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Prototype routines for GPU quantization written using PyTorch.

License: MIT License

C++ 21.74% Python 54.96% Cuda 23.30%

protoquant's Introduction

PROTOQUANT - Dynamic Quantization with Tensor Subclassing

The protoquant package provides dynamic vector-wise quantization and quantized arithmetic using torch.tensor subclassing.

This dynamnic quantization support is directed at a broad range of applications, and currently tested with the PyTorch Transformner API and Better Transformers implementation with a focus on GPU inference.

The focus on testing for Transformer Inference is non-limiting and protoquant is broadly applicable to support broad uses for using dynamic inference with PyTorch.

Installation

You need to clone the repo with recursive submodules.

git clone --recurse-submodules https://github.com/facebookexperimental/protoquant.git

If you forget to, you can always fix this using this trick

Once the repository is cloned, you will NEED to be on a GPU machine, and then pip install -e . works.

If you really want to compile on a CPU machine, see here

License

MIT

protoquant's People

Contributors

cpuhrsch avatar facebook-github-bot avatar hdcharles avatar msaroufim avatar yuguo68 avatar

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