[Th7SrSSKrSSKrSSKrSSKrSSKJrJr SSKrSSK J r SSK J r SSK Jr SSKJr SS KJr "S S 5r"S S 5r"SS5r\"5rg)a_ This module provides the public comptime interface to TorchDynamo, enabling users to execute arbitrary Python code during symbolic evaluation of their programs. The comptime interface allows inspection and modification of TorchDynamo's compilation process while it is running. This can be useful for: - Debugging compilation issues - Inspecting intermediate state - Adding custom guards or graph breaks - Analyzing symbolic shapes and values Example usage: import torch from torch._dynamo.comptime import comptime def my_model(x): # Print the compile-time known information about x comptime.print(x) # Print the current FX graph being constructed comptime.print_graph() # Force a value to be treated as static if comptime(lambda ctx: ctx.get_local("x").is_dynamic()): comptime.force_static(x) # Add a manual graph break comptime.graph_break() Note: While this API provides significant flexibility, it intentionally avoids exposing internal implementation details of TorchDynamo to maintain compatibility across versions. N)OptionalUnion) free_symbolsunimplemented_v2) CellVariable)ConstantVariable)SymNodeVariablec\rSrSrSrSSjrSrSrSrSS \ \ S\ \ \ R44S jjrS rS rS rSrSrSrS\4SjrSrg) ComptimeVar6a A ComptimeVar represents a Python value, at some particular point in time, in the Python code we are symbolically evaluating with torchdynamo. This must be distinguished from a runtime value, as at compile-time there are some properties of the variable we do not know (for example, if the ComptimeVar represents a Tensor, we only know metadata about the tensor; we do NOT know what the actual data in the Tensor is.) returnNcXlgN_ComptimeVar__variable)selfvs N/var/www/auris/envauris/lib/python3.13/site-packages/torch/_dynamo/comptime.py__init__ComptimeVar.__init__Asc6URR5$)a Returns an fx.Proxy (or tuple/list of fx.Proxy) representing this variable in the FX graph we are assembling to pass to the user compiler. This method only works for variables we actually track in the FX graph, aka Tensors (and ints, if you are compiling with dynamic shapes). In particular, if you have a list or tuple of tensors, you will get a list/tuple of proxies (not a single proxy representing the entire list/tuple). )ras_proxyrs rrComptimeVar.as_proxyDs''))rc6URR5$)z+ Returns True if as_proxy() would succeed. )ris_proxyrs rrComptimeVar.is_proxyRs''))rcdURR5RRS$)a@ Returns a "fake" value (either a FakeTensor or a SymInt) representing the variable in question. This only works for variables that denote Tensor or int. You can use this to query metadata; e.g., v.as_fake().size(0) will tell you the compile-time known size of the tensor. WARNING: Do NOT mutate the returned tensor. example_value)rrnodemetars ras_fakeComptimeVar.as_fakeXs('')..33ODDrdimc@UR5RU5$)zw Returns the size of the tensor (if dim is None) or the size at the dimension dim. The returned size may be a SymInt. )r%size)rr's rr)ComptimeVar.sizeds ||~""3''rc6URR5$)zL Returns what type(v) would have returned for the variable at compile time. )r python_typers rr,ComptimeVar.python_typeks **,,rc6URR5$)ay Returns the Python value this variable would have, but only if it is completely known at compile-time (e.g., it is constant). WARNING: Do NOT mutate the returned constant. The returned constant may or may not correspond to the actual value this variable may take on at runtime; for example, if the variable in question is a constant list, we may return a copy of that list. )ras_python_constantrs rr/ComptimeVar.as_python_constantrs1133rc6URR5$)z3 Returns True if as_python_constant would succeed. )ris_python_constantrs rr2ComptimeVar.is_python_constant~s1133rc[UR[5(a*[URR5n[ U5$g)NF) isinstancerr rsym_numbool)rfss r is_dynamicComptimeVar.is_dynamics3 doo 7 7doo556B8Orc[UR[5(aURR5 g[UR[5(ag[ SURS[ UR5S35e)zG Forces that a value is static, inducing a guard on its specific value z cannot force z (z) staticN)r5rr evaluate_exprr AssertionErrortypers r force_staticComptimeVar.force_staticsh doo 7 7 OO ) ) + )9 : :  043H2IR rcUR$)z Returns the internal data structure VariableTracker that Dynamo uses to represent variables at compile time. There are no BC guarantees on this API and WE RESERVE THE RIGHT TO BREAK YOUR CODE if you rely on it. rrs r1_i_will_not_complain_if_bc_breaks_VariableTracker=ComptimeVar._i_will_not_complain_if_bc_breaks_VariableTrackersrc6URR5$r)r debug_reprrs r__repr__ComptimeVar.__repr__s))++r) __variablerNr)__name__ __module__ __qualname____firstlineno____doc__rrrr%rintrtorchSymIntr)r,r/r2r9r?rBstrrF__static_attributes__rrr r 6sk ** E( (sELL7H1I(- 44  ,#,rr c\rSrSrSrSSjrSS.S\S\4S jjrSS jr S r S r S SS.Sjr Sr SrSS.SjrSSS.SjrSSS.SjrSSS.SjrSSS.SjrSS.SjrSrSrSrg)ComptimeContextz This context class provides access to a public API for Dynamo's internals. If there is something here you would find useful that is missing, please file a feature request at https://github.com/pytorch/pytorch/ rNcXlgr_ComptimeContext__tx)rtxs rrComptimeContext.__init__s rr stacklevelnamecURU5nURUn[U[5(a.[ UR R RU55$[ U5$)z< Retrieve the compile-time known information about a local. )_ComptimeContext__get_txsymbolic_localsr5r r output side_effects load_cell)rr_r^r[vars r get_localComptimeContext.get_locals\]]: &  & c< ( (ryy55??DE E3rc$[SUSUS3/S9 g)z Manually trigger a graph break zComptimeContext graph breakz>## Y ##rT)verbosefilec[URRRR SUS9R US9 g)zg Print the partially constructed FX graph that would be passed to the user compiler after compilation. r)ryrzN)printrZrcrs python_codesrc)rryrzs r print_graphComptimeContext.print_graphs8  II   " " . .vw . G K KRV rc@[URR5$r)rVrZparentrs rrComptimeContext.parentstyy//00rcZURn[U5HnURnM U$r)rZranger)rr^r[_s r__get_txComptimeContext.__get_txs( YYz"AB# rr|c*[[U5US9 g)Nr|)r}repr)rrvrzs rr}ComptimeContext.prints d3id#r)rzr^cURU5n[[R"URUR UR RS9R5US9 g)z Print the current series of opcodes being executed (not including parent frames), including where you are in the particular opcode stream. )current_offsetr|N)rar}disBytecodef_code instructionsinstruction_pointeroffset)rrzr^r[s r print_disasComptimeContext.print_disassQ ]]: & LL !r/E/EFMM ce  rcURU5nURHn[SUR53US9 M g)a Print the current Python value stack. Note that this is NOT the same as the traceback; use print_bt() to print that. Note that at stacklevel=0, this will typically be empty, as comptime cannot currently be used in an expression context where there would be intermediates on the stack. If you would find this useful, please file a bug at https://github.com/pytorch/pytorch/ NB: Stack grows downwards in our print z- r|N)rastackr}rE)rrzr^r[ss rprint_value_stack!ComptimeContext.print_value_stacks8]]: &A Bq||~&'d 3rcURU5nURR5H"upE[USUR 53US9 M$ g)z Print all of the locals available in the current context. By default this view is very limited; you can get more information about any individual local using get_local(). z = r|N)rarbitemsr}rE)rrzr^r[krs r print_localsComptimeContext.print_locals sI ]]: &&&,,.DA QCs1<<>*+$ 7/rc ,/nURU5nUb1URUR55 [USS5nUbM1[ SR [ RR[U55R55US9 g)ao Print the user code backtrace, starting at the beginning of the frame Dynamo started evaluating. Note that this MAY NOT go all the way to the torch.compile invocation, as we may have done a graph break and are compiling an intermediate frame as the starting point. If you think the other behavior would be better, file a bug at https://github.com/pytorch/pytorch/ Nrr|) raappend frame_summarygetattrr}join traceback StackSummary from_listreversedformat)rrzr^rr[s rprint_btComptimeContext.print_bts} ]]: &n LL))+ ,Xt,Bn  GGI**44Xe_ELLN O rc[SRS[URRR 555US9 g)z Print the currently installed guards for the Dynamo context. This does NOT include guards associated with variables that may or may not be installed in the future if those variables are used.  c3:# UHn[U5v M g7fr)r).0guards r /ComptimeContext.print_guards..2sT4S5e &4Ssr|N)r}rsortedrZrcguards)rrzs r print_guardsComptimeContext.print_guards(s6  IITF499;K;K;R;R4ST T rcUR$)z Returns the internal data structure InstructionTranslator that Dynamo uses to track state of symbolic evaluation. There are no BC guarantees on this API and WE RESERVE THE RIGHT TO BREAK YOUR CODE if you rely on it. rYrs r7_i_will_not_complain_if_bc_breaks_InstructionTranslatorGComptimeContext._i_will_not_complain_if_bc_breaks_InstructionTranslator6syyrc0[R"U5 gr)timesleep)rsecs rrComptimeContext.sleep?s  3r)__txrI)zComptimeContext.graph_break)rJrKrLrMrNrrRr rgrprsrwrrrar}rrrrrrrrSrTrrrVrVs 23  c  {    & &* 1 "&$#'1  )- 4$(A8 $ &$(  rrVc$\rSrSr\S4Sj5r\S5r\S5r\S5r\SS.S j5r \SS.S j5r \SS.S j5r \SS.S j5r \SS.S j5r \S5r\S5r\S5r\S5r\S5rSrg) _ComptimeiCcgrrTrTrr_Comptime.EsrcU"5 g)zJfn gets called at compile time in TorchDynamo, calls fallback_fn otherwiseNrT)fn fallback_fns r__call___Comptime.__call__Ds   rc[S5 g)Nc"UR5$r)rpctxs rr'_Comptime.graph_break..M S__.rcomptimerTrrrp_Comptime.graph_breakK ./rc(^[SU4Sj5 g)NcBURURS55$)Ne)r}rgrs rr!_Comptime.print..QsSYYs}}S'9:rc>[T5$r)r}rsrrrQs E!Hrrrs`rr}_Comptime.printOs:.UrrrrTrrr_Comptime.print_graphSrrrr]c[S5 g)Nc`URURS5R5S-S9$Nr^rr])rrgr/rs rr'_Comptime.print_disas..Zs,==6IIKaO(rrr]s rr_Comptime.print_disasW  rc[S5 g)Nc`URURS5R5S-S9$rrrgr/rs rr-_Comptime.print_value_stack..b.--==6IIKaO.rrr]s rr_Comptime.print_value_stack_rrc[S5 U$)Nc`URURS5R5S-S9$rrrs rr8_Comptime.print_value_stack_and_return..mrrr)rr^s rprint_value_stack_and_return&_Comptime.print_value_stack_and_returnjs  rc[S5 g)Nc`URURS5R5S-S9$r)rrgr/rs rr(_Comptime.print_locals..vs.((==6IIKaO)rrr]s rr_Comptime.print_localssrrc[S5 g)Nc`URURS5R5S-S9$r)rrgr/rs rr$_Comptime.print_bt..~s, ==6IIKaO%rrr]s rr_Comptime.print_bt{rrc[S5 g)Nc"UR5$r)rrs rr(_Comptime.print_guards..s S--/rrrTrrr_Comptime.print_guardss /0rc[S5 g)NcBURURS55$Nrv)rwrgrs rr)_Comptime.assert_static..sS..s}}U/CDrrrvs rrw_Comptime.assert_statics DErc[S5 g)Nc@URS5R5$r)rgr?rs rr(_Comptime.force_static..sS]]51>>@rrrs rr?_Comptime.force_statics @Arc Sn[U5 g)a Like pdb breakpoint(), but drop into pdb whenever this line of code is compiled by dynamo. Use it by putting this in your model code:: from torch._dynamo.comptime import comptime comptime.breakpoint() And then, inside pdb, you can access 'ctx' to query things about the compilation context:: (Pdb) !ctx.print_bt() (Pdb) !ctx.print_locals() (Pdb) p ctx.get_local("attention").as_fake() cNUR5n[R"5 gr)rbuiltins breakpoint) inner_ctxrs rinner#_Comptime.breakpoint..inners""$C    !rNr)r s rr _Comptime.breakpoints& " rc[S5 g)Nc^URURS5R55$)Nr)rrgr/rs rr!_Comptime.sleep..sSYYs}}U';'N'N'PQrr)rs rr_Comptime.sleeps QRrrTN)rJrKrLrM staticmethodrrpr}rrrrrrrrwr?r rrSrTrrrrCs2!- 00NN00"#  ()  67#$     11FFBB0SSrr)rNr rrrtypingrrrP%torch.fx.experimental.symbolic_shapesrexcr variablesr variables.constantr variables.tensorr r rVrrrTrrrs`"H " >!#0-l,l,bYYxgSgST ;r