o Ti,@sddlZddlmZddlmZddlZddlmZddlmZddlm Z m Z ddl m Z dd l mZdd lmZdd lmZddlZd ejd edejfddZdS)N)nn)Dict)layers)QUANTIZATION_LAYER_MAPPINGS)$get_AsyncPartitionedParameterSwapperrecursive_setattr)logger)deque)ContextManagers)QuantizationContextmodel ds_configreturncsFi}d}d|vs Jd|dd}t|}d|vo'd|dvo'|dddkd|vo1d|dv}t_r@tt||d gnt}|ttd d | }|s^|j fd d d t |}t |dkr| \} } d} d} |D]\} }| | vr| dusJ| d| d| | } |} qv| durqbr| j| jjtjksJdtt| | | }r| jt|| || |vrg|| <|| | |d7}tt |dkshWdn1swYtjtd|dd}|D]\} }|d| d7}|D] } |d| d7}qqt||S)a@[Experimental] Apply group-wise weight quantization to model. Replace layers module according to config_list Args: model (nn.Module): A nn.Module ds_config (Dict, optional): The ds_config dictionary. use None for non-deepspeed managed model. Returns: nn.Module: Quantized nn.Module rweight_quantizationz/Please provide quantization config in ds_configpost_init_quantzero_optimizationstage offload_param)config_dict_or_path param_swappercSst|dtvSNr)typer named_modulera/home/ubuntu/.local/lib/python3.10/site-packages/deepspeed/inference/quantization/quantization.py6sz6_init_group_wise_weight_quantization..cs"r |djjS|djSr)weight ds_tensornumelris_zero3_enabledrrr:s)keyNz( matched multiple quantization key word z and z!Model weight is expected in half.rz0Group-wise weight quantization summary: convert z$ node(s) to quantized implementation zKey: z, matched modules:  )rrr#r r contextlibsuppresslistfilter named_modulessortr lenpopleftitemsr all_gatherdtypetorchfloat16rr partitionrappendgccollectquantized_weight_registryclearr info)r rmatched_module_list_by_keymatched_module_countquantization_config nvme_swapperis_offloading_enabled context_mgr module_list module_namemodule matched_keymatched_quantization_configr$config new_module summary_strrr"r$_init_group_wise_weight_quantizations            /  rI)r2rtypingrr6 deepspeed.inference.quantizationrrutilsrrdeepspeed.utils.loggingr collectionsr transformers.utils.genericr quantization_contextr r'ModulerIrrrrs