[ThLSSKrSSKrSSKrSSKJrJrJrJr SSKrSSK J r J r SSK J r Jr SSKJrJrJrJr SSKJr SSKJrJr SSKJr SS KJr SS KJr SS KJ r SS K!J"r"J#r#J$r$J%r%J&r&J'r'J(r( SS K)J*r* SSK+J,r,J-r-J.r. SSK/J0r0 SSK1J2r2 SSK3J4r4J5r5 SSK6J7r7J8r8 SSK9J:r:J;r; SSKr>J?r?J@r@JArAJBrB SSKCJDrDJErEJFrFJGrGJHrHJIrIJJrJJKrKJLrL /SQrM\R\R\R\R\R\R\R\R\R\R/ rX\R\RRRR\R\RRRR0r`S\RRTS\.S\b\c\RR4S\b\c\f\c\g44S\b\c\4SS4 SjrhS\RRTS\.S\b\c\RR4S\b\c\f\c\g44S\b\c\4SS4 S jriS\.S!\-SS4S"jrjS#\RRS\k4S$jrlS\,S\b\c\4S\k4S%jrmS&\*S'\ SS4S(jrnS)\S\.S!\-SS4S*jroS+\.S\b\c\f\c\g44S\b\c\4S\f\c\c44S,jrpS\.S!\-SS4S-jrqS\.S\b\c\RR4S\\RR4S.jrrS\.S\b\c\RR4S\RRTS/\kS'\\ SS4 S0jrsS?S\.S\b\c\RR4S1\t\cS\b\c\4S'\ S2\kS/\kSS4S3jjruS\.S4\.S!\-SS4S5jrvS\.S!\-S\b\c\RR4S6\b\\b\g\g44S7\t\.SS4 S8jrwS@S\*S/\kS9\\7\b\c\4S4S:\kS;\kS<\\\b\c\4S4S'\\ \b\c\4S4S2\kS=\kS\*4S>jjrxg)AN)AnyCallableOptionalUnion) CUSTOM_KEYNUMERIC_DEBUG_HANDLE_KEY) BackendConfigget_native_backend_config)get_fused_module_classesget_pattern_to_dtype_configsget_qat_module_classes-get_root_module_to_quantized_reference_module)_is_activation_post_process)qconfig_equals QConfigAny)QConfigMapping) QuantType)_remove_qconfig) DeQuantStub) _parent_name"activation_is_statically_quantizedget_qparam_dictget_swapped_custom_module_classis_per_channelto_underlying_dtypeweight_is_quantized) GraphModule)ArgumentGraphNode)type_before_parametrizations)quantized_decomposed_lib)convert_eq_obsupdate_obs_for_equalization)ConvertCustomConfigPrepareCustomConfig)_is_observed_module_is_observed_standalone_module)lower_to_fbgemm))_compare_prepare_convert_qconfig_mappings_generate_node_name_to_qconfig&_is_qconfig_supported_by_dtype_configs_update_qconfig_for_fusion_update_qconfig_for_qat) _get_module_is_custom_module_lstm_is_custom_module_mhaassert_and_get_unique_devicecollect_producer_nodescreate_getattr_from_valueget_custom_module_class_keys graph_module_from_producer_nodesnode_arg_is_weight)convertconvert_custom_moduleconvert_standalone_moduleconvert_weighted_modulemodelnodemodulesnode_name_to_scopenode_name_to_qconfigreturnc .^URnUce[UR[5(de[ XT5upgX!Rn[ US5(aUR X5 g[U4Sj[UR5[URR55-55n U (d[U5(dJURU5 URURS5 URU5 SSS5 gUR n Sn [ US5(a UR"n Sn U [$;GaU (GdSn SnUR'5unn[)UR*5(a[-UR.5n[0R2R4R6R8n[0R2R4R:R8nUR<nUR>n[AU 5nUUUUUUS .nO[0R2R4RBR8n[0R2R4RDR8n[GU5n[-U5nUR<nUR>n[AU 5nUUUUUS .nURU5 URSnU/nURI5H]unnUS ;a@[U[F[,45(d%[KXXg-U-U5nURMU5 MLURMU5 M_ UROX[QU505nU/US S-nURSU[QU5U "UU55nURU5 [TURV;at[XURV[T;aY[TURV;a0URV[T'URV[T[XURV[T[X'URU5 SSS5 gU (GaSn [0R2R4RBRZn[AU 5nU[0R\[0R^4;dS 5eUR<nUR>n[aUS[0Rb5n[aUS[0Rd"[0Rf5Rh5n UUU US.n[jUn!URU5 URSnURS/n"URI5Hunn#U"RMU#5 M UROSU![QU"505n$UROS[lRnU$S405n%UROS[lRnU$S 405n&USnUSnUSn U%U&UUU S .nSSS5 URU5 URSnU/nURI5H2unnUS ;aUnURMU5 M!URMU5 M4 UROX[QU505nU/US S-n[0R2R4RDRZnURSU[QU5U "UU55nURU5 [XURV;a$URV[XURV[X'URU5 SSS5 gU [0Rp:Xa[0R2R4RrRtn'URU5 URSnUROSU'U[0Rp405n(UROSU'U([0RF405n)URU)5 URU5 SSS5 gg!,(df  g=f!,(df  g=f!,(df  GNK=f!,(df  g=f!,(df  g=f)aUReplace activation_post_process module call node with quantize and dequantize node working with decomposed Tensor Before: ... -> observer_0(x) -> ... After: ... -> torch.ops.quantized_decomposed.quantize_per_tensor(x, ...) -> torch.ops.quantized_decomposed.dequantize_per_tensor() -> ... or quantize_per_channel and dequantize_per_channel Nr9c3<># UHn[UT5v M g7fN_has_none_qconfig.0nrAs X/var/www/auris/envauris/lib/python3.13/site-packages/torch/ao/quantization/fx/convert.py M_replace_observer_with_quantize_dequantize_node_decomposed..y#:A !122:rF is_dynamicc0nSUR;a1URSRnU[R:waSU0nU$)Nval out_dtype)metadtypetorchfloat32) dequantize_op input_nodedequantize_op_kwargs dq_out_dtypes rKadd_dequantize_op_kwargs\_replace_observer_with_quantize_dequantize_node_decomposed..add_dequantize_op_kwargssE! JOO #%??5177Lu}},(3\'B$## call_function)_scale_ _zero_point__axis_ _quant_min_ _quant_max__dtype_)r`rarcrdrer`rar"zVonly uint8 and int8 are supported in reference flow for dynamic quantization right nowqschemeeps)rcrd_eps_rercrdre);graph isinstancetargetstr_get_module_path_and_prefixhasattrr9alllistargsuserskeys_is_conversion_supportedinserting_beforereplace_all_uses_with erase_noderUrPSUPPORTED_QDTYPEScalculate_qparamsrrgintch_axisrVopsquantized_decomposedquantize_per_channeldefaultdequantize_per_channel quant_min quant_maxrquantize_per_tensordequantize_per_tensorfloatitemsr5append create_nodetupler_rrTrtensoruint8int8getattrper_tensor_affinefinforWrh_QSCHEME_TO_CHOOSE_QPARAMS_OPoperatorgetitemfloat16convert_element_typeno_fuse)*r=r>r?r@rArj module_pathprefixactivation_post_processskip_replacementrUrPr\ node_type quantize_opscale zero_pointr|rXrrdtype_qparamsrYquantize_op_inputskey value_or_node qparam_nodequantized_node dq_inputsdequantized_nodergrhchoose_qparams_opchoose_qparams_op_inputsvaluechoose_qparams_node scale_nodezero_point_nodedtype_convert_opconvert_fp16_nodeconvert_fp32_nodes* ` rK:_replace_observer_with_quantize_dequantize_node_decomposedr[s$ KKE    dkk3 ' '' '5 "6K&kk2& 22''4dii4 (9#::78OPP # #D )  & &tyy| 4   T "*  $ ) )EJ& 55,77 $ !!:$ *. 3EEGz 199 : :199:G))88MMUUK ..EEMM 099I/99I(/F *!((! G ))88LLTTK!II::PPXXM%LEZJ/99I/99I(/F *((! G # #D )1J", &-mmo"]55"=5#,??#<k&:S&@-#K'--k:'--m<)'6,#...@(A2N((+=ab+AAI$22i ( C    & &'7 8dii', *0EE%5%:%::8:$))*5NRiiO*O, %%j12JK   T "_* )` $ ii44HHOO %U+%++uzz22  - 2,55 +55 19e>U>UV-uekk%--6P6T6TU %$  :'B  # #D )1J(, ! ~ $%mmo U)//6.#("3"3!2E:R4SUW# **!1!14G3KRJ$//!1!14G3KRO .I .II&E% /(( G)*: # #D )1J", &-mmo"]55#0K&--k:'--m<'6#...@(A2N((+=ab+AAI"II::PPWWM$22i ( C    & &'7 8'4994BF)),C %%&>?   T "K* )L %--  9999NNVV  # #D )1J % 1 1!1J 3NPR! !& 1 1!14Eu{{3SUW!   & &'8 9   T "* ) k* ) @* )^* ):* )R* )s@'0aE6aCa#8D)a5=A:b a a # a25 b bc ^Uce[UR[5(deURn[ XT5upgX!Rn[ U4Sj[ UR5[ URR55-55n U (d[U5(dJURU5 URURS5 URU5 SSS5 gURn Sn [US5(a UR n U ["R$["R&["R(["R*["R,4;GabU (GdZSn Sn UR/5up[1UR25(a-[5UR65nUUUU S.n["R8n O+[;U5n[5U5nXU S.n["R<n URU5 URSnU/nUR?5HBunnUS ;a%[AXXg-U-U5nURCU5 M1URCU5 MD UREX[GU505nURIS U4S 9nURU5 URU5 SSS5 gU (aSn ["RJn ["RLRNRPS ;nU US .nURU5 URSnU/nUR?5HunnURCU5 M UREX[GU505nURIS U4S 9nURU5 URU5 SSS5 gU ["RR:XaSn Sn SU 0nURU5 URSnU/nUR?5HunnURCU5 M UREX[GU505nURIS U4S 9nURU5 URU5 SSS5 gg!,(df  g=f!,(df  g=f!,(df  g=f!,(df  g=f)zReplace activation_post_process module call node with quantize and dequantize node Before: ... -> observer_0(x) -> ... After: ... -> torch.quantize_per_tensor(x, ...) -> x.dequantize() -> ... Nc3<># UHn[UT5v M g7frErFrHs rKrLB_replace_observer_with_quantize_dequantize_node..rNrOrFrPr_)r`rarbre)r`rarerf dequantize)rr)fbgemmx86)re_reduce_range_ call_methodtore)*rkrlrmrjrnrprqrrrsrtrurvrwrxrUrorPrVquint8qint8qint32 float8_e5m2 float8_e4m3fnrzrrgr{r|rrrrr5rrrrquantize_per_tensor_dynamicbackends quantizedenginer)r=r>r?r@rArjrrrrrUrPrrrrr|rrYrrrrrr reduce_rangers ` rK/_replace_observer_with_quantize_dequantize_nodergs    dkk3 ' '' ' KKE5 "6K&kk2dii4 (9#::78OPP # #D )  & &tyy| 4   T "*  $ ) )EJ& 55,77         $ *. 3EEG 199 : :199:G *! G  44K%LEZJ"'PUVG33K # #D )1J", &-mmo"]55#<k&:S&@-#K'--k:'--m<'6#...@(A2N %00^DU0V   & &'7 8   T "-* ). $ 77 ~~//66:KK #|D  # #D )1J", %mmo U"))%0.#...@(A2N %00^DU0V   & &'7 8   T "* ) %-- !  e$  # #D )1J", %mmo U#))%0. #...@(A2N %00^DU0V   & &'7 8   T "* ) u* ) T* )B* ) * )s30R B8R2B R.5B R? R R+. R<? S rjcURSn[U[5(d SU35eURU5 UR U5 [ X!5 g)Nrz@Expecting the for call custom module node to be a Node, but got )rrrkr rwrx_insert_dequantize_node)r>rjcall_custom_module_nodes rK6_replace_observer_or_dequant_stub_with_dequantize_nodersi#iil   d IJaIbcd  67 T3;r^rcURnSn[US5(a URnU[;=(a U(+=(d U=(d U[R :H$)NFrP)rUrorPryrVr)rrUrPs rKrurus[ # ) )EJ& 55,77  # # 8Z "  " EMM !r^c|[U[5=(a& URU;=(a XRSL$)zTCheck if a node has a qconfig of None, i.e. user requested to not quantize the node N)rkr name)r>rAs rKrGrGs9 4 4 II- - 4  +t 3r^observedbackend_configcURRHenURS:waMURH@nU(dM [ X#5(dM[ U5nUcM.[ X5nU"5 MB Mg g)zExtract the subgraph that produces the weight for dynamic quant or weight only quant node and run the subgraph to observe the weight. Note that the observers of dynamic quant or weight only quant ops are run during the convert step. r_N)rjnodesoprrr8r4r7)rrr>node_argweight_observer_nodesweight_observer_modules rK_run_weight_observersr#so $$ 77o %  Hx.t>>(>x(H%(0)I*&'("%r^argc[U[5(aAURS:Xa1URS:Xa!URSnUR X5 g[U[ [45(aUHn[XAU5 M g[U[5(a$UR5Hn[XAU5 M g[R"S[U535 g)zwIf the arg is a dequantize Node, or a list/tuple/dict of dequantize Node, we'll recursively remove the dequantize Node rrrz6Unsupported node type in recursive remove dequantize: N)rkr rrlrrreplace_input_withrqr"_maybe_recursive_remove_dequantizedictvalueswarningswarntype)rr>rj quantize_node arg_elements rKrr9s#t=!8SZZ<=W  3 C$ ' 'K .{% H C  ::rjdequantize_node user_nodes rKrrsW   t $++L4'Bdjj)I/,,TC* % $ $s.A"A"" A0cURR5HAnURS:XdMU[UR5n[ U5(dM?Us $ g)zP If the node is observed, return the observer instance. Otherwise, return None. call_moduleN)rsrtrrmrlr)r>r?maybe_obs_node maybe_obss rK_maybe_get_observer_for_nodersR**//+    -N$9$9 :;I*955  , r^ is_referencecU(a/[RRRRnO.[RRRR nU[ UR5nURSRn[UR5n[[U55Hn X;dM Xn U RS:XdM U RS:XdM2U RSn URX5 [U R 5S:XdMmUR"R%U 5 M URSR&n [U 5S:a&U SS:XdS5e[)XR"5 U"XdS9n [+UR5up[-XX5 X[ UR5'g)a|Converts a observed standalone module to a quantized standalone module by calling the fx convert api, currently using the same `is_reference` flag as parent, but we may changing this behavior in the future (e.g. separating quantization and lowering for standalone module as well) Args: - node: The call_module node of the observed standalone module - modules: named_module of original model - model: original model - is_reference: a flag from parent provided by user to decide if we want to produce a reference model or a fbgemm/qnnpack model - backend_config: backend configuration of the target backend of quantization _observed_graph_module_attrsrrrzCurrently only quantized)rN)rVao quantization quantize_fxconvert_to_reference_fx convert_fxrmrlrT&standalone_module_input_quantized_idxsrqrrrangelenrrrsrjrx'standalone_module_output_quantized_idxsrrsetattr)r>r?r=rr convert_fnobserved_standalone_modulesm_input_quantized_idxsrridxrrsm_output_quantized_idxsquantized_standalone_module parent_namers rKr;r;s*XX**66NN XX**66AA /6c$++6F.G8==&,,  ?DSY  ))Cvv&3::+E # '';syy>Q&KK**3/  :>>& -- #$q('*a/K1KK/( kk2#-"#%T[[1K G $D ;C r^observed_node_names is_decomposedcdU[UR5nURnSn [U5n [ Xz5(aAUR n UR 5n[UR5up[XX5 URU;n Ub[X5(dU (dg[U5nUR[U5/5n[X5(dg[U5nU(dgSnUn[ U[ R"R$R&R(5(aUnUSnSU0n[ U[ R$R*5(apUR-5nUR-5nU"UR.5 U"UR05 [3U5n[3U5nUR5UUS.5 GOX[ U[ R$R6[ R$R845(aUR:H~n[=UU5(dMUR?S5(dM.[AUU5nUR-5n U RB[ RD:XaU "U5 [3U 5UU'M OU SLnU(a3UR-5n [GU5nU(aU RIU5 U(+nU(aU(aU(dU "UR,5 UR5[3U 55 [KU5nUR[MU5S5nUcS[MU535eUROUU5nUbUUS'g[UR5up[XU U5 g)aConvert a weighted module to reference quantized module in the model If the QConfig of a QAT module is not set, the module will still be converted to a float module. Args: - node: The call_module node of the observed standalone module - modules: named_module of original model - observed_node_names: names for the set of observed fx node, we can skip this conversion if the node is not observed Nrr ) weight_ih weight_hhweightz3No reference quantized module class configured for )(rmrlqconfigr rkweight_fake_quantto_floatrrrrGr getrr-rrVrr intrinsic _FusedModule RNNCellBaserr rrupdateLSTMGRU_flat_weights_namesro startswithrrUrr3rrr! from_float) r>r?r rArr roriginal_modulerweight_post_processqat_module_classesr r is_observedpattern_to_dtype_configs dtype_configsis_weight_quantized fused_module float_module wq_or_wq_dictweight_post_process_ihweight_post_process_hhweight_qparams_ihweight_qparams_hhwnris_ptqdeviceis_qat)root_module_to_quantized_reference_moduleref_qmodule_cls ref_qmodules rKr<r<s<&c$++./O)11G/?/66.??)224(5 $d<))22K  T 8 8 r2rjs rK,_remove_previous_dequantize_in_custom_moduler4os 4  Z Mi[YZ ||}$)9)9\)I  >>!+<= y 1 $   Y ' %*J$r^custom_module_class_mapping(statically_quantized_custom_module_nodescU[UR5nURn[U5(GaUR U5 [ X5(a[ UR5S:Xa>[URS[5(a[ URS5S:XdeURunup[U[5(de[U[5(de[U [5(de[XU5 [XU5 [X U5 O[X5(a[ UR5S:XdeURupn [U [5(de[U [5(de[U [5(de[X U5 [X U5 [X U5 OHURSn [U [5(de[X U5 [X5nUceXl[XSU5nUR!U5n[#UR5unn[%UUUU5 g)aConverts an observed custom module to a quantized custom module based on `custom_module_class_mapping` For static quantization, we'll also remove the previous `dequantize` node and attach the observer node for output to the module, the observer for the node will be converted to a dequantize node instead of quantize-dequantize pairs later in the graph. In the end we would have a quantized custom module that has the same interface as a default quantized module in nn.quantized namespace, i.e. quantized input and quantized output. Args: - node: The call_module node of the observed standalone module - graph: The graph containing the node - modules: named_module of original model - custom_module_class_mapping: mapping from observed custom module class to quantized custom module class, used to swap custom modules - statically_quantized_custom_module_nodes: we'll add the custom module node if we find it is statically quantized, this will be used later when converting observers to quant/dequant node pairs, if the observed node is a statically quantized custom module nodes, we'll convert the observer to a dequantize node, this is to keep the interface the same as the default quantized module. TODO: maybe we want to redesign this part to align with reference model design as well, but there has been some discussions around the interface, so we can do it later. r"rN)rmrlrraddr1rrrrkrr r4r2rrr from_observedrr)r>rjr?r5r6observed_custom_modulerinputshidden0hidden1queryrrrrquantized_custom_module_classquantized_custom_moduler rs rKr:r:s>%S%56$,,G)'22044T: !$ 0 0DII!#tyy|U33 ! %* +,099 (V'gfd++ ++gt,, ,,gt,, ,, 8u M 8 N 8 N "4 1 1tyy>Q& && $ EeT** **c4(( ((eT** ** 8e L 8E J 8e L))A,Cc4(( (( 8E J&B4&Q #*6 66=T :%DW%!<II%T[[1K GK $(?@r^convert_custom_configis_standalone_module_remove_qconfig_flagqconfig_mappingkeep_original_weightsc l Uc [5n[U[5(a0[R"S[ SS9 [R "U5n[U[5(a9[R"S[ SS9 U(a[R "U5OSn[R"U5nUb[U[5(de[U[5(a0[R"S[ SS9 [R "U5nUc [5n[U5(dS5eURSn U Rn U Rn U R n U R"n [UR%S S 95nU(aU R&n[R"U5nU R((a [+XV5 [-X5 [/X5 [1UUUR2XZ5nU R55HFunnUU;d S US 35eUUcM[7UUU5(aM5S USUSUU35e Un [9UR:5nUR:nU R<b[?X5n[AXU5 [CX5 SnU RDnU RFn[IU5n[KURM55nU5n[QU5n[S5n[UUR2RV5GHnURXS:Xa'Un US- nU U;a[[UUR25 M9M;URXS:Xa[]U5S:XaM\Un!UR^Sn"[U"[T[J45(a%UHn#[aU"U#U!UR25 M M[U"[b[45(a!SU;a[aU"U!UR25 MM[R"S[eU"535 GMURXS:XdGM![gUU5n$U$ce[iU$5(aYUR^Sn%U%U;a[kUUR25 GMpU(a[mUUUU U 5 GM[oUUUU U 5 GM[U$[p5(a[kUUR25 GM[sU$5(a[uUXX5 GM[wU$5[SU5RyU5RyU5;a8[wU$5U;a[wU$S5U;aGMC[{UUU U UUU5 GMW[wU$5U;dGMi[}UUR2UUU5 GM UR2R5 [XR25nU(d [X X5nU(a [U5 UR5 URRSS5 U$)a+ We will convert an observed model (a module with observer calls) to a reference quantized model, the rule is simple: 1. for each observer module call in the graph, we'll convert it to calls to quantize and dequantize functions based on the observer instance 2. for weighted operations like linear/conv, we need to convert them to reference quantized module, this requires us to know whether the dtype configured for the weight is supported in the backend, this is done in prepare step and the result is stored in observed_node_names, we can decide whether we need to swap the module based on this set Args: * `is_standalone_module`: when this flag is True, it means we are quantizing a submodule that is not inlined in parent module, and will be quantized separately as one unit. * `is_decomposed`: a boolean flag to indicate whether we want to use the quantize operator for decomposed quantized tensor (torch.ops.quantized_decomposed.quantize_per_tensor) or default/standalone quantized tensor (torch.quantize_per_tensor) Returns: a quantized standalone module, whether input/output is quantized is specified by prepare_custom_config, with input_quantized_idxs, output_quantized_idxs, please see docs for :func:`~torch.ao.quantization.prepare_fx` for details NzPassing a convert_custom_config_dict to convert is deprecated and will not be supported in a future version. Please pass in a ConvertCustomConfig instead.r8) stacklevelzPassing a QConfig dictionary to convert is deprecated and will not be supported in a future version. Please pass in a QConfigMapping instead.zPassing a backend_config_dict to prepare is deprecated and will not be supported in a future version. Please pass in a BackendConfig instead.z-incoming model must be produced by prepare_fxrF)remove_duplicatez Expected key z in convert node_name_to_qconfigz Expected k zD to have the same value in prepare and convert QConfigMappings, but z was updated to r placeholderr"outputz1Unsupported node type for output_quantized_idxs: r)Er&rkrrr FutureWarning from_dictrcopydeepcopyr r r(rTr@prepare_custom_configr rA named_modulesrFr.r/r.r+r,rjrrr6observed_to_quantized_mapping!equalization_node_name_to_qconfigr%r$rinput_quantized_indexesoutput_quantized_indexesrrrtr r setrqrrrrrrrr rr0rrrrrr)r;r!unionr<r:eliminate_dead_coderr*rdelete_all_unused_submodulespop)&r=rrCrDrErFrr rGobserved_graph_module_attrsr@rQr rAr?prepare_qconfig_mapping modules_copyconvert_node_name_to_qconfigkvcustom_module_classesr5weight_eq_obs_dictplaceholder_node_seen_cntinput_quantized_idxsoutput_quantized_idxsr/root_module_classesrfused_module_classesr6r>cur_placeholder_node_idx return_noderLrmodrs& rKr9r9sL$ 3 5'..  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