*iH ddlZddlZddlZddlZddlmZddlmZddlm Z ddl m Z m Z m Z ddlZddlmZddlmZddlmcmZddlmcmZddlmZmZddlmZddlm Z m!Z!gd Z"d e#d e$e#e#ffd Z%d ee&dejNde(e#e ffdZ)dejNde(e#e fdej$jTfdZ+d)dej$jTd ej$jTfdZ,dejTd ejTfdZ-dejTde.ejNde.ejNde.ejNfdZ/ej`ejbejdejfejhejjejlejnejpejrejnejtejvg Z (`foo.bar`, `baz`) .r)rsplit)rparentnames d/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/torch/fx/experimental/optimization.py _parent_namer$%s3 MM#q)MVT6!9 ,,B ,,patternnodemodulescjt|jdk(ry|jd|f}t||D]z\}}t|tj sy|j dk7ryt|jtsy|j|vryt||j|uszyy)NrF call_moduleT) lenargszip isinstancefxNodeoprstrtype)r&r'r(nodes expected_type current_nodes r#rr/s 499~"&))A,!5E'*7E': # |,0 ??m +,--s3   g -  ++, -] B  r% new_modulect|jts!tdt |jt |j\}}|||j<t ||||y)NExpected str target, got )r.rr2AssertionErrorr3r$setattr)r'r(r7 parent_namer"s r#rrCsa dkk3 '8dkk9J8KLMM$T[[1K%GDKK GK $ 3r%modelctjtjftjtjftj tj ftjtjfg}|stj|}|r$t|tjjstj|}n|}t|j!}tj|j"}|D]}|j$D]}t'|||st)|j*dj,dkDr8||j*dj.} ||j.} | j0sp|dtjtjtj fvr t3| | } n t5| | } t7|j*d|| |j9|j*d|j;|tj||S)z Fuses convolution/BN and linear/BN layers for inference purposes. Will deepcopy your model by default, but can modify the model inplace as well. rr)nnConv1d BatchNorm1dConv2d BatchNorm2dConv3d BatchNorm3dLinearcopydeepcopyr.torchr/ GraphModulesymbolic_tracedict named_modulesgraphr4rr+r,usersrtrack_running_statsr r rreplace_all_uses_with erase_node) r=inplaceno_tracepatternsfx_modelr( new_graphr&r' first_layerbn fused_layers r#rrMs BNN# BNN# BNN# BNN# H  e$ :eUXX-A-AB$$U+8))+,G hnn-I+OO +D%gtW=tyy|))*Q.%diil&9&9: T[[)--1:"))RYY !BB"3K"DK"5k2"FK#DIIaL';G**499Q<8$$T* ++" >>(I ..r%ctj|}Gddtjj}||j S)z5 Removes all dropout layers from the module. cDeZdZdedeedfdeeefdeffd Z xZ S)&remove_dropout..DropoutRemoverrr,.kwargsrct|j|tjr*t |dk7rt dt ||dSt ||||S)Nrz Expected 1 arg for Dropout, got r)r. submodulesr?Dropoutr+r:superr*)selfrr,r^ __class__s r#r*z2remove_dropout..DropoutRemover.call_module|s\$//&12::>t9>(+KCPTI;)WXXAww*64@@r%) __name__ __module__ __qualname__r tupler rLr2rr* __classcell__)rds@r#DropoutRemoverr]{sE A  A(-hm(< AFJ3PS8n A  A Ar%rj)r/rKrI Transformer transform)r=rVrjs r#rrusB  'H A-- A ( # - - //r% orig_moduler4inputsoutputscZ tj}i |D]"}|j|j}| |<$|D]}|j | fd}| |<|j |Dcgc]} | c}|j tj||Scc}w)z Given lists of nodes from an existing graph that represent a subgraph, returns a submodule that executes that subgraph. c|SN)xenvs r#z"extract_subgraph..s s1vr%)r/Graph placeholderr" node_copyoutputlintrJ) rmr4rnrorWinputnew_noder'rzrus @r#rrs I"$C((4E &&t-=>D 8fc&k89 NN >>+y 119s/ B(c,tj|Srr) th_mkldnnMkldnnBatchNorm)a_s r#rvrvs!:!:1!=r%ci}|D]}|jdk(st|jts!t dt |j||j}t |t vsot t ||tj}t|tjst dt |tj|||<t||||S)z For each node, if it's a module that can be preconverted into MKLDNN, then we do so and create a mapping to allow us to convert from the MKLDNN version of the module to the original. r*r9zExpected nn.Module, got )r1r.rr2r:r3 mkldnn_maprIfloatr?ModulerGrHr)r4r( old_modulesr' cur_moduler7s r#rrs /1K ? 77m #dkk3/$'@dkkAR@S%TUU -JJ:-'Z(89*ekkR !*bii8(+CDDTCU)VWW*.-- *C J'#D':> ? r%rc|D]q}|jdk(st|jts!t dt |j||j}||vsbt ||||sy)za Maps each module that's been changed with `modules_to_mkldnn` back to its original. r*r9N)r1r.rr2r:r3r)r4r(rr'rs r#rrssL 77m #dkk3/$'@dkkAR@S%TUU -J[(#D';z3JK Lr%c,eZdZdejfdZy)rfx_graphc<||_g|_g|_g|_yrr)rr4 start_nodes end_nodes)rcrs r#__init__zMklSubgraph.__init__s  $& *,(*r%N)rerfrgr/rwrrsr%r#rrs++r%rcDdddtdtffd }|S)aW This generates a heuristic that can be passed into `optimize_for_inference` that determines whether a subgraph should be run in MKL by running it with the example_inputs. Example usage: heuristic = gen_mkl_autotuner(example_inputs, iters=10) fast_model = optimization.optimize_for_inference(model, heuristic) NrNrc|j} F|jj |jj t j |Dcgc]!}t j|j#c}tttj|jDcgc]}|jdc}}t |j || fd}|fd}t#j$j t'j) |fd}||kScc}wcc}w)NrctD] }| tj}tD] }| tj|z Srr)rangetime)frbeginiterswarmups r# benchmarkz?gen_mkl_autotuner..use_mkl_heuristic..benchmarksO6]  IIKE5\  99;& &r%cDcgc]}|jc}Dcgc]}|jc}Scc}wcc}wrr) to_mkldnnto_dense)i sample_inputs submodules r#rvz>gen_mkl_autotuner..use_mkl_heuristic.. s<&/1WA!++-1W&X!" 1Ws ?AcSrrrs)rrsr#rvz>gen_mkl_autotuner..use_mkl_heuristic..s =(Ar%)rr owning_modulerr propagaterIrandnshaperlistr/r0rr,rr4rrNrLrM)rN input_nodesr' output_argsrmkl_time no_mkl_timerrexample_inputsrVrrrs @@r#use_mkl_heuristicz,gen_mkl_autotuner..use_mkl_heuristics''  ~~33H..44K h  ) ). 9=HITTZZ0I 4=EOO*TD499Q<*TU $Xu{{KU  '    OO ! ! ((* +    AB +%%3J*Ts &D>.E )rbool)rrrrrVrs``` @@r#rrs0HK & & & &D r%rNc2t|jdkDS)z This is a heuristic that can be passed into `optimize_for_inference` that determines whether a subgraph should be run in MKL by checking if there are more than 2 nodes in it )r+r4)rNs r#rrs u{{ a r%c>eZdZdZdefdZdedefdZdedefdZy ) rc0dg|z|_dg|z|_y)Nrr!size)rcns r#rzUnionFind.__init__%s,06A: !sQw r%vc@||j|<d|j|<y)Nrr)rcrs r#make_setzUnionFind.make_set)s A ! r%rc|j|}||k(r|S| td|j||j|<tt|j|S)NzParent is None)r!r:findrint)rcrpars r#rzUnionFind.find-sVkk!n 8H ; !12 23 ACQ((r%rbc|j||j|}}||k(r|S|j||j|kr||}}||j|<|j|xx|j|z cc<yrr)rrr!)rcrrs r#joinzUnionFind.join6soyy|TYYq\1 6H 99Q<$))A, &aqA A !  ! $ r%N)rerfrgrrrrrrsr%r#rr$s9'#)c)c)%c%c%r%r pass_configtracercdddtid}|i}|j||dr t|}|dr t|}|ddur|St |dt s t d d|dvr t d |dd}|}|jtj|tj|jt |j}Gd d t}tj D]}|j"} |j$d k(r||j&} t)| t*vr|j,} t/| j1d} | | j2t4j6k7r t9d| j:t5j:dk7rWt9d|j$dk(r=|j&t*vr |j,} n|j&t<vr |j>} | |j"k7s| |j>k(rtAd|jBDsFjE|5tjF|jBfd} dddtItJtjLjN |_!jQ|5jSdd|f} |jU| |f| _!dddtWtj |}|_,j D]}|j$dk(s|j&dk(s#|jBd}t|jZ}|D]D}|j$dk(s|j&dk(s#|jU|j]|Ft_|jZdk(sj]|t_j }ta|fd}tcj D]7\}}|j$dk(r(|j&dk(r||_2jg|>|j$dk(rJ|j&dk(r;||jBd t9d||jBd|_4|jjDcgc],}t |tjlr||||.}}t_|dk(rtAd|Dr t9dto|}|d|_8|ddD]}js|d|:tufd}j D]}tw|dr7|jy|jpj j{|tw|dr7|jy|jdj|j{|tw|d s|jy|jhj~j{||jD]q}||r |j||j~zD]3}|jBd}|jU|j]|5t|j ||sd}j D]&}|j&dk(s|j&dk(s"|dz }(tjtjd!|jtj|}|S#1swYxYw#1swYlxYwcc}w)"a Performs a set of optimization passes to optimize a model for the purposes of inference. Specifically, the passes that are run are: 1. Conv/BN fusion 2. Dropout removal 3. MKL layout optimizations The third optimization takes a function `use_mkl_heuristic` that's used to determine whether a subgraph should be explicitly run in MKL layout. Note: As FX does not currently handle aliasing, this pass currently assumes nothing aliases. If that isn't true, use at your own risk. T heuristic) conv_bn_fusermkldnn_layout_optimizeNrrrFz+mkldnn_layout_optimize config is not a dictz4Heuristic not found in mkldnn_layout_optimize configceZdZdZdZdZy)*optimize_for_inference..MklSupportrrN)rerfrgNOYESUNKNOWNrsr%r# MklSupportrls r%rr*z)this pass is only for torch.float modulescpuz!this pass is only for CPU modules call_functionc3:K|]}|jdk(yw)rN)r).0args r# z)optimize_for_inference..sI3::3Isc*jd|fS)Nr) call_method)rrs r#rvz(optimize_for_inference..s)=)=kA4)Pr%rrrrct|drj|jSt|drj|jSy)Ncolor start_color)hasattrrrr)rufs r# get_colorz)optimize_for_inference..get_colors@ 1g 77177# # 1m $771==) )r%z!Expected color for to_dense inputc3$K|]}|du ywrrrs)rrs r#rz)optimize_for_inference..s1191szFound None in cur_colorsrctSrr)r)rsr#rvz(optimize_for_inference..s H@Ur%rr end_colorzmkldnn conversions: %s)Grupdaterrr.rL RuntimeErrortracerGrHr/rJrootrMrrr4rr1rr3mkldnn_supportedrnext parametersdtyperIrr:devicemkldnn_supported_unknownranyr,inserting_beforemap_argrrhr'r inserting_after create_noderQrrrOrRr+r enumeraterrrall_input_nodesr0sortedrrrrrappendrrvaluesrlogging getLoggerreinfor{)r=rrdefault_pass_configr cur_tracerr(rr'supports_mkldnnrsample_parameter mkldnn_argsdense_xrprv_noderOuser num_nodesrcur_idxr cur_colors other_color mkldnn_graphsrNprvmkldnn_conversionsresultrrs @@r#rr@s&#."?  {+>*U +,u%34= )*BCT JHII-.FGGQRR+,DEkRJ e 45HNN:??H-$()<)<)>$?GTX^^$"'$-- 77m # -JJ#33",..#' (=(=(?#F #/'--<,G(..%,,u2EE,-PQQ WW '{{..",.. 88","4"4 jmm +*"4"44ItyyII**40  jjIIP   U277#3#34kBDI))$/ '"..}j4'R**73 $w  ' '?"'J$D$8'BK&H * 77m # z(Ayy|H$E .77m+ {0J..x8''- .4::!###D) *HNN#I 9 B$#8>>24  77m # {(B&D  KK WW %$++*C1&.$%HII&tyy|4DN--a)Q<+! J:!#1j11$%?@@ +J#ADJ)!"~ 4  1 {3 4-42-88U,VMJ 4 ! "''$**- . 4 4 ; ;D A 4 ' "''$"2"23 4 @ @ G G M 4 % "''$..1 2 < < C CD I J%%'= '))EOO; *iil**3/##D) * %++w < =$ ;;+ % )B ! # $ h$$%=?QR MMO ^^E8 ,F MK   ' 'fs$$]".]/1]<"], /]9 )FF) r)LrGroperatorr collectionsrcollections.abcrenumrtypingrrrrItorch.fxr/torch.nnr?torch.nn.functional functionalFtorch.utils.mkldnnutilsmkldnnr torch.fx.noder r torch.fx.passes.shape_propr torch.nn.utils.fusionr r __all__r2rhr$r3r0rLrrrrrrrrBrFrCReLU MaxPool2d AvgPool2dAdaptiveAvgPool2drelu transposesigmoid avg_pool2dadaptive_avg_pool2draddmulr MkldnnConv2d MkldnnLinearrrrrrrrrTracerrrsr%r#r#s  #$&& &&*0H  --sCx- d^#%7759#s(^(4 ''4 cN48=4%/%/588??%/P0"))0 0(22 =2 M2"''] 2.IIIINNGGLLLL JJ OO MMFFLL&%LL(,,7IIy%%IIy%%NN= T"'']T#ryy.5I,L =L #ryy. !Lbii*+L$++.b + $ %%<-1 iir 88??r$sCx.)r Or XX__ rr%