IR2Vec
is a LLVM IR based framework to generate distributed representations for the source code in an unsupervised manner, which can be used to represent programs as input to solve machine learning tasks that take programs as inputs.
This repo contains the source code and relevant information described in the paper (arXiv). Please see here for more details.
IR2Vec: LLVM IR Based Scalable Program Embeddings, S. VenkataKeerthy, Rohit Aggarwal, Shalini Jain, Maunendra Sankar Desarkar, Ramakrishna Upadrasta, and Y. N. Srikant
LLVM Version | Branch |
---|---|
LLVM 17.0.6 | main |
LLVM 16.0.1 | llvm16 |
LLVM 14.0.1 | llvm14 |
LLVM 12.0.0 | llvm12 |
LLVM 10.0.1 | llvm10 |
LLVM 8.0.1 | llvm8 |
IR2Vec
can be installed in different ways to accommodate individual preferences and requirements effectively. You may select to install via a user-friendly Python wheel setup if you are a Python user, or opt for a C++ based installation if you are looking to integrate with a compiler pass or necessitate advanced control and enhanced integration capabilities. The detailed setup steps are mentioned in the following sections.
If you prefer working with Python, you can easily install IR2Vec
using pip
.
pip install -U ir2vec
Now, you can import and use IR2Vec in your Python projects. Make sure you have a good understanding of Python and its package management system.
We are actively working on improving the Python interfaces and providing better support. If you find any good-to-have interfaces that you may need for your use case missing, please feel free to raise a request.
If you're a C++ developer and require low-level control, optimization, or integration with C++ projects, you can build IR2Vec
from source. First, ensure the below requirements are satisfied, then follow the steps mentioned in the Building from source section.
pip3 install --user lit
pip3 install --user filecheck
(Experiments are done on an Ubuntu 20.04 machine)
mkdir build && cd build
wget https://gitlab.com/libeigen/eigen/-/archive/3.3.7/eigen-3.3.7.tar.gz
tar -xvzf eigen-3.3.7.tar.gz
mkdir eigen-build && cd eigen-build
cmake ../eigen-3.3.7 && make
cd ../
cmake -DLT_LLVM_INSTALL_DIR=<path_to_LLVM_build_dir> -DEigen3_DIR=<path_to_eigen_build_dir> [-DCMAKE_INSTALL_PREFIX=<install_dir>] ..
make [&& make install]
This process would generate ir2vec
binary under build/bin
directory, libIR2Vec.a
and libIR2Vec.so
under build/lib
directory.
To ensure the correctness, run make check
IR2Vec
can be used either as a stand-alone tool using binary or can be integrated with any third-party tools using libraries. Please see below for the usage
instructions.
ir2vec -\<mode> -o \<output-file> -level \<p|f> -class \<class-number> -funcName=\<function-name> \<input-ll-file>
mode
- can be one of sym
/fa
sym
denotes Symbolic representationfa
denotes Flow-Aware representationo
- file in which the embeddings are to be appended; (Note : If file doesn’t exist, new file would be created, else embeddings would be appended)level
- can be one of chars p
/f
.
p
denotes program level
encodingf
denotes function level
encodingclass
- non-mandatory argument. Used for the purpose of mentioning class labels for classification tasks (To be used with the level p
). Defaults to -1. When, not equal to -1, the pass prints class-number
followed by the corresponding embeddingsfuncName
- also a non-mandatory argument. Used for generating embeddings only for the functions with given name. level
should be f
while using this optionPlease use --help
for further details.
Format of the output embeddings in output_file
level
is p
:<class-number> <Embeddings>
class-number would be printed only if it is not -1
level
is f
<function-name> = <Embeddings>
For all functions
ir2vec -fa -o <output_file> -level <p|f> -class <class-number> <input_ll_file>
For a specific function
ir2vec -fa -o <output_file> -level f -class <class-number> -funcName=\<function-name\><input_ll_file>
For all functions
ir2vec -sym -o <output_file> -level <p|f> -class <class-number> <input_ll_file>
For a specific functionir2vec -sym -o <output_file> -level f -class <class-number> -funcName=\<function-name\> <input_ll_file>
The libraries can be installed by passing the installation location to the CMAKE_INSTALL_PREFIX
flag during cmake
followed by make install
.
The interfaces are available in IR2Vec.h
. External projects that would like to use IR2Vec
can access the functionality
using these exposed interfaces on including IR2Vec.h
from the installed location after linking statically or dynamically.
LT_LLVM_INSTALL_DIR
during cmake.The following template can be used to link IR2vec libraries on a cmake based project.
set(IR2VEC_INSTALL_DIR "" CACHE PATH "IR2Vec installation directory")
include_directories("${IR2VEC_INSTALL_DIR}/include")
target_link_libraries(<your_executable_or_library> PUBLIC ${IR2VEC_INSTALL_DIR}/lib/<libIR2Vec.a or libIR2Vec.so>)
And then pass the location of IR2Vec's install prefix to DIR2VEC_INSTALL_DIR
during cmake.
The following example snippet shows how to query the exposed vector representations.
#include "IR2Vec.h"
// Creating object to generate FlowAware representation
auto ir2vec =
IR2Vec::Embeddings(<LLVM Module>, IR2Vec::IR2VecMode::FlowAware);
// Getting Instruction vectors corresponding to the instructions in <LLVM Module>
auto instVecMap = ir2vec.getInstVecMap();
// Access the generated vectors
for (auto instVec : instVecMap) {
outs() << "Instruction : ";
instVec.first->print(outs());
outs() << ": ";
for (auto val : instVec.second)
outs() << val << "\t";
}
// Getting vectors corresponding to the functions in <LLVM Module>
auto funcVecMap = ir2vec.getFunctionVecMap();
// Access the generated vectors
for (auto funcVec : funcVecMap) {
outs() << "Function : " << funcVec.first->getName() << "\n";
for (auto val : funcVec.second)
outs() << val << "\t";
}
// Getting the program vector
auto pgmVec = ir2vec.getProgramVector();
// Access the generated vector
for (auto val : pgmVec)
outs() << val << "\t";
Description: Initialize IR2Vec embedding for an LLVM IR file.
Parameters:
file_path
: str - Path to the .ll
or .bc
file.encoding_type
: str - Choose fa
(Flow-Aware) or sym
(Symbolic).level
: str - Choose p
for program-level or f
for function-level.Returns:
IR2VecObject
: Initialized object for accessing embeddings.Example:
import ir2vec
initObj = ir2vec.initEmbedding("/path/to/file.ll", "fa", "p")
Description: Gets the program-level vector representation.
Parameters: optional
Returns:
progVector
: ndarray - The program-level embedding vector.Example:
# Getting the program-level vector
progVector = initObj.getProgramVector()
Description: Gets function-level vectors for all functions in the LLVM IR file.
Parameters: optional
Returns:
functionVectorMap
: dict - A dictionary where keys are function names and values are ndarrays containing function-level embedding vectors.Example:
# Getting function-level vectors
functionVectorMap = initObj.getFunctionVectors()
Description: Gets instruction-level vectors for all instructions in the LLVM IR file.
Parameters: optional
Returns:
instructionVectorsList
: list - A list of list where each list contains instruction corresponding embedding vectors as values.Example:
# Getting instruction-level vectors
instructionVectorsList = initObj.getInstructionVectors()
import ir2vec
import numpy as np
# IR2Vec Python APIs can be used in two ways. As shown below.
initObj = ir2vec.initEmbedding("/path/to/file.ll", "fa", "p")
#Approach 1
progVector1 = ir2vec.getProgramVector(initObj)
functionVectorMap1 = ir2vec.getFunctionVectors(initObj)
instructionVectorsList1 = ir2vec.getInstructionVectors(initObj)
#Approach 2
progVector2 = initObj.getProgramVector()
functionVectorMap2 = initObj.getFunctionVectors()
instructionVectorsList2 = initObj.getInstructionVectors()
# Both the approaches would result in same outcomes
assert(np.allclose(progVector1,progVector2))
for fun, funcObj in functionVectorMap1.items():
assert fun == funcObj["demangledName"]
functionOutput1 = ir2vec.getFunctionVectors(
initObj,
funcObj["actualName"],
)
functionOutput2 = initObj.getFunctionVectors(
funcObj["actualName"]
)
assert(np.allclose(functionOutput1[fun]["vector"],functionOutput2[fun]["vector"]))
Binaries, Libraries (.a and .so), and whl files are autogenerated for every relevant check-in using GitHub Actions. Such generated artifacts are tagged along with the successful runs of Publish
and Build Wheels
actions.
The results mentioned in the experiment's scripts/the published version are not updated for this branch. The experimental results for this branch would be different when compared to the published version. For comparison, use the release corresponding to v0.1.0.
@article{VenkataKeerthy-2020-IR2Vec,
author = {VenkataKeerthy, S. and Aggarwal, Rohit and Jain, Shalini and Desarkar, Maunendra Sankar and Upadrasta, Ramakrishna and Srikant, Y. N.},
title = {{IR2Vec: LLVM IR Based Scalable Program Embeddings}},
year = {2020},
issue_date = {December 2020},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {17},
number = {4},
issn = {1544-3566},
url = {https://doi.org/10.1145/3418463},
doi = {10.1145/3418463},
journal = {ACM Trans. Archit. Code Optim.},
month = dec,
articleno = {32},
numpages = {27},
keywords = {heterogeneous systems, representation learning, compiler optimizations, LLVM, intermediate representations}
}
Please feel free to raise issues to file a bug, pose a question, or initiate any related discussions. Pull requests are welcome :)
IR2Vec is released under a Apache License v2.0 with LLVM Exceptions License. See the LICENSE file for more details.