llama.cpp/gguf-py
Gabe Goodhart 3d6bf6919f
llama : add IBM Granite MoE architecture (#9438)
* feat(gguf-py): Add granitemoe architecture

This includes the addition of new tensor names for the new moe layers.
These may not be correct at this point due to the need for the hack in
gguf_writer.py to double-check the length of the shape for these layers.

Branch: GraniteMoE

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat(convert_hf_to_gguf): Add GraniteMoeModel

GraniteMoe has the same configuration deltas as Granite

Branch: GraniteMoE

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(granitemoe convert): Split the double-sized input layer into gate and up

After a lot of staring and squinting, it's clear that the standard mixtral
expert implementation is equivalent to the vectorized parallel experts in
granite. The difference is that in granite, the w1 and w3 are concatenated
into a single tensor "input_linear." Rather than reimplementing all of the
math on the llama.cpp side, the much simpler route is to just split this
tensor during conversion and follow the standard mixtral route.

Branch: GraniteMoE

Co-Authored-By: alex.brooks@ibm.com

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat(granitemoe): Implement granitemoe

GraniteMoE follows the mixtral architecture (once the input_linear layers
are split into gate_exps/up_exps). The main delta is the addition of the
same four multipliers used in Granite.

Branch: GraniteMoE

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* Typo fix in docstring

Co-Authored-By: ggerganov@gmail.com

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(conversion): Simplify tensor name mapping in conversion

Branch: GraniteMoE

Co-Authored-By: git@compilade.net
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(convert): Remove unused tensor name mappings

Branch: GraniteMoE

Co-Authored-By: git@compilade.net
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(convert): Sanity check on merged FFN tensor sizes

Branch: GraniteMoE

Co-Authored-By: git@compilade.net
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Allow "output" layer in granite moe architecture (convert and cpp)

Branch: GraniteMoE

Co-Authored-By: git@compilade.net
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(granite): Add missing 'output' tensor for Granite

This is a fix for the previous `granite` architecture PR. Recent snapshots
have included this (`lm_head.weights`) as part of the architecture

Branch: GraniteMoE

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-09-25 10:06:52 +03:00
..
examples gguf-py : fix double call to add_architecture() (#8952) 2024-08-10 08:58:49 +03:00
gguf llama : add IBM Granite MoE architecture (#9438) 2024-09-25 10:06:52 +03:00
scripts gguf_dump.py: fix markddown kv array print (#8588) 2024-07-20 17:35:25 +10:00
tests ggml-quants : ternary packing for TriLMs and BitNet b1.58 (#8151) 2024-09-05 21:48:47 -04:00
LICENSE gguf : make gguf pip-installable 2023-08-25 09:26:05 +03:00
pyproject.toml build(nix): Package gguf-py (#5664) 2024-09-02 14:21:01 +03:00
README.md convert-*.py: GGUF Naming Convention Refactor and Metadata Override Refactor (#7499) 2024-07-18 20:40:15 +10:00

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.

  1. Bump the version in pyproject.toml.
  2. Create a tag named gguf-vx.x.x where x.x.x is the semantic version number.
git tag -a gguf-v1.0.0 -m "Version 1.0 release"
  1. 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:

  1. Bump the version in pyproject.toml.
  2. Build the package:
python -m build
  1. 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.