o ý°“iã @sàddlmZmZddlZddlmZmZddlmZm Z m Z ddl m Z ddl mZdgZ ddeed eed eed efd d „Z  ddejjd eedeefdd„Z  ddejjd eedeed eefdd„ZdS)é)ÚListÚOptionalN)ÚCompressionFormatÚSparsityStructure)ÚQuantizationArgsÚQuantizationStrategyÚQuantizationType)Úis_module_quantized)ÚloggerÚ,infer_and_set_per_module_quantization_formatÚ input_argsÚ weight_argsÚsparsity_structureÚreturncCsÐt|ƒtjk}|duo|du}|jdkr&|jtjjkr&|jdkr#tj Stj S|rL|jdvo3|jtj jk}|s9tj S|rI|j tjjtjjfvrItjStjS|jtjjkr[|jdkr[tjS|jtj jkretjStj S)a  Using the weight and input quantization args as well as an optional sparsity structure, determine the compression format that should be applied to a given module :param input_args: input quantization parameters :param weight_args: weight quantization parameters :param sparsity_structure: optional (global) modle sparsity structure :return CompresssionFormat for the module Néé )rér)rÚTWO_FOURÚnum_bitsÚtyperÚFLOATÚvalueÚ group_sizerÚmxfp4_pack_quantizedÚnvfp4_pack_quantizedÚINTÚnaive_quantizedÚstrategyrÚCHANNELÚGROUPÚ marlin_24Úpack_quantizedÚfloat_quantizedÚ int_quantized)r r rÚis_24_structureÚis_weight_onlyÚ is_valid_pack©r'úT/home/ubuntu/.local/lib/python3.10/site-packages/compressed_tensors/config/format.pyÚ_get_quant_compression_formats2 ÿ   þþ r)ÚmoduleÚquantization_formatcCsŠ|jj}|jj}|durdSt|||ƒ}|dur(||jkr"t d¡||j_dS|jjdur>|jj|jkrsJ  ýÿþý ü9ýÿþ ý/ýÿþýü