In this directory, we provide the torch scripts for the experiments in QuaRot.
Currently, we only support LLaMa-2 models. You can simply run the main.py to reproduce the results in the paper. The most important arguments are:
--model: the model name (or path to the weights)--bsz: the batch size for PPL evaluation--rotate: whether we want to rotate the model--lm_eval: whether we want to run LM-Eval for Zero-Shot tasks--tasks: the tasks for LM-Eval--cal_dataset: the calibration dataset for GPTQ quantization--a_bits: the number of bits for activation quantization--w_bits: the number of bits for weight quantization--v_bits: the number of bits for value quantization--k_bits: the number of bits for key quantization--w_clip: Whether we want to clip the weights--a_clip_ratio: The ratio of clipping for activation--k_clip_ratio: The ratio of clipping for key--v_clip_ratio: The ratio of clipping for value--w_asym: Whether we want to use asymmetric quantization for weights--a_asym: Whether we want to use asymmetric quantization for activation--v_asym: Whether we want to use asymmetric quantization for value--k_asym: Whether we want to use asymmetric quantization for key--a_groupsize: The group size for activation quantization--w_groupsize: The group size for weight quantization--v_groupsize: The group size for value quantization--k_groupsize: The group size for key quantization
For example, to run the perplexity of LLaMA2-7B model with quantizing all weights and activations, you can run the following command:
For QuaRot
python main.py \
--methods quarot \
--model /data/BaseLLMs/llama2-7b-hf \
--rotate --a_bits 4 --v_bits 4 --k_bits 4 --w_bits 4 --w_clip
For SpinQuant
python main.py \
--methods spinquant \
--model /data/BaseLLMs/llama2-7b-hf \
--rotate --a_bits 4 --v_bits 4 --k_bits 4 --w_bits 4 --w_clip