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If this is something you are still interested in, then you need to run a traced model through the onnx export flow. The onnx file generated in the process is specific to Caffe2. Hi it is currently only possible to convert quantized model to Caffe2 using ONNX.

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If you are going to use a GPU you can install optimum with pip install optimum. This will install all required packages including transformers, torch, and onnxruntime. Our first step is to install Optimum with the onnxruntime utilities and evaluate. i am unable to install onnxruntime with pip3. onnx quantization onnxruntime static-quantization. For activation ONNXRuntime supports only uint8 format for now, and for weight ONNXRuntime supports both.

It supports dynamic quantization with IntegerOps and static quantization with QLinearOps.

During quantization the floating point real values are mapped to an 8 bit quantization space and it is of the form: VAL_fp32 = Scale * (VAL_quantized - Zero_point) Scale is a positive real number used to map the floating point numbers to a quantization space. Quantization in ONNX Runtime refers to 8 bit linear quantization of an ONNX model. Generally, it is not a big issue for final result. When and why do I need to try U8U8? On x86-64 machines with AVX2 and AVX512 extensions, OnnxRuntime uses VPMADDUBSW instruction for U8S8 for performance, but this instruction suffer saturation issue.

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator OnnxRuntime Quantization on GPU only support S8S8 format.

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