README.md

pytorch.cpp

Running PyTorch Models for Inference using GGML

Directory Structure

• conversion.py - Converts weights of a PyTorch model to GGML format
• model.py - Sample PyTorch model for training a neural network to learn 2 input truth table
• main.cpp - Main driver program for running inference using ggml

Getting Started

Train Model

• Run the following command to train the model on your choice of truth table:

python3 model.py xor
OR
python3 model.py and
OR
python3 model.py or

• This will save the weights in the assets folder with name model.pth

Convert PyTorch Model Weights to GGML fprmat

• It is a binary format that is designed for fast loading and saving of models, and for ease of reading.
• Usually your model weights per layer are stored with dimensions shape, actual dimensions and then actual weights tightly.
• Packing needs to be done in binary format such that it can be loaded using GGML C/C++ code.
• Run following command to convert your PyTorch model weights stored in assets/model.pth to GGML format:

python3 conversion.py

Compile and Run Inference using GGML

• Refer main.cpp for referring to load and predict functions.
• load loads the GGML format and reads the weights to initialize GGML params per layer specific to the model and initialize context.
• predict uses the initialized model and perform the vector calculations as a forward pass would do eventually.
• Run following command to include GGML headers:

git clone https://github.com/ggerganov/ggml

• Compile and create the excutable for running the inference:

mkdir build && cd build
cmake ..
make

• Run the inference:

./bin/pytorch.cpp

License

This project is licensed under MIT License.