llm-sepweight 0.9.2

Microlib for converting LLM weights into sepweight format

llm_sepweight

The llm_sepweight microlib is designed to manage the weights of large language models (LLMs) by organizing them into a unified format called sepweight.

Every LLM has roughly the same three parts:

1. begin - the part of the model which computes the embeddings before the layers
2. mid - a number of (most commonly transformer) layers
3. end - the part of the model which converts the hidden state into a prediction for the next token

sepweight essentially mirrors the state dict of the LLM into the filesystems, meaning that you will (roughly) have one file per each of these components of the LLM.

This format enables the distributed execution of LLMs by separating the model weights into distinct segments that can be individually managed and accessed as needed.

This microlib provides methods for:

1. dump-ing from different formats to sepweight
2. load-ing the state dict needed to run a part of the LLM

The only dependency is torch.

Microlib docs are available on https://microlib.org/llm_sepweight.html

Installation

pip install llm_sepweight

Quick Example

To convert an existing state dict into sepweight, you need to provide:

• decider is a function which will be called for each key in the state dict, and has to decide whether that key should be part of the begin, mid, or end section and the the new name of the key. Example
• state_dict - is just your usual PyTorch state dict
• out_path is the directory, in which you want the result to be stored.

import llm_sepweight

llm_sepweight.dump(
    decider=decider,
    state_dict=state_dict,
    out_path=out_path
)

You could have multiple state dicts (for example coming from multiple files), it's ok to call dump_to_directory with each of them. The result will be combined state dict of all the state dicts provided for a given out_path.

Goal format

llm_sepweight allows you to convert different formats to its own directory format, which is very simple. Let's have a look at an example:

├── begin.pth
├── end.pth
├── mid.00000.pth
├── mid.00001.pth
├── mid.00002.pth
├── mid.00003.pth
├── mid.00004.pth
└── mid.00005.pth

All the weights are stored in a directory in usual .pth files.

This format is very simple and allows great flexibility. For example, a node running layers 0 to 3 would only need to download the begin, mid.00000, mid.00001, mid.00000 files.

Why do we need it?

There are all sorts of different formats for storing the weights of an LLM - .pth files, safetensors, H5, arrow, GGUF, etc.

Moreover, there is a lot of difference in the naming of the transformer layers, of the start embedding, and of the final head. llm_sepweight aims to provide functions, through which you can convert different formats into a sepweight format. The sepweight format is a unified, simple format that allows you to treat the weights of all LLMs in the same way when running nodes in distributed way.