This research project is for academic and non-production purposes. Your suggestions and contributions are warmly welcomed.

RepDL is a specialized library designed to facilitate reproducible deep learning by guaranteeing bitwise identical outcomes across various hardware platforms for identical training or inference tasks.

Citation:

@misc{xie_repdl_2025,
	title = {{RepDL}: {Bit}-level {Reproducible} {Deep} {Learning} {Training} and {Inference}},
	url = {https://arxiv.org/abs/2510.09180},
	author = {Xie, Peichen and Zhang, Xian and Chen, Shuo},
	year = {2025},
	note = {arXiv: 2510.09180},
}

Before setting up RepDL, ensure that PyTorch and the corresponding CUDA version are installed on your system.

To build and install RepDL, execute the following commands in your terminal:

git clone https://github.com/microsoft/RepDL.git  
cd RepDL  
pip install .  

The easiest way to enable reproducible inference for an existing PyTorch model is by using the following code

import repdl
model = repdl.from_torch_module(model)

For reproducible training, refer to the example script located at examples/mnist_training.py. The output of this script is consistent across different devices, as shown below:

Hash of the initial model: 2a2d133895b1684e55d0f152ead2914b55adc551d9790a4b4585309b79c60362
Hash of the trained model: 31ee86a7f75dbd76bac22f209617eb7349f93b0ede116dba924f328cac3013f1
Test accuracy of the model on the 10000 test images: 0.9804
Hash of the logits: 0ca34dcd37105b4af690c46a62407b4b9b097d1063864c6d99f4309aeb4bca3e

Many operations in PyTorch are non-reproducible, even if torch.use_deterministic_algorithms(True) is set. For example, the following operations on a tensor x: torch.mm(x, x), torch.div(x, 10), and torch.sqrt(x), can lead to various results on different devices. However, with RepDL, the equivalent operations: repdl.ops.mm(x, x), repdl.ops.div(x, 10), and repdl.ops.sqrt(x) will produce identical results regardless of the device used. The script of this example is located at examples/reproducible_ops.py.

RepDL defines reproducible operations in repdl.ops, and implements them in repdl.backend. Building on these operations, RepDL provides PyTorch-compatible functions and modules in repdl.nn. Currently, only a subset of functions and modules is available.

To expand RepDL, you can write your own reproducible operations, functions, or modules following the guidelines below:

• Add custom operations to repdl/ops.py, put the C++ and CUDA implementations of the operations in the backend directory, and register them in backend/cpu/bind.cpp and backend/cuda/bind.cpp.
• Add custom differentiable functions, i.e., functions compatible with PyTorch's .backward() automatic differentiation, to repdl/func.py.
• Add PyTorch-compatible differentiable functions and modules to the corresponding locations, such as repdl/nn/functionals.py and repdl/optim.py.
• Ensure that your implementations are reproducible by fixing the order of floating-point operations, avoiding instructions that do not comply with the IEEE-754 standard, and using correctly rounded mathematical functions rather than inaccurate ones.

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