9j /ddlZddlZddlmZddlmZddlmZddlZddl m Z m Z m Z m Z mZmZmZmZddlmZddlmZddlmZdd lmZGd d ZGd d eZGddZGddeZy)N)Callable)partial)Any) 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 k/media/conek/DATA/Code/OCR/venv/lib/python3.12/site-packages/torch/_inductor/autoheuristic/autoheuristic.py__init__zLocalFeedback.__init__!s &choicec$|j|Srrrrs r__call__zLocalFeedback.__call__$s''r) __name__ __module__ __qualname____doc__rr r rr 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$eZdZUdZeeefed< ddegefde ede dzde de d e e dzd eee gefdzd dfd Zd efd Zd efdZ ddede e dzd e edzfdZded efdZed e fdZd e fdZd e fdZdeded dfdZy) AutoHeuristica AutoHeuristic is a framework that allows one to collect data, learn a heuristic (i.e. a regression tree) and generate the heuristic to code. This class allows one to collect data. The collected data can then be used to train a heuristic (see torchgen/autoheuristic/). collected_feedbackNfallbackchoicesfeedbackcontextnameaugment_context preconditionrc||_||_||_||_||_i|_||_tttjj|j|j|_ ||_ |jsytjj j"dk(r|j%|_n)tjj j"|_tjj j)|jrB|j5|jD]%}|j|} |j+|| 'yyy)a Initializes an instance of the AutoHeuristic class. Args: fallback: A callable that returns a Choice when the heuristic is unsure which choice to make, or AutoHeuristic is in data collection mode. choices: A list of possible choices the heuristic can make. feedback: An instance of LocalFeedback that provides feedback for a given choice. context: Context to store with each choice and feedback. name: A string that identifies the heuristic. augment_context: An optional list of AHOperation instances that augment the context. precondition: A callable that returns a boolean indicating whether AutoHeuristic should run. NDEFAULT)r+r,r-r.r/r*r0rrtorchcudaget_device_capabilitymetadatar1satisfies_precondition _inductorconfigautoheuristic_log_pathget_default_log_pathlog_pathcollect_autoheuristic save_data) rr+r,r-r.r/r0r1r feedback_vals rrzAutoHeuristic.__init__8s.!      "$." ! # JJ , , . LL II   )**,  ?? ! ! 8 8I E 557DM!OO22IIDM ?? ! ! 7 7 B}}("ll9F#'==#8LNN6<89) Crcn|jduxs&|j|j|jSr)r1r7r.)rs rr8z$AutoHeuristic.satisfies_preconditionls4  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. )r8r+r4r9r:use_autoheuristicr/r0r.apply_operationsrr7 get_decisionr,)r controllerdecisions r get_choicezAutoHeuristic.get_choiceqs**,==? " ?? ! ! 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) r8r4r9r:rCr/r0r.rDrr7get_decisions_rankedappend)rrIrJrFr,rs rget_top_k_choiceszAutoHeuristic.get_top_k_choicess**, ?? ! ! 3 3DII >##/ --d.B.BC3  J!55e $v-3c,6GGG T (C  -A GGDIIe$t+ , - - -s '$EENNr)r!r"r#r$dictr r __annotations__rlistrrrrrrboolrr8rHintrNrrQ staticmethodrXr<rir?r%rrr)r)/sR VX-..59GK292v:&29f29 $& 29  29  29k*T129 I6<=D29 29h  F4?C+/9t+; f ,9V993c)C)--h-4-rr)ceZdZdZ ddegedzfdeedeedede dee dzd ee ege fdzd dffd Z deedeed dfd Zd edzfd Z ddedee dzd eedzfdZxZS)AutoHeuristicSelectAlgorithmz AutoHeuristicSelectAlgorithm is a subclass of AutoHeuristic that allows one to collect data and learn a heuristic when one wants to use AutoHeuristic for kernel choice selection. Nr+r, input_nodesr.r/r0r1rc ||_i|_|D]}||j|j<!t|jj } dt ffd } t || | d||||tjjj|jr$|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)rchoicestr2choicerrkeysrrsuperrr4r9r:r>r/r8register_global_feedback) rr+r,rr.r/r0r1r choices_strr __class__s ` rrz%AutoHeuristicSelectAlgorithm.__init__s '9; FF?ED ! !&"9"9"; < F4005578  6c 6          OO " " 8 8 C++-  ) )+w ?. Drcp ddlm}mm dtdtdt t tfdtdttdtt d tgt t tffd t t tfd 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_keytimingsr/rr, profiled_timeprecompile_timesrNc |}||k7ry |||} | |k7ry|jD]%\} } j| j| 'yr)itemsr?r)rrrr/rr,rrcurrent_inputs_keycurrent_precompile_keyrtimerrrs rstore_global_feedbackzTAutoHeuristicSelectAlgorithm.register_global_feedback..store_global_feedbacksn"3;!? !]2%:('& "&):: '  @ v668$? @r) torch._inductor.select_algorithmrrrrrrrfloatrrrr/r) rrr,rr inputs_keyprecompile_keyfeedback_saverrrs ` @@rrz5AutoHeuristicSelectAlgorithm.register_global_feedback s   @ @" @,-. @  @ c  @ ,'  @$B\5-@(A$AB @#<#67 @ @*'{3 .tyy*gN !6 NS>*rcZ|j}|jj|dSr)rHrrPrs rget_choice_callerz.AutoHeuristicSelectAlgorithm.get_choice_caller4s'"$$((66rrIrJcp|j||}|y|Dcgc]}|j|c}Scc}wr)rNr)rrIrJr,rs rget_top_k_choices_callerz5AutoHeuristicSelectAlgorithm.get_top_k_choices_caller8s=((@ ?rsi $    ,;7 ( (9h-h-VdE=dEr