A quantization interval refers to the spacing between two consecutive quantization levels in an A/D converter, determined by the number of quantization or resolution levels. It plays a crucial role in minimizing quantization noise by providing a narrower interval with more quantization bits. ...
Quanto is device agnostic, meaning you can quantize and run your model regardless if you are on CPU/GPU/ MPS (Apple Silicon). Quanto is also torch.compile friendly. You can quantize a model with quanto and call `torch.compile` to the model to compile it for faster generation. This featur...
what's meaning of the debug info? The debug info is the min/max of the max of input. Please refer the code. neural-compressor/neural_compressor/adaptor/torch_utils/waq/utils.py Lines 286 to 291 in 24419c9 def cal_scale(input_max_abs, weights, alpha, weight_max_lb=1e-5): we...
meaning the most important or impactful rows (determined by sampled inputs and outputs) are processed first. This method aims to place most of the quantization error (inevitably introduced during quantization) on less significant weights. This approach improves...
axis=1. These settings offer a good balance between quality, vram usage and speed. If you want better results with the same vram usage, switch toaxis=0and use the ATEN backend. If you want to use lower likenbits=2, you should useaxis=0with a low group-size via HQQ+, meaning adding...
Quanto is device agnostic, meaning you can quantize and run your model regardless if you are on CPU/GPU/ MPS (Apple Silicon). Quanto is also torch.compile friendly. You can quantize a model with quanto and call `torch.compile` to the model to compile it for faster generation. This featur...