i} pSSKrSSKrSSKJrJr SSKJr SSKJrJ r J r J r J r J r JrJr SSKrSSKJr SSKJr SSKJr SSKJr SS KJr SS KJr SS KJr SS KJ r J!r!J"r" SS K#J$r$ SSK%J&r& SSK'J(r( SSK)J*r* \RVRXRZR\r/\RVRXR`R\r1\"SS9S\ \RdRLS\ \RdRLS\34Sj5r4S\RdRjS\RdRjS\34Sjr6S\RdRjSS4Sjr7S\RdRjS\*S\RpRrS\\RdRL\ \RdRL\ \RdRL44Sjr:S\S\ \;4S jrS%\RdR~S\ \RdRL4S&jr@S%\RdR~S\ \RdRL4S'jrAS\RdRjSS4S(jrBS\RdRjS\;4S)jrCS*\SS4S+jrDS*\SS4S,jrES#\RdRLS\34S-jrF"S.S/5rG"S0S15rH"S2S35rI"S4S5\(5rJS\RdRjS\ \;\\;\ \\K444S6jrLg)7N) defaultdict OrderedDict) lru_cache)AnyDictIterableListOptionalSetTupleUnion)ExportedProgram)duplicate_constant_node)setting_python_recursive_limit)executorch_call_delegate)ops)create_submodule_from_nodes)tabulate) is_bufferis_lifted_tensor_constantis_param)has_free_symbols)Node)OperatorSupportBase)SourcePartition)maxsize node_left node_rightreturncz[U[RR5(a[U[RR5(aURUR:Xa{UR UR :Xaa[ UR5[ UR5:Xa5[S[URUR555(dgg[ [U55[ [U55:wag[X5Hup#[X#5(aM g g)Nc3<# UHup[X5v M g7fN) is_same_node).0arg_left arg_rights \/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/executorch/exir/backend/utils.py is_same_node..2s&,'HX11,sFT) isinstancetorchfxrtargetoplenall_input_nodesallziplistr#)rrn_leftn_rights r'r#r#&s)UXX]]++ :uxx}}0U0U   !2!2 2.Y../3z7Q7Q3RR+.--z/I/I,  tI 3tJ'7#8 8"99OF00 :  graph_left graph_rightc[[URR55[[URR55:wag[ S5 [ URRURR5Hup#[ X#5(aM SSS5 g SSS5 g!,(df  g=f)NFi0uT)r/r3graphnodesrr2r#)r7r8rrs r'is_identical_graphr<Cs 4   && '(C[5F5F5L5L0M,NN ' .%(    " "K$5$5$;$;& !I!77 / .&  /  / . sAC+C5C C graph_modulecpURRHnUR[:XarURSR S:XaS[ URR55nUH(nURS4URSS-UlM* MMUR[:XdM[ URR55nUSR S:XdMUSnURS/4UlM URR5 UR5 g)Nr placeholderoutput) r:r;r-T_QuantPerTensorargsr.r3userskeysT_DQuantPerTensoreliminate_dead_code recompile)r=node node_users dequant_node output_nodes r'#remove_first_quant_and_last_dequantrMUs""(( ;;* *yy|-/!$**//"34 $.L)-1,:K:KAB:O(OL%%/0 [[- -djjoo/0J!}8+(m %)YYq\N#4  )**,r6 partitionreplacement_opc/n/n/n/nURVs/sHowUR;dMUPM nnURHnURHPn [U [R R 5(dM.XR;dM?URU 5 MR URR5H%n XR;dMURU5 M' M UHnUR[:XdMX7/- nM UH?nURR5Hn U R[:XdMXJ/- nM MA [U5S:dS5e[U5S:dS5eX8-U-n [X SSS9upURR!U 5 URR#UU RU R$S9nU R'U5 URR)U 5 U R*UlSSS5 UR-5 WX44$s snf!,(df  N'=f) a Replaces partition with the op specified by replacement_op. It's also expected that the nodes contained in partition are sourced from a quantized module as this function searches for the quantization pattern to consume along with the nodes in the partition, to be then replaced by replacement_op. Args: graph_module: The graph module from which this partition was sourced. partition: Partition to be replaced. replacement_op: The op to replace paritition with. Returns: Tuple: First element in the tuple is the new replaced module. The second and third node lists in the returned tuple consist of the dq and q nodes that were consumed along with this partition to be replaced by the replacement_op. r@z2Dequant nodes missing in node list to be replaced.z0Quant nodes missing in node list to be replaced.to_be_replacedT)skip_legalize_graph)kwargsN)r;paramsrCr*r+r,rappendrDrEr-rFrBr/rr:inserting_before call_functionrSreplace_all_uses_with erase_nodemetarH)r=rNrO dequant_nodes quant_nodes input_nodes output_nodesrIpartition_nodesarguser node_list submodulecall_module_node replaced_ops r'#replace_quantized_partition_with_oprfks*MKKL(1XyGWGW;WtOX 99C#uxx}}--3oo3M""3'JJOO%D??*##D)&   ;;+ + V #M JJOO%D{{..v% & }  "X$XX " { q T"TT /+=I"=!1t#I    , ,-= >"((66   ! !#**7 ..{;%%&67+00  ?  44aYJ ? >sH9H90A-H>> I tagged_exported_program copied_nodescrURRHnURS:XdMURU;dM'[ 5nUR H.nUR URRSS55 M0 [U5S:XdMUR5URS'M g)a Assign new tag to the copied nodes. Before the pass constant_0 (tag_10) ------------------> op_b (tag_10) constant_0_copy (tag_10) -------------> op_a (tag_11) After the pass constant_0 (tag_10) ------------------> op_b (tag_10) constant_0_copy (tag_11) -------------> op_a (tag_11) r?delegation_tagNr@) r:r;r.namesetrDaddrZgetr/pop)rgrhrI users_tagras r'_assign_new_tagrqs (--33 77m #yyL(E  JJDMM$))--0@$"GH'y>Q&2;--/DII./4r6tagc `[5nURRHnURR SS5U:XdM%UR S:XdM7UR HznURR SS5nXQ:wdM&URR SS5(a[SUSUS US US 3 5eURUR5 M| M [5nUHnUR[X55nM URU5n[X5 g) a If the constants node is shared by different tagged nodes, like constant_0 ----> op_b (tag_10) |-------------> op_a (tag_11) we make default as constant_0 is duplicated to constant_0_1, constant_0_2, unless the node is tagged with "no_copy" constant_0 ------------------> op_b (tag_10) constant_0_copy -------------> op_a (tag_11) backend can estimate how much they want to duplicate the constant node, either error out or default to duplicate rjr?Nno_copyFzconstant data node (z) is tagged with (z) but has user (z) which has tag ()) rlr:r;rZrnr.rD RuntimeErrorrmrkunionrrq) rgrrcandidate_nodesrIrarprhcandidate_nodecandidate_node_with_copiess r'_maybe_duplicate_constant_nodesr|s!eO'--33 99==)2 .# 5ww-' JJD $ .> EI '99==E::"."6tf   sAAAAcURVs/sH9nURS:XdMURRS5(dM7UPM; sn$s snf)z/ Returns the list of delegates from the graph. get_attrlowered_module_)r;r.rk startswithrs r' get_delegatesrsQ KK D 77j  %)YY%9%9:K%L   sAA Ac,[[U55 g)z# Print the formatted graph string. N)printformat_delegated_graph)r=s r'print_delegated_graphrs  ./r6cURRVs0sHWnURS:XdMURR S5(dM7UR[ XR5_MY nnSnSnURRHnXCUR 5S3- nURS:XdM,URR S5(dMNX!RnXCS-SURS3- nXCS-S3- nURRRHnUUS -UR 5S3- nM M U$s snf) a Return the formatted graph string of including lowered_module (both backend id and original graph) together with the graph module. Example output: graph(): %arg0_1 : [num_users=2] = placeholder[target=arg0_1] %arg1_1 : [num_users=2] = placeholder[target=arg1_1] %arg2_1 : [num_users=2] = placeholder[target=arg2_1] %lowered_module_0 : [num_users=1] = get_attr[target=lowered_module_0] backend_id: BackendWithCompilerDemo lowered graph(): %arg0_1 : [num_users=1] = placeholder[target=arg0_1] %arg1_1 : [num_users=1] = placeholder[target=arg1_1] %arg2_1 : [num_users=1] = placeholder[target=arg2_1] %aten_mm_default : [num_users=1] = call_function[target=executorch.exir.dialects.edge._ops.aten.mm.default](args = (%arg0_1, %arg1_1), kwargs = {}) %aten_add_tensor : [num_users=1] = call_function[target=executorch.exir.dialects.edge._ops.aten.add.Tensor](args = (%aten_mm_default, %arg2_1), kwargs = {}) return [aten_add_tensor] %executorch_call_delegate : [num_users=1] = call_function[target=torch.ops.higher_order.executorch_call_delegate](args = (%lowered_module_0, %arg0_1, %arg1_1, %arg2_1), kwargs = {}) %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%executorch_call_delegate, 0), kwargs = {}) %aten_sub_tensor : [num_users=1] = call_function[target=executorch.exir.dialects.edge._ops.aten.sub.Tensor](args = (%getitem, %arg0_1), kwargs = {}) %lowered_module_1 : [num_users=1] = get_attr[target=lowered_module_1] backend_id: BackendWithCompilerDemo lowered graph(): %aten_sub_tensor : [num_users=1] = placeholder[target=aten_sub_tensor] %arg1_1 : [num_users=1] = placeholder[target=arg1_1] %arg2_1 : [num_users=1] = placeholder[target=arg2_1] %aten_mm_default_1 : [num_users=1] = call_function[target=executorch.exir.dialects.edge._ops.aten.mm.default](args = (%aten_sub_tensor, %arg1_1), kwargs = {}) %aten_add_tensor_1 : [num_users=1] = call_function[target=executorch.exir.dialects.edge._ops.aten.add.Tensor](args = (%aten_mm_default_1, %arg2_1), kwargs = {}) return [aten_add_tensor_1] %executorch_call_delegate_1 : [num_users=1] = call_function[target=torch.ops.higher_order.executorch_call_delegate](args = (%lowered_module_1, %aten_sub_tensor, %arg1_1, %arg2_1), kwargs = {}) %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%executorch_call_delegate_1, 0), kwargs = {}) return [getitem_1] rrz z graph():  r~z backend_id: zlowered graph(): ) r:r;r.rkrgetattr format_node backend_idoriginal_module)r=rIlowered_module_dictindentgraph_format_strlowered_modulenode_in_lowered_modules r'rr&saD!&&,,,D 77j  4%)YY%9%9:K%L 4 7<33, F#""((ht'7'7'9&:"== 77j TYY%9%9:K%L%L0;N A:,l>;T;T:UUW X X  A:,.@ A A *8*H*H*N*N*T*T& zl#9#E#E#G"HK +U ) #sEE$E edge_programc[5nURRHnURS:XdM[ X5(d"[ X5(d[ X5(dMGURHPnURURR;dM)[R"S5 URU5 MR M URRHnURS:XdM[ X5(d"[ X5(d[ X5(dMGX!;dMN[5nURH5nURRSS5nUcM$URU5 M7 [!U5S:a[R"SUS35 [!U5S:dMUR#5URS'M g) a Util function for partitioners. This function tags the const/param/buffers nodes whose users all belong within the same partition. This should be called after tagging all other nodes. Any const/param/buffer which is used as input to a subgraph, will be tagged with the same tag as that subgraph. Throw error when const/param/buffers is used across different partitions. That is the underlying data will be owned by multiple delegates. r?z@The buffer node is a mutated buffer node, which is not constant.rjNr@>\%A%A%S%SSLLZ#&&t, ( )""(( 77m # \ ( (,,(<<)E  JJD#yy}}-=tDH+! h/'y>A%LLVW`Vab__ y>A%2;--/DII./+)r6cZURRGHnSnURS:XaK[X5(a;URH+nUR UR R;dM)Sn O U(dMj[5nURH5nURRSS5nUcM$URU5 M7 [U5S:a[R"SUS35 [U5S :dMUR5URS'GM g) a Util function for partitioners. This function tags the mutated buffer nodes whose users all belong within the same partition. This should be called after tagging all other nodes. Any buffer which is used as input to a subgraph, will be tagged with the same tag as that subgraph. Throw error when buffers is used across different partitions. That is the underlying data will be owned by multiple delegates. Fr?TrjNr@rrr)r:r;r.rrDrkrrrlrZrnrmr/rrro)rrIis_mutated_buffer_noderrrars r'tag_mutated_bufferrs""((!& 77m # ,(E(E!ZZ >>\%A%A%S%SS-1*( " !I 99==)94@'MM(+#9~! RS\R]^[[ 9~!.7mmo *+/)r6cF[URSR5$)z% Check if the node shape is dynamic. val)rrZshapers r'is_shape_dynamicrs DIIe,22 33r6c \rSrSrSrS S\4SjjrS\\\ \ \ 4\\ \ \ 444Sjr SS\ \\\\4S \ \\ \\ \ 4S \ \\ \ 4S\\ \ 44S jjrS rg)DelegateMappingBuilderiaK Profiling helper class for building Delegate Mappings. Delegate Mappings are mappings from delegate debug identifiers to node debug handles. Specifically this is used to log within backend delegates Args: generated_identifiers (bool, optional): Whether identifier keys are generated automatically. Defaults to False. generated_identifierscFXl[[5UlSUlg)Nr)_generated_identifiersrrl_debug_handle_map _next_index)selfrs r'__init__DelegateMappingBuilder.__init__s$&;#   !"r6rc URR5VVs0sHupU[[U55_M snn$s snnf)a Returns: Union[Dict[int, Tuple[int]], Dict[str, Tuple[int]]]: A map of delegate debug identifier to a list of debug handles The keys (identifier) are either integers or strings The values are a sorted tuple of integer debug handles )ritemstuplesorted)rkvs r'get_delegate_mapping+DelegateMappingBuilder.get_delegate_mappings=150F0F0L0L0NO0N5##0NOOOs!ANr;handles identifiercUR(aUb[SUS35eUbX0R;a [S5eUSLUSL- (d [S5eUc>UR(a"URnU=RS- slO [S5eUbD[ U[ 5(aUOU/Vs1sHnUR RS5iM nnO [ U[[ 45(aUOU/nUVs1sH ofcMUiM nn[U5S :Xa [S 5eXpRU'U$s snfs snf) a Add a new delegate mapping entry If self._generated_identifiers = False: - A new identifier must be provided, else an exception is thrown If self._generated_identifiers = True: - New identifiers are generated incrementally, 0 indexed - Identifiers cannot be manually provided, else an exception is thrown Args: nodes (Union[Node, List[Node]]): A (list of) Node(s) handles (Union[int, List[Optional[int]]]): A (list of) debug handle(s) identifier (Optional[Union[int, str]]): Debug identifier corresponding to the Node(s) Note: Exactly one of nodes and handles must be provided Note: If a debug handle is missing or None, it is skipped Returns: Union[int, str]: Delegate debug identifier inserted NzEBuilders using generated identifiers can't manually add identifiers: z. Failed to add or update entryzjThis delegate debug identifier was already inserted. Duplicate delegate debug identifiers are not allowed.zOnly one of nodes or handles must be provided. Either both were provided or neither were provided. Failed to add or update entry.r@z6No identifier provided. Failed to add or update entry. debug_handlerz2No valid debug handles found. Failed to add entry.) r Exceptionrrr*r rZrnrr/)rr;rrrInew_debug_handleshandlefiltered_debug_handless r'insert_delegate_mapping_entry4DelegateMappingBuilder.insert_delegate_mapping_entrys>  & &:+AWXbWcdCD   !j4J4J&J|  d"wd':;T   **!--   A% L  '1&=&=UE7J!JD n-J !  &gt}==G9  "3" !2vF!2 "  % &! +PQ Q.Dz*%!" s:%EEE)rrr)F)NNN)__name__ __module__ __qualname____firstlineno____doc__boolrr rrr strrr rr r__static_attributes__r6r'rrs"d" P tCsO$d3c ?&;; < P48=A04 MdDJ./0M%T(3-%8 89:MU38_- M sCx MMr6rc\rSrSrSrS\R 4SjrS\RRS\ SS4S jr S\ 4S jr S rg) WhyNoPartitioni%aW Simple helper class for partitioners to log why a node was not lowered. Example usage: # In your backend partitioner file(s) why = WhyNoPartition(logger=your_backend_logger) # hypothetical function that checks if a node can be lowered if not can_be_lowered(node): why(node, "This node was not lowered because ...") loggerc,XlSUlSUlg)NrtrrIreason)rrs r'rWhyNoPartition.__init__3s -1  r6rIrrNcRXlX lURRU5 gr")rIrrdebugrrIrs r'__call__WhyNoPartition.__call__8s   $r6c<SURSURS3$)NzWhyNoPartition: Node z was not partitioned because .)rIrrs r'__str__WhyNoPartition.__str__=s!&tyyk1Nt{{m[\]]r6r)rrrrrrLoggerrr+r,rrrrrrr6r'rr%sG w~~  UXX]] C D ^^r6rc|\rSrSrSrSrS\RRS\ 4Sjr S\ 4Sjr S \ S \ S\ 4S jr S rg )WhyNoPartitionReporteriAa Helper class for partitioners to gather why nodes were not lowered in a single report. If a node is reported multiple times, only the first report is included. Example usage: # In your backend partitioner file(s) reporter = WhyNoPartitionReporter() # hypothetical function that checks if a node can be lowered if not can_be_lowered(node): reporter.report_reject(node, "This node was not lowered because ...") # Back in partitioner logger.info(reporter.get_table_report()) c"[5Ulgr")r_rejected_nodesrs r'rWhyNoPartitionReporter.__init__Ss M r6rIrc@XR;aX RU'gg)zNReport a node that was rejected from a partition, along with a reason for why.Nrrs r' report_reject$WhyNoPartitionReporter.report_rejectXs! ++ +)/  & ,r6rc/nURHnURS:XdURS:XaM%[URSS5=n(d URnURR SS5nUR URX4URU/5 M [U5S:a[U/SQS S S 9$g ) uReturns a string containing a table listing all rejected nodes. The table looks something like this: ╒══════════════════════════╤══════════════════════════╤═════════════════════════════════════╤═════════════════════════════════════╕ │ Node name │ Target │ Torch func │ Reason │ ╞══════════════════════════╪══════════════════════════╪═════════════════════════════════════╪═════════════════════════════════════╡ │ aten_convolution_default │ aten.convolution.default │ ('conv2d_1', 'builtin_function_or_m │ Convolution needs to have │ │ │ │ ethod.conv2d') │ kernel_y<=64, │ │ │ │ │ kernel_x*kernel_y<=4096, got kernel │ │ │ │ │ (2, 65) │ ╘══════════════════════════╧══════════════════════════╧═════════════════════════════════════╧═════════════════════════════════════╛ r?rA_opNtorch_fn-r)z Node nameTargetz Torch funcReason fancy_grid#)tablefmt maxcolwidthszNo nodes rejected.) rr.rr-rZrnrUrkr/r)r reject_reportrIr-rs r'get_table_report'WhyNoPartitionReporter.get_table_report]s ((Dww-'477h+>%dkk5$??F?yy}}Z5H  Fd.B.B4.HI  ) }  !?%  (r6operator_supportmessagec[XU5$)zHWrap the operator_support, reporting rejects with the specified message.)ReportRejected)rrrs r' wrap_check!WhyNoPartitionReporter.wrap_check}s.g>>r6rN)rrrrrrr+r,rrrrrrrrr6r'rrAsQ" 0%((--00 (#(@? 3?>A? ?r6rcd\rSrSrSrS\S\4SjrS\RRS\ 4Sjr S r g ) rizeClass for wrapping a OperatorSupportBase, reporting rejects with the specified message to `reporter`.rreporterc(XlX lX0lgr")rrr)rrrrs r'rReportRejected.__init__s !1  r6rIrcURRX5nU(d%URRX R5 U$r")ris_node_supportedrrr)r submodulesrI is_supporteds r'r ReportRejected.is_node_supporteds7,,>>zP  MM ' 'll ;r6)rrrN)rrrrrrrrr+r,rrrrrr6r'rrs7o-)%((--Dr6rcpSSKJn 0nURRHnURS:XdMUR R S5(dM7[XR S5nUcMR[XA5(dMdURURUR4X#R 'M U$)a Extracts the payload for delegates from a graph module that has been lowered to one or more backends. This function iterates through all lowered modules (delegates) in the graph and returns a dictionary mapping each delegate's name to a tuple containing: - backend_id: The name/identifier of the backend - compile_specs: A list of backend-specific compilation specifications - processed_bytes: The delegate blob created by the backend's preprocess method Args: graph_module: A torch.fx.GraphModule that may contain lowered backend modules. This is typically obtained from an EdgeProgramManager or ExecutorchProgram via `.exported_program().graph_module`. Returns: Dict[str, Tuple[str, List[Any], bytes]]: A dictionary where: - Keys are the delegate names (e.g., "lowered_module_0", "lowered_module_1") - Values are tuples of (backend_id, compile_specs, processed_bytes) Example: >>> edge = to_edge(export(model, inputs)) >>> edge = edge.to_backend(MyPartitioner()) >>> payloads = get_delegated_payload(edge.exported_program().graph_module) >>> for name, (backend_id, specs, data) in payloads.items(): ... print(f"{name}: backend={backend_id}, data_size={len(data)}") r)LoweredBackendModulerrN) &executorch.exir.lowered_backend_moduler r:r;r.rkrrr*r compile_specsprocessed_bytes)r=r delegate_payloadsrIrs r'get_delegated_payloadrs<LAC""(( 77j TYY%9%9:K%L%L$\99dCN)j//#--"00"220!)), ) r6)Mrr collectionsrr functoolsrtypingrrrr r r r r r+'executorch.exir.backend.backend_detailsrKexecutorch.exir.backend.canonical_partitioners.duplicate_constant_node_passrexecutorch.exir.commonrexecutorch.exir.delegaterexecutorch.exir.dialects._opsrexir_opsrrrtorch._export.utilsrrr%torch.fx.experimental.symbolic_shapesr torch.fx.noder torch.fx.passes.operator_supportr*torch.fx.passes.utils.source_matcher_utilsredgequantized_decomposedquantize_per_tensordefaultrBdequantize_per_tensorrFr,rr# GraphModuler<rM_opsOpOverloadPacketrfrrqr|rGraphrrrrrrrrrrrbytesrrr6r'r+s)0III CB=9NNNB@F==55IIQQMM66LLTT 4 &' 8$$38883G3G $((&& ,J5((&&J5J5JJ//J5 588==$uxx}}-tEHHMM/B BC J5ZB,Bc(B6$I,$I $I $IN %((-- D   D4G D,?0(<(<001)=)=1#1h+BO+B+B\>_>>D4588==4T4ood^^8@?@?F((/((&&/ #uS$s)U*+ +,/r6