llvm-ir2vec - IR2Vec and MIR2Vec Embedding Generation Tool =========================================================== .. program:: llvm-ir2vec SYNOPSIS -------- :program:`llvm-ir2vec` [*subcommand*] [*options*] DESCRIPTION ----------- :program:`llvm-ir2vec` is a standalone command-line tool for IR2Vec and MIR2Vec. It generates embeddings for both LLVM IR and Machine IR (MIR) and supports triplet generation for vocabulary training. The tool provides three main subcommands: 1. **triplets**: Generates numeric triplets in train2id format for vocabulary training from LLVM IR. 2. **entities**: Generates entity mapping files (entity2id.txt) for vocabulary training. 3. **embeddings**: Generates IR2Vec or MIR2Vec embeddings using a trained vocabulary at different granularity levels (instruction, basic block, or function). The tool supports two operation modes: * **LLVM IR mode** (``--mode=llvm``): Process LLVM IR bitcode files and generate IR2Vec embeddings * **Machine IR mode** (``--mode=mir``): Process Machine IR (.mir) files and generate MIR2Vec embeddings The tool is designed to facilitate machine learning applications that work with LLVM IR or Machine IR by converting them into numerical representations that can be used by ML models. The `triplets` subcommand generates numeric IDs directly instead of string triplets, streamlining the training data preparation workflow. .. note:: For information about using IR2Vec and MIR2Vec programmatically within LLVM passes and the C++ API, see the `IR2Vec Embeddings `_ section in the MLGO documentation. OPERATION MODES --------------- The tool operates in two modes: **LLVM IR mode** and **Machine IR mode**. The mode is selected using the ``--mode`` option (default: ``llvm``). Triplet Generation and Entity Mapping Modes are used for preparing vocabulary and training data for knowledge graph embeddings. The Embedding Mode is used for generating embeddings from LLVM IR using a pre-trained vocabulary. The Seed Embedding Vocabulary of IR2Vec is trained on a large corpus of LLVM IR by modeling the relationships between opcodes, types, and operands as a knowledge graph. For this purpose, Triplet Generation and Entity Mapping Modes generate triplets and entity mappings in the standard format used for knowledge graph embedding training (see for details). See `llvm/utils/mlgo-utils/IR2Vec/generateTriplets.py` for more details on how these two modes are used to generate the triplets and entity mappings. Triplet Generation ~~~~~~~~~~~~~~~~~~ With the `triplets` subcommand, :program:`llvm-ir2vec` analyzes LLVM IR and extracts numeric triplets consisting of opcode IDs, type IDs, and operand IDs. These triplets are generated in the standard format used for knowledge graph embedding training. The tool outputs numeric IDs directly using the ir2vec::Vocabulary mapping infrastructure, eliminating the need for string-to-ID preprocessing. Usage: .. code-block:: bash llvm-ir2vec triplets input.bc -o triplets_train2id.txt Entity Mapping Generation ~~~~~~~~~~~~~~~~~~~~~~~~~ With the `entities` subcommand, :program:`llvm-ir2vec` generates the entity mappings supported by IR2Vec in the standard format used for knowledge graph embedding training. This subcommand outputs all supported entities (opcodes, types, and operands) with their corresponding numeric IDs, and is not specific for an LLVM IR file. Usage: .. code-block:: bash llvm-ir2vec entities -o entity2id.txt Embedding Generation ~~~~~~~~~~~~~~~~~~~~ With the `embeddings` subcommand, :program:`llvm-ir2vec` uses a pre-trained vocabulary to generate numerical embeddings for LLVM IR or Machine IR at different levels of granularity. Example Usage for LLVM IR: .. code-block:: bash llvm-ir2vec embeddings --mode=llvm --ir2vec-vocab-path=vocab.json --ir2vec-kind=symbolic --level=func input.bc -o embeddings.txt Example Usage for Machine IR: .. code-block:: bash llvm-ir2vec embeddings --mode=mir --mir2vec-vocab-path=vocab.json --level=func input.mir -o embeddings.txt OPTIONS ------- Common options (applicable to both LLVM IR and Machine IR modes): .. option:: --mode= Specify the operation mode. Valid values are: * ``llvm`` - Process LLVM IR bitcode files (default) * ``mir`` - Process Machine IR (.mir) files .. option:: -o Specify the output filename. Use ``-`` to write to standard output (default). .. option:: --help Print a summary of command line options. Subcommand-specific options: **embeddings** subcommand: .. option:: The input LLVM IR/bitcode file (.ll/.bc) or Machine IR file (.mir) to process. This positional argument is required for the `embeddings` subcommand. .. option:: --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) .. option:: --function= Process only the specified function instead of all functions in the module. **IR2Vec-specific options** (for ``--mode=llvm``): .. option:: --ir2vec-kind= Specify the kind of IR2Vec embeddings to generate. Valid values are: * ``symbolic`` - Generate symbolic embeddings (default) * ``flow-aware`` - Generate flow-aware embeddings Flow-aware embeddings consider control flow relationships between instructions, while symbolic embeddings focus on the symbolic representation of instructions. .. option:: --ir2vec-vocab-path= Specify the path to the IR2Vec vocabulary file (required for LLVM IR embedding generation). 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. .. option:: --ir2vec-opc-weight= Specify the weight for opcode embeddings (default: 1.0). This controls the relative importance of instruction opcodes in the final embedding. .. option:: --ir2vec-type-weight= Specify the weight for type embeddings (default: 0.5). This controls the relative importance of type information in the final embedding. .. option:: --ir2vec-arg-weight= Specify the weight for argument embeddings (default: 0.2). This controls the relative importance of operand information in the final embedding. **MIR2Vec-specific options** (for ``--mode=mir``): .. option:: --mir2vec-vocab-path= Specify the path to the MIR2Vec vocabulary file (required for Machine IR embedding generation). The vocabulary file should be in JSON format and contain the trained vocabulary for embedding generation. .. option:: --mir2vec-kind= Specify the kind of MIR2Vec embeddings to generate. Valid values are: * ``symbolic`` - Generate symbolic embeddings (default) .. option:: --mir2vec-opc-weight= Specify the weight for machine opcode embeddings (default: 1.0). This controls the relative importance of machine instruction opcodes in the final embedding. .. option:: --mir2vec-common-operand-weight= Specify the weight for common operand embeddings (default: 1.0). This controls the relative importance of common operand types in the final embedding. .. option:: --mir2vec-reg-operand-weight= Specify the weight for register operand embeddings (default: 1.0). This controls the relative importance of register operands in the final embedding. **triplets** subcommand: .. option:: The input LLVM IR or bitcode file to process. This positional argument is required for the `triplets` subcommand. **entities** subcommand: No subcommand-specific options. OUTPUT FORMAT ------------- Triplet Mode Output ~~~~~~~~~~~~~~~~~~~ In triplet mode, the output consists of numeric triplets in train2id format with metadata headers. The format includes: .. code-block:: text MAX_RELATIONS= ... Each line after the metadata header represents one instruction relationship, with numeric IDs for head entity, relation, and tail entity. The metadata header (MAX_RELATIONS) provides counts for post-processing and training setup. Entity Mode Output ~~~~~~~~~~~~~~~~~~ In entity mode, the output consists of entity mapping in the format: .. code-block:: text ... The first line contains the total number of entities, followed by one entity mapping per line with tab-separated entity string and numeric ID. 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 ----------- :program:`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 -------- :doc:`../MLGO` For more information about the IR2Vec algorithm and approach, see: `IR2Vec: LLVM IR Based Scalable Program Embeddings `_. For more information about the MIR2Vec algorithm and approach, see: `RL4ReAl: Reinforcement Learning for Register Allocation `_.