bert.cpp

Note: Now that BERT support has been merged into llama.cpp, this repo is semi-defunct. The implementation in llama.cpp is substantially faster and has much better model support. Still happy to accept PRs if they do come along though.

This is a ggml implementation of the BERT embedding architecture. It supports inference on CPU, CUDA and Metal in floating point and a wide variety of quantization schemes. Includes Python bindings for batched inference.

This repo is a fork of original bert.cpp as well as embeddings.cpp. Thanks to both of you!

Install

Fetch this repository then download submodules and install packages with

git submodule update --init
pip install -r requirements.txt

To fetch models from huggingface and convert them to gguf format run something like the following (after creating the models directory)

python bert_cpp/convert.py BAAI/bge-base-en-v1.5 models/bge-base-en-v1.5-f16.gguf

This will convert to float16 by default. To do float32 add f32 to the end of the command.

Build

To build the C++ library for CPU/CUDA/Metal, run the following

# CPU
cmake -B build . && make -C build -j

# CUDA
cmake -DGGML_CUBLAS=ON -B build . && make -C build -j

# Metal
cmake -DGGML_METAL=ON -B build . && make -C build -j

On some distros, when compiling with CUDA, you also need to specify the host C++ compiler. To do this, I suggest setting the CUDAHOSTCXX environment variable to your C++ bindir.

Execute

All executables are placed in build/bin. To run inference on a given text, run

# CPU / CUDA
build/bin/main -m models/bge-base-en-v1.5-f16.gguf -p "Hello world"

# Metal
GGML_METAL_PATH_RESOURCES=build/bin/ build/bin/main -m models/bge-base-en-v1.5-f16.gguf -p "Hello world"

To force CPU usage, add the flag -c.

Python

You can also run everything through Python, which is particularly useful for batch inference. For instance,

from bert_cpp import BertModel
mod = BertModel('models/bge-base-en-v1.5-f16.gguf')
emb = mod.embed(batch)

where batch is a list of strings and emb is a numpy array of embedding vectors.

Quantize

You can quantize models with the command (using the f32 model as a base seems to work better)

build/bin/quantize models/bge-base-en-v1.5-f32.gguf models/bge-base-en-v1.5-q8_0.gguf q8_0

or whatever your desired quantization level is. Currently supported values are: q8_0, q5_0, q5_1, q4_0, and q4_1. You can then pass these model files directly to main as above.