This library implements constrained decoding (also called constrained sampling or structured outputs) for Large Langauge Models (LLMs). It can enforce arbitrary context-free grammar on the output of LLM and is fast - on the order of 1ms of CPU time per token (for 100k tokenizer) with negligible startup costs.
Following grammar formats are supported:
llguidance- internal (JSON-based) format- regular expressions (following Rust regex crate syntax)
- a large subset of JSON schemas (but see issue)
- context-free grammars in (a subset of) Lark format
The internal format is most powerful and can be generated by the following libraries:
- Guidance (Python)
- guidance.ts (TypeScript)
- hopefully more to come!
The library can be used from:
The library is currently integrated in:
- Guidance - library for interacting with LLMs; uses either llama.cpp or HF Tranformers
- LLGTRT - OpenAI-compatible REST server using NVIDIA's TensorRT-LLM
The integration is ongoing in:
- onnxruntime-genai - draft PR
- mistral.rs - preliminary PR
- llama.cpp - preliminary PR; note that llama.cpp is fully integrated in Guidance above via Python bindings
Given a context-free grammar, a tokenizer, and a prefix of tokens, llguidance computes a token mask - a set of tokens from the tokenizer - that, when added to the current token prefix, can lead to a valid string in the language defined by the grammar. Mask computation takes approximately 1ms of single-core CPU time for a tokenizer with 100k tokens. While this timing depends on the exact grammar, it holds, for example, for grammars derived from JSON schemas. There is no significant startup cost.
The library implements a context-free grammar parser using Earley’s algorithm on top of a lexer based on derivatives of regular expressions. Mask computation is achieved by traversing the prefix tree (trie) of all possible tokens, leveraging highly optimized code.
LM-format-enforcer and llama.cpp grammars are similar to llguidance in that they dynamically build token masks for every step of the decoding process. Both are significantly slower - the former due to clean Python code and the latter due to the lack of a lexer and use of a backtracking parser, which, while elegant, is inefficient.
Outlines builds an automaton from constraints and then pre-computes token masks for all automaton states, making sampling fast but inherently limiting constraint complexity and introducing significant startup cost and memory overhead. Llguidance computes token masks on the fly and has essentially no startup cost. The lexer’s automata are built lazily and are typically much smaller, as the context-free grammar imposes the top-level structure.
In llguidance, online mask computation takes approximately 1ms of CPU time per sequence in a batch. Thus, with 16 cores and a 10ms forward pass, the library can handle batch sizes up to 160 without slowing down the model. (Note that a 10ms forward pass for small batch sizes typically increases to 20ms+ for batch sizes of 100-200.)
- install rust; 1.75 or later
If you just need the C or Rust library (llguidance_parser),
check the parser directory.
For Python bindings:
- install python 3.9 or later; very likely you'll need a virtual env/conda
- run
./scripts/install-deps.sh - to build and after any changes, run
./scripts/test-guidance.sh
This builds the Python bindings for the library and runs the tests (which mostly live in the Guidance repo - it will clone it).
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