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Uses the graph signature to identify only user inputs (excluding parameters, buffers, and other non-input placeholders). Args: exported_program: The exported program to analyze low: Lower bound for random uniform values (default: -1.0) high: Upper bound for random uniform values (default: 1.0) Returns: Tuple of randomly generated tensors matching the input specs namer?valNrr)rrr)setrz input_specskindr# USER_INPUThasattrargaddrrordrAr: isinstancerMTensortuplerrrappendr ) r[rrruser_input_namesspecr7rrrtensorinputs_flattenedrs r4generate_sample_inputsrsR(Mu 00<< 99 ,, ,txx(( $$TXX]]3= !&&,, 77m #  99, ,  DII ))E"CEE#u||,,cii( g&&73+@+@cii(  U%6.uDV$$V,/-2'}5 r6c 0n [UUUUUS9n [U UUUSS9n [U [U UUUUS9/SSS9n U R 5n U R R SRSR[R:waM[S[RSU R R SRSR35eU $) N)rrrTroperator_blocklistoperator_allowlistnn_module_blocklistnn_module_allowlistr}transform_passescompile_configrExpected delegate ID , but got ) rr!rr to_executorchexecutorch_programexecution_plan delegatesrer rG RuntimeError)rrrrrrrrrcompile_optionsexported_graphprogram edge_programrs r4export_model_to_vulkanrAs)O' #% N% G/ #5#5$7$7    L&335 --<zAll values are within toleranceF)as_tuplerFound z" values exceeding tolerance (atol=z, rtol=)zShowing first z errors:z;Index -> tensor1_value, tensor2_value, abs_error, rel_errorz -> z.6fz, z , abs_err=z , rel_err=)rlistrlenprint enumeratezipprint_tensor_comparison_errorsrMrrabsanynonzerominrangetolistitem)tensor1tensor2atolrtol max_errorsit1t2abs_diffrel_difftolerance_mask error_indices total_errorsidxval1val2abs_errrel_errs r4rrsJ'D%=))j4-.P.P w<3w< ' +CL>c'l^L M $S%:;KAx M!O4 5 *24z J<  gu|| , ,Jw 4U4U ?@}} %  tGMM?CDyy*+H 'T!H o(/:N      /0MM.5AM &&q)L F<. B4&PTvUV WX N3z89 BC GH3z0 1M$++-.|  "|  "-$$&-$$& e4SzD:Z}JwWZm \ 2r6c [U[5(a[UR55n[U[5(d[U[5(GaP[ U5[ U5:wa [ XX#5 gU(a8[R"USUSX#S9nU(d[ USUSX#5 U$Sn[[ U55Hn[X[R5(aE[R"XXX#S9(d$[SUS35 [ XXX#5 SnMgMi[X[5(a4XX:Xd([SUS35 [XSX/35 SnMM[SUS [X535 M U$[R"USXUS9nU(d[ USXU5 U$) z Helper function that checks if model output and reference output are equal with some tolerance. Returns True if equal, False otherwise. Fr)rrTz === Output z comparison failed ===rzWARNING: Output z has type )rrrvaluesrrrrMallcloserrrrtype) model_output ref_outputrrfirst_output_onlyresultrs r4check_outputs_equalr s*k***++-. *e$$ :t(D(D z?c,/ / *>$T aS0FGH6(OZ]D"' s33'?jm; aS0FGH 1jo5FGH!&< ,QCz$z}:M9NOP!,"M QTR *<?Jd S r6reference_modelrrc[UR5n[U5upxURS[ U55n [U"U65up[ U U UUUS9$)a^ Utility function that accepts an already lowered ExecuTorch program, executes it with the provided sample input, and checks the output for correctness. Args: executorch_program: Already lowered ExecutorchProgramManager sample_inputs: Sample inputs to run the program with reference_model: Reference model to generate reference outputs for comparison atol: Absolute tolerance for output comparison rtol: Relative tolerance for output comparison first_output_only: Whether to compare only the first output Returns: bool: True if outputs match within tolerance, False otherwise forwardrrr )rbufferr run_methodrr ) rrrrrr executorch_modulerrr r s r4run_and_check_outputrsq099K9R9RS'}5%// 5AQ;RSL!-!@AMJ   +  r6c/nUH]n[U[R5(a!URUR 55 MCUR[ U55 M_ [ U5$r.)rrMrrclonerr)rsample_inputs_copy input_vals r4make_copy_of_inputsrHsY" i . .  % %ioo&7 8  % %hy&9 : # # $$r6rc U (a[U[U5UUUU U 5n O[U[U5UUUU U S9n [U R5n [ U5upU R S[U55nU"[U56n[UUUUUS9(dgUbKUHEn[ U5unnU R S[U55nU"U6n[UUUUUS9(aME g g)a Helper testing function that takes a torch.nn.Module and lowers it to Vulkan with the given sample inputs. It then runs the lowered module and compares its outputs with the outputs of the eager module. Returns: bool: True if all comparisons pass, False otherwise. rrrFT) rrrrrr rrr )rrrrr test_inputsr rrrrxnnpackrrrrr r  test_inputtest_inputs_flatteneds r4lower_module_and_test_outputr Rs&.4   .      4   . 11 3 3 99K9R9RS&}5$// 5AQ;RSL+M:;J   +  %J'3J'? $ !1,775!67L +J&"3 & r6expected_outputs method_namect[U5upE[U[UUS9/S9/n[X5n[ U5nU$)a Create a bundled program containing the model and test cases for correctness testing. Args: executorch_program: The ExecutorchProgramManager to bundle sample_inputs: Sample inputs for the model expected_outputs: Expected outputs from running the model with sample_inputs method_name: Name of the method to test (default: "forward") Returns: Serialized bundled program as bytes )inputsr!)r" test_cases)r rrrr) rrr!r"rr test_suitesbundled_programbundled_buffers r4create_bundled_programr)sY&'}5 #+%5  K%%7EOB/RN r6 output_path et_programcBUc [UUUUS9nUc0n[X5"U0UD6/n[X45up[U[ U5UU5n UR S5(dUS-n[ US5n U RU 5 SSS5 U$!,(df  U$=f)a  Export a bundled .pte file containing the model and test cases. Args: model: The PyTorch model to export sample_inputs: Sample inputs for the model output_path: Path where the bundled .pte file should be saved (should end with .bpte) method_name: Name of the method to test (default: "forward") et_program: Optional pre-exported ExecutorchProgramManager. If None, will export to Vulkan dynamic_shapes: Optional dynamic shapes for export Returns: str: Path to the saved bundled program file N)rrz.bptewb)rgetattrr r)rendswithopenwrite) rrr*r"rr+rr!rr bp_bufferfiles r4save_bundled_programr4s0+  ')    3]TmTU' 'EF'  I    ( (!G+  k4 D 9 !  ! s 3B BcURS5(dUS-n[US5nURU5 SSS5 U$!,(df  U$=f)z Save an ExecutorchProgramManager as a .pte file. Args: executorch_program: The ExecutorchProgramManager to save output_path: Path where the .pte file should be saved (should end with .pte) Returns: str: Path to the saved .pte file z.pter-N)r/r0 write_to_file)rr*r3s r4save_executorch_programr7sY    ' '!F*  k4 D((. !  !  s A A operator_listc*[R"S5nUR[R5 UR S[ U5S35 SnUR 5RRGH;n[R"U5(dM!URnUR[RRR:Xd2UR[RRR :XaNUR"Sn[%US5(aUR'5nO[%US5(a UR(nXQ;dMUS- nUR SUS UR+535 [R,"U5nUR S U35 GM> US:XaUR S 5 g UR SUS35 g ![.a nUR S U35 S nAGMS nAff=f)z Print the input/output information for all occurrences of specified operators in the edge program. Args: edge_program: The edge program created by to_edge_transform_and_lower operator_list: List of operators to search for in the graph zSearching for occurrences of z operators in the graph...rrrGrnz Occurrence :  z Error getting I/O string: Nz=No occurrences of the specified operators found in the graph.rz. total occurrences of the specified operators.)logging getLoggersetLevelINFOinforr[rordutilsis_torch_op_nodetargetrMops higher_orderauto_functionalizedauto_functionalized_v2argsrrrG format_node node_io_str Exception) rr8loggeroccurrence_countr7rD first_argio_stres r4print_occurrencesrR!s  r "F OOGLL! KK 'M(:';;UV--/55;;  ! !$ ' '[[F uyy55III;;%))"8"8"O"OO IIaL 9f--&^^-FY 33&//F& A%  k*:);2d>N>N>P=QRSD"..t4FKK"VH .-<41 ST %&&T U !DKK">qc BCCDs *G(( H2H  Hc B^^^^^^^^^^^^[R"S5mTR[R5 TR S5 [ TTSS9R 5n[ UTTSS9n[U/SSS9n 0mU R5RRHn [R"U 5(dM U Rn U R[RR R":Xd2U R[RR R$:XaNU R&Sn [)U S 5(aU R+5n O[)U S 5(a U R,n U T;aTU ==S - ss'MS TU 'M [/TR155n TR S [3U 5S 35 [5U U4SjSS9nTR S5 UH,nTR SUR+5STUS35 M. SmS[6S[6S[84UUUUUUUUU4 SjjmS[6S[6S[:[6[644UUU4SjjmTR S[3U5S35 T"U/5unnTR STS35 TR S[3U5S35 UH,nTR SUR+5S TUS!35 M. TR S"[3U5S35 UH,nTR S#UR+5S TUS!35 M. [=U U5 [3U 5T- nTR S$TS%[3U 5S&US'35 UUTU TS(.$))a Fast binary search utility function to determine which operators work correctly when delegated to Vulkan. This function uses a binary search approach to efficiently find bad operators: 1. Split operators into two halves (least frequent first, most frequent second) 2. Test each half to see if it produces correct output 3. Add good halves to known_good_ops and recursively search bad halves 4. Continue until all operators are classified Args: model: The PyTorch model to test sample_inputs: Sample inputs for the model atol: Absolute tolerance for output comparison rtol: Relative tolerance for output comparison dynamic_shapes: Optional dynamic shapes for export test_inputs: Optional additional test inputs first_output_only: Whether to compare only the first output Returns: dict: Dictionary with keys: - 'good_operators': List of operators that work correctly - 'bad_operators': List of operators that cause failures - 'operator_frequencies': Dictionary mapping operators to their occurrence count - 'all_operators': List of all unique operators found in the graph - 'test_count': Number of tests performed r:z5Starting fast binary search operator ablation test...T)rrNrrrrGrnrz unique operators in the graphc>TU$r.r)rAoperator_frequenciess r4"op_ablation_test..s &:2&>r6)keyreversezAOperator frequencies (sorted by occurrence, most frequent first):r<r;z occurrences ops_to_testknown_good_opsr,c > T S- m X-nT RST S[U5S[U5S35 [T T TT TTTUS9nT RSU(aSOS 35 T RS 5 UH%nT RS UR535 M' T RS 5 UH%nT RS UR535 M' U$![anT RS U35 SnAgSnAff=f)zSTest if a set of operators works correctly when combined with known good operators.rnzTest z : Testing z operators with z known good)rrrrrrr rr<u✓ PASSu✗ FAIL Known good: * z Tested ops:z ! Error: NF)rArr rrL)rZr[test_allowlistsuccessrArQrrr rMrrr test_countrs r4test_operator_set+op_ablation_test..test_operator_sets a '5 JU(d//4$[U5S:XadUSnT"U/U5(a(T RSUR5S35 U//4$T RSUR5S35 /U/4$[U5S-nUSUnXSnT RS[U5S [U5S [U535 T RS 5 UH%nT RS UR535 M' T RS 5 UH%nT RS UR535 M' T RS5 UH%nT RS UR535 M' /n/nT"XA5nU(a@T RS[U5S35 URU5 URU5 T"XQ5n U (a/T RS[U5S35 URU5 U(d[T RS[U5S35 T "XA5upURU 5 URU 5 URU 5 U (dJT RS[U5S35 T "XQ5upURU 5 URU 5 Xg4$)z} Recursively find bad operators using binary search. Returns: Tuple of (good_operators, bad_operators) from ops_to_test rnrz Single operator z is GOODz is BADrNz Splitting z operators: z + r]r^z First half ops:z Second half ops:z First half (z$ ops) is good - adding to known goodz Second half (z ops) is bad - recursing)rrArextend)rZr[rAmid first_half second_halfgood_opsbad_opsfirst_half_goodsecond_half_goodsub_goodsub_badfind_bad_operatorsrMrbs r4ro,op_ablation_test..find_bad_operatorssr6M { q QB "~66 0 8DEtRx 0 7CDB4x+!# #& !$'  [)*,s:6Gs3{K[J\ ]  O$ B KK$rwwyk* +!  '(B KK$rwwyk* +  ()B KK$rwwyk* ++JG  KKs://ST  OOJ '  ! !* -,[I  KKK 011UV  OOK ( KK,s:&77OP Q 2: N H OOH % NN7 #  ! !( + KK-K(8'99QR S 2; O H OOH % NN7 #  r6z === Starting binary search on z operators ===z" === Binary search complete after z tests ===zGood operators (z):u ✓ z (frequency: rzBad operators (u ✗ z Efficiency: z tests instead of z (saved z tests))good_operators bad_operatorsrU all_operatorsra)r=r>r?r@rAr!rrr[rordrBrCrDrMrErFrGrHrIrrrGrkeysrsortedrrPr rR)rrrrrrr rrrr7rDrOrsoperators_by_frequencyrArqrrefficiency_gainrorMrUrarbs``````` @@@@@r4op_ablation_testrxTsF  r "F OOGLL! 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GU\\ * * <()3WX_XeXeWfflmtmxmxmzmmnBmCCJKRKVKVKXK]K]K_J``ijqjwjwixxy z  GS ! ! \*+71#S BC \*+71#SgHIr6)ir_in_target_subgraph)r)g?NN)gr)MbP?r )rrF)r皙?F) rrNNFNNNNF)r)rNNN)rrNNF)rr)fr= collectionsrcopyrenumrrtypingrrr r executorch.backends.vulkan.utilsbackendsvulkanrBrM9executorch.backends.vulkan.partitioner.vulkan_partitionerr ,executorch.backends.vulkan.vulkan_preprocessr 9executorch.backends.xnnpack.partition.xnnpack_partitionerr 7executorch.backends.xnnpack.quantizer.xnnpack_quantizerrr.executorch.backends.xnnpack.xnnpack_preprocessrexecutorch.devtoolsr*executorch.devtools.bundled_program.configrr-executorch.devtools.bundled_program.serializerexecutorch.exirrr#executorch.exir.backend.backend_apir#executorch.exir.backend.partitionerrrrexecutorch.exir.backend.utilsrr&executorch.exir.lowered_backend_modulerr,executorch.extension.pybindings.portable_librexecutorch.extension.pytreer torch.exportr!torch.export.exported_programr"torch.export.graph_signaturer#!torch.fx.passes.infra.partitionerr$ torch.fx.passes.operator_supportr%'torchao.quantization.pt2e.quantize_pt2er&r'r)rUrN GraphModulerrLrqrrrrrfloatrrrrrr nnModulerPrrr bytesr)r4r7rRdictrxr}rrr6r4rs#--00 WFXJ.VRL P592H@N . F ; H& .((&&. ..  .0'&--&#& & &R%t%      XX <"NN7%7 7 7 5<<  7z   5v   %R:<A JIN3t   *XX__*0*&* *Z%uSz%eCj%   M 88??M&M Mh! (0(s*+(3i( (  (^!598 88??8&88 8 12 8 8v0 20 40 l  l 88??l&l l^ JS JC Jr6