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* Deepseek MLA Optimizations Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com> * Make MLA optional * Remove some unnecessary copies in the MLA attention * Deepseek MLA Optimizations V2 (#195) * Avoid allocating MHA KV cache when MLA is turned on * Added missing gguf-py file * Added final optimizations Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com> * Make sure we do have wk_b and wv_b before enabling MLA --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com> Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com> * Use type_k and type_v to set the types of the MLA caches They were hard-coded at f16. On my Ryzen-7950X with native bf16 support I get a fairly significant PP performance boost with bf16 KV-cache: PP-4096 = 320 t/s up from 292 t/s with fp16 KV-cache. * Better gemm strategy when nth > nhead It gives a ~10% PP performance boost for DeepSeek-Lite with 32 threads (with or without MLA). Before this commit, when nth > nhead heads were processed sequentially with all nth threads participating in each matrix multiplication. Now we ind the gcd of nhead and nth and split threads into nth/gcd groups, each group processing nhead/gcd heads. --------- Co-authored-by: Saood Karim <saood05@gmail.com> Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com> Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
gguf
This is a Python package for writing binary files in the GGUF (GGML Universal File) format.
See convert_hf_to_gguf.py as an example for its usage.
Installation
pip install gguf
API Examples/Simple Tools
examples/writer.py — Generates example.gguf in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model.
scripts/gguf_dump.py — Dumps a GGUF file's metadata to the console.
scripts/gguf_set_metadata.py — Allows changing simple metadata values in a GGUF file by key.
scripts/gguf_convert_endian.py — Allows converting the endianness of GGUF files.
scripts/gguf_new_metadata.py — Copies a GGUF file with added/modified/removed metadata values.
Development
Maintainers who participate in development of this package are advised to install it in editable mode:
cd /path/to/llama.cpp/gguf-py
pip install --editable .
Note: This may require to upgrade your Pip installation, with a message saying that editable installation currently requires setup.py.
In this case, upgrade Pip to the latest:
pip install --upgrade pip
Automatic publishing with CI
There's a GitHub workflow to make a release automatically upon creation of tags in a specified format.
- Bump the version in
pyproject.toml. - Create a tag named
gguf-vx.x.xwherex.x.xis the semantic version number.
git tag -a gguf-v1.0.0 -m "Version 1.0 release"
- Push the tags.
git push origin --tags
Manual publishing
If you want to publish the package manually for any reason, you need to have twine and build installed:
pip install build twine
Then, follow these steps to release a new version:
- Bump the version in
pyproject.toml. - Build the package:
python -m build
- Upload the generated distribution archives:
python -m twine upload dist/*
Run Unit Tests
From root of this repository you can run this command to run all the unit tests
python -m unittest discover ./gguf-py -v
TODO
- Include conversion scripts as command line entry points in this package.