llvm-ir2vec - IR2Vec and MIR2Vec Embedding Generation Tool¶

SYNOPSIS¶

llvm-ir2vec [subcommand] [options]

DESCRIPTION¶

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:

triplets: Generates numeric triplets in train2id format for vocabulary training from LLVM IR.
entities: Generates entity mapping files (entity2id.txt) for vocabulary training.
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 <https://github.com/thunlp/OpenKE/tree/OpenKE-PyTorch?tab=readme-ov-file#data-format> 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, llvm-ir2vec analyzes LLVM IR or Machine IR and extracts numeric triplets consisting of opcode 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 vocabulary mapping infrastructure, eliminating the need for string-to-ID preprocessing.

Usage for LLVM IR:

llvm-ir2vec triplets --mode=llvm input.bc -o triplets_train2id.txt

Usage for Machine IR:

llvm-ir2vec triplets --mode=mir input.mir -o triplets_train2id.txt

Entity Mapping Generation¶

With the entities subcommand, llvm-ir2vec generates the entity mappings supported by IR2Vec or MIR2Vec in the standard format used for knowledge graph embedding training. This subcommand outputs all supported entities with their corresponding numeric IDs.

For LLVM IR, entities include opcodes, types, and operands. For Machine IR, entities include machine opcodes, common operands, and register classes (both physical and virtual).

Usage for LLVM IR:

llvm-ir2vec entities --mode=llvm -o entity2id.txt

Usage for Machine IR:

llvm-ir2vec entities --mode=mir input.mir -o entity2id.txt

:::{note} For LLVM IR mode, the entity mapping is target-independent and does not require an input file. For Machine IR mode, an input .mir file is required to determine the target architecture, as entity mappings vary by target (different architectures have different instruction sets and register classes). :::

Embedding Generation¶

With the embeddings subcommand, 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:

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:

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/bitcode file (.ll/.bc) or Machine IR file (.mir) to process. This positional argument is required for the triplets subcommand. :::

entities subcommand:

:::{option} The input Machine IR file (.mir) to process. This positional argument is required for the entities subcommand when using --mode=mir, as the entity mappings are target-specific. For --mode=llvm, no input file is required as IR2Vec entity mappings are target-independent. :::

OUTPUT FORMAT¶

Triplet Mode Output¶

In triplet mode, the output consists of numeric triplets in train2id format with metadata headers. The format includes:

MAX_RELATION=<max_relation_count>
<head_entity_id> <tail_entity_id> <relation_id>
<head_entity_id> <tail_entity_id> <relation_id>
...

Each line after the metadata header represents one instruction relationship, with numeric IDs for head entity, tail entity, and relation type. The metadata header (MAX_RELATION) indicates the maximum relation ID used.

Relation Types:

For LLVM IR (IR2Vec): : - 0 = Type relationship (instruction to its type)

1 = Next relationship (sequential instructions)
2+ = Argument relationships (Arg0, Arg1, Arg2, …)

For Machine IR (MIR2Vec): : - 0 = Next relationship (sequential instructions)

1+ = Argument relationships (Arg0, Arg1, Arg2, …)

Entity IDs:

For LLVM IR: Entity IDs represent opcodes, types, and operands as defined by the IR2Vec vocabulary.

For Machine IR: Entity IDs represent machine opcodes, common operands (immediate, frame index, etc.), physical register classes, and virtual register classes as defined by the MIR2Vec vocabulary. The entity layout is target-specific.

Entity Mode Output¶

In entity mode, the output consists of entity mappings in the format:

<total_entities>
<entity_string>      <numeric_id>
<entity_string>      <numeric_id>
...

The first line contains the total number of entities, followed by one entity mapping per line with tab-separated entity string and numeric ID.

For LLVM IR, entities include instruction opcodes (e.g., “Add”, “Ret”), types (e.g., “INT”, “PTR”), and operand kinds.

For Machine IR, entities include machine opcodes (e.g., “COPY”, “ADD”), common operands (e.g., “Immediate”, “FrameIndex”), physical register classes (e.g., “PhyReg_GR32”), and virtual register classes (e.g., “VirtReg_GR32”).

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