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ggml

Roadmap / Manifesto

Tensor library for machine learning

Note that this project is under active development.
Some of the development is currently happening in the llama.cpp and whisper.cpp repos

Features

  • Written in C
  • 16-bit float support
  • Integer quantization support (4-bit, 5-bit, 8-bit, etc.)
  • Automatic differentiation
  • ADAM and L-BFGS optimizers
  • Optimized for Apple Silicon
  • On x86 architectures utilizes AVX / AVX2 intrinsics
  • On ppc64 architectures utilizes VSX intrinsics
  • No third-party dependencies
  • Zero memory allocations during runtime

Updates

Whisper inference (example)

With ggml you can efficiently run Whisper inference on the CPU.

Memory requirements:

Model Disk Mem
tiny 75 MB ~280 MB
base 142 MB ~430 MB
small 466 MB ~1.0 GB
medium 1.5 GB ~2.6 GB
large 2.9 GB ~4.7 GB

GPT inference (example)

With ggml you can efficiently run GPT-2 and GPT-J inference on the CPU.

Here is how to run the example programs:

# Build ggml + examples
git clone https://github.com/ggerganov/ggml
cd ggml
mkdir build && cd build
cmake ..
make -j4 gpt-2 gpt-j

# Run the GPT-2 small 117M model
../examples/gpt-2/download-ggml-model.sh 117M
./bin/gpt-2 -m models/gpt-2-117M/ggml-model.bin -p "This is an example"

# Run the GPT-J 6B model (requires 12GB disk space and 16GB CPU RAM)
../examples/gpt-j/download-ggml-model.sh 6B
./bin/gpt-j -m models/gpt-j-6B/ggml-model.bin -p "This is an example"

# Install Python dependencies
python3 -m pip install -r ../requirements.txt

# Run the Cerebras-GPT 111M model
# Download from: https://huggingface.co/cerebras
python3 ../examples/gpt-2/convert-cerebras-to-ggml.py /path/to/Cerebras-GPT-111M/
./bin/gpt-2 -m /path/to/Cerebras-GPT-111M/ggml-model-f16.bin -p "This is an example"

The inference speeds that I get for the different models on my 32GB MacBook M1 Pro are as follows:

Model Size Time / Token
GPT-2 117M 5 ms
GPT-2 345M 12 ms
GPT-2 774M 23 ms
GPT-2 1558M 42 ms
--- --- ---
GPT-J 6B 125 ms

For more information, checkout the corresponding programs in the examples folder.

Using Metal (only with GPT-2)

For GPT-2 models, offloading to GPU is possible. Note that it will not improve inference performances but will reduce power consumption and free up the CPU for other tasks.

To enable GPU offloading on MacOS:

cmake -DGGML_METAL=ON -DBUILD_SHARED_LIBS=Off ..

# add -ngl 1
./bin/gpt-2 -t 4 -ngl 100 -m models/gpt-2-117M/ggml-model.bin -p "This is an example"

Using cuBLAS

# fix the path to point to your CUDA compiler
cmake -DGGML_CUBLAS=ON -DCMAKE_CUDA_COMPILER=/usr/local/cuda-12.1/bin/nvcc ..

Using clBLAST

cmake -DGGML_CLBLAST=ON ..

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Tensor library for machine learning

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  • C 60.1%
  • Cuda 15.1%
  • C++ 10.7%
  • Metal 5.4%
  • Objective-C 4.8%
  • CMake 1.8%
  • Other 2.1%