llama.cpp

Inference of Facebook's LLaMA model in pure C/C++

!!! IMPORTANT !!!

Commit 007a8f6f459c6eb56678fdee4c09219ddb85b640 added support for all LLaMA models, but introduced breaking changes. If you generated any models before that commit, you must regenerate them after updating to latest master.

TEMPORARY NOTICE: Currently the quantized models run only on Apple Silicon. On other architectures, you can use the F16 models, but they will be much slower. Support will be added later

Description

The main goal is to run the model using 4-bit quantization on a MacBook.

• Plain C/C++ implementation without dependencies
• Apple silicon first-class citizen - optimized via Arm Neon and Accelerate framework
• Mixed F16 / F32 precision
• 4-bit quantization support
• Runs on the CPU

This was hacked in an evening - I have no idea if it works correctly.

Here is a typical run using LLaMA-7B:

make -j && ./main -m ./models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -t 8 -n 512
I llama.cpp build info:
I UNAME_S:  Darwin
I UNAME_P:  arm
I UNAME_M:  arm64
I CFLAGS:   -I.              -O3 -DNDEBUG -std=c11   -fPIC -pthread -DGGML_USE_ACCELERATE
I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC -pthread
I LDFLAGS:   -framework Accelerate
I CC:       Apple clang version 14.0.0 (clang-1400.0.29.202)
I CXX:      Apple clang version 14.0.0 (clang-1400.0.29.202)

make: Nothing to be done for `default'.
main: seed = 1678486056
llama_model_load: loading model from './models/7B/ggml-model-q4_0.bin' - please wait ...
llama_model_load: n_vocab = 32000
llama_model_load: n_ctx   = 512
llama_model_load: n_embd  = 4096
llama_model_load: n_mult  = 256
llama_model_load: n_head  = 32
llama_model_load: n_layer = 32
llama_model_load: n_rot   = 128
llama_model_load: f16     = 2
llama_model_load: n_ff    = 11008
llama_model_load: ggml ctx size = 4529.34 MB
llama_model_load: memory_size =   512.00 MB, n_mem = 16384
llama_model_load: .................................... done
llama_model_load: model size =  4017.27 MB / num tensors = 291

main: prompt: 'Building a website can be done in 10 simple steps:'
main: number of tokens in prompt = 15
     1 -> ''
  8893 -> 'Build'
   292 -> 'ing'
   263 -> ' a'
  4700 -> ' website'
   508 -> ' can'
   367 -> ' be'
  2309 -> ' done'
   297 -> ' in'
 29871 -> ' '
 29896 -> '1'
 29900 -> '0'
  2560 -> ' simple'
  6576 -> ' steps'
 29901 -> ':'

sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000


Building a website can be done in 10 simple steps:
1) Select a domain name and web hosting plan
2) Complete a sitemap
3) List your products
4) Write product descriptions
5) Create a user account
6) Build the template
7) Start building the website
8) Advertise the website
9) Provide email support
10) Submit the website to search engines
A website is a collection of web pages that are formatted with HTML. HTML is the code that defines what the website looks like and how it behaves.
The HTML code is formatted into a template or a format. Once this is done, it is displayed on the user's browser.
The web pages are stored in a web server. The web server is also called a host. When the website is accessed, it is retrieved from the server and displayed on the user's computer.
A website is known as a website when it is hosted. This means that it is displayed on a host. The host is usually a web server.
A website can be displayed on different browsers. The browsers are basically the software that renders the website on the user's screen.
A website can also be viewed on different devices such as desktops, tablets and smartphones.
Hence, to have a website displayed on a browser, the website must be hosted.
A domain name is an address of a website. It is the name of the website.
The website is known as a website when it is hosted. This means that it is displayed on a host. The host is usually a web server.
A website can be displayed on different browsers. The browsers are basically the software that renders the website on the user’s screen.
A website can also be viewed on different devices such as desktops, tablets and smartphones. Hence, to have a website displayed on a browser, the website must be hosted.
A domain name is an address of a website. It is the name of the website.
A website is an address of a website. It is a collection of web pages that are formatted with HTML. HTML is the code that defines what the website looks like and how it behaves.
The HTML code is formatted into a template or a format. Once this is done, it is displayed on the user’s browser.
A website is known as a website when it is hosted

main: mem per token = 14434244 bytes
main:     load time =  1332.48 ms
main:   sample time =  1081.40 ms
main:  predict time = 31378.77 ms / 61.41 ms per token
main:    total time = 34036.74 ms

And here is another demo of running both LLaMA-7B and whisper.cpp on a single M1 Pro MacBook:

https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4

Usage

Here are the step for the LLaMA-7B model:

# build this repo
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

# obtain the original LLaMA model weights and place them in ./models
ls ./models
65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model

# install Python dependencies
python3 -m pip install torch numpy sentencepiece

# convert the 7B model to ggml FP16 format
python3 convert-pth-to-ggml.py models/7B/ 1

# quantize the model to 4-bits
./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin 2

# run the inference
./main -m ./models/7B/ggml-model-q4_0.bin -t 8 -n 128

For the bigger models, there are a few extra quantization steps. For example, for LLaMA-13B, converting to FP16 format will create 2 ggml files, instead of one:

ggml-model-f16.bin
ggml-model-f16.bin.1

You need to quantize each of them separately like this:

./quantize ./models/13B/ggml-model-f16.bin   ./models/13B/ggml-model-q4_0.bin 2
./quantize ./models/13B/ggml-model-f16.bin.1 ./models/13B/ggml-model-q4_0.bin.1 2

Everything else is the same. Simply run:

./main -m ./models/13B/ggml-model-q4_0.bin -t 8 -n 128

The number of files generated for each model is as follows:

7B  -> 1 file
13B -> 2 files
33B -> 4 files
65B -> 8 files

When running the larger models, make sure you have enough disk space to store all the intermediate files.

Limitations

• Not sure if my tokenizer is correct. There are a few places where we might have a mistake:
  • 26c0846629/convert-pth-to-ggml.py (L79-L87)
  • 26c0846629/utils.h (L65-L69) In general, it seems to work, but I think it fails for unicode character support. Hopefully, someone can help with that
• I don't know yet how much the quantization affects the quality of the generated text
• Probably the token sampling can be improved
• x86 quantization support not yet ready. Basically, you want to run this on Apple Silicon. For now, on Linux and Windows you can use the F16 ggml-model-f16.bin model, but it will be much slower.
• The Accelerate framework is actually currently unused since I found that for tensors shapes typical for the Decoder, there is no benefit compared to the ARM_NEON intrinsics implementation. Of course, it's possible that I simlpy don't know how to utilize it properly. But in any case, you can even disable it with LLAMA_NO_ACCELERATE=1 make and the performance will be the same, since no BLAS calls are invoked by the current implementation