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Contributors

The project differentiates between 3 levels of contributors:

Contributors: people who have contributed before (no special privileges)
Collaborators (Triage): people with significant contributions, who may be responsible for some parts of the code, and are expected to maintain and review contributions for the code they own
Maintainers: responsible for reviewing and merging PRs, after approval from the code owners

AI Usage Policy

[!IMPORTANT] This project does not accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity.

Detailed information regarding permissible and restricted uses of AI can be found in the AGENTS.md file.

Code that is initially generated by AI and subsequently edited will still be considered AI-generated. AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (e.g., generating repeated lines with minor variations).

If AI is used to generate any portion of the code, contributors must adhere to the following requirements:

Explicitly disclose the manner in which AI was employed.
Perform a comprehensive manual review prior to submitting the pull request.
Be prepared to explain every line of code they submitted when asked about it by a maintainer.
Using AI to respond to human reviewers is strictly prohibited.

For more info, please refer to the AGENTS.md file.

Pull requests (for contributors & collaborators)

Before submitting your PR:

Search for existing PRs to prevent duplicating efforts
llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the examples in the ggml repository. simple shows the bare minimum for using ggml. gpt-2 has minimal implementations for language model inference using GPT-2. mnist demonstrates how to train and evaluate a simple image classifier
Test your changes:
- Execute the full CI locally on your machine before publishing
- Verify that the perplexity and the performance are not affected negatively by your changes (use llama-perplexity and llama-bench)
- If you modified the ggml source, run the test-backend-ops tool to check whether different backend implementations of the ggml operators produce consistent results (this requires access to at least two different ggml backends)
- If you modified a ggml operator or added a new one, add the corresponding test cases to test-backend-ops
Create separate PRs for each feature or fix:
- Avoid combining unrelated changes in a single PR
- For intricate features, consider opening a feature request first to discuss and align expectations
- When adding support for a new model or feature, focus on CPU support only in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly

After submitting your PR:

Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability
Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR
If your PR becomes stale, rebase it on top of latest master to get maintainers attention
Consider adding yourself to CODEOWNERS to indicate your availability for fixing related issues and reviewing related PRs

Pull requests (for maintainers)

Squash-merge PRs
Use the following format for the squashed commit title: <module> : <commit title> (#<issue_number>). For example: utils : fix typo in utils.py (#1234)
Optionally pick a <module> from here: https://github.com/ggml-org/llama.cpp/wiki/Modules
Let other maintainers merge their own PRs
When merging a PR, make sure you have a good understanding of the changes
Be mindful of maintenance: most of the work going into a feature happens after the PR is merged. If the PR author is not committed to contribute long-term, someone else needs to take responsibility (you)

Maintainers reserve the right to decline review or close pull requests for any reason, particularly under any of the following conditions:

The proposed change is already mentioned in the roadmap or an existing issue, and it has been assigned to someone.
The pull request duplicates an existing one.
The contributor fails to adhere to this contributing guide.

Coding guidelines

Avoid adding third-party dependencies, extra files, extra headers, etc.
Always consider cross-compatibility with other operating systems and architectures
Avoid fancy-looking modern STL constructs, use basic for loops, avoid templates, keep it simple
Vertical alignment makes things more readable and easier to batch edit
Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, void * ptr, int & a
Use sized integer types such as int32_t in the public API, e.g. size_t may also be appropriate for allocation sizes or byte offsets
Declare structs with struct foo {} instead of typedef struct foo {} foo
- In C++ code omit optional struct and enum keyword whenever they are not necessary
```
// OK
llama_context * ctx;
const llama_rope_type rope_type;

// not OK
struct llama_context * ctx;
const enum llama_rope_type rope_type;
```
(NOTE: this guideline is yet to be applied to the llama.cpp codebase. New code should follow this guideline.)
Try to follow the existing patterns in the code (indentation, spaces, etc.). In case of doubt use clang-format (from clang-tools v15+) to format the added code
For anything not covered in the current guidelines, refer to the C++ Core Guidelines
Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
Matrix multiplication is unconventional: C = ggml_mul_mat(ctx, A, B) means C^T = A B^T \Leftrightarrow C = B A^T.

Naming guidelines

Use snake_case for function, variable and type names
Naming usually optimizes for longest common prefix (see https://github.com/ggml-org/ggml/pull/302#discussion_r1243240963)
```
// not OK
int small_number;
int big_number;

// OK
int number_small;
int number_big;
```

Enum values are always in upper case and prefixed with the enum name

enum llama_vocab_type {
    LLAMA_VOCAB_TYPE_NONE = 0,
    LLAMA_VOCAB_TYPE_SPM  = 1,
    LLAMA_VOCAB_TYPE_BPE  = 2,
    LLAMA_VOCAB_TYPE_WPM  = 3,
    LLAMA_VOCAB_TYPE_UGM  = 4,
    LLAMA_VOCAB_TYPE_RWKV = 5,
};

The general naming pattern is <class>_<method>, with <method> being <action>_<noun>

llama_model_init();           // class: "llama_model",         method: "init"
llama_sampler_chain_remove(); // class: "llama_sampler_chain", method: "remove"
llama_sampler_get_seed();     // class: "llama_sampler",       method: "get_seed"
llama_set_embeddings();       // class: "llama_context",       method: "set_embeddings"
llama_n_threads();            // class: "llama_context",       method: "n_threads"
llama_adapter_lora_free();    // class: "llama_adapter_lora",  method: "free"

The get <action> can be omitted
The <noun> can be omitted if not necessary
The _context suffix of the <class> is optional. Use it to disambiguate symbols when needed
Use init/free for constructor/destructor <action>

Use the _t suffix when a type is supposed to be opaque to the user - it's not relevant to them if it is a struct or anything else
```
typedef struct llama_context * llama_context_t;

enum llama_pooling_type llama_pooling_type(const llama_context_t ctx);
```
(NOTE: this guideline is yet to be applied to the llama.cpp codebase. New code should follow this guideline)
C/C++ filenames are all lowercase with dashes. Headers use the .h extension. Source files use the .c or .cpp extension
Python filenames are all lowercase with underscores
(TODO: abbreviations usage)

Preprocessor directives

(TODO: add guidelines with examples and apply them to the codebase)
```
#ifdef FOO
#endif // FOO
```

Code maintenance

Existing code should have designated collaborators and/or maintainers specified in the CODEOWNERS file reponsible for:
- Reviewing and merging related PRs
- Fixing related bugs
- Providing developer guidance/support
When adding or modifying a large piece of code:
- If you are a collaborator, make sure to add yourself to CODEOWNERS to indicate your availability for reviewing related PRs
- If you are a contributor, find an existing collaborator who is willing to review and maintain your code long-term
- Provide the necessary CI workflow (and hardware) to test your changes (see ci/README.md)
New code should follow the guidelines (coding, naming, etc.) outlined in this document. Exceptions are allowed in isolated, backend-specific parts of the code that do not interface directly with the ggml interfaces. (NOTE: for legacy reasons, existing code is not required to follow this guideline)

Documentation

Documentation is a community effort
When you need to look into the source code to figure out how to use an API consider adding a short summary to the header file for future reference
When you notice incorrect or outdated documentation, please update it

Resources

The Github issues, PRs and discussions contain a lot of information that can be useful to get familiar with the codebase. For convenience, some of the more important information is referenced from Github projects:

https://github.com/ggml-org/llama.cpp/projects

10 KiB Raw Blame History