ȅiX ~SSKrSSKrSSKJr SSKrSSKJs Js Js J r SSK Js Js J r SSK Jr SSKJrJr SSKJr SSKJr SSKJr SSKJr SS KJrJr \R8Rr"S S \R<5rS \S \S\S\ \!\"\4S\#\\44 Sjr$S \S \S\ \!\"\4S\#\RJ\&-\RJ\'-4S-4Sjr(S \S \S\4Sjr)S \S \S\'4Sjr*S \S\+\'4Sjr,S \S \S\!4Sjr-S\S\!S\4Sjr.S\SS4Sjr/Sr0\0S\RJS\RJS\RJ4Sj5r1\0S\RJS\RJS\RJ4Sj5r2\0S\RJS\RJS\RJ4S j5r3S \S\44S!jr5S \S \S"\'S\4S#jr6g)$N)Callable)FakeQuantizeBase ObserverBase)_is_activation_post_process)getattr_from_fqn) GraphModule)Node)NSNodeTargetType NSResultsTypec\rSrSr\R "5r\R "5r\R "5r\R "5r \R "5r Sr g)NodeInputOrOutputTypeN) __name__ __module__ __qualname____firstlineno__enumautoFP32INT8FP16UNKNOWN FP32_OR_INT8__static_attributes__rN/home/james-whalen/.local/lib/python3.13/site-packages/torch/ao/ns/fx/utils.pyrrs; 99;D 99;D 99;DiikG 99;Lrrnodegm logger_clsnode_type_to_io_type_mapreturnc4 ^USnUSnUSnUSnUSnUSn USn USn URS :XGa URU;a [R[R4$URU;a [R[R4$URU;a [R [R 4$URU;aJ[ XS 5n [U [5(d[S [U 535e[XX#5un nX4$[R[R4$URS :XGaURS :wa[S URS35e[UR[5(d![S[UR535e[XR5m[U4SjU 55n[TU[ ["45(dU(aJ[ XS 5n [U [5(d[S [U 535e[XX#5un nX4$[U4SjU55n[U4SjU 55nU(a [R[R4$U(a [R [R 4$[R[R4$URS:XGa{URS:XaZ[ XS 5n[U[5(d[S [U535e[UXU5un nU[R4$URS:Xa[ XS 5n[U[5(d[S [U535e[UXU5un n[ XS5nU[$R&La[US35eU[R4$URU ;aJ[ XS 5n [U [5(d[S [U 535e[XX#5un nX4$[R[R4$[R[R4$)Nfuns_io_type_fp32funs_io_type_fp16funs_io_type_int8funs_io_type_fp32_or_int8mods_io_type_fp32mods_io_type_int8mods_io_type_fp32_or_int8meths_io_type_fp32_or_int8 call_functionrExpected Node, got call_modulezExpected call_module, got ''zExpected str, but got c3<># UHn[TU5v M g7fN isinstance.0 target_typemods r 7get_node_first_input_and_output_type..Rs"1 8  sK ( (8c3<># UHn[TU5v M g7fr2r3r5s rr9r:f") 0  sK ( (0r;c3<># UHn[TU5v M g7fr2r3r5s rr9r:jr=r; call_method dequantizetor z handling needs to be added)optargetrrrrget_normalized_nth_inputr4r AssertionErrortype$get_node_first_input_and_output_typerstrranyrrtorchfloat16)rr r!r"FUNS_IO_TYPE_FP32FUNS_IO_TYPE_FP16FUNS_IO_TYPE_INT8FUNS_IO_TYPE_FP32_OR_INT8MODS_IO_TYPE_FP32MODS_IO_TYPE_INT8MODS_IO_TYPE_FP32_OR_INT8METHS_IO_TYPE_FP32_OR_INT8 first_arg_prev_node_input_typeprev_node_output_type"is_known_fp32_or_int8_input_moduleis_known_fp32_input_moduleis_known_int8_input_module prev_nodecur_node_dtype_targetr8s @rrGrG&s11DE01DE01DE 89T U01DE01DE 89T U!9:V!W ww/! ;;+ +)..0E0J0JK K ;;+ +)..0E0J0JK K [[- -)..0E0J0JK K [[5 501=Ii..$':4 ?:K%LMM5z %%*A A)113H3P3PQ Q M ! 77m # #>twwiq!IJ J$++s++ #9$t{{:K9L!MN Nr;;/-01 81 . * sZ7GH I I111=Ii..$':4 ?:K%LMM5z %%*A A%() 0) & "&)) 0) & " &)..0E0J0JK K ')..0E0J0JK K)113H3P3PQ Q M ! ;;, &11=Ii..$':4 ?:K%LMM52+C %%*+@+E+EF F [[D 11=Ii..$':4 ?:K%LMM52+C %% %=Tq$I !$EMM9$,--HI*+@+E+EF F [[6 601=Ii..$':4 ?:K%LMM5z %%*A A%--/D/L/LMM%--/D/L/LMMrc^[XS5n[U[5(dgUSnSnURS:Xa~UR[ R La U"X1SS5$UR[R[R[R[R4;a U"X1SS5$gURS :XGa[UR[5(d![S [UR535e[XR5m[T[ R"[ R$[ R&[(R*[ R,[ R.[ R0[ R2[ R4[ R6[ R8[ R:[ R<[ R>[ R@[ RB[ RD[ RF[(RH[(RJ[(RL[(R*[(RN[(RP45(aTRRTRT4$[WU4S jU55nU(a [YX1U5$g) zo Returns the qparams (scale, zero_point) of the first input to `node`, if they can be inferred from the graph. rNr+c<[XU5n[XU5n[U[5(d[S[ U535e[UR [ 5(d![S[ UR 535e[U[5(d[S[ U535e[UR [ 5(d![S[ UR 535e[XR 5n[XR 5nXg4$)Nr.Expected str, got )rDr4r rErFrCrHr)rr scale_arg_idx zp_arg_idx scale_nodezp_node scale_objzp_objs r _get_scale_zp_from_function_args@get_node_input_qparams.._get_scale_zp_from_function_argss-d F *4Z@*d++ #6tJ7G6H!IJ J*++S11 #5d:;L;L6M5N!OP P'4(( #6tG}o!FG G'..#.. #5d7>>6J5K!LM M$R):):; !"nn5""rr-r r/r^c3<># UHn[TU5v M g7fr2r3)r6r7 module_objs rr9)get_node_input_qparams..s"1 8  z; / /8r;)-rDr4r rBrCrJquantize_per_tensortoqaddadd_relumulmul_relurHrErFrnnqLinearConv1dConv2dnniq ConvReLU2dConv3d BatchNorm2d BatchNorm3dConvTranspose1dConvTranspose2dELU GroupNormInstanceNorm1dInstanceNorm2dInstanceNorm3d LayerNorm Hardswish LeakyReLUReLU6BNReLU2dBNReLU3d ConvReLU1d ConvReLU3d LinearReLUscale zero_pointrIget_node_input_qparams)rr r"rZrRrerWrjs @rrrs)15I i & & 89T U #||&   u88 83I1aH H   #''3<<#,,!O O3I1aH H  &)**C00 #5d9;K;K6L5M!NO O%b*:*:;      #### """"""      1   :$$j&;&;< <-01 81 . * .))9QR R rczURS:XGa[XR5n[U5(Ga[ UR 5S:wa![ S[ UR 535e[UR S[5(d$[ S[UR S535eUR Sn[UR[5(d![ S[UR535e[XR5n[U5(a[ UR 5S:wa![ S[ UR 535e[UR S[5(d$[ S[UR S535eUR SnU$)ak If node is not an observer, returns it. If node is an observer, navigates up the graph and returns the first parent which is not an observer. For example, graph: (node_non_obs), node = node_non_obs : returns node_non_obs graph: (node_non_obs -> obs0), node = obs0 : returns node_non_obs graph: (node_non_obs -> obs0 -> fq0), node = fq0 : returns node_non_obs r/r z)Expected node.args to have length 1, got rr.r^) rBrrCrlenargsrEr4r rFrHrr node_objs rreturn_first_non_observer_noders_ ww-#B 4 &x 0 0499~"$?DII?OPdiilD11$':4 ! ;M:N%OPP99QQ&(CC NCST"$))A,55(+>tDIIaL?Q>R)STTyy| KrcURS:Xa5[XR5n[U[R 5(agg)a/ Assumes that all non-param args occur first. Returns the number of non-param args expected for a node. For example, for F.linear(x, weight, bias) Returns 1, because x is a non-param arg and weight and bias are params. For lstm_mod(x, hid) Returns 2, because both x and hid are non-param args. r/rgr )rBrrCr4nnLSTMrs rget_number_of_non_param_argsr+s7" ww-#B 4 h ( ( rc([UR5S:Xa/$URS:XaUR[R [R RR [R 4;dQUR[R[R RR[R4;a>[S5Vs/sH&n[URU5[LdM$UPM( nnU$S/$s snf)a  Returns the indices of args of the node which we should attach loggers to, if input logging is enabled. For example, * for (x + y), returns [0, 1] * for (1 + y), returns [1] * for (x + 1), returns [0] * for (linear(x, w, b)) returns [0] * by default, returns [0] rr-rg) rrrBrCrJrnops quantizedoperatorrprangerFr )riresults r get_arg_indices_of_inputs_to_logrEs 499~  ww/!  599#6#6#:#:HLLII ;;599eii&9&9&=&=x||L L"1XDXdiil);t)C!XD 3JEs #DDcdSnURS;a"[R"UR5nU$URS:Xak[ UR[ 5(d![ S[UR535e[XR5n[R"U5nU$)z| Returns a string representation of the type of the function or module pointed to by this node, or '' for other node types. )r-r?r/r^) rBrJtypenamerCr4rHrErFr)rr r7 target_mods rget_target_type_strr]s K ww22nnT[[1  M !$++s++ #5d4;;6G5H!IJ J%b++6 nnZ0 rresults model_namec 0nUR5Hmup4SnUR5HEnUR5H.upxXq:Xa$[U5S:Xa [S5eUSSnM.M0 MG UbXBU'MiXBU'Mo U$)a Rekeys the layer name of a results dictionary to use node names from `model_name`. For example, transforms {'base_op_1_0': {'node_output': {'model_a': [{'ref_node_name': 'linear1', ...}]}}} into {'linear1': {'node_output': {'model_a': [{'ref_node_name': 'linear1', ...}]}}} Note: we cannot use these node names directly because they are not guaranteed to be consistent across models. This is why we extract the results first and rekey afterwards. Nrz(Expected list_of_results to be not empty ref_node_name)itemsvaluesrrE) rr new_resultsold_layer_nameresult_type_to_resultsnew_layer_namemodel_name_to_resultscur_model_namelist_of_resultss r'rekey_logger_info_on_node_name_of_modelrms,K29--/.%;%B%B%D !3H3N3N3P/!/?+q0,-WXX%4Q%7%HN 4Q&E  %*@ '*@ '3B rcSnUR5HQnUR5H:nUR5H#upE[U5S:dMUSScM!Un O O O U(a|UR5HgnUR5HPnX1nUR5H5upEXA:XaM [[U55HnXgSnXUS'M M7 MR Mi gg)aa If `fqn` entries are filled in for one of the models in `results`, copies them over to any models which do not have them filled out. A common use case benefitting from this is comparing a model prepared by quantization to a quantized model. In this case, the model prepared by quantization would have `fqn` entries, and the quantized model would not. Nrfqn)rrrr) rmodel_name_with_fqnsrrr model_resultsref_model_resultsrrs rmaybe_add_missing_fqnsrs ").."2%;%B%B%D !-B-H-H-J) }%)$Q'.:/9, .K  &E #3&-nn&6 ")?)F)F)H%$9$O!1F1L1L1N-J!9 "3}#56/25925a(/7 2O*I'7rc^^UU4SjmT$)Nc>Utp#n[U[5(a[U[5(d*[U[5(aJ[U[5(a5/n[X#5H"upgXg/UQ7nUR T "U0UD65 M$ U$[U[ R 5(aa[U[ R 5(aBUR(aUR5nUR(aUR5nUR[ R:wdUR[ R:wagX#/UQ7nT "U0UD6$r2) r4tuplelistzipappendrJTensor is_quantizedr@dtypefloat) rkwargsa0a1a_otherrel0el1new_argsfinners rrGmaybe_dequantize_first_two_tensor_args_and_handle_tuples..inners r5 ! !jU&;&; r4 ZD%9%9GK/w/uh9&9:(N ELL ) )jU\\.J.J]]_]]_ 88u{{ "bhh%++&=%W%(%f%%rr)rrs`@r8maybe_dequantize_first_two_tensor_args_and_handle_tuplesrs&2 Lrxyc[R"U5n[R"X- 5nS[R"X#- 5-$)z Computes the SQNR between `x` and `y`. Args: x: Tensor or tuple of tensors y: Tensor or tuple of tensors Return: float or tuple of floats )rJnormlog10)rrPsPns r compute_sqnrrs7 AB AE B  BG$ $$rc|[R"X- S-R5US-R5- 5$)z Computes the normalized L2 error between `x` and `y`. Args: x: Tensor or tuple of tensors y: Tensor or tuple of tensors Return: float or tuple of floats rg)rJsqrtsumrrs rcompute_normalized_l2_errorrs1 ::!|((*adZZ\9 ::rcURSS5nURSS5n[RRR X5$)z Computes the cosine similarity between `x` and `y`. Args: x: Tensor or tuple of tensors y: Tensor or tuple of tensors Return: float or tuple of floats r )reshaperJr functionalcosine_similarityrs rcompute_cosine_similarityrs? !RA !RA 88   0 0 66rcURS:XakUR[R[R[ R[ R[R [R4;agg)Nr-FT)rBrCrJrnrprcatstack)rs rop_type_supports_shadowingr sQ ww/! ;; II II LL LL II KK    ridxc tURUSS9nUbtUupE[U5[U5-U::a&[SUS[U5[U5-35eU[U5:aXB$[UR 55U$[UR 5[UR 5-U::a:[SUS[UR 5[UR 5-35eU[UR 5:aUR U$U[UR 5-n[UR R 55U$![a [UR 5[UR 5-U::a;[SUS[UR 5[UR 5-35SeU[UR 5:aUR Us$U[UR 5-n[UR R 55Us$f=f)zi Given a node, gets the n'th input to that node, normalizing args and kwargs to the best of its ability. T)normalize_to_only_use_kwargsNzIndex z out of range: total = )normalized_argumentsrrErrrr RuntimeError)rr rnorm_args_and_kwargs norm_args norm_kwargs kwargs_idxs rrDrDs %:#88 T 9  +%9 "I9~K 00C7$SE!8Y#kJZ9Z8[\S^# ~%K..01#66499~DKK 00C7$SE!8TYY#dkkJZ9Z8[\S^#yy~% 3tyy>1 DKK..01*==  : tyy>C , , 3 4S^c$++FV5V4WX  TYY 99S> !s499~-J **,-j9 9 :s,A*E-E BE=EBH76>H76H7)7rrcollections.abcrrJtorch.ao.nn.intrinsic.quantizedaor intrinsicrrvtorch.ao.nn.quantizedrrtorch.nntorch.ao.quantizationrrtorch.ao.quantization.observerrtorch.ao.quantization.utilsrtorch.fxrtorch.fx.graphr ns_typesr r rrmEnumrdictrHsetrrGrrintrrrrrrrrrrrrboolrrDrrrrs $ ..##@F8 5 ii DII DN DNDNDN#3,<(=#=> DN  "7 78 DNNU UU#3,<(=#=>U 5<<% !3 34t; Up# ## #L     44DI0 d    % %%%P 6M 6d 6F:: %ELL %U\\ %ell %: % : ;5<< ;ELL ;U\\ ;: ;:77%,,75<<7:7& T d *:4*:[*:s*:t*:r