mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2025-02-06 08:30:33 +01:00
ff227703d6
* initial porting of previous LLG patch * update for new APIs * build: integrate llguidance as an external project * use '%llguidance' as marker to enable llg lark syntax * add some docs * clarify docs * code style fixes * remove llguidance.h from .gitignore * fix tests when llg is enabled * pass vocab not model to llama_sampler_init_llg() * copy test-grammar-integration.cpp to test-llguidance.cpp * clang fmt * fix ref-count bug * build and run test * gbnf -> lark syntax * conditionally include llguidance test based on LLAMA_LLGUIDANCE flag * rename llguidance test file to test-grammar-llguidance.cpp * add gh action for llg test * align tests with LLG grammar syntax and JSON Schema spec * llama_tokenizer() in fact requires valid utf8 * update llg * format file * add $LLGUIDANCE_LOG_LEVEL support * fix whitespace * fix warning * include <cmath> for INFINITY * add final newline * fail llama_sampler_init_llg() at runtime * Link gbnf_to_lark.py script; fix links; refer to llg docs for lexemes * simplify #includes * improve doc string for LLAMA_LLGUIDANCE * typo in merge * bump llguidance to 0.6.12
52 lines
3.6 KiB
Markdown
52 lines
3.6 KiB
Markdown
# LLGuidance Support in llama.cpp
|
|
|
|
[LLGuidance](https://github.com/guidance-ai/llguidance) is a library for constrained decoding (also called constrained sampling or structured outputs) for Large Language Models (LLMs). Initially developed as the backend for the [Guidance](https://github.com/guidance-ai/guidance) library, it can also be used independently.
|
|
|
|
LLGuidance supports JSON Schemas and arbitrary context-free grammars (CFGs) written in a [variant](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md) of Lark syntax. It is [very fast](https://github.com/guidance-ai/jsonschemabench/tree/main/maskbench) and has [excellent](https://github.com/guidance-ai/llguidance/blob/main/docs/json_schema.md) JSON Schema coverage but requires the Rust compiler, which complicates the llama.cpp build process.
|
|
|
|
## Building
|
|
|
|
To enable LLGuidance support, build llama.cpp with the `LLAMA_LLGUIDANCE` option:
|
|
|
|
```sh
|
|
cmake -B build -DLLAMA_LLGUIDANCE=ON
|
|
make -C build -j
|
|
```
|
|
|
|
This requires the Rust compiler and the `cargo` tool to be [installed](https://www.rust-lang.org/tools/install).
|
|
|
|
## Interface
|
|
|
|
There are no new command-line arguments or modifications to `common_params`. When enabled, grammars starting with `%llguidance` are passed to LLGuidance instead of the [current](../grammars/README.md) llama.cpp grammars. Additionally, JSON Schema requests (e.g., using the `-j` argument in `llama-cli`) are also passed to LLGuidance.
|
|
|
|
For your existing GBNF grammars, you can use [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) to convert them to LLGuidance Lark-like format.
|
|
|
|
## Performance
|
|
|
|
Computing a "token mask" (i.e., the set of allowed tokens) for a llama3 tokenizer with 128k tokens takes, on average, 50μs of single-core CPU time for the [JSON Schema Bench](https://github.com/guidance-ai/jsonschemabench). The p99 time is 0.5ms, and the p100 time is 20ms. These results are due to the lexer/parser split and several [optimizations](https://github.com/guidance-ai/llguidance/blob/main/docs/optimizations.md).
|
|
|
|
## JSON Schema
|
|
|
|
LLGuidance adheres closely to the JSON Schema specification. For example:
|
|
|
|
- `additionalProperties` defaults to `true`, unlike current grammars, though you can set `"additionalProperties": false` if needed.
|
|
- any whitespace is allowed.
|
|
- The definition order in the `"properties": {}` object is maintained, regardless of whether properties are required (current grammars always puts required properties first).
|
|
|
|
Unsupported schemas result in an error message—no keywords are silently ignored.
|
|
|
|
## Why Not Reuse GBNF Format?
|
|
|
|
GBNF lacks the concept of a lexer.
|
|
|
|
Most programming languages, including JSON, use a two-step process: a lexer (built with regular expressions) converts a byte stream into lexemes, which are then processed by a CFG parser. This approach is faster because lexers are cheaper to evaluate, and there is ~10x fewer lexemes than bytes.
|
|
LLM tokens often align with lexemes, so the parser is engaged in under 0.5% of tokens, with the lexer handling the rest.
|
|
|
|
However, the user has to provide the distinction between lexemes and CFG symbols. In [Lark](https://github.com/lark-parser/lark), lexeme names are uppercase, while CFG symbols are lowercase.
|
|
The [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) can often take care of this automatically.
|
|
See [LLGuidance syntax docs](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#terminals-vs-rules) for more details.
|
|
|
|
## Error Handling
|
|
|
|
Errors are currently printed to `stderr`, and generation continues. Improved error handling may be added in the future.
|