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...
In practice these features alongside int4 weight only quantization allow us toreduce peak memory by ~55%, meaning we can Llama3.1-8B inference with a130k context length with only 18.9 GB of peak memory.More details can be foundhere
In language tasks, a few words/tokens in a sentence exhibit more importance than words to understand the overall meaning of a sentence better, leading to different patterns of self-attention applied to different parts of the input. In vision applications, a few regions in the input image may ...
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...
In language tasks, a few words/tokens in a sentence exhibit more importance than words to understand the overall meaning of a sentence better, leading to different patterns of self-attention applied to different parts of the input. In vision applications, a few regions in the input image may ...
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...
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...
In practice these features alongside int4 weight only quantization allow us to reduce peak memory by ~55%, meaning we can Llama3.1-8B inference with a 130k context length with only 18.9 GB of peak memory. More details can be found here Quantization Aware Training Post-training quantization can...
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...