o h$@sddlZddlZddlmZddlmZmZmZddlm Z ddl Z ddl m Z ddl m Z ddlmZdd lmZ ed d Zed d ZeddZeddddZddZeee dZededeedZededeeeddeje dddZed edee dZed!edGd"d#d#eZGd$d%d%eZed&e j j!fd'd(Z"ed&e j j!fd)d*Z#ed&e j j!fd+d,Z$ed&e j j!fd-d.Z%ej&Gd/d0d0Z'd&e j j!fd1d2Z(Gd3d4d4Z)dS)5N) import_module)AnyListOptional)#min_cut_rematerialization_partition)_guards) ts_compile) aot_autograd)register_debug_backendcCs|SNgmfake_tensor_inputsr r V/var/www/html/ai/venv/lib/python3.10/site-packages/torch/_dynamo/backends/debugging.pyeagersrcs4ddlm}fdd}||dd|}||S)Nr)make_fxcstjj|Sr torchfx Interpreterrunargsrr r runnable_gmz'pre_dispatch_eager..runnable_gmT) pre_dispatch)"torch.fx.experimental.proxy_tensorrprint_readable)rrrrpre_dispatch_gmr rrpre_dispatch_eagers  r"csddlmfdd}|S)Nr)SchemaCheckModecs<tjj|WdS1swYdSr rrr#rr rinner,s$zeager_debug..inner)#torch._subclasses.schema_check_moder#)rrr%r r$r eager_debug%s r'ts)namecCs tj|Sr )rjitscriptrr r r torchscript3s r,csfdd}d|_|S)Ncstj|Sr )rrr boxed_runrfx_gr rr:rzboxed_nop..runT) _boxed_call)r/example_inputsrr r.r boxed_nop9s r2) fw_compiler partition_fn aot_eager)r) compiler_fn)r3aot_eager_default_partitionercCs tdS)Nztorch._inductor.compile_fx)rselect_decomp_tabler r r rVsr9inductor)compiler)r3 bw_compilerdecompositionsr4aot_eager_decomp_partitionaot_tsc@ eZdZdS)ReluCompileErrorN__name__ __module__ __qualname__r r r rrAkrAc@r@)TestingOnlyCompileErrorNrBr r r rrGorFrGrcCs$|jjD] }|jtjkrtq|Sr )graphnodestargetrrelurArr1noder r rrelu_compile_error_TESTING_ONLYss  rNcCs4|jjD]}|jtjkrtj|_d|_q||S)N)FReluRuntimeError)rHrIrJrrK_assertr recompilerLr r rrelu_runtime_error_TESTING_ONLY{s  rRcCs>|jjD]}|jtjkrtj|_|jddf|_q||S)Nrr )rHrIrJrrKaddrrQrLr r r relu_accuracy_error_TESTING_ONLYs  rTcCs:|jjD] }|jdkr nq|S|D]}|jstq|S)N call_function)rHrIopis_leafrG)rr1rMtr r r#non_leaf_compile_error_TESTING_ONLYs  rYc@seZdZUdZeejjed<e ed<e ed<ee ed<e ed<dZ e eejj ed<dZe eejed <dZe eed <d d ZdS) 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_timesc CsXd|jd}|d|jd7}|d|jd7}|d7}t|jD])\}}|d|dd7}|d |jd7}|d 7}|jD] }|d |d7}q@q"|jdurv|d 7}t|jD]\}}|d |dd7}|D] }|d|d7}qjqZ|jdur|d7}t|jD]\}}|d|dd7}|dt |7}q|j dur|d|j d7}|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: ) r\r]r_ enumerater^reason user_stackr`rastrrb) selfoutputidx break_reason frame_summaryopsrViguardr r r__str__s4    zExplainOutput.__str__)rCrDrE__doc__rrr GraphModule__annotations__intrr`rNoderarGuardrbrgrpr r r rrZs  rZcCsT||dd|jjD}|t|7}|||jjr#||j|||||fS)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. cSsg|] }|jdkr|jqS)rU)rVrJ).0rMr r r sz)_explain_graph_detail..)appendrHrIlencompile_subgraph_reason graph_break)rr[r_r`r^rmr r r_explain_graph_details    r}c@s8eZdZdZddZdejjfddZde fdd Z d S) 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()) cCs,ddlm}|||_g|_d|_g|_dS)Nr )lookup_backendr)registryrbackendr[r_r^)rhrrr r r__init__s   zExplainWithBackend.__init__rcCs2t||j|jg|j\}|_|_}|_|||Sr )r}r[r_r^r)rhrr1_r r r__call__s zExplainWithBackend.__call__returncCs(t|j}t|j||d|j|j}|S)Nr )rzr[rZr^r_)rhr\rir r rris zExplainWithBackend.outputN) rCrDrErqrrrrrrrZrir r r rr~s r~)* dataclasses functools importlibrtypingrrrfunctorch.compilerrrtorch._functorch.compilersrcommonr rr register_backendrr"r'r,r2r5r7partialr>r? ExceptionrArGrrrrNrRrTrY dataclassrZr}r~r r r rsr                  0