iiW ddlZddlZddlZddlmZmZmZddlmZm Z m Z m Z m Z ddl Z ddlmcmZddlZddl mZmZddlmZej.GddZGdd Zd ed efd Zd ed eeeffdZd ed efdZded e fdZ!dddede d e fdZ"ded e fdZ#ded e fdZ$defdZ%dZ&dejNdeedfde(eefd eeejReffdZ*d Z+ded e fd!Z,d e fd"Z-d#Z.dejNfd$Z/dejNfd%Z0dejNd e fd&Z1dejNd e edffd'Z2 d>deede(eefd(ed ee jffd)Z4d*fd+Z5d,fd-Z6Gd.d/Z7d0e jfd e jffd1Z8de jrj,d e fd2Z:dejNd ee;ee;effd3Z2sR ^^D !F 6{a**248 9  !9fQi rct|\}}d|vr|jd\}}nd}ttj|}t||}t||S)N.default)r>r:getattrtorchops)r6 namespacenamer nspackets r lookup_oprI>sV%h/OIt d{Ch I &B R F 68 $$ropcBt|tsJ|jdvS)N>atenprimprims) isinstancer rE)rJs r is_builtinrPIs" b* %% % <<4 44rF)allow_valid_viewschemarQcfd}t|tjjr||Sddlm}t|t r|j |}t||sJ||S)afCheck if the schema is functional. An operator is functional if: - it does not mutate any of its inputs - If no view are allowed - it does not return a view on any of its inputs - If valid views are allowed - it is not a view or a view with a single input Tensor and single output Tensor - it has at least one return c|jry|j}t|dkDxrtd|D}d}d}t |t j rw|jD],}t |jt js(|dz }.|jD],}t |jt js(|dz }.nt |tjj rl|jjD]"}|jjs|dz }$|jD]"}|jjs|dz }$|r xr |dk(xr|dk(S|jsyy)NFrc3jK|]+}|jduxr|jj -ywr) alias_infois_write).0rs r z>is_functional_schema..is_functional..^s35 GHALL $ BQ\\-B-B)B B5 s13r9T) is_mutablereturnsr;anyrOrCFunctionSchema argumentstype TensorTypetorchgenmodel flat_non_outis_tensor_like) rRretsis_non_mutating_viewnum_tensor_inputsnum_tensor_outputsargretargumentret_argrQs r is_functionalz+is_functional_schema..is_functionalZsu   ~~"4y1} 5 LP5 2  fe22 3'' +chh(8(89%*% +~~ ,chh(8(89&!+& , = = >",,99 +==//1%*% +">> ,<<..0&!+& , #!Q&B+=+B ~~rr)r^)rOrCr r^torchgen.modelrparse)rRrQrnr^s ` ris_functional_schemarqNsd"H&%((112V$$.&#%%f- fn -- -   rtypc F|tjtjjk(xs|tjtjtjjk(xs|tjtjtjjk(xsZ|tjtjtjtjjk(Sr)r ListTyperaget OptionalTyperrs ris_tensorlist_like_typerxs r{{2==,,.// U "++boobmm.?.?.ABC C U "//"++bmm.?.?.A"BC C U "//"++boobmm>O>O>Q.R"ST T rc|tjjk(xs4|tjtjjk(Sr)r rarurvrws ris_tensor_like_typerzs: "--##% % T @Q@Q@S0T)TTrc|jdk7ry|j}t|jdk7ry|jdjy|jdjj }t|dk7ryt t|}t|jdkry|jd}|jy|jjsy|jj }t|dk7ry|t t|k7ry|jddD]}|jyy)aNCheck if an op is an inplace aten op, i.e. it mutates and returns the first arg. 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N)r;r_rrrF)rRrrrrs r zip_schemars v CIF $; ;; ; 3v''( ) " ??yyF"F499---  D >??tyyF':F499--- DGm  sCCc \ddlm}|j}t|tj j s tdd}g}|jD]}t|tjjtjjjfr|j||ct|tjjjtt fr&|j|Dcgc] }|| c}tdt#|t%j&|j(j*|}||}|j-|j.t!|j0j3t|fScc}w)Nr)FunctionSchemaGenzfx_node's target must be a hop.c|jjdd}|;|jdk(sJt|jj |j }|S)Nvalget_attr)metarurJrBgraph owning_moduletarget)nodemeta_vals r_collect_example_valz5hop_schema_from_fx_node.._collect_example_valsM99==-  77j( ((tzz77EHrzUnsupported arg type )torchgen.gen_schema_utilsrrrOrC_opsHigherOrderOperator RuntimeErrorrfxNoderrimmutable_collectionsimmutable_listlistrr`r. signaturerbind from_example_namer_items) rrhoprexample_inputsrjx bound_argsexample_outputs rhop_schema_from_fx_nodersW; ++C c5::99 :<==NyyD cEHHMM588==+=+=> ?  ! !"6s"; <  %((00??uM   ! !C"Hq#7#:"H I!6tCykBC CD*N):):3<<)H)M)M *J *$/N  ) ) 5--3356n9M8O #Is>F) ct|tsJt|ry|j}|jsyt |j dkDryy)NFrT)rOr rPr|r[r;r\)rJrRs rcan_generate_trivial_fake_implrsJ b* %% %"~ ZZF    6>>Q rc$ttddS)zIf an op was defined in C++ and extended from Python using the torch.library APIs, returns if we require that there have been a m.set_python_module("mylib.ops") call from C++ that associates the C++ op with a python module. REQUIRES_SET_PYTHON_MODULET)rBr r rrrequires_set_python_moduler!s ?$@$ GGrct|tjjjsJtjj j ||jf\}}|Dcgc]w}t|tjr[tjj|jtjjjr t|y}}|j||||Scc}wr)rOrCutils_python_dispatchTorchDispatchMode_pytree tree_flattenvaluesTensorr _dispatch_keyshas DispatchKeyPythonr`__torch_dispatch__) curr_mode op_overloadrrargs_flattened_aoverload_typess rhandle_dispatch_moder*s i!=!=!O!O PP P ++88$ 9PQNA  a & HH # #A & * *588+?+?+F+F G QN  ' ' ^T6 RRs1A.=s6q||6sr]r_rRs rhas_kwarg_only_argsr<s 6V%5%56 66rc|jD];}t|jst|js.|js;yy)NTF)r_rzr`rxr)rRrs rhas_kwarg_only_tensorsr@sD   #AFF+/Fqvv/N ||   rc:td|jDS)z Given a schema, returns True if the schema has a Tensor arg. A Tensor arg is any arg with a type annotation that might involve Tensor. c3tK|]0}t|jxst|j2ywr)rzr`rxrs rrZz!has_tensor_arg..Os3  QVV $ G(?(G Gs68rrs rhas_tensor_argrJs$ !! rct|jD]C\}}|jtjj us1|j dk(sA|cSy)zx Given a schema, returns the id of the `device: torch.device` argument. If it does not exist, returns None. deviceN) enumerater_r`r DeviceObjTyperurF)rRindexrjs rget_device_arg_indexrUsR   0 01 s 88r''++- -#((h2FL rallowed_nestingc#Kfd}|D]}||Ed{|jD]}||Ed{y7,7 w)Nc3Kt|tjr|ydkDr9t|ttfr"t t|idz Ed{yyy7w)Nrr9)rOrCrrr iter_tensors)rjrs rcheckziter_tensors..checkcsV c5<< (I q ZeT]%C#E#JOa4GH H H&D HsAA$A"A$)r)rrrrrjkwargs ` rrr`sVI : <  sA A$A AA A cyNz???r r rrrorc|Dchc]7}t|tjs|jj9}}|}t|t s|f}t |iD]_}|jj}|jj|vrt|d|d|j|aycc}w)zO custom operators' outputs must not alias any inputs or other outputs.  (with implementation in ): The output of this custom operator (1) must not also be an input to this custom operator and (2) may not alias any inputs to this custom operator or other returns. The most common way to trigger this error is if we have y = custom_op(x) and y and x are the same Tensor. Please instead return a clone of the offending output tensor(s) (e.g. return x.clone()) or refactor the custom operator to not return y.N) rOrCruntyped_storage_cdatarrradd) rFprevresult get_moduletstorages tuple_resulttensorkeys rcheck_aliasing_constraintros59XqJq%,,.check_onesi==K/"ww}}o%5dii[@Yww'($15IDU#VWWX  rF)rrOrCrrziprequalisnanallpytreetree_unflattenrrrJr|rzr`rxr]) rrreal_post_hashesprepostrwas_mutated_argswas_mutated_kwargsrrwas_any_mutateds ` rrzMutationChecker.checksk^^ )ELL9KNt C  !!5!57GH  T#u||,D%,,1O C& & ?YY[__&=4::<+;+;+=>   06/D/D 0 ,,", GGOO-/A"  1 D+ #499-$ ,(3+6+>%C DT$0- 1  s .FBFN)rrrrr'rr rrrrs %1rrrcZ|jjjS)zNSome inexpensive hash. Used as a quick and dirty indicator for tensor mutation)detachfloatmean)rs rrrs 88:    " " $$rct|ry|j}tjj |drytj j j|}|tjj |drytj jjj|}|jjytjj |dryy|jyy)zIf an operator (that stays alive until FakeTensorMode) has a Fake kernel. Don't use this if the operator decomposes before FakeTensorMode. TCompositeImplicitAutogradCompositeExplicitAutogradMetaF)rrrCr %_dispatch_has_kernel_for_dispatch_key_library custom_ops_maybe_get_opdefsimple_registry singletonfind fake_implkernel _abstract_fn)rJrFopdefentrys rhas_fake_kernelr$s&b) 88D xx55 ) NN % % 6 6t J b>66)) )+uxx||V%O%OPP r)rr impure_randomr8cJddlm}ddlm}t |t j jr*t|dd}| |jry||vry||yt |t j jrk|t jjjt jjjfvr|rt|dkDr|d|vS||yy|rt|ddry|t vryt|dd}| |jry||vryy) a An operator is impure if it: - Mutates its inputs (has a mutable schema) - Has nondeterministic/random behavior that mutates RNG state - Is explicitly marked as effectful via torch.library._register_effectful_op Args: op: The operator to check (function, OpOverload, HigherOrderOperator, etc.) args: Optional arguments that would be passed to the callable kwargs: Optional keyword arguments that would be passed to the callable impure_random: Whether to treat random operations as impure (default: True) Returns: bool: True if the callable has side effects, False otherwise r) _get_effect)_side_effectful_functionsr|NTF_nondeterministic_seeded)torch._higher_order_ops.effectsr: torch.fx.noder;rOrCrr rBr[rrD higher_orderauto_functionalizedauto_functionalized_v2r;_RANDOM_FUNCTIONS)rJrrr8r:r;rRs r is_impurerC?s.<7"ejj++,Y-  &"3"3 * * r? &"ejj445 II " " 6 6 II " " 9 9  D A Aw";;; r? &%?G  RD )F f// && r)r9)Q dataclassesr.r0collections.abcrrrtypingrrrr r rCtorch.utils._pytreerrrrbr r torch._opsr dataclassrr"intrr5rr>rIboolrPrqrxrzrrr^dictArgumentrrrrrrrrrrrrrrrrr$rr(r3needs_exact_stridesneeds_contiguous_stridesneeds_fixed_stride_orderr4r1r-randrandnrandintrandperm rand_like randn_like randint_likenormalpoisson bernoulli multinomialrBrCr rrr\s 88:: $$%! *** 3 3   c  eCHo  %% %5:5$5 CH9!9!49!D9!zUSUTU#j#L'$    %*38_ >B38n  eBKK$%& 4#L z d  HDHS$7 1 172#4#42,,!2!2uS$Y7GFG  *  "38n  ?B   ell  >K4IV 27171t%5<<%ELL%  --$<  1 1 eDItCy