QPyTorch is a low-precision arithmetic simulation package in PyTorch. It is designed to support researches on low-precision machine learning, especially for researches in low-precision training.
Notably, QPyTorch supports quantizing different numbers in the training process with customized low-precision formats. This eases the process of investigating different precision settings and developing new deep learning architectures. More concretely, QPyTorch implements fused kernels for quantization and integrates smoothly with existing PyTorch kernels (e.g. matrix multiplication, convolution).
Recent researches can be reimplemented easily through QPyTorch. We offer an example replication of WAGE in a downstream repo WAGE. We also provide a list of working examples under Examples.
Note: QPyTorch relies on PyTorch functions for the underlying computation, such as matrix multiplication. This means that the actual computation is done in single precision. Therefore, QPyTorch is not intended to be used to study the numerical behavior of different accumulation strategies.
Note: QPyTorch, as of now, have a different rounding mode with PyTorch. QPyTorch does round-away-from-zero while PyTorch does round-to-nearest-even. This will create a discrepancy between the PyTorch half-precision tensor and QPyTorch's simulation of half-precision numbers.
requirements:
- Python >= 3.6
- PyTorch >= 1.0
- GCC >= 4.9 on linux
Install other requirements by:
pip install -r requirements.txtInstall QPyTorch through pip:
pip install qtorchFor more details about compiler requirements, please refer to PyTorch extension tutorial.
See our readthedocs page.
- Low-Precision VGGs and ResNets using fixed point, block floating point on CIFAR and ImageNet. lp_train
- Reproduction of WAGE in QPyTorch. WAGE
- Implementation (simulation) of 8-bit Floating Point Training in QPyTorch. IBM8
- Tianyi Zhang
- Zhiqiu Lin
- Guandao Yang
- Christopher De Sa