Ph|ddlZddlZddlmZmZmZddlmZmZddlm Z m Z ddl m Z ddl mZddlmZddlmZdd lmZdd lmZddlZddlZdd lmZmZmZmZmZmZm Z ddl!Z!d Z"d Z#ejHejJjHe!jHejLejJjLe!jLhZ'dZ(dZ)GddZ*dee+efdee+eeffdZ,dededede-de-de+dede e+dej\j^fdZ0dej\j^dededefdZ1 d5dede-de-dedede e+d eee+efd!ed"ee ed#e ed$e ee+efddfd%Z2 d5dede-d&e+d'eed(eed eee+efd#e ed$e ee+efddfd)Z3ded*ee+eefd+ed,ee+efddf d-Z4d ej\j^fd.Z5d/ede fd0Z6d1e defd2Z7defd3Z8 d6d4Z9y)7N)Node GraphModuleGraph)get_target_type_strget_normalized_nth_input)NSSingleResultValuesType NSResultsType)_maybe_get_fqn)QConfigMapping) QConfigAny)getattr_from_fqn) _MatchResult)tree_map)ListDictSetTupleCallableAnyOptionalshadowshadow_wrapperctd|d|SN_)SHADOW_NODE_NAME_PREFIX subgraph_idxsubgraph_candidate_idxs iC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\torch/ao/ns/fx/n_shadows_utils.py_get_attr_namer!+s% &a ~Q7M6N OOctd|d|Sr)SHADOW_WRAPPER_NODE_NAME_PREFIXrs r _get_attr_wrapper_namer%.s- .a ~Q?U>V WWr"ceZdZdZdZdZy) OutputPropa Output propagation (modeled from shape propagation). Given a GraphModule and an example input, saves the output flowing through each node on `node.traced_result`. Code based on the example from https://pytorch.org/docs/stable/fx.html#the-interpreter-pattern c||_|j|_t|jj|_yN)modgraphdict named_modulesmodules)selfr*s r __init__zOutputProp.__init__<s-YY DHH2245 r"c t|}i fd}dtffd }jjD]S}|jdk(r t |}n|jdk(r||j }n|jdk(r3|j ||ji||j}n|jdk(rC||j^}}||j}t||j |i|}nN|jdk(r?j|j ||ji||j}ttjr||_| |j<Vy) Nc^tjjj|fdS)Nc"|jSr))name)nenvs r z8OutputProp.propagate..load_arg..Fs s166{r")torchfxr+map_arg)ar6s r load_argz&OutputProp.propagate..load_argEs88>>))!-BC Cr"targetc |jd}j}t|D]=\}}t||s t ddj |d|t ||}?|S)N.z#Node referenced nonexistent target )splitr* enumeratehasattr RuntimeErrorjoingetattr)r= target_atomsattr_itriatomr/s r fetch_attrz(OutputProp.propagate..fetch_attrHsq!<<,LxxH$\24x.&)LSXXVbcedeVfMgLh'ijj"8T23Or" placeholderget_attr call_function call_method call_module)iterstrr+nodesopnextr=argskwargsrEr. isinstancer8Tensor traced_resultr4) r/rU args_iterr<rJnoderesultself_objrVr6s ` @r propagatezOutputProp.propagateAsJJ " D  JJ$$Dww-'iJ&#DKK0O+$htyy&9SXdkk=RSM)"*499"54!$++.74;;7HHM)2dkk2HTYY4Ga8TXT_T_K`a&%,,/%+"#C N#%&r"N)__name__ __module__ __qualname____doc__r0r^r"r r'r'2s6 #r"r'matchesreturncZt}i}g}|jD]\}}|jd||f|D]j\}}d}|dD]l}t|tr||vrd}|j |+t|t sJ|D]+}t|tsJ||vrd}|j |-n|rg} t|ddk(r|d} nt|ddk(sJdttfd} t|ddtrt|ddtr|d} ngt|ddt r|dd\} } |dd} | | | | } n3t|ddt r|dd\} } |dd} | | | | } | j| ||<m|S)NrFTrec|||g}d}d}d}|D]?}|jd}tt|j} ||vr|}7| |vr|}>|}A|||J|jd|usJ|jd|usJ|||gS)Nr)rUrTrPusers) node_anode_bnode_crR first_nodemid_node last_noder5prev_nnext_ns r _order_nodesz*_get_dedup_subgraphs.._order_nodess0!  AVVAYF!$qww-0FU*%& u,$% #$"-(2F)**}}Q':555 ~~a(H444!8Z88r") setitemsinsertrWraddtuplelenrreverse)rd seen_nodessubgraphs_dedupmatches_items_reversedr4 cur_matchwas_seen node_or_tupler[ list_of_nodesrsrkrlrms r _get_dedup_subgraphsrfs JO >@"==?i%%a$ ):;+2i&q\M"-. J.#H}-"-777)D%dD111z)#'NN4( *1*<   y|  !%aLMy|$) )) 9T  9()A,q/40Z ! QQU5V )! IaLOU3!*1a"1a ,VVV D IaLOU3!*1a"1a ,VVV D   -k2n r"modelrnrprr qconfig_str logger_clsfqnc |d}||j|jd|d|dt||t||tjjdd|| }d|_|S)z Given a model and a linear subgraph starting from `first_node` and ending with `last_node`, creates a logger for the end of this subgraph. subgraph_rrrF)r4rr NODE_OUTPUTvalueenabled) rrnrprrrrrlogger_mod_origs r _get_logger_for_subgraphrsz {  L>#9":;Iu-J. ,,22    O $O r"c  Gddtjj}|}tjj |}|j }t |j jD]}|j||}|j} |j} d} d} d} ||urpg}i}t}i}dtdtfd}|jD]}t|tr|||||}|j|0t|t t"frWg}|D]>}t|tr|j|||||.|j|@|j||j||jj%D]j\}}t|tr|||||||<%t|t t"fr+g}|D]}|||||}|j| |||<f|||<lt#|}n|j&t(vsJg}t+|jdkDr|jdd D]}t|tjj,rX|j/j1}d | }| dz } t3||||j5|}|j|t|t6t8tj:fr|j|t=d t?|d t#|}|j@d k(rVtC||j&}tEjF|}d | }t3|||| dz } |jI||}nr|j@dk(r|jK|j&|}nE|j@dk(r|jM|j&|}nt=|j@d||urnt+|jNjQdk(s J|dtStU|jNjQ}|j} |j} | dz } | | kDr t=d|jW||jY|S)aa Input: a model, and a linear subgraph within the model from first_node to last_node. Output: a new submodule containing a copy of the subgraph, with the inputs to the first node becoming the inputs to the submodule, and all other nodes in the subgraph being copied. Example inputs: `model`: a module with graph x0 -> op1 -> x1 -> op2 -> x2 | arg1 `first_node`: op1 `last_node`: op2 Example output: a new module with graph input1 -> op1_copy -> x1 -> op2_copy -> output1 | arg1 ceZdZdZy))create_submodule_from_subgraph..Mcyr)rc)r/xs r forwardz1create_submodule_from_subgraph..M.forwards r"N)r_r`rarrcr"r Mrs r"rrdgr[c"d}|jdzt|z|vr#|dz }|jdzt|z|vr#|jdzt|z}|j||j|}|||j<|S)Nrrrg)r4rQrwrK)rr[ seen_namesold_name_to_new_nodecountercur_namerKs r _add_placeholderz8create_submodule_from_subgraph.._add_placeholder:s ii#oG 4 BqLGii#oG 4 B99s?S\9x(mmH5 2=$TYY/""r"rgNmod_z arg of type z not handled yetrOrMrNz not supported yetz) has more than 1 users, not supported yetziteration limit exceeded)-r8nnModuler9symbolic_tracer+reversedrR erase_noderUrVrtrrrWappendlistrxrur=BINARY_FUNCTIONSry ParameterclonedetachsetattrrKfloatintdtypeAssertionErrortyperSr copydeepcopyrOrMrNrjkeysrTrPoutput recompile) rrnrprmgmrr[ cur_node_orig cur_args_origcur_kwargs_orig cur_name_idxiteration_limit cur_iteration cur_args_copycur_kwargs_copyrrrargpnew_arg inner_arg kwarg_namekwarg new_kwarg inner_kwarg cur_node_copymod_namenew_arg_placeholderorig_mod orig_mod_copys r create_submodule_from_subgraphrsFEHHOO A   #B A( T)M!&&M#**OLOM  J &M O#&5J46  # # $ #%))c4((3 ,@BA!((+dE]3 G%( %i6#NN+; !9j:N,PQ$NN95 &) "((1!((-*$&3%9%9%?%?%A! EeT*2B5*.B3DOJ/e}5 "I', ,{J8LN!((+(-3 %** '.. ? * !;< !?  I!'  $$)HH$9$9$E$E$P$P%~%Q%W !%,(*%\ !)>>w/R\Q]]v@ww>]#1#2.N  ! !"7 8$5%%" %" ! +<9OP 2y)))I458: XX % %&7 8 H!c4(OOC(dE]3CZPSTUPVX\E]%( %i6$OOI6&) 'J#-#4#4#:#:#<ii.'0Jt$ D%=9c)n+4 8,5 $=XHxx++,=H59<3  I|5K L0#7#<1GH 2y)))I/ XX % %h /XX)))8Y:OXZ)[F-3 ' *0LLN}1 0j9 8J0 /sK$%M$ A M0 M0#M07M0 A#M01 M0=AM0%&M<$M-0M9<N match_namenodes_in_this_subgraphqconfig_mappingsctd|Dry|d}|d} t||d} t| tr| D cgc]} | j} } nTt| t r d| D} n:t | dr| jf} n td|jdzyd } tt|d zD]%}|dk(r ||d z }||j}|#d } n| s td |jdzyt||}dg}tt|d zD]}t||||| ||| ||| ycc} w) z Given a model `mt` and a subgraph_idx, creates the needed copies of the subgraph for all qconfigs, and instruments them with loggers. c3>K|]}t|t ywr))rWr).0r[s r zEcreate_n_transformed_and_logged_copies_of_subgraph..Ns" *D tT ""*sNrc34K|]}|jywr))rY)rrs r rzEcreate_n_transformed_and_logged_copies_of_subgraph..]s=9a!//9srYz%unable to get example input for node z , skippingFrgTz-unable to find at least one qconfig for node )anyrrWrrYrxrBprint format_noderangeryr4r r)rrrrrrrrrnrp prev_noderrfound_at_least_one_qconfigrrrrrs r 2create_n_transformed_and_logged_copies_of_subgraphr:s(  *  '*J&r*I)R;I)T"3<=9a!//9= Iu %=9= 9o .'557N 7))+,J78 9  "'"',<(=(A"B !Q &  ))?!)C D &z7  )- & !#C" & ='')**56 7 R (C:>"',<(=(A"B:  4j s8 79J !  ##Co>sE r|rrc %&ddlm}m}d%t}i}i&t |j j }d} |D]'} | jdvs| |vr%| } d} | 6| d| d}} | D]}|j||| j}|d} n| | }} | rv| j}t|| || |g|gddd | d }d}|j j D]%}|jd k(s|j|k(s#|}n|J||| <|&| <nM| | n| g}d }d}t| |}t|| || ||||}t| |}t||rJt!||||}|j j#|5|j j%||fi }|}ddd| }d}d}||vr"|| ur|j&} |j(}!n)|}"t+|"g|j&dd} |j(}!|j j#|5|j j-|j|j| |!}||}ddd||k7r(t/|j0j3dk(sJt5t7|j0j3}|jj9t:rJ|}||vr"d}t|| || ||||}t| |}t||rJt!||||j j#|5|j j%|||fi }ddd||| <|&| <| dz } *|j=t}|D]} | jdvs| |vr%| } | #| d| d}} | D]}|j|n| | }} %&fd}#|| }$|$Jt?|#|$j&|$_t?|#|$j(|$_|j=y#1swYxYw#1swYxYw#1swYxYw)a~ Given a model, a model graph partition (currently a set of matched subgraphs) and instructions how to transform each subgraph (currently quantizing it according to qconfig_mapping), modifies the model graph to create an alternate path through the original graph, with each of the subgraphs quantized. This is useful to compare propagation error of a transformation such as quantization. For example, given layer op0 and op1, there are four cases when handling op1: 1. op0 and op1 quantized 2. op0 and op1 unquantized 3. op0 quantized, op1 unquantized 4. op0 unquantized, op1 quantized Example input, case 1: .. code:: x0_0 -> op0_0 -> x1_0 -> log -----> op1_0 -> x2_0 -> log \ \ \ \ # noqa: W605 ---> op0_1 -> x1_1 ----> clog op1_1 -> x2_1 ----> clog Example output, case 1: .. code:: x0_0 -> op0_0 -> x1_0 -> log -----> op1_0 -> x2_0 -> log \ \ \ # noqa: W605 ---> op0_1 -> x1_1 ----> clog -> op1_1 -> x2_1 ----> clog rrc>|jD] }||vs|cSyr))values)r[r|subgraphs r _get_subgraph_containing_nodez?create_add_loggers_graph.._get_subgraph_containing_nodes''..0Hx1r")rKrLrFNrTshadow_wrapper__1rOrrrgc|t|ts|S|jdvr|S|}||g}|d}|}|S)zt If unshadowed `node` has a shadow version, return that. If not, return `node`. )rKrLr)rWrrS)r[ prev_subgraphprev_first_nodeprev_shadow_outputr"orig_first_node_to_shadow_out_noder|s r maybe_remap_node_to_shadowz.maybe_remap_node_to_shadowrsa dD) ww55 :o'M$!% +A.O2?C % %r") rrrrtrr+rRrSrwr4rr=r rr!rBrrrOrUrVrx create_noderyrjrrTrP startswithrrr)'rr|rrrr nodes_to_skip!orig_first_node_to_shadow_in_node orig_nodescur_subgraph_idxr5maybe_subgraphinsert_submodule_copyrnrp node_to_skiprrexpected_shadow_targetnew_shadow_modmaybe_shadow_modsubgraph_to_userrrrrinsertion_pointrrrfirst_node_copyrrfirst_arg_for_copyr cur_shadow_inputrr s' ` @@r create_add_loggers_graphrsLS0EM)+%)+&ekk''(J  448 8A'^ !$8#9d (77G6H%K "!N$)KK$5$5 #&&-7(//3II%5N %6 "- --@V\30O''79OPIui0 00 E9o 6'O,,_=00YL1="(> 'M M"O?2 J.,11H!.!5!5J)6&$&8%R=;M;Mab;Q%RSH!.!5!5J[[00A$)KK$;$;%((%,, " %M'.*7B!I-}22779:a??? $T-*=*=*B*B*D%E F (--889PQQQ"/5 ?2:&' "6z9.>@V3S:O''79OPIui0 00 E9o 6,,_=00]I$>r1K> =L -j 9=J .z :AUX OO.EM  448 8A=&BA4>=s$"P(7P50!Q(P2 5P? Q cd}|jjD]7}|jdk7r|dz }|dk7rt|jdk(sJt t |jj}d}|jtjk(rE|j\}}}}} t||j} t||j} | | || f}na|jtjk(sJ|j\}}}} t||j} t||j} | | | f}|j|fcSy)NrrKrgrh)r+rRrSryrjrTrPrr=r8quantize_per_channelrUr quantize_per_tensor) rplaceholders_seenshadow_n quant_noder_weight scale_nodezp_nodeaxisr scale_valzp_vals r $_get_weight_info_from_shadow_wrapperr*sU"((.. ;;- ' Q  ! 8>>"a'''$x~~22456     : : :8B 5GZ$( 1 13I%0F!647H$$(A(AA AA2>? JJ 188>>?Gaf Nr"r?c tjt}tt |dj }|d|j D]]\}}|jd\}}}|d|}|dd|dd|dd|dd|dd|dd |dd d } | |||<_t|S) a Creates a comparison of results Input: { 'model': { 'node_output': { 'subgraph_0_0': [ 'values': [torch.tensor(...), ...], ... 'ref_node_name': ..., 'ref_node_target_type': ..., 'qconfig_str': ..., 'comparisons': [], ... 'comparison_fn_name': '', 'fqn': '...', ], 'subgraph_0_1': [ 'values': [torch.tensor(...), ...], ... 'ref_node_name': ..., 'ref_node_target_type': ..., 'qconfig_str': ..., 'comparisons': [torch.tensor(...), ...], ... 'comparison_fn_name': '...', 'fqn': '...', ], ... }, }, } Output: { 'subgraph_0': { '0': { 'ref_node_name': '...', 'ref_node_target_type': ..., 'values': [torch.tensor(...), ...], 'qconfig_str': None, 'comparisons': [torch.tensor(...), ...], ... 'comparison_fn_name': '...', 'fqn': '...', }, '1': { 'ref_node_name': '...', 'ref_node_target_type': ..., 'values': [torch.tensor(...), ...], 'qconfig_str': '...', 'comparisons': [torch.tensor(...), ...], ... 'comparison_fn_name': '...', 'fqn': '...', }, }, } rrrr1r2rrrr5r6)r1r2rrrr5r6) collections defaultdictr,rTrPrrur@) r?!subgraph_name_to_subgraph_results key_to_usesubgraph_name_with_idxsubgraph_candidate_results subgraph_strrr subgraph_namesubgraph_resultss r group_results_by_subgraphr^@sr.9-D-DT-J%d77+00234J G Z ( . . 0 ; : # ( ( - ; l$:'.,8 8:?K$>q$ABX$Y-a0703H=5a8G5a8G"rsz   7048=( BBB""2 II LL LL II LL LL PX22hj #|# $j #tDz/jX" """ "  "  "" #" XX__"H{ 88??{{{ {N-16:EEE E E  E # E#'tCO'<"=EE"&htn!5E )E$DcN3E E\-16:^#^#^#^#!I ^# >* ^# #'tCO'<"= ^# )^#$DcN3^# ^#@| |#tDz/*|$|sJ/ |  ||11hqqqhS3}S3S3lMM`:. :.r"