Welcome to Pychop’s documentation!¶

Using low precision can achieve extra speedup while reducing storage and energy costs. The intention of Pychop, motivated by the software chop in Matlab developed by Nick Higham, is to simulate the low precision formats based on single and double precisions, which are prevalent on modern machines. Pychop is a Python package for simulating low precision and quantization in modern machines. It supports NumPy, Torch, or JAX backend.

Pychop mainly contains three modules for quantization simulation:

Chop: for low precision floating point simulation
Chopi: for integer quantization
Chopf: for fixed point quantization

Features¶

The Pychop class offers several key advantages that make it a powerful tool for developers, researchers, and engineers working with numerical computations:

Customizable Precision

Users can specify the number of exponent (exp_bits) and significand (sig_bits) bits, enabling precise control over the trade-off between range and precision. For example, setting exp_bits=5 and sig_bits=4 creates a compact 10-bit format (1 sign, 5 exponent, 4 significand), ideal for testing minimal precision scenarios.

Multiple Rounding Modes

Supports a variety of rounding strategies, including deterministic (toward_zero, nearest_even, nearest_odd) and stochastic (stochastic_prop, stochastic_equal) methods. This flexibility allows experimentation with different quantization effects, which is crucial for machine learning models where rounding impacts training dynamics and inference accuracy.

Hardware-Independent Simulation

Emulates low-precision arithmetic within standard float32 or float64 precision, eliminating the need for specialized hardware. This makes it accessible for prototyping and testing on any PyTorch-supported platform, from CPUs to GPUs, without requiring custom FPGA or ASIC implementations.

Support for Denormal Numbers

The optional support_denormals parameter enables handling of subnormal numbers, extending the representable range near zero. This is particularly valuable for applications requiring high fidelity at small magnitudes, such as scientific simulations or deep learning with small gradients.

GPU Acceleration

Leveraging PyTorch’s tensor operations and device support (device parameter), Pychop can run efficiently on GPUs. This allows for fast, vectorized processing of large datasets, making it suitable for large-scale experiments in machine learning and numerical optimization.

Reproducible Stochastic Rounding

The seed parameter ensures reproducibility in stochastic rounding modes, critical for debugging and comparing results across runs. This is a significant advantage in research settings where consistent outcomes are needed to validate hypotheses. To ensure consistency with MATLAB’s chop software, Pychop closely follows its API. For the first four rounding modes, it produces identical results given the same user-defined parameters. For stochastic rounding modes (rmode 5 and 6), both tools yield the same output when provided with identical random numbers.

Ease of Integration

Built on PyTorch, the class integrates seamlessly with existing PyTorch workflows, including neural network training pipelines and custom numerical algorithms. The input value can be any array-like object, automatically converted to a tensor, enhancing usability.

Installation guide¶

Pychop has the following dependencies for its functionality:

numpy>=1.21

pandas>=2.0

torch (only for Torch use)

jax (only for JAX use)

To install the current release via PIP use either of them according to one’s need:

pip install pychop

To check if the instalment is successful, one can load the package, simple use

import pychop

Floating point information¶

To give a shot, one may print information of various precision formats:

from pychop import float_params
float_params()

Note

The documentation is still on going. We welcome the contributions in any forms.

Contents: