sg.ddlZddlZddlmZddlmZmZmZmZm Z ddl Z ddl m Z m Z mZmZmZmZmZmZddlmZddlmZddlmZddlmZGd d ZGd d eZGd dZGddeZ y)N)partial)AnyCallableDictListOptional) AHContext AHMetadata AHOperationChoice CHOICE_COLFeedback FEEDBACK_COLget_metadata_str_from_log)LearnedHeuristicController) ChoiceCaller) cache_dir)get_gpu_shared_memoryc<eZdZdZdeegefddfdZdedefdZy) LocalFeedbacka7 To be able to collect data for a choice, a function providing feedback given a choice has to be provided. LocalFeedback can be used when AutoHeuristic should immediately run the function to collect feedback for each choice (see pad_mm.py, where the autotuning happens locally, for an example). feedback_fnreturnNc||_yNr)selfrs ^/var/www/html/venv/lib/python3.12/site-packages/torch/_inductor/autoheuristic/autoheuristic.py__init__zLocalFeedback.__init__ s &choicec$|j|Srrrr s r__call__zLocalFeedback.__call__#s''r) __name__ __module__ __qualname____doc__rr rrr#rrrrs9 'HfXx-?$@'T'(v(((rrceZdZdZy)InconsistentMetadataz Exception that is thrown when AutoHeuristic tries to log data to a file where the metadata stored in the file does not match the metadata it would store if the file didn't exist. N)r$r%r&r'r(rrr*r*'srr*c$eZdZUdZeeefed< ddegefde ede e de de d e e ed e eee gefd dfd Zd efd Zd efdZ ddede e e d e e efdZded efdZed e fdZd e fdZd e fdZdeded dfdZy) AutoHeuristica AutoHeuristic is a framework that allows one to collect data, learn a heuristic (i.e. a regression tree) and generate the heuristic to code. This class allows one to collect data. The collected data can then be used to train a heuristic (see torchgen/autoheuristic/). collected_feedbackNfallbackchoicesfeedbackcontextnameaugment_context preconditionrc||_||_||_||_||_i|_||_tttjj|j|j|_ ||_ |jsytjj j"dk(r|j%|_n)tjj j"|_tjj j)|jrB|j5|jD]%}|j|} |j+|| 'yyy)a Initializes an instance of the AutoHeuristic class. Args: fallback: A callable that returns a Choice when the heuristic is unsure which choice to make, or AutoHeuristic is in data collection mode. choices: A list of possible choices the heuristic can make. feedback: An instance of LocalFeedback that provides feedback for a given choice. context: Context to store with each choice and feedback. name: A string that identifies the heuristic. augment_context: An optional list of AHOperation instances that augment the context. precondition: A callable that returns a boolean indicating whether AutoHeuristic should run. NDEFAULT)r.r/r0r1r2r-r3r rtorchcudaget_device_capabilitymetadatar4satisfies_precondition _inductorconfigautoheuristic_log_pathget_default_log_pathlog_pathcollect_autoheuristic save_data) rr.r/r0r1r2r3r4r feedback_vals rrzAutoHeuristic.__init__7s.!      "$." ! # JJ , , . LL II   )**,  ?? ! ! 8 8I E 557DM!OO22IIDM ?? ! ! 7 7 B}}("ll9F#'==#8LNN6<89) Crcn|jduxs&|j|j|jSr)r4r:r1)rs rr;z$AutoHeuristic.satisfies_preconditionks4  D( D,=,= MM4<<-  rc|js|jStjjj |j r|j%|jj|jt|j|j}|j}||jvr|jS||S|jS)a  Returns the chosen option based on the value of autoheuristic_use. If self.name is one of the comma separated strings in autoheuristic_use, it queries a learned heuristic to make a decision. Otherwise, it returns the fallback option. )r;r.r7r<r=use_autoheuristicr2r3r1apply_operationsrr: get_decisionr/)r controllerdecisions r get_choicezAutoHeuristic.get_choiceps**,==? " ?? ! ! 3 3DII >##/ --d.B.BC3  J"..0Ht||+}}&#}}rtop_kalways_includedc|jsytjjj |j r|j %|jj|j t|j|j}|j|}|y||D]}||vs|j||Syr) r;r7r<r=rFr2r3r1rGrr:get_decisions_rankedappend)rrLrMrIr/r s rget_top_k_choiceszAutoHeuristic.get_top_k_choicess**, ?? ! ! 3 3DII >##/ --d.B.BC3  J!55e $v-3c,6GGG T (C  -A GGDIIe$t+ , - - -s '$EENNr)r$r%r&r'rr r__annotations__rrrrr rur r boolrr;rKintrQrrT staticmethodr[r?rlrBr(rrr,r,.sS VX-..8 $v, ,9V993c)C)--h-4-rr,ceZdZdZ ddegeefdeedeede de deee d eee e ge fd dffd Zdeedeed dfd Zd eefd Z ddedeee d eeefdZxZS)AutoHeuristicSelectAlgorithmz AutoHeuristicSelectAlgorithm is a subclass of AutoHeuristic that allows one to collect data and learn a heuristic when one wants to use AutoHeuristic for kernel choice selection. Nr.r/ input_nodesr1r2r3r4rc ||_i|_|D]}||j|j<!t|jj } dt ffd } t || | d||||tjjj|jr$|jr|j||yyy)a< The arguments choices, input_nodes and name have to match the ones used in the call to autotune_select_algorithm(), e.g. if the following call is made autotune_select_algorithm(name, choices, input_nodes, layout), the same name, choices and input_nodes have to be used here. rc8}|y|jS)Nunsure)autoheuristic_id)fallback_choicer.s r fallback_strz;AutoHeuristicSelectAlgorithm.__init__..fallback_strs"&jO&"335 5rN)rchoicestr2choicerlistkeysrusuperrr7r<r=rAr2r;register_global_feedback) rr.r/rr1r2r3r4r choices_strr __class__s ` rrz%AutoHeuristicSelectAlgorithm.__init__s '9; FF?ED ! !&"9"9"; < F4005578  6c 6          OO " " 8 8 C++-  ) )+w ?. Drc ddlm}mm dtdtdt t tfdtdttdtt d d f fd }|} j||}t|||}||y ) zk Registers a callback in select_algorithm, which is called with the timing of each choice. r)add_feedback_savercreate_inputs_keycreate_precompile_key ah_inputs_keyah_precompile_keytimingsr2rr/rNc |}||k7ry |||}||k7ry|jD]%\}} j|j| 'yr)itemsrBr) rrrr2rr/current_inputs_keycurrent_precompile_keyr timerrrs rstore_global_feedbackzTAutoHeuristicSelectAlgorithm.register_global_feedback..store_global_feedbacksn"3;!? !]2%:('& "&):: '  @ v668$? @r) torch._inductor.select_algorithmrrrrurrfloatrrr2r) rrr/rr inputs_keyprecompile_keyfeedback_saverrrs ` @@rrz5AutoHeuristicSelectAlgorithm.register_global_feedback s   @ @" @,-. @  @ c  @ ,'  @ @&'{3 .tyy*gN !6 NS>*rcZ|j}|jj|dSr)rKrrSr"s rget_choice_callerz.AutoHeuristicSelectAlgorithm.get_choice_caller1s'"$$((66rrLrMcp|j||}|y|Dcgc]}|j|c}Scc}wr)rQr)rrLrMr/r s rget_top_k_choices_callerz5AutoHeuristicSelectAlgorithm.get_top_k_choices_caller5s=((@ ?E $|$ %Err)!rgr_ functoolsrtypingrrrrrr71torch._inductor.autoheuristic.autoheuristic_utilsr r r r r rrr:torch._inductor.autoheuristic.learned_heuristic_controllerrtorch._inductor.irr%torch._inductor.runtime.runtime_utilsrtorch._inductor.utilsrr Exceptionr*r,rr(rrrsi 66    ,;7 ( (9h-h-VbE=bEr