default: assert(0); return "";
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}
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}
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bool mShouldLog;
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Severity mSeverity;
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};
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//! \class Logger
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//!
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//! \brief Class which manages logging of TensorRT tools and samples
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//!
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//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
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//! and supports logging two types of messages:
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//!
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//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
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//! - Test pass/fail messages
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//!
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//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
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//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
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//!
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//! In the future, this class could be extended to support dumping test results to a file in some standard format
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//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
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//!
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//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
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//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
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//! library and messages coming from the sample.
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//!
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//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
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//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
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//! object.
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class Logger : public nvinfer1::ILogger
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{
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public:
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Logger(Severity severity = Severity::kWARNING)
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: mReportableSeverity(severity)
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{
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}
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//!
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//! \enum TestResult
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//! \brief Represents the state of a given test
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//!
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enum class TestResult
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{
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kRUNNING, //!< The test is running
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kPASSED, //!< The test passed
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kFAILED, //!< The test failed
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kWAIVED //!< The test was waived
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};
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//!
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//! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
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//! \return The nvinfer1::ILogger associated with this Logger
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//!
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//! TODO Once all samples are updated to use this method to register the logger with TensorRT,
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//! we can eliminate the inheritance of Logger from ILogger
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//!
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nvinfer1::ILogger& getTRTLogger()
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{
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return *this;
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}
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//!
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//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
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//!
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//! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
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//! inheritance from nvinfer1::ILogger
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//!
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void log(Severity severity, const char* msg) noexcept override
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{
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LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
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}
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//!
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//! \brief Method for controlling the verbosity of logging output
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//!
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//! \param severity The logger will only emit messages that have severity of this level or higher.
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//!
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void setReportableSeverity(Severity severity)
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{
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mReportableSeverity = severity;
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}
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//!
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//! \brief Opaque handle that holds logging information for a particular test
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//!
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//! This object is an opaque handle to information used by the Logger to print test results.
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//! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
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//! with Logger::reportTest{Start,End}().
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//!
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class TestAtom
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{
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public:
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TestAtom(TestAtom&&) = default;
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private:
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friend class Logger;
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TestAtom(bool started, const std::string& name, const std::string& cmdline)
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: mStarted(started)
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, mName(name)
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, mCmdline(cmdline)
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{
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}
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bool mStarted;
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std::string mName;
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std::string mCmdline;
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};
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//!
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//! \brief Define a test for logging
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//!
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//! \param[in] name The name of the test. This should be a string starting with
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//! "TensorRT" and containing dot-separated strings containing
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//! the characters [A-Za-z0-9_].
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//! For example, "TensorRT.sample_googlenet"
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//! \param[in] cmdline The command line used to reproduce the test
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//
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//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
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//!
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static TestAtom defineTest(const std::string& name, const std::string& cmdline)
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{
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return TestAtom(false, name, cmdline);
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}
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//!
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//! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
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//! as input
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//!
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//! \param[in] name The name of the test
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//! \param[in] argc The number of command-line arguments
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//! \param[in] argv The array of command-line arguments (given as C strings)
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//!
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//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
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static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
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{
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auto cmdline = genCmdlineString(argc, argv);
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return defineTest(name, cmdline);
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}
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//!
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//! \brief Report that a test has started.
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//!
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//! \pre reportTestStart() has not been called yet for the given testAtom
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//!
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//! \param[in] testAtom The handle to the test that has started
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//!
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static void reportTestStart(TestAtom& testAtom)
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{
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reportTestResult(testAtom, TestResult::kRUNNING);
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assert(!testAtom.mStarted);
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testAtom.mStarted = true;
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}
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//!
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//! \brief Report that a test has ended.
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//!
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//! \pre reportTestStart() has been called for the given testAtom
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//!
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//! \param[in] testAtom The handle to the test that has ended
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//! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
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//! TestResult::kFAILED, TestResult::kWAIVED
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//!
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static void reportTestEnd(const TestAtom& testAtom, TestResult result)
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{
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assert(result != TestResult::kRUNNING);
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assert(testAtom.mStarted);
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reportTestResult(testAtom, result);
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}
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static int reportPass(const TestAtom& testAtom)
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{
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reportTestEnd(testAtom, TestResult::kPASSED);
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return EXIT_SUCCESS;
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}
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static int reportFail(const TestAtom& testAtom)
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{
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reportTestEnd(testAtom, TestResult::kFAILED);
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return EXIT_FAILURE;
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}
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static int reportWaive(const TestAtom& testAtom)
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{
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reportTestEnd(testAtom, TestResult::kWAIVED);
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return EXIT_SUCCESS;
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}
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static int reportTest(const TestAtom& testAtom, bool pass)
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{
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return pass ? reportPass(testAtom) : reportFail(testAtom);
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}
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Severity getReportableSeverity() const
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{
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return mReportableSeverity;
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}
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private:
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//!
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//! \brief returns an appropriate string for prefixing a log message with the given severity
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//!
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static const char* severityPrefix(Severity severity)
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{
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switch (severity)
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{
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case Severity::kINTERNAL_ERROR: return "[F] ";
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case Severity::kERROR: return "[E] ";
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case Severity::kWARNING: return "[W] ";
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case Severity::kINFO: return "[I] ";
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case Severity::kVERBOSE: return "[V] ";
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default: assert(0); return "";
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}
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}
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//!
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//! \brief returns an appropriate string for prefixing a test result message with the given result
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||||
//!
|
||||
static const char* testResultString(TestResult result)
|
||||
{
|
||||
switch (result)
|
||||
{
|
||||
case TestResult::kRUNNING: return "RUNNING";
|
||||
case TestResult::kPASSED: return "PASSED";
|
||||
case TestResult::kFAILED: return "FAILED";
|
||||
case TestResult::kWAIVED: return "WAIVED";
|
||||
default: assert(0); return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
|
||||
//!
|
||||
static std::ostream& severityOstream(Severity severity)
|
||||
{
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief method that implements logging test results
|
||||
//!
|
||||
static void reportTestResult(const TestAtom& testAtom, TestResult result)
|
||||
{
|
||||
severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
|
||||
<< testAtom.mCmdline << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief generate a command line string from the given (argc, argv) values
|
||||
//!
|
||||
static std::string genCmdlineString(int argc, char const* const* argv)
|
||||
{
|
||||
std::stringstream ss;
|
||||
for (int i = 0; i < argc; i++)
|
||||
{
|
||||
if (i > 0)
|
||||
ss << " ";
|
||||
ss << argv[i];
|
||||
}
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
Severity mReportableSeverity;
|
||||
};
|
||||
|
||||
namespace
|
||||
{
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_INFO(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_INFO(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_WARN(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_WARN(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_ERROR(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_ERROR(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
|
||||
// ("fatal" severity)
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_FATAL(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_FATAL(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
#endif // TENSORRT_LOGGING_H
|
||||
323
mlp/mlp.cpp
Normal file
323
mlp/mlp.cpp
Normal file
@ -0,0 +1,323 @@
|
||||
#include "NvInfer.h" // TensorRT library
|
||||
#include "iostream" // Standard input/output library
|
||||
#include "logging.h" // logging file -- by NVIDIA
|
||||
#include <map> // for weight maps
|
||||
#include <fstream> // for file-handling
|
||||
#include <chrono> // for timing the execution
|
||||
|
||||
// provided by nvidia for using TensorRT APIs
|
||||
using namespace nvinfer1;
|
||||
|
||||
// Logger from TRT API
|
||||
static Logger gLogger;
|
||||
|
||||
const int INPUT_SIZE = 1;
|
||||
const int OUTPUT_SIZE = 1;
|
||||
|
||||
/** ////////////////////////////
|
||||
// DEPLOYMENT RELATED /////////
|
||||
////////////////////////////*/
|
||||
std::map<std::string, Weights> loadWeights(const std::string file) {
|
||||
/**
|
||||
* Parse the .wts file and store weights in dict format.
|
||||
*
|
||||
* @param file path to .wts file
|
||||
* @return weight_map: dictionary containing weights and their values
|
||||
*/
|
||||
|
||||
std::cout << "[INFO]: Loading weights..." << file << std::endl;
|
||||
std::map<std::string, Weights> weightMap;
|
||||
|
||||
// Open Weight file
|
||||
std::ifstream input(file);
|
||||
assert(input.is_open() && "[ERROR]: Unable to load weight file...");
|
||||
|
||||
// Read number of weights
|
||||
int32_t count;
|
||||
input >> count;
|
||||
assert(count > 0 && "Invalid weight map file.");
|
||||
|
||||
// Loop through number of line, actually the number of weights & biases
|
||||
while (count--) {
|
||||
// TensorRT weights
|
||||
Weights wt{DataType::kFLOAT, nullptr, 0};
|
||||
uint32_t size;
|
||||
// Read name and type of weights
|
||||
std::string w_name;
|
||||
input >> w_name >> std::dec >> size;
|
||||
wt.type = DataType::kFLOAT;
|
||||
|
||||
uint32_t *val = reinterpret_cast<uint32_t *>(malloc(sizeof(val) * size));
|
||||
for (uint32_t x = 0, y = size; x < y; ++x) {
|
||||
// Change hex values to uint32 (for higher values)
|
||||
input >> std::hex >> val[x];
|
||||
}
|
||||
wt.values = val;
|
||||
wt.count = size;
|
||||
|
||||
// Add weight values against its name (key)
|
||||
weightMap[w_name] = wt;
|
||||
}
|
||||
return weightMap;
|
||||
}
|
||||
|
||||
ICudaEngine *createMLPEngine(unsigned int maxBatchSize, IBuilder *builder, IBuilderConfig *config, DataType dt) {
|
||||
/**
|
||||
* Create Multi-Layer Perceptron using the TRT Builder and Configurations
|
||||
*
|
||||
* @param maxBatchSize: batch size for built TRT model
|
||||
* @param builder: to build engine and networks
|
||||
* @param config: configuration related to Hardware
|
||||
* @param dt: datatype for model layers
|
||||
* @return engine: TRT model
|
||||
*/
|
||||
|
||||
std::cout << "[INFO]: Creating MLP using TensorRT..." << std::endl;
|
||||
|
||||
// Load Weights from relevant file
|
||||
std::map<std::string, Weights> weightMap = loadWeights("../mlp.wts");
|
||||
|
||||
// Create an empty network
|
||||
INetworkDefinition *network = builder->createNetworkV2(0U);
|
||||
|
||||
// Create an input with proper *name
|
||||
ITensor *data = network->addInput("data", DataType::kFLOAT, Dims3{1, 1, 1});
|
||||
assert(data);
|
||||
|
||||
// Add layer for MLP
|
||||
IFullyConnectedLayer *fc1 = network->addFullyConnected(*data, 1,
|
||||
weightMap["linear.weight"],
|
||||
weightMap["linear.bias"]);
|
||||
assert(fc1);
|
||||
|
||||
// set output with *name
|
||||
fc1->getOutput(0)->setName("out");
|
||||
|
||||
// mark the output
|
||||
network->markOutput(*fc1->getOutput(0));
|
||||
|
||||
// Set configurations
|
||||
builder->setMaxBatchSize(1);
|
||||
// Set workspace size
|
||||
config->setMaxWorkspaceSize(1 << 20);
|
||||
|
||||
// Build CUDA Engine using network and configurations
|
||||
ICudaEngine *engine = builder->buildEngineWithConfig(*network, *config);
|
||||
assert(engine != nullptr);
|
||||
|
||||
// Don't need the network any more
|
||||
// free captured memory
|
||||
network->destroy();
|
||||
|
||||
// Release host memory
|
||||
for (auto &mem: weightMap) {
|
||||
free((void *) (mem.second.values));
|
||||
}
|
||||
|
||||
return engine;
|
||||
}
|
||||
|
||||
void APIToModel(unsigned int maxBatchSize, IHostMemory **modelStream) {
|
||||
/**
|
||||
* Create engine using TensorRT APIs
|
||||
*
|
||||
* @param maxBatchSize: for the deployed model configs
|
||||
* @param modelStream: shared memory to store serialized model
|
||||
*/
|
||||
|
||||
// Create builder with the help of logger
|
||||
IBuilder *builder = createInferBuilder(gLogger);
|
||||
|
||||
// Create hardware configs
|
||||
IBuilderConfig *config = builder->createBuilderConfig();
|
||||
|
||||
// Build an engine
|
||||
ICudaEngine *engine = createMLPEngine(maxBatchSize, builder, config, DataType::kFLOAT);
|
||||
assert(engine != nullptr);
|
||||
|
||||
// serialize the engine into binary stream
|
||||
(*modelStream) = engine->serialize();
|
||||
|
||||
// free up the memory
|
||||
engine->destroy();
|
||||
builder->destroy();
|
||||
}
|
||||
|
||||
void performSerialization() {
|
||||
/**
|
||||
* Serialization Function
|
||||
*/
|
||||
// Shared memory object
|
||||
IHostMemory *modelStream{nullptr};
|
||||
|
||||
// Write model into stream
|
||||
APIToModel(1, &modelStream);
|
||||
assert(modelStream != nullptr);
|
||||
|
||||
|
||||
std::cout << "[INFO]: Writing engine into binary..." << std::endl;
|
||||
|
||||
// Open the file and write the contents there in binary format
|
||||
std::ofstream p("../mlp.engine", std::ios::binary);
|
||||
if (!p) {
|
||||
std::cerr << "could not open plan output file" << std::endl;
|
||||
return;
|
||||
}
|
||||
p.write(reinterpret_cast<const char *>(modelStream->data()), modelStream->size());
|
||||
|
||||
// Release the memory
|
||||
modelStream->destroy();
|
||||
|
||||
std::cout << "[INFO]: Successfully created TensorRT engine..." << std::endl;
|
||||
std::cout << "\n\tRun inference using `./mlp -d`" << std::endl;
|
||||
|
||||
}
|
||||
|
||||
/** ////////////////////////////
|
||||
// INFERENCE RELATED //////////
|
||||
////////////////////////////*/
|
||||
void doInference(IExecutionContext &context, float *input, float *output, int batchSize) {
|
||||
/**
|
||||
* Perform inference using the CUDA context
|
||||
*
|
||||
* @param context: context created by engine
|
||||
* @param input: input from the host
|
||||
* @param output: output to save on host
|
||||
* @param batchSize: batch size for TRT model
|
||||
*/
|
||||
|
||||
// Get engine from the context
|
||||
const ICudaEngine &engine = context.getEngine();
|
||||
|
||||
// Pointers to input and output device buffers to pass to engine.
|
||||
// Engine requires exactly IEngine::getNbBindings() number of buffers.
|
||||
assert(engine.getNbBindings() == 2);
|
||||
void *buffers[2];
|
||||
|
||||
// In order to bind the buffers, we need to know the names of the input and output tensors.
|
||||
// Note that indices are guaranteed to be less than IEngine::getNbBindings()
|
||||
const int inputIndex = engine.getBindingIndex("data");
|
||||
const int outputIndex = engine.getBindingIndex("out");
|
||||
|
||||
// Create GPU buffers on device -- allocate memory for input and output
|
||||
cudaMalloc(&buffers[inputIndex], batchSize * INPUT_SIZE * sizeof(float));
|
||||
cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float));
|
||||
|
||||
// create CUDA stream for simultaneous CUDA operations
|
||||
cudaStream_t stream;
|
||||
cudaStreamCreate(&stream);
|
||||
|
||||
// copy input from host (CPU) to device (GPU) in stream
|
||||
cudaMemcpyAsync(buffers[inputIndex], input, batchSize * INPUT_SIZE * sizeof(float), cudaMemcpyHostToDevice, stream);
|
||||
|
||||
// execute inference using context provided by engine
|
||||
context.enqueue(batchSize, buffers, stream, nullptr);
|
||||
|
||||
// copy output back from device (GPU) to host (CPU)
|
||||
cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost,
|
||||
stream);
|
||||
|
||||
// synchronize the stream to prevent issues
|
||||
// (block CUDA and wait for CUDA operations to be completed)
|
||||
cudaStreamSynchronize(stream);
|
||||
|
||||
// Release stream and buffers (memory)
|
||||
cudaStreamDestroy(stream);
|
||||
cudaFree(buffers[inputIndex]);
|
||||
cudaFree(buffers[outputIndex]);
|
||||
}
|
||||
|
||||
void performInference() {
|
||||
/**
|
||||
* Get inference using the pre-trained model
|
||||
*/
|
||||
|
||||
// stream to write model
|
||||
char *trtModelStream{nullptr};
|
||||
size_t size{0};
|
||||
|
||||
// read model from the engine file
|
||||
std::ifstream file("../mlp.engine", std::ios::binary);
|
||||
if (file.good()) {
|
||||
file.seekg(0, file.end);
|
||||
size = file.tellg();
|
||||
file.seekg(0, file.beg);
|
||||
trtModelStream = new char[size];
|
||||
assert(trtModelStream);
|
||||
file.read(trtModelStream, size);
|
||||
file.close();
|
||||
}
|
||||
|
||||
// create a runtime (required for deserialization of model) with NVIDIA's logger
|
||||
IRuntime *runtime = createInferRuntime(gLogger);
|
||||
assert(runtime != nullptr);
|
||||
|
||||
// deserialize engine for using the char-stream
|
||||
ICudaEngine *engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
|
||||
assert(engine != nullptr);
|
||||
|
||||
// create execution context -- required for inference executions
|
||||
IExecutionContext *context = engine->createExecutionContext();
|
||||
assert(context != nullptr);
|
||||
|
||||
float out[1]; // array for output
|
||||
float data[1]; // array for input
|
||||
for (float &i: data)
|
||||
i = 12.0; // put any value for input
|
||||
|
||||
// time the execution
|
||||
auto start = std::chrono::system_clock::now();
|
||||
|
||||
// do inference using the parameters
|
||||
doInference(*context, data, out, 1);
|
||||
|
||||
// time the execution
|
||||
auto end = std::chrono::system_clock::now();
|
||||
std::cout << "\n[INFO]: Time taken by execution: "
|
||||
<< std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
|
||||
|
||||
|
||||
// free the captured space
|
||||
context->destroy();
|
||||
engine->destroy();
|
||||
runtime->destroy();
|
||||
|
||||
std::cout << "\nInput:\t" << data[0];
|
||||
std::cout << "\nOutput:\t";
|
||||
for (float i: out) {
|
||||
std::cout << i;
|
||||
}
|
||||
std::cout << std::endl;
|
||||
}
|
||||
|
||||
int checkArgs(int argc, char **argv) {
|
||||
/**
|
||||
* Parse command line arguments
|
||||
*
|
||||
* @param argc: argument count
|
||||
* @param argv: arguments vector
|
||||
* @return int: a flag to perform operation
|
||||
*/
|
||||
|
||||
if (argc != 2) {
|
||||
std::cerr << "[ERROR]: Arguments not right!" << std::endl;
|
||||
std::cerr << "./mlp -s // serialize model to plan file" << std::endl;
|
||||
std::cerr << "./mlp -d // deserialize plan file and run inference" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
if (std::string(argv[1]) == "-s") {
|
||||
return 1;
|
||||
} else if (std::string(argv[1]) == "-d") {
|
||||
return 2;
|
||||
}
|
||||
return -1;
|
||||
}
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
int args = checkArgs(argc, argv);
|
||||
if (args == 1)
|
||||
performSerialization();
|
||||
else if (args == 2)
|
||||
performInference();
|
||||
return 0;
|
||||
}
|
||||
248
mlp/mlp.py
Normal file
248
mlp/mlp.py
Normal file
@ -0,0 +1,248 @@
|
||||
import argparse
|
||||
import os
|
||||
import numpy as np
|
||||
import struct
|
||||
|
||||
# required for the model creation
|
||||
import tensorrt as trt
|
||||
|
||||
# required for the inference using TRT engine
|
||||
import pycuda.autoinit
|
||||
import pycuda.driver as cuda
|
||||
|
||||
# Sizes of input and output for TensorRT model
|
||||
INPUT_SIZE = 1
|
||||
OUTPUT_SIZE = 1
|
||||
|
||||
# path of .wts (weight file) and .engine (model file)
|
||||
WEIGHT_PATH = "./mlp.wts"
|
||||
ENGINE_PATH = "./mlp.engine"
|
||||
|
||||
# input and output names are must for the TRT model
|
||||
INPUT_BLOB_NAME = 'data'
|
||||
OUTPUT_BLOB_NAME = 'out'
|
||||
|
||||
# A logger provided by NVIDIA-TRT
|
||||
gLogger = trt.Logger(trt.Logger.INFO)
|
||||
|
||||
|
||||
################################
|
||||
# DEPLOYMENT RELATED ###########
|
||||
################################
|
||||
def load_weights(file_path):
|
||||
"""
|
||||
Parse the .wts file and store weights in dict format
|
||||
:param file_path:
|
||||
:return weight_map: dictionary containing weights and their values
|
||||
"""
|
||||
print(f"[INFO]: Loading weights: {file_path}")
|
||||
assert os.path.exists(file_path), '[ERROR]: Unable to load weight file.'
|
||||
|
||||
weight_map = {}
|
||||
with open(file_path, "r") as f:
|
||||
lines = [line.strip() for line in f]
|
||||
|
||||
# count for total # of weights
|
||||
count = int(lines[0])
|
||||
assert count == len(lines) - 1
|
||||
|
||||
# Loop through counts and get the exact num of values against weights
|
||||
for i in range(1, count + 1):
|
||||
splits = lines[i].split(" ")
|
||||
name = splits[0]
|
||||
cur_count = int(splits[1])
|
||||
|
||||
# len of splits must be greater than current weight counts
|
||||
assert cur_count + 2 == len(splits)
|
||||
|
||||
# loop through all weights and unpack from the hexadecimal values
|
||||
values = []
|
||||
for j in range(2, len(splits)):
|
||||
# hex string to bytes to float
|
||||
values.append(struct.unpack(">f", bytes.fromhex(splits[j])))
|
||||
|
||||
# store in format of { 'weight.name': [weights_val0, weight_val1, ..] }
|
||||
weight_map[name] = np.array(values, dtype=np.float32)
|
||||
|
||||
return weight_map
|
||||
|
||||
|
||||
def create_mlp_engine(max_batch_size, builder, config, dt):
|
||||
"""
|
||||
Create Multi-Layer Perceptron using the TRT Builder and Configurations
|
||||
:param max_batch_size: batch size for built TRT model
|
||||
:param builder: to build engine and networks
|
||||
:param config: configuration related to Hardware
|
||||
:param dt: datatype for model layers
|
||||
:return engine: TRT model
|
||||
"""
|
||||
print("[INFO]: Creating MLP using TensorRT...")
|
||||
# load weight maps from the file
|
||||
weight_map = load_weights(WEIGHT_PATH)
|
||||
|
||||
# build an empty network using builder
|
||||
network = builder.create_network()
|
||||
|
||||
# add an input to network using the *input-name
|
||||
data = network.add_input(INPUT_BLOB_NAME, dt, (1, 1, INPUT_SIZE))
|
||||
assert data
|
||||
|
||||
# add the layer with output-size (number of outputs)
|
||||
linear = network.add_fully_connected(input=data,
|
||||
num_outputs=OUTPUT_SIZE,
|
||||
kernel=weight_map['linear.weight'],
|
||||
bias=weight_map['linear.bias'])
|
||||
assert linear
|
||||
|
||||
# set the name for output layer
|
||||
linear.get_output(0).name = OUTPUT_BLOB_NAME
|
||||
|
||||
# mark this layer as final output layer
|
||||
network.mark_output(linear.get_output(0))
|
||||
|
||||
# set the batch size of current builder
|
||||
builder.max_batch_size = max_batch_size
|
||||
|
||||
# create the engine with model and hardware configs
|
||||
engine = builder.build_engine(network, config)
|
||||
|
||||
# free captured memory
|
||||
del network
|
||||
del weight_map
|
||||
|
||||
# return engine
|
||||
return engine
|
||||
|
||||
|
||||
def api_to_model(max_batch_size):
|
||||
"""
|
||||
Create engine using TensorRT APIs
|
||||
:param max_batch_size: for the deployed model configs
|
||||
:return:
|
||||
"""
|
||||
# Create Builder with logger provided by TRT
|
||||
builder = trt.Builder(gLogger)
|
||||
|
||||
# Create configurations from Engine Builder
|
||||
config = builder.create_builder_config()
|
||||
|
||||
# Create MLP Engine
|
||||
engine = create_mlp_engine(max_batch_size, builder, config, trt.float32)
|
||||
assert engine
|
||||
|
||||
# Write the engine into binary file
|
||||
print("[INFO]: Writing engine into binary...")
|
||||
with open(ENGINE_PATH, "wb") as f:
|
||||
# write serialized model in file
|
||||
f.write(engine.serialize())
|
||||
|
||||
# free the memory
|
||||
del engine
|
||||
del builder
|
||||
|
||||
|
||||
################################
|
||||
# INFERENCE RELATED ############
|
||||
################################
|
||||
def perform_inference(input_val):
|
||||
"""
|
||||
Get inference using the pre-trained model
|
||||
:param input_val: a number as an input
|
||||
:return:
|
||||
"""
|
||||
|
||||
def do_inference(inf_context, inf_host_in, inf_host_out):
|
||||
"""
|
||||
Perform inference using the CUDA context
|
||||
:param inf_context: context created by engine
|
||||
:param inf_host_in: input from the host
|
||||
:param inf_host_out: output to save on host
|
||||
:return:
|
||||
"""
|
||||
|
||||
inference_engine = inf_context.engine
|
||||
# Input and output bindings are required for inference
|
||||
assert inference_engine.num_bindings == 2
|
||||
|
||||
# allocate memory in GPU using CUDA bindings
|
||||
device_in = cuda.mem_alloc(inf_host_in.nbytes)
|
||||
device_out = cuda.mem_alloc(inf_host_out.nbytes)
|
||||
|
||||
# create bindings for input and output
|
||||
bindings = [int(device_in), int(device_out)]
|
||||
|
||||
# create CUDA stream for simultaneous CUDA operations
|
||||
stream = cuda.Stream()
|
||||
|
||||
# copy input from host (CPU) to device (GPU) in stream
|
||||
cuda.memcpy_htod_async(device_in, inf_host_in, stream)
|
||||
|
||||
# execute inference using context provided by engine
|
||||
inf_context.execute_async(bindings=bindings, stream_handle=stream.handle)
|
||||
|
||||
# copy output back from device (GPU) to host (CPU)
|
||||
cuda.memcpy_dtoh_async(inf_host_out, device_out, stream)
|
||||
|
||||
# synchronize the stream to prevent issues
|
||||
# (block CUDA and wait for CUDA operations to be completed)
|
||||
stream.synchronize()
|
||||
|
||||
# create a runtime (required for deserialization of model) with NVIDIA's logger
|
||||
runtime = trt.Runtime(gLogger)
|
||||
assert runtime
|
||||
|
||||
# read and deserialize engine for inference
|
||||
with open(ENGINE_PATH, "rb") as f:
|
||||
engine = runtime.deserialize_cuda_engine(f.read())
|
||||
assert engine
|
||||
|
||||
# create execution context -- required for inference executions
|
||||
context = engine.create_execution_context()
|
||||
assert context
|
||||
|
||||
# create input as array
|
||||
data = np.array([input_val], dtype=np.float32)
|
||||
|
||||
# capture free memory for input in GPU
|
||||
host_in = cuda.pagelocked_empty((INPUT_SIZE), dtype=np.float32)
|
||||
|
||||
# copy input-array from CPU to Flatten array in GPU
|
||||
np.copyto(host_in, data.ravel())
|
||||
|
||||
# capture free memory for output in GPU
|
||||
host_out = cuda.pagelocked_empty(OUTPUT_SIZE, dtype=np.float32)
|
||||
|
||||
# do inference using required parameters
|
||||
do_inference(context, host_in, host_out)
|
||||
|
||||
print(f'\n[INFO]: Predictions using pre-trained model..\n\tInput:\t{input_val}\n\tOutput:\t{host_out[0]:.4f}')
|
||||
|
||||
|
||||
def get_args():
|
||||
"""
|
||||
Parse command line arguments
|
||||
:return arguments: parsed arguments
|
||||
"""
|
||||
arg_parser = argparse.ArgumentParser()
|
||||
arg_parser.add_argument('-s', action='store_true')
|
||||
arg_parser.add_argument('-d', action='store_true')
|
||||
arguments = vars(arg_parser.parse_args())
|
||||
# check for the arguments
|
||||
if not (arguments['s'] ^ arguments['d']):
|
||||
print("[ERROR]: Arguments not right!\n")
|
||||
print("\tpython mlp.py -s # serialize model to engine file")
|
||||
print("\tpython mlp.py -d # deserialize engine file and run inference")
|
||||
exit()
|
||||
|
||||
return arguments
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = get_args()
|
||||
if args['s']:
|
||||
api_to_model(max_batch_size=1)
|
||||
print("[INFO]: Successfully created TensorRT engine...")
|
||||
print("\n\tRun inference using `python mlp.py -d`\n")
|
||||
else:
|
||||
perform_inference(input_val=4.0)
|
||||
|
||||
3
mlp/mlp.wts
Normal file
3
mlp/mlp.wts
Normal file
@ -0,0 +1,3 @@
|
||||
2
|
||||
linear.weight 1 3fff7e32
|
||||
linear.bias 1 3c138a5a
|
||||
Loading…
Reference in New Issue
Block a user