EntroPack

General-purpose tensor compression for PyTorch

EntroPack is a general-purpose tensor compression library for PyTorch. It supports lossless compression for exact recovery and lossy compression with a target bitrate to balance storage and reconstruction accuracy. GPU encoding and decoding compress tensors and restore them in their original shape and dtype.

Documentation · 中文

• Flexible bitrates. Compress each weight matrix at any non-integer target bitrate, or preserve every input bit with a lossless scheme.
• Multiple dtypes. Compress tensors in 16 floating-point, integer, and Boolean dtypes, such as FP32, BF16, FP16, FP8, and INT8.
• Easy to use. Compress and restore tensors through a common API, and save them with state_dict. Compressed linear layers provide an integration for model weights.

Installation

Use Python 3.10 or later and install a CUDA-enabled build of PyTorch 2.10 or later for your environment.

Install from source (recommended)

git clone https://github.com/modelscope/entropack.git
cd entropack
pip install -e ".[cuda13]"

Install from PyPI

PyPI releases may lag behind source updates. Install from source for the latest features.

pip install "entropack[cuda13]"

Both installation methods above use CUDA 13 and include the matching CuPy package. For CUDA 12, replace cuda13 with cuda12 in either command. If a compatible CuPy is already installed, use pip install -e . for source installation or pip install entropack for PyPI.

See Quick start for environment requirements and usage examples.

Get started

Direct tensor compression

This example compresses a 2D BF16 tensor at a target of 3.5 bits per parameter, then decompresses it to a tensor with the original shape and dtype:

import torch
import entropack as ep

tensor = (torch.randn(256, 256, device="cuda") * 0.02).to(torch.bfloat16)
config = ep.EntroPackLossyConfig(target_bpp=3.5)

compressed = ep.compress(tensor, config)
restored = ep.decompress(compressed, config)

print(f"Target: {config.target_bpp:.2f} bits per parameter")
print(f"Stored: {compressed.actual_bpp:.2f} bits per parameter")
print(restored.shape, restored.dtype)

target_bpp specifies the target bitrate in bits per parameter (bpp). For general tensors, each tensor element counts as one parameter when computing bpp. actual_bpp reports the stored rate, including metadata. Finite targets with 0.001 <= target_bpp <= 11 are supported, including non-integer values. See Compression configuration for target limits by dtype.

Compressed Linear

CompressedLinear.from_linear compresses an existing torch.nn.Linear's weights using the supplied Config and returns a new Compressed Linear. Call it as layer(x) to compute the output, just as with an ordinary linear layer:

import torch
import entropack as ep

linear = torch.nn.Linear(256, 256, dtype=torch.bfloat16, device="cuda")
config = ep.EntroPackLossyConfig(target_bpp=4.0)
layer = ep.CompressedLinear.from_linear(linear, config=config)
x = torch.randn(8, 256, dtype=torch.bfloat16, device="cuda")

with torch.inference_mode():
    output = layer(x)
print(output.shape, f"{layer.compressed_bits:.2f} bits per weight")

See Compressed Linear usage for model replacement and checkpoint examples.

Pre-quantized models

We provide pre-quantized weight packages produced by EntroPack for representative diffusion models on ModelScope. These packages can be loaded using the quantization features of DiffSynth-Studio:

• Z-Image-Turbo: DiT and text encoder.
• Qwen-Image-2.1: DiT and text encoder.
• MiniMax-H3: two DiTs, FL2VA and Ref2VA, sharing the text encoder and video VAE.

Each model provides DiT and text-encoder packages at 4/5/6/7/8 bpp, plus:

• FP8@5 (dit_fp8_5bpp): FP8 W8A8 weight codes compressed to 5 bpp with EntroPack, using FP8 GEMM for inference.
• Extreme mixed-bit: bitrate allocation based on layer sensitivity, reaching 2.3 bpp for Z-Image-Turbo and 3.0 bpp for Qwen-Image-2.1 and MiniMax-H3.

Configuration

Config defines the compression scheme and its settings. It is passed to tensor compression and decompression functions or to a compressed Linear layer's constructor.

Config	Compression and use cases
DFloat11Config()	Specialized lossless compression for BF16 tensors. Decompression restores every input bit, for applications requiring exact recovery.
TileANSConfig()	Lossless compression for BF16, FP16, FP32, FP8, INT8, and other supported dtypes. The compression ratio depends on the input data distribution.
EntroPackLossyConfig(target_bpp=...)	Lossy compression of 2D floating-point and integer tensors. target_bpp specifies the target bits per parameter in [0.001, 11], including non-integer values, to balance storage size and reconstruction accuracy.

See Compression configuration for scheme selection and the complete parameter reference.

Performance

On one NVIDIA H20, EntroPack compresses the weights of all 276 linear layers in Z-Image-Turbo's diffusion transformer at a 4 bpp target in 3.5 seconds. The compressed weights occupy 4.02 bpp, with 7.18% relative L2 reconstruction error. Inference with these weights takes 544.7 ms per denoising step, only 7.7% above the original BF16 model's 505.7 ms.

Documentation

Guide	Contents
Quick start	Install and run tensor compression and Compressed Linear examples
Compression configuration	Choose a scheme and look up supported dtypes and parameters
Tensor compression	Encode, decode, inspect storage, move data, and save or load tensors
Compressed Linear usage	Replace model layers, use low-precision computation, and manage checkpoints
API reference	Look up functions, classes, and properties

Compression principles: DFloat11, tile-ANS, and EntroPack Lossy Compression.

Acknowledgements

EntroPack's design is inspired by DFloat11, dahuffman, DietGPU, and tile-ANS.

License

Apache License 2.0.

Citation

@misc{zhang2026entropackfastaccurateentropycoded,
      title={EntroPack: Fast and Accurate Entropy-Coded Weight Compression at Arbitrary Bitrates},
      author={Hong Zhang and Zhongjie Duan and Yingda Chen},
      year={2026},
      eprint={2609.34185},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2609.34185},
}