Skip to main content

AtomQuant: Quantization For Human.

Project description

Atom Quant

Atom Quant AKA: aq is a easy quantization lib supports most decent and fashion quantization method through torch.fx. Unlike original pytorch fx quantization support, we add a fully deploy chain from PTQ and QAT quantization to exporting onnx and then shiping to target inference framework.

atomquant can be easily use to quant any model without a specific dataloader or evaluator, you can even evaluator quantization performance without any GT.

We also support different quantization from vendor package, such as onnxruntime, pytorch_quantization, make it more easy to use and with fully examples.

There are 3 main components in atomquant:

  • onnx: directly quantize on onnx model (via onnxruntime);
  • atom: Our built-in quantization method;
  • tensorrt: Quantization specific for convert to TensorRT engine usage;

Install

atomquant can be installed via:

pip install atomquant

Model Zoo

Here, we provide some models quantized for coco, it devided into CPU use, or TensorRT use. Related training code also available:

Examples

  1. Quant Classification

  2. Quant GPT3

  3. Quant VITS

  4. Quant AlphaPose

  5. Quant YOLOv7

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

atomquant-0.0.1.tar.gz (1.9 kB view details)

Uploaded Source

File details

Details for the file atomquant-0.0.1.tar.gz.

File metadata

  • Download URL: atomquant-0.0.1.tar.gz
  • Upload date:
  • Size: 1.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.26.0 requests-toolbelt/0.9.1 urllib3/1.26.7 tqdm/4.62.3 importlib-metadata/4.10.1 keyring/23.5.0 rfc3986/1.5.0 colorama/0.4.4 CPython/3.9.7

File hashes

Hashes for atomquant-0.0.1.tar.gz
Algorithm Hash digest
SHA256 0a82ab4d973b50d54fb3664f045f705dd96c736c8e4edfcbe8d4d0f773212a1d
MD5 7818565ab3ee00dbe9429e473989a3d3
BLAKE2b-256 e7d9bda26c00612087767a3aaec57b50f87be27dd0de7117e180701fed7eea74

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page