g^(h<dZddlZddlZddlZddlmZddlmZmZddl Z ddl m Z ddl m Z ddl mZddlmZd d lmZd d lmZej.eZed Zd ZdZedZedZedZeddZ dZ!dZ"d8dZ#de jHjJde&fdZ'dZ( d9dZ)ede)ee!d Z*ed!e*d"Z+ed#e+d$Z,ed%e,d&Z-ed'e-ee(Z.ed)e.Gd*d+e/Z0Gd,d-e/Z1ed.e jdjffd/Z4ed.e jdjffd0Z5ed.e jdjffd1Z6ed.e jdjffd2Z7ejpGd3d4Z9d.e jdjffd5Z:Gd6d7Z;y):aD This module provides debugging backends for TorchDynamo to help diagnose and troubleshoot compilation and execution issues. It includes: Key Debugging Backends: - eager: Simple pass-through backend that runs models in eager mode - eager_noexcept: Similar to eager but with additional exception handling - eager_debug: Adds schema validation checks for custom operators - aot_eager: Uses AOT Autograd with nop compiler for debugging - aot_eager_decomp_partition: Uses TorchInductor decompositions for debugging - torchscript: Compiles using TorchScript for debugging JIT-related issues Testing and Development Tools: - Backends for inducing specific errors (compile/runtime/accuracy) - ExplainOutput class for detailed graph compilation analysis - Utilities for cross-referencing and mode management - Tools for graph detail inspection and break reason analysis These backends are primarily used for: 1. Debugging graph breaks and compilation failures 2. Testing error handling and recovery mechanisms 3. Analyzing performance bottlenecks 4. Validating operator schemas and decompositions N) import_module)AnyOptional)#min_cut_rematerialization_partition)_guards)config) ts_compile) aot_autograd)register_debug_backendc J|rtjd||jS)Nz&eager backend ignoring extra kwargs %s)logwarningforward)gmfake_tensor_inputskwargss ^/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/torch/_dynamo/backends/debugging.pyeagerr/s  .fn@s@  &D    % &  r) contextlibr)r$r%rs` @rrr:s% Irc B|rtjd|fd}|S)Nz/eager_noexcept backend ignoring extra kwargs %sc |S#t$r/}tjjj d|d}~wwxYw)Nz7Unexpected exception when running generated GraphModule) Exceptiontorch_dynamoexcTorchDynamoException)argsers rinnerzeager_noexcept..innerSsE t9  --##88I  s A*;A)rr)rrrr0s` reager_noexceptr1Ls!  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((rrs)rE compiler_fnT)rorraot_eager_default_partitionerc d|rtjd|ddlm}ddi}|j dx}r|j |t j|5tttdtjtd  ||cdddS#1swYyxYw) Nz;aot_eager_decomp_partition backend ignoring extra kwargs %sr)CompilerBisectorunlift_effect_tokensTaot_eager_decomp_partitionc4tdjSNztorch._inductor.compile_fxrselect_decomp_tablerlrrz,aot_eager_decomp_partition..s=,$!!#rinductorcompilerrorpdecompositionsrq) rr!torch._inductor.compiler_bisectorrwget_config_changeupdatefunctorch_configpatchr rmrjrkr)rrrrwconfig_patchesbisect_changess rryrys  I6 C,d3N);;$~ n-    / "  |$$$#**3j     " " " "s AB&&B/ryc ttjt|tjt|dtjtd||S)Nrc4tdjSr{r|rlrrr~z6aot_eager_decomp_partition_with_mode..s} (   rrrr)r rjrkrUr)rrrkwargs r$aot_eager_decomp_partition_with_moders\ <%%&9E%%&9E && /*     rrc tjsdntj}tj|5t||fi|cdddS#1swYyxYw)Nrg)ri)rrirry)rrr config_vals r#aot_eager_decomp_partition_crossrefrsY  44   2 2   Z @L)".@KFKLLLs AAr)roaot_tsc eZdZy)ReluCompileErrorN__name__ __module__ __qualname__rlrrrr"rrc eZdZy)TestingOnlyCompileErrorNrrlrrrr&rrrrc|jjD]%}|jtjk(s!t |Sr)graphnodestargetr*relurrrQnodes rrelu_compile_error_TESTING_ONLYr*s6# ;;%** $" "# Irc|jjD]<}|jtjk(s!tj |_d|_>|j|S)N)FReluRuntimeError)rrrr*r_assertr. recompilers rrelu_runtime_error_TESTING_ONLYr2sK4 ;;%** $--DK3DI4LLN Irc|jjD]K}|jtjk(s!tj |_|j ddf|_M|j|S)Nrr )rrrr*raddr.rrs r relu_accuracy_error_TESTING_ONLYr<sX* ;;%** $))DK1q)DI*LLN Irc|jjD]}|jdk(sn|S|D]}|jrt|S)N call_function)rrr]is_leafr)rrQrts r#non_leaf_compile_error_TESTING_ONLYrGsT 77o %  *yy) )* IrceZdZUdZeej jed<e ed<e ed<ee ed<e ed<dZ e eej jed<dZe eej ed <dZe eed <d efd Zy) ExplainOutputzu This is the output of :func:`torch._dynamo.explain()` There is no reason to create this class directly. graphs graph_countgraph_break_count break_reasonsop_countN ops_per_graph out_guards compile_timesr^cd|jd}|d|jdz }|d|jdz }|dz }t|jD]C\}}|d|dzdz }|d |j dz }|d z }|j D] }|d |dz } E|j>|d z }t|jD]!\}}|d |dzdz }|D] }|d|dz } #|j?|dz }t|jD]"\}}|d|dzdz }|dt|z }$|j|d|jdz }|S)Nz Graph Count:  zGraph Break Count: z Op Count: zBreak Reasons: z Break Reason r z: z Reason: z User Stack: z zOps per Graph: z Ops z z Out Guards: z Guard zCompile Times: ) rrr enumeraterreason user_stackrrstrr) selfoutputidx break_reason frame_summaryopsr]iguards r__str__zExplainOutput.__str__hs !1!1 2"5'(>(>'?rBBJt}}oR00$$!*4+=+=!> 5 C ay4 4F  \%8%8$9< >F r)rrr__doc__listr*r6 GraphModule__annotations__intrrrNoderrGuardrrrrlrrrrVs %% && M37M8D/0704Jgmm,-4#'M8C='rrcV|j||jjDcgc]}|jdk(s|j }}|t |z }|j||j jr|j|j |||||fScc}w)a This function is a utility which processes a torch.fx.GraphModule and accumulates information about its ops, graph breaks, and other details. It is intended to be used by the ExplainWithBackend class and `torch._dynamo.explain()` to provide details from Dynamo's graph capture. Parameters: gm (torch.fx.GraphModule): The GraphModule to be processed. graphs (list): A list that accumulates all the GraphModules processed. op_count (int): The total count of operations in all GraphModules processed so far. ops_per_graph (list): A list that accumulates the operations of each GraphModule. break_reasons (list): A list that accumulates the reasons for breaks in each GraphModule. Returns: tuple: A tuple containing the processed GraphModule, the updated lists of graphs, operations per graph, and break reasons, and the updated operation count. r)appendrrr]rlencompile_subgraph_reason graph_break)rrrrrrrs r_explain_graph_detailrs( MM"#%88>> P4TWW5O4;; PC P CH !!--R778 vx == Qs B&B&cXeZdZdZddZdej jfdZde fdZ y) ExplainWithBackenda This class is intended to be used as a backend for `torch.compile`. It is composable with other backends. When used in this way, it accumulates information about graph breaks, ops, and other info and provides a string representation summarizing this information. Attributes: backend (str): The name of the backend to use for optimization. graphs (list): A list of the graphs captured by TorchDynamo. op_count (int): The total number of operations in all optimized graphs. break_reasons (list): A list of graph break reasons with stack traces. Example Usage: def fn(x): x = torch.sigmoid(x) return x torch._dynamo.reset() eb = ExplainWithBackend("inductor") optimized_fn = torch.compile(fn, backend=eb) result = optimized_fn(torch.randn(5)) print(eb.output()) r^NcTddlm}|||_g|_d|_g|_y)Nr )lookup_backendr)registryrbackendrrr)rrrs r__init__zExplainWithBackend.__init__s',%g.   rrct||j|jg|j\}|_|_}|_|j ||Sr)rrrrr)rrrQ_s r__call__zExplainWithBackend.__call__sK@U  T]]B0B0BA =DK4+=||B//rct|j}t|j||dz |j|j}|S)Nr )rrrrr)rrrs rrzExplainWithBackend.outputsB$++&  KK  !O    MM   r)r^N) rrrrrr*r6rrrrrlrrrrs.0 0588//0  rrr)NN)rCrIrRrUr\_ops OpOverloadboolrdrmrsruryrrrr)rrr6rrrrr dataclassrrrrlrrrsf2# A71 @g! @$"     t  5 --5$5X (ky9 ,$! (6S": %3M  /4 L .3 * -hF3  y  i (<(<(<(<)=)= EHH,@,@   ---`> ><11r