llvm-ir2vec - IR2Vec Embedding Generation Tool

SYNOPSIS

llvm-ir2vec [options] input-file

DESCRIPTION

llvm-ir2vec is a standalone command-line tool for IR2Vec. It generates IR2Vec embeddings for LLVM IR and supports triplet generation for vocabulary training. It provides two main operation modes:

  1. Triplet Mode: Generates triplets (opcode, type, operands) for vocabulary training from LLVM IR.

  2. Embedding Mode: Generates IR2Vec embeddings using a trained vocabulary at different granularity levels (instruction, basic block, or function).

The tool is designed to facilitate machine learning applications that work with LLVM IR by converting the IR into numerical representations that can be used by ML models.

Note

For information about using IR2Vec programmatically within LLVM passes and the C++ API, see the IR2Vec Embeddings section in the MLGO documentation.

OPERATION MODES

Triplet Generation Mode

In triplet mode, llvm-ir2vec analyzes LLVM IR and extracts triplets consisting of opcodes, types, and operands. These triplets can be used to train vocabularies for embedding generation.

Usage:

llvm-ir2vec --mode=triplets input.bc -o triplets.txt

Embedding Generation Mode

In embedding mode, llvm-ir2vec uses a pre-trained vocabulary to generate numerical embeddings for LLVM IR at different levels of granularity.

Example Usage:

llvm-ir2vec --mode=embeddings --ir2vec-vocab-path=vocab.json --level=func input.bc -o embeddings.txt

OPTIONS

--mode=<mode>

Specify the operation mode. Valid values are:

  • triplets - Generate triplets for vocabulary training

  • embeddings - Generate embeddings using trained vocabulary (default)

--level=<level>

Specify the embedding generation level. Valid values are:

  • inst - Generate instruction-level embeddings

  • bb - Generate basic block-level embeddings

  • func - Generate function-level embeddings (default)

--function=<name>

Process only the specified function instead of all functions in the module.

--ir2vec-vocab-path=<path>

Specify the path to the vocabulary file (required for embedding mode). The vocabulary file should be in JSON format and contain the trained vocabulary for embedding generation. See llvm/lib/Analysis/models for pre-trained vocabulary files.

--ir2vec-opc-weight=<weight>

Specify the weight for opcode embeddings (default: 1.0). This controls the relative importance of instruction opcodes in the final embedding.

--ir2vec-type-weight=<weight>

Specify the weight for type embeddings (default: 0.5). This controls the relative importance of type information in the final embedding.

--ir2vec-arg-weight=<weight>

Specify the weight for argument embeddings (default: 0.2). This controls the relative importance of operand information in the final embedding.

-o <filename>

Specify the output filename. Use - to write to standard output (default).

--help

Print a summary of command line options.

Note

--level, --function, --ir2vec-vocab-path, --ir2vec-opc-weight, --ir2vec-type-weight, and --ir2vec-arg-weight are only used in embedding mode. These options are ignored in triplet mode.

INPUT FILE FORMAT

llvm-ir2vec accepts LLVM bitcode files (.bc) and LLVM IR files (.ll) as input. The input file should contain valid LLVM IR.

OUTPUT FORMAT

Triplet Mode Output

In triplet mode, the output consists of lines containing space-separated triplets:

<opcode> <type> <operand1> <operand2> ...

Each line represents the information of one instruction, with the opcode, type, and operands.

Embedding Mode Output

In embedding mode, the output format depends on the specified level:

  • Function Level: One embedding vector per function

  • Basic Block Level: One embedding vector per basic block, grouped by function

  • Instruction Level: One embedding vector per instruction, grouped by basic block and function

Each embedding is represented as a floating point vector.

EXIT STATUS

llvm-ir2vec returns 0 on success, and a non-zero value on failure.

Common failure cases include:

  • Invalid or missing input file

  • Missing or invalid vocabulary file (in embedding mode)

  • Specified function not found in the module

  • Invalid command line options

SEE ALSO

Machine Learning - Guided Optimization (MLGO)

For more information about the IR2Vec algorithm and approach, see: IR2Vec: LLVM IR Based Scalable Program Embeddings.