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
-
Quant Classification
-
Quant GPT3
-
Quant VITS
-
Quant AlphaPose
-
Quant YOLOv7
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