i%SSKrSSKrSSKrSSKrSSKJr SSKJrJr SSK J r J r J r J r JrJrJrJrJrJr SSKrSSKJr SSKJr SSKJr SSKJrJr SS KJ r J!r! SS K"J#r# SS K$J%r% SS KJ&r& SS K'J(r(J)r)J*r* SSK+J,r, SSK-J.r. 0r/\ \0\ S\\144\2S'"SS5r3S\RLRXS\44Sjr5S\RLRXS\44Sjr6S`S\RLRXS\%S\1S\1S\\)SS4 Sjjr7S\\ S\\,4Sjr8S`S\,S\\)S\44S jjr9S!\\,S\\)S\\%4S"jr:S\\)S\44S#jr;S`S!\\,S\\)S\\%4S$jjrS`S0\RLR~S\\)S\\%4S1jjr@\"S2S355rA\"S4S555rB\"S6S755rC\"S8S955rDSbS:\\BS;\1S\14S<jjrES=\BS\%S\44S>jrFS=\BS\%S\14S?jrGScS:\\BS\%S@\4S\B4SAjjrHS\RLRXS\\\%\\%44SBjrIS0\RLR~S\44SCjrJSbS&SD.SE\1SF\\%S0\RLR~S\)SG\1S@\4S\D4SHjjjrK"SISJ5rLSE\1SF\\%S0\RLR~S\)SG\1S\D4 SKjrMS0\RLR~S\\,4SLjrNS0\RLR~S\\,4SMjrOS0\RLR~S\\,4SNjrPS0\RLR~S\\,4SOjrQS0\&R~S\\%4SPjrRS0\&R~S\\%4SQjrSS0\&R~SR\\%SS4SSjrTST\U\1SU\U\1S\U\14SVjrVSdSW\ S\U\14SX\RLR~SE\1SY\RLRXS\\)SZ\4S\U\14S[jjrWS`SW\ S\U\14S0\RLR~SE\1S\\)S\U\14 S\jjrXSeSW\ S\U\14S0\RLR~SE\1S\\)SZ\4S]\4S^\4S\U\14S_jjrYg)fN) defaultdict) dataclassfield) AnyCallablecastDictIterableListOptionalSetTupleUnion)memory) while_loop)executorch_call_delegate)internal_assert InternalError)is_inplace_variantis_out_variant)TensorShapeDynamism) TensorSpec)fx)ConstantArgumentExportGraphSignature InputKind)Node) tree_flatten.REGISTERED_ALGOSc F\rSrSrSrSS\R RS\S\S\S\ \ S S4 S jjr \ SS \ S \ S \S \4Sjj5r\ S\\S\\S \4Sj5r\ S \ S \ S \4Sj5r\ S \ S \ S \4Sj5r\ S \ S \ S \4Sj5rSS\S \4SjjrSSjrSrg)Verifier0z Verify if the outcome of a memory planning algorithm makes sense. E.g., make sure tensors having overlapping lifetime does not have overlapping storage/buffer. N graph_modulealloc_graph_inputalloc_graph_outputalloc_mutable_buffersgraph_signaturereturnc@XlXPlX lX0lX@lgN)r#r'r$r%r&)selfr#r$r%r&r's ^/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/executorch/exir/memory_planning.py__init__Verifier.__init__7s").!2"4%:"lhs_specrhs_specaccept_both_nonecURUR:wagURcURcU$URUR:H$)z Given two `TensorSpec`, return if their `mem_obj_id` are the same. Note that if both are None, this function will return True if `accept_both_none` is True and False otherwise. F)mem_id mem_obj_id)clsr0r1r2s r,mem_obj_id_matchVerifier.mem_obj_id_matchEsM ??hoo -    &8+>+>+F# #""h&9&999r/lhs_ivlrhs_ivlcUSUS:d USUS:agUSUS:=(a USUS:*=(d USUS:=(a USUS:*$)zW The passed in intervals are inclusive in both sides. Return if they have overlapping. rF)r6r9r:s r, has_overlapVerifier.has_overlapWst 1: "gaj71:&= gaj(EWQZ71:-E AJ'!* $ Awqz)A r/cURnURn[USSL=(a USSLUS35 [USSL=(a USSLUS35 URX45$)Nrr<z should have valid start and end)lifetimerr>)r6r0r1 lhs_lifetime rhs_lifetimes r,lifetime_overlapVerifier.lifetime_overlapes(( ((  O4 ' GLO4,Gj8 9   O4 ' GLO4,Gj8 9 |::r/c/nURUR:wagX4Hn[URS:US35 [[UR[ 5=(a URS:US35 UR URURUR-S- /5 M UR"U6nU$)NFrz& should have non-zero allocated memoryz$ should have specified memory offsetr<)r4rallocated_memory isinstance mem_offsetintappendr>)r6r0r1 intervalsspecr>s r,storage_overlapVerifier.storage_overlapss ??hoo -(D %%*&>?  4??C0IT__5I&<=    $//D4I4I"IA"MN )ooy1 r/cSURSURS3nUSURSURSURS3- nUSURSURSUR3- nU$)Nzlhs life time: z, rhs lifetime:  z lhs: mem_id z storage: z, z rhs: mem_id )rAr4rIrG)r6r0r1messages r,_debug_message_from_specs"Verifier._debug_message_from_specss h//00@ARAR@SST U  \(//!2*X=P=P&>x&R#*9d>S>S??(6x7Y7YZb7m6no  00DD'>xjxjY 1$3:';8r/c ^URn[URRUR5[ URR5-nU(a1UR UR:wa[R"S5 gSnSnSnSnSS1URRVs1sHowRiM sn-nSU;d SU35e[URRUR5mURRHn U RU;dM[U 5=n (dM)[XR5(aME[[U4SjU 55n [!U 5S:XaURnM{[#S U 55n [#S U 55n [%S U 55U :XdS 5eU RS:XaU nX\-nMU nXl-nM ['URS 9n SU;a>U (a7UcS5eU(d&X0R :XdSUSUR 35eUcS5eU(d'X@R:XdSUSUR35eggs snf)z alloc_graph_input / alloc_graph_output indicates if memory for graph input/output is allocated by the compiler. If not, the runtime will set them using buffers provided by users. zlHaving overlapping graph input/output tensors while the allocation decision for graph input/output mismatch.NF placeholderoutputzgraph module has no output: c>>UR(+=(a UT;$r*)const)rMmutable_buffer_specss r,4Verifier.verify_graph_input_output..s^&= (<<&=r/rc3R# UHoSL=(d URSLv M g7fr*rI.0rMs r, 5Verifier.verify_graph_input_output..s& LQDDL?DOO4$??E%'c3r# UH-nUSL=(d UR[R:Hv M/ g7fr*)shape_dynamismrDYNAMIC_UNBOUNDrss r,rurvs>1!&DLR**.A.Q.QQR %s57c3R# UHoSL=(d URSLv M g7fr*rrrss r,rurv s%VPU Ct(CCPUrwz;Either all or non of the tensors should be allocated memory)r'zgraph_input_allocated not setzMisallocate graph input: z v.s. zgraph_output_allocated not setzMisallocate graph output )r#get_graph_input_tensorsr`rar'get_graph_output_tensorsr$r%loggingdebugop_get_mutable_buffer_specsget_node_tensor_specs_is_mutable_bufferr^filterlenanyall_do_user_inputs_exist)r+r#overlapgraph_input_allocatedgraph_output_allocatedhas_dynamic_unbound_inputhas_dynamic_unbound_outputnode check_listndspecs allocatedhas_dynamic_unbound_tensoruser_inputs_existrns @r,verify_graph_input_output"Verifier.verify_graph_input_outputs (( *    $ $d&:&: $\%7%7%=%= >? ..$2I2II MM~   $!%$)!%*"#X. , 2 2 8 82 8GG 82  :%T)El^'TT% 9    $ $d&:&: $$**Buu "!6r!:::%b*>*>??=u:?-1-D-D* LQ  .11!&1.* VPUVV !QQQ!55M),5)-K--6*.L.E+J2$BVBVW J &+<(4 U6U U4,)-C-CCe./D.EVDLbLbKcdeC&1S3SS1)&*A*AA c*+A*B&I`I`Hab cA*s2 s3J )r$r%r&r#r'r*TF)r(N)__name__ __module__ __qualname____firstlineno____doc__torchr GraphModuleboolr rr- classmethodrr7r rJr>rDrNstrrSrgr__static_attributes__r=r/r,r!r!0sc;? ;hh** ;  ;! ; $ ; ""67 ;  ;RV:!:-7:KO: ::"  $s)  d3i  D     ; ;j ;T ; ;zZD(!-7 :?7267 7rYcr/r!rr(c(URS:H=(a} [UR[RR 5=(aH [ URRRURRR5$N call_function) rrHtargetr_ops OpOverloadr_schemaname overload_namers r,_is_out_var_noder(sa ?" X t{{EJJ$9$9 : X 4;;..33T[[5H5H5V5V Wr/c(URS:H=(a} [UR[RR 5=(aH [ URRRURRR5$r) rrHrrrrrrrrrs r,_is_inplace_noder0sb ?" t{{EJJ$9$9 :  KK   $ $dkk&9&9&G&G r/rMnode_idx max_node_idxgscURupVURS:XaSnO UbXR:aUOUnURS:Xa[X5(aUnO UbXb:aUOUnXV/Ulg)a  Update the lifetime of the tensor to cover node_idx. A tensor's lifetime are represented by the index of the first and last node referring that tensor in its inputs/outputs. Arguments: spec: the TensorSpec for the tensor node_idx: extend the tensor's lifetime to cover node_idx rjrN)rArr)rrMrrrstartends r,update_tensor_lifetimer:sd JE ww-!MU-=5 ww-$6t$@$@+hSLDMr/inputsc[[U55SVs/sHn[U[5(dMUPM sn$s snf)zH This method need return Node object embedded inside List/Dict as well. r)rr^rHr)rrs r, filter_nodesrYs5&d6l3A6 O62*R:NB6 OO Os AAr'c UcgURS:Xap[UR[5(aQURUR;a7URURnX!R R 5;agg)zP Check if the node is mutable buffer according to the provided graph signature. FrjT)rrHrrinputs_to_buffersbuffers_to_mutatevalues)rr'fqns r,rr`sk ww- dkk3 ' '{{o???%77 D;;BBDD r/rac[5nUH8n[X15(dM[U5HnURU5 M M: U$r*)setrradd)rar'rnrrMs r,rrssF-0E d 4 4-d3$((.4 r/cnUcg[[SUR55n[SU55$)NFc<UR[R:H$r*)kindr USER_INPUT)inputs r,ro'_do_user_inputs_exist..s%** (<(<.sR!E uyy"2 33 D%))//Cc+BC C D sA,A.)r^r input_specsr)r' user_inputss r,rr~sG <  ' ' K ! r/c[5nUHJnURS:XdM[X15(aM'[U5HnUR U5 M ML U$)Nrj)rrrrr)rar'graph_input_tensorsrrMs r,r|r|sR% 77m #,>t,U,U-d3#''-4 r/c[5nUH8nURS:XdM[U5HnURU5 M M: U$)Nrk)rrrr)ragraph_output_tensorsrrMs r,r}r}sE5 77h -d3$((.4 r/TrXrYrZrWr\r]r[ignore_dynamic_unbound_tensorc ## [5n U(a [X5O [5n U(a [U5O [5n [5n U(a [X5n UGHnURS;aMU(a[ U5(aM/[ U5=n(dMC[U5(aMU[X5(a U(aMnU(a[URS;=(d UR[R[R[R[ R"R$R&[([ R"R$R*[,4;SURSUR35 UHnUcMU (a UR.[0R2:XaM/U(aUU ;aM>U(aUU ;aMMU(aUU ;aM\U(a4UR4(a#UR6R9SS5(dMU(aUU ;aMU R;U5 Uv M GM g7f)a Collect specs from the passed in nodes. Do filtering as controlled by arguments. Arguments: ignore_graph_input: ignore graph input tensors from placeholder nodes ignore_const: whether to ignore the const ignore_out_var_node: whether to ignore out variant node dedup: whether do dedup do_assertion: whether to assert the filtered nodes belong to a resticted set like alloc, getitem )get_attr)rjrkzUnexpected op z , target Nweight_has_gradientF)rr|r}rrrrrrrrrallocviewoperatorgetitemrops higher_ordercond exir_whilemap_implrryrrzrmmetagetr)rar'rXrYrZrWr\r]r[r unique_specrrrnrrrMs r,r_r_s,%K;M7SVSX,? 'CE -0E8P 77l "  #3D#9#9 .t444  D ! !  d 4 49O   44 ;;LLKK$$II**//II**33, ! 4;;-@ D|.''+>+N+NN "d.A&A"t/C'C%$2F*FJJ &;UCC;&OOD)J?EsI#I%r#c [5n[URR5S- n[ URR5H|upE[ [ [R"U/URURR555USSSSSSS9H!n[XVXCU5 URU5 M# M~ U$)zC Set the lifetime for all the tensors encountered in the Fx graph. r<F)rXrWr\r]r[r)rrr`rarbr_r itertoolschainargskwargsrrr)r#r'rrrrrMs r,update_all_tensors_lifetimers EE|))//014L#L$6$6$<$<=, $DKK Lr/c.\rSrSr%Sr\\S'\\S'Srg)AllocationSpeci,zA AllocationSpec is used to represent the allocation of a tensor. offsetrMr=N) rrrrrrJ__annotations__rrr=r/r,rr,s K r/rcv\rSrSr%Sr\\S'\\S'\\S'\\S'\\S'\"\S9r \ \ \S 'S \ 4S jr S rg ) SharedObjecti8aC We define the concept of shared object, which represents a segment in the memory buffer that can be shared by multiple tensors. In order to check if a shared object is available for a tensor, we maintain the last_used_index attribute. The shared object will be available for nodes with index greater than last_used_index. idxrsizefirst_used_indexlast_used_index)default_factory allocationsr(c SURSURSURSURUR4S3 $)NzSharedObject(idx=z , offset=z, size=z , lifetime=[z]))rrrrr)r+s r,__repr__SharedObject.__repr__Os_"488*Idkk]'$))T`aeavavx|yMyMbMaNNPQ Qr/r=N)rrrrrrJrrr^rr rrrrr=r/r,rr8sF H K I(-d(CKn%CQ#Qr/rc8\rSrSr%Sr\\S'\\S'\\S'Srg)SpecAllocResultiSa!These are the values that a memory plannig algorithm assigns to a spec. These are not directly written back into the spec object, but are used to track the allocation decisions and assigned back to the spec object in the end, based on which algorithm is picked as the best performing one. r4r5rIr=N)rrrrrrJrrr=r/r,rrSs KOOr/rc>\rSrSr%Sr\\\4\S'\ \ \S'Sr g)MemoryAlgoResulti`aThis is the result returned by a memory planning algorithm that is invoked by memory_planning_algorithm_suite. It contains the allocation decisions of that algorithm for all the specs, and the size of the buffer that was used for different memory hierarchies. spec_dictbufsizesr=N) rrrrrr rrrr rJrr=r/r,rr`s# J/003ir/rshared_objectsinput_total_sizecDUnUHnX#lX#R- nM U$)z Assign concrete location in the buffer for each SharedObject.offset. Assuming all the passed in shared objects belong to the same memory buffer. )rr)rr total_sizesobjs r,materialize_bufferrls,"J ii  r/rcURH[nURSURRS:aM/URSURRS:aM[ g g)z7 Check if a shared object and a tensor do not overlap. r<rFT)rrArM)rrMrs r,_does_not_overlapr{s\!! MM! uzz2215 5}}Q%**"5"5a"88 " r/c6SnURHnURSURRS:d*URSURRS:aMY[URURR -U5nM U$Nrr<)rrArMmaxrrG)rrM max_offsetrs r,(_find_max_overlapping_allocations_offsetr sJ!! MM! uzz2215 5}}Q%**"5"5a"88  (C(CCZP " r/allow_overlapping_allocationsc*SnUHn[XA5(dMURUR:dS5eUn[URUR S5Ul[ URUR S5Ul[SU5nURRU5 O UcU(aUHn[XA5nUS:dMXaR-UR::dM4Un[URUR S5Ul[ URUR S5Ul[Xa5nURRU5 O Uc[[U5SURUR SUR S5n[SU5nURRU5 UR SUlUR SUlURU5 U$)a Pick the available shared object to which to assign this spec, or create a new one Algorithm details Previous: Look at every spec in chronological order. Find if previously allocated object allows it to fit in. If not, allocate a new object. New: - Sort all the specs by allocation size - Process the specs in order - If the spec's size in smaller than previously allocated buckets: - Conditions under which previously allocated bucket can be used: - Lifetime of the spec does not overlap with lifetime of the bucket. - In this case allocate spec to that bucket and expand its lifetime. - Spec is allocated at offset = 0 in this bucket. - Add this spec to allocated object's list of specs. - Lifetime of the spec overlaps with lifetime of the bucket, partially or fully (e.g. spec's lifetime subset of bucket's lifetime) - If none of the specs in the bucket overlaps with spec's lifetime. - Allocate spec to the bucket at offset = 0. - Add this spec to the bucket's list of specs. - Expand bucket's lifetime accounting for added spec's lifetime. - If one or more specs in the bucket overlaps with spec's lifetime. - Collect offsets (at which the given overlapping spec is allocated in the bucket). of all the overlapping specs, and find the max offset. - Allocate spec to the bucket at offset = max_offset + max_offset_spec_size. - Add this spec to the bucket's list of specs. - Expand bucket's lifetime accounting for added spec's lifetime. - If none of these conditions are met, allocate a new bucket. - Add spec to this bucket. - Update bucket's lifetime to that of the spec. - If the spec's size is larger than previously allocated buckets, allocate a new bucket. - Size and lifetime of this bucket is that of the spec Proof of correctness: - If allocating a new bucket, it is correct. - If allocating spec to an existing bucket, whose lifetime does not overlap with any of the previously allocated specs' lifetime, then the allocation is correct. Proof of correctness by induction when adding spec to an existing bucket: - If all previous allocations in the given bucket are correct: - Then the new one being added must be correct because when the requested allocation overlaps with one or more previous allocations, we find the largest offset among all the overlapping allocations, and allocate the new spec at that offset. Hence, the allocation at such an offset, will not overlap with any previous allocations. Base case: A newly added allocation within a bucket with single allocation is correct: because a) it must fit and b) its lifetime must not overlap with object's lifetime. This holds true because of the following invariants: - Once a bucket is created, it is never resized. - All the allocations within a bucket follow this: - Span, defined by allocation's offset + size, of two allocations can only overlap, if their timelines do not overlap. NzAllocation specs are not sortedrr<)rrrGminrrAr rrrrKr rr)rrMr pickedrallocation_specr s r,pick_shared_objrspF T ( (99 5 55 X7X X5F$'(=(=t}}Q?O$PD !#&t';';T]]1=M#ND ,Q5O    % %o 6 ~7"DA$MJA~ 5 55B!F,/0E0Et}}UVGW,XD)+.t/C/CT]]STEU+VD(&4Z&FO&&--o>#~     ! ! MM!  MM!   )D1!!/2"&--"2!%q!1f% Mr/c UR[R:XaVURSn[ U[ R R5(deURRS5nOURRS5n[ U[5(aU/n[ U[[45(d/$UVs/sH6n[ U[[[[ [#S545(aM4UPM8 sn$s snf)za Return the list of the tensor specs for the node or empty list if the node has no tensor specs. rrMN)rrrrrHrrrrrrr^tuplerJrrrtype)rbaserrMs r,rrs {{fkk!yy|$ .... f% f%%$$ edE] + +  dS%sDJ$GH    s 3D D cURRHpnUR[:XdM[ URR UR SR5nSURR5;dMp g g)NrxnnpackTF) r`rarrgetattr owning_moduler backend_idlower)r#rlowered_modules r,_contains_xnnpack_delegaterso""(( ;;2 2$""00$))A,2E2ENN55;;== ) r/)r  alignmentr extra_paddingc [0/5n0n[[5nSSKn /n UHn U R XSS9 M U R 5 U Hn UR RU [SSS55n U RcSU l OU RU l XR U 'U RU5 [XRU U5X{'M [U5S:XaSS/n GO6S/[UR55S--n SnUHnSn[USS5=n(a+[!U[5(de[U5U:aUUn[#XU5X'X==U- ss'XHnUR$HmnUR&n UR RU S5n U c SU S35eUR(U lUR,UR,-U lUS- nMo M M [U5U:XdS [U5S US 35e[0R2"S U 35 XlU$) aGreedy algorithm to allocate memory for tensors in the graph. Args: alignment: Memory alignment requirement specs: Set of TensorSpec objects with updated lifetimes graph_module: Graph module graph_signature: Graph signature extra_padding: Additional padding to add to each memory buffer (in bytes) allow_overlapping_allocations: If set to true, allows for allocations that overlap in their lifetime but are at different offsets in the storage. By default true. This flag is added to allow for Vulkan to use MemoryPlanningPass with overlapping allocations disabled Returns: MemoryAlgoResult containing the allocation decisions rNcUR$r*)rG)xs r,rogreedy..Es 8J8Jr/keyr<input_mem_buffer_sizeszSpec z not found.z-All specs should be processed but there were z specs and processed z specsz#greedy algorithm returns bufsizes: )rrr^bisectinsortreverserrrr4realignrrr keysrrHrrrMrr5rrIr~rr)rrr#r'r r  greedy_resultspec2objrr( sorted_specsrMspec_alloc_result total_sizesnum_specs_processedr4rrrrs r,greedyr3!ss2%R,MH &N L l.J K*3377oaQRTU>VW ;; '(  $'+{{  $(9% Y( 33 4  )   >a!f cS!4!4!67!;< $F "<1I4PPxP!(D1111x=6)'/'7$"4&(8#K   = 0  '.!--E ::D)6(?(?(C(CD$(O%,8SE${:SS83788%037;;3M%0'1,'./%2 M0 0 { :3x=/I^_r^ssy z { 0 MM7 }EF( r/c \rSrSrSS\\\S\4SS4SjjrS\ S\ \ S \ RRS \S \ S\\ 4 S jrS rg)MemoryPlanningAlgorithmSuiteiN algo_list.r(c$Uc[/nXlgr*)r3r6)r+r6s r,r-%MemoryPlanningAlgorithmSuite.__init__s  I@Ir/rrr#r'r c^ 0m URHUn[U[R5(aURR nO [ USS5nU"UUUUU5T U'MW [T R5Vs1sHn[UR5iM sn5S:XdS5e[T U 4SjS9n [R"SU 35 T U Rn T U RHHn T U RU n U RU lU RU lU R U lMJ U $s snf)a Memory planning algorithm suite that runs a list of memory planning algorithms and returns the result of the algorithm that minimizes the total memory usage. Args: graph_module: The graph module to allocate memory for alignment: Memory alignment requirement graph_signature: Optional graph signature alloc_graph_input: Whether to allocate memory for graph input alloc_graph_output: Whether to allocate memory for graph output allow_overlapping_allocations: Whether to allow overlapping allocations algo_list: List of memory planning algorithms to run specs: Optional set of TensorSpec objects with updated lifetimes. If None, they will be calculated from the graph_module. Returns: List of buffer sizes for each memory hierarchy rNr<zVDifferent memory planning algorithms should have the same number of buffers allocated.c4>[TUR5$r*)sumr)kmem_algo_resultss r,ro7MemoryPlanningAlgorithmSuite.__call__..sC0@0C0L0L,Mr/r%z,Best memory planning algo for this model is )r6rH functoolspartialfuncrrrrrrr~rrr4rIr5)r+rrr#r'r algormem_algo_result best_algorrMr0r=s @r,__call__%MemoryPlanningAlgorithmSuite.__call__s\6NND$ 1 122yy))tZ6%) & T " # ,<+B+B+D+D001+D     d d d  "M   DYKPQ#I.77%Y/99D 0 ; E Ed K +22DK/::DO/::DO : /sE)r6r*)rrrrr r rrr-rJr rrrrrrErr=r/r,r5r5sFJJD#/?*?!@ABJ JDD:Dhh** D . D  D cDr/r5c ([0/5nS[[S[S[S[4Sjn[USS5nUcSS/n[ [[U5nUHnUR R U[SSS55n URcS U lOURU lXR U'URU5 U"XyRUR5U l M [R"S U35 XulU$) aNaive algorithm to allocate memory for tensors in the graph. This algorithm simply allocates memory for each tensor sequentially without reusing memory. Args: alignment: Memory alignment requirement specs: Set of TensorSpec objects with updated lifetimes graph_module: Graph module graph_signature: Graph signature extra_padding: Additional padding to add to each memory buffer (in bytes) Returns: MemoryAlgoResult containing the allocation decisions rr4rr(cU[U5:a$URS/U[U5- S--5 XnX==U- ss'U$r)rextend)rr4rrets r, _allocate_bufnaive.._allocate_bufsJ S] " OOQC6CM#9A#=> ?I% r/r'Nrr<z"naive algorithm returns bufsizes: )rr rJrrrrrr4r+rGrIr~rr) rrr#r'r  naive_resultrKrrMr0s r,naiverNs*$B+LS 333|%=tDHq6DIx(H(2266t_QPQST=UV ;; '(  $'+{{  $'8t$ Y'4 ..0E0E( $  MM6xjAB$ r/c## URRH:nUR[RR R LdM6Uv M< g7fr*)r`rarrrrrr#rs r,get_cond_nodesrQ <  && 99 ..33 3H' A A Ac#x# URRHnUR[LdMUv M g7fr*)r`rarrrPs r,get_while_nodesrUs,  && 99 "H's+: :c## URRH:nUR[RR R LdM6Uv M< g7fr*)r`rarrrrrrPs r, get_map_nodesrWs<  && 99 ..77 7H'rSc## URRH:nUR[RR R LdM6Uv M< g7fr*)r`rarrrrscanrPs r,get_scan_nodesrZrRrSc[5nURRn[U5S:aP[ [ [ U555n[URS5SHnURU5 M U$)NrrM) rr`rarnextiterreversedrrr)r# return_specsra last_noderMs r,get_return_specsra#si5L    $ $E 5zA~huo./  !78;D   T "< r/c[5nURRnUHHnURS:XdM[ UR S5SHnUR U5 M MJ U$)NrjrMr)rr`rarrrr)r#rrarrMs r,get_input_specsrc-sa%K    $ $E 77m #$TYYv%67:%; r/allspecsc[U5n[U5n[[5nUHQnUR[ R :XdM#XR;dM*XS;dM1XERSRU5 MS [URR5n[[URR5[US- SS55HxupxURU/5n U (dM URR!U5 U H.nURR#[$R&U45 M0 SSS5 Mz UR)5 g!,(df  M=f)a Insert calls to free for dynamic unbound tensors that goes out of lifetime. Only handle the module itself. Submodule is handles in separate calls of this function. NOTE: this method will invalidate lifetime recorded in TensorSpec because of extra free node added to the graph. r<rN)rarcrr^ryrrzrArKrr`razipr^rangerinserting_afterrrfree recompile) r#rdr_ridx_to_dead_specsrM num_nodesrr dead_specss r,insert_calls_to_freern7s+$L1L",/K#D)   #6#F#F F(' mmA. / 6 6t < L&&,,-I##))*E)a-R,H'**8R8      / / 5"""00tgF#6 5 6 5s 5E  E/ r new_bufsizesc[U5[U5:a*URS/[U5[U5- -5 [[U55Hn[XX5X'M U$)zCombine two buffer size lists.r)rrIrgr )rrois r,_merge_bufsizesrr`s_ 8}s<((s<03x=@AB 3|$ %(+|7 & Or/rBparent_graph_modulesubmodule_noder$c >URS:Xde[XR5n[R"SUR S35 [ UUUUUSS9nURRSU05 [R"SUR SU35 U$) z7Apply algo to nodes in a submodule of the graph module.rzPlanning memory for submodule z...T)r$r%non_const_buffer_sizeszBuffer sizes for submodule z: ) rrrr~rr apply_algorupdate)rBrsrrtr'r$ submodulers r,_handle_submodulerzis    ** *+-B-BCI MM2>3F3F2GsKL + HNN3X>? MM/0C0C/DBxjQR Or/c^^^^^ /m S S[RRS[SS4UUU UU4Sjjjn[ T5HqnU"[ [RRUR S55 U"[ [RRUR S55 Ms [T5HqnU"[ [RRUR S55 U"[ [RRUR S55 Ms [T5H9nU"[ [RRUR S5S S 9 M; [T5H9nU"[ [RRUR S5S S 9 M; T $) zApply algo to map/cond/while/scan nodes in the graph module. This method will popuate graph_module.meta["non_const_buffer_sizes"] for all submodules and return a bufsizes list that is the maximum size of all buffers. rtr$r(Nc :>[TTTUTUS9n[TU5 g)Nr$)rzrr)rtr$current_bufsizesrBrrr#r's r,_handle*_apply_algo_to_submodules.._handles/-     /   "23r/r<rTr}r) rrrrrQrrrUrWrZ) rBr#rr'r cond_node while_nodemap_node scan_noders ```` @r,_apply_algo_to_submodulesrsJH#( 4  4 4  4 4$L1 UXX]]INN1$567UXX]]INN1$5672&l3 UXX]]JOOA$678UXX]]JOOA$6784",/UXX]]HMM!$45N0$L1 UXX]]INN1$56$O2 Or/r%r&c 2[X5n[URRUSU(+U(+U(+S9n[ XX#5n XlSn [ U5(aSn U"UUUUU 5n [X5 URRSU 05 U $)a Recursively apply algo to graph_module and its submodules for control flow. Algo implementation should handle one of two meta entries for submodules: 1. input_mem_buffer_sizes: List of int offset bytes. Memory allocated by `algo` should start at the offset specified by this list; OR 2. non_const_buffer_sizes: List of bufsizes for planned memory in submodule. `algo` should reserve the space specified by this list for the lifetime of the submodule node (e.g. cond, while, map). TODO: Missing optimizations: 1. To handle maps, we set `alloc_graph_input=True`, which allocates appropriate space for mapped arg but ends up allocating extra space for `operand` arg. The memory for operands is unused. 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