(formerly llama2.f90)
Hackable large language model inference in pure Fortran. Builds to a ~100k executable that can be run efficiently on a CPU and has zero external dependencies. Between this and sibling project https://github.com/rbitr/ferrite you can create and customize a retrieval augmented (RAG) or other complete language model system.
See the ssm folder for mamba state space model inference code
https://github.com/rbitr/llm.f90/tree/optimize16/purefortran
Current single-core performance of 7.3 tok/s vs 7.4 tok/s for llama.cpp
https://github.com/rbitr/llm.f90/tree/dev/phi2/phi2
An implementation of the phi-2 model from Microsoft.
The base implementation in the master branch runs on a single core only. See the roadmap below for more info on what has been done and what is planned.
git clone https://github.com/rbitr/llm.f90
cd llm.f90
makeThis is a 1.1B parameter llama model converted into 32-bit gguf. See https://huggingface.co/Tensoic/Tiny-Llama-openhermes-1.1B-step-715k-1.5T for the model info
wget https://huggingface.co/SDFASDGA/llm/resolve/main/ggml-model-f32.gguf$ ./llm -m ggml-model-f32.gguf -t 0.9 -v -n 96 -p "I stopped posting on knitting forums because"
Embedding dimension: 2048
Hidden dimension: 5632
Layers: 22
Heads: 32
kv Heads: 4
Vocabulary Size: 32000
Sequence Length: 2048
head size 64
kv head Size 256
loaded embedding weights: 65536000
loaded rms att weights: 45056
loaded wq weights: 92274688
loaded wk weights: 11534336
loaded wv weights: 11534336
loaded wo weights: 92274688
loaded ffn norm weights: 45056
loaded w1 (gate) weights: 253755392
loaded w2 (down) weights: 253755392
loaded w3 (up) weights: 253755392
loaded output norm weights: 2048
loaded classifier weights: 65536000
loading tokens
found 32000 tokens
found 32000 scores
maximum token length 48
Loaded weights
I stopped posting on knitting forums because I couldn't find the
knitters I was looking for, and it often seemed that no longer
interested knitters would post here. Groups like Ravelry and
Knitters' Yarn Added are important to all of us, and I really thought
it was time for our own site to be my own place to showcase my work,
to find people who were interested in knitting, and on and on.
Inference time: 22.5280018 seconds
4.21697426 tokens/second
Timings
1 26.6666660
2 0.00000000
3 0.00000000
4 192.000000
5 17.3333340 The base version currently hard codes the model parameters. This is trivially changed with some uncommenting that will let you load any llama2 model. For anything much bigger (depending on your computer) the suggested branch is https://github.com/rbitr/llama2.f90/tree/version_0 than implements 16-bit floats and parallelism but has not been optimized. To use this branch you will have to get a .gguf version of the model and then convert it as described in the readme.
Models may load slightly faster if you convert to the "ak" file format (from Andrej Karpathy's llama2.c) and load that instead.
If you want to use llm.f90 for a project and need support, please get in touch. See the motivation section below for information about the "philosophy". We want any features added to not add complexity, so for example quantization will be written as a separate program.
- ✅ Speed: currently matches llama.cpp for single thread 32-bit operation (tested on a single intel machine so ymmv) Update: try a competitive 16-bit implementation at
optimize16/purefortran(not actually pure Fortran, it calls a C SIMD routine - 🚧 Parallelism: see https://github.com/rbitr/llama2.f90/tree/version_0 (also with 16-bit quantization)
- 🚧 Quantization: see https://github.com/rbitr/llama2.f90/tree/f16_convert and https://github.com/rbitr/llama2.f90/tree/four_bit_dev for 16-bit and 4-bit respectively
- 🔜 Support for other models
- 🔜 Test on other architectures machines (Apple, other ARM, etc). Please open issues for any feedback.
- 🔜 ...
Note that 🚧 means features that have a "legacy" implementation that works but uses older model file formats and may have other breaking changes. The plan is to roll these into the current master branch while preserving speed optimizations and direct loading of gguf files.
See here for why language models inferenece should be self-hosted for most non-trivial uses. A big reason for this is that LLMs are still a new and rapidly evolving technology and that being able to "hack" the implementation is important to make the best use of them. A corollary to being able to hack the implementation is being able to easily understand and modify the code. The requirements for a hackable model are at odds with the requirements for a framework that has lots of composable parts and works across many platforms. There is a niche for, is something that's dead simple, where the only abstraction is linear algebra and matrix operations, but is also fast enough to run inference at competitive speeds on normal hardware.
Pytorch is a full featured framework but is highly abstracted and not optimized for CPU inference. Llama.cpp / ggml is well optimized for a wide range of hardware and has a simpler project structure compared to pytorch that increases hackability. However as of writing, ggml.c is 20k lines and llama.cpp is 7k. The hand optimization across many platforms plus big range of options (all of which make it a good, full featured software project) make it heavy to work with. Llama2.c (the names are confusing and I may change the name of this project) is very hackable (although less than when it started) and simple to understand. It is not optimized; while in principle it could be, it will still be a C program that requires memory management and manual vector / matrix operations.
| Pytorch | llama.cpp | llama2.c | llm.f90 | |
|---|---|---|---|---|
| Good abstraction | x | x | ||
| Broad hardware support | x | x | ||
| Simple & Hackable | x | x | ||
| Fast | x | x | ||
| Memory and linalg | x | x |
The plan is to retain the hackability of llama2.c, but with the speed of Llama.cpp (currently we achieve comparable speeds on CPU) and the matrix and memory support of Fortran. So far optimization has not significantly diminished the readability or understandability of the code. The goal is not a framework that can be called from other programs, but example source code that can be modified directly for custom use. The hope is that such modifications will be as easy or easier than working with a high level framework. At the same time, we provide the capability of running an LLM from the command line.
Additional options, such as quantization (under development), are preferred to be added as in dedicated programs instead of as branches of one main program. Likewise if we decide to support another model. In this way (hopefully) we keep everything simple and easy to use and hack elsewhere.
