U ,-e$@sddlZddlZddlmZddlmZmZmZddlm Z ddl Z ddl m Z ddl m Z ddlmZdd lmZed d Zed d ZeddZeddddZddZeee dZededeedZededeeeddeje dddZed edee dZed!edGd"d#d#eZGd$d%d%eZee j j!d&d'd(Z"ee j j!d&d)d*Z#ee j j!d&d+d,Z$ee j j!d&d-d.Z%ej&Gd/d0d0Z'e j j!d&d1d2Z(Gd3d4d4Z)dS)5N) import_module)AnyListOptional)#min_cut_rematerialization_partition)_guards) ts_compile) aot_autograd)register_debug_backendcCs|SNgmfake_tensor_inputsr r a/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/torch/_dynamo/backends/debugging.pyeagersrcs4ddlm}fdd}||dd|}||S)Nr)make_fxcstjj|Sr torchfx Interpreterrunargsrr r runnable_gmsz'pre_dispatch_eager..runnable_gmT)Z pre_dispatch)Z"torch.fx.experimental.proxy_tensorrZprint_readable)rrrrZpre_dispatch_gmr rrpre_dispatch_eagers   rcsddlmfdd}|S)Nr)SchemaCheckModec s0 tjj|W5QRSQRXdSr rrrrr rinner,szeager_debug..inner)Z#torch._subclasses.schema_check_moder)rrr r rr eager_debug%s r!ts)namecCs tj|Sr )rZjitscriptrr r r torchscript3sr%csfdd}d|_|S)Ncstj|Sr )rrrZ boxed_runrfx_gr rr:szboxed_nop..runT)Z _boxed_call)r'example_inputsrr r&r boxed_nop9s r)) fw_compiler partition_fn aot_eager)r#Z compiler_fn)r*aot_eager_default_partitionercCs tdS)Nztorch._inductor.compile_fx)rZselect_decomp_tabler r r rVsr.Zinductor)compiler)r*Z bw_compilerZdecompositionsr+aot_eager_decomp_partitionaot_tsc@s eZdZdS)ReluCompileErrorN__name__ __module__ __qualname__r r r rr2ksr2c@s eZdZdS)TestingOnlyCompileErrorNr3r r r rr7osr7rcCs$|jjD]}|jtjkrtq|Sr )graphnodestargetrrelur2rr(noder r rrelu_compile_error_TESTING_ONLYss  r>cCs4|jjD]}|jtjkrtj|_d|_q||S)N)FZReluRuntimeError)r8r9r:rr;Z_assertr recompiler<r r rrelu_runtime_error_TESTING_ONLY{s   r@cCs>|jjD](}|jtjkrtj|_|jddf|_q||S)Nrr )r8r9r:rr;addrr?r<r r r relu_accuracy_error_TESTING_ONLYs   rBcCs:|jjD]}|jdkrq q|S|D]}|js$tq$|S)N call_function)r8r9opZis_leafr7)rr(r=tr r r#non_leaf_compile_error_TESTING_ONLYs  rFc@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 Cs^d|jd}|d|jd7}|d|jd7}|d7}t|jD]R\}}|d|dd7}|d |jd7}|d 7}|jD]}|d |d7}qqD|jdk r|d 7}t|jD]6\}}|d |dd7}|D]}|d|d7}qq|jdk r<|d7}t|jD]0\}}|d|dd7}|dt |7}q |j dk rZ|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: ) rIrJrL enumeraterKreasonZ user_stackrMrNstrrO) selfoutputidxZ break_reason frame_summaryopsrDiguardr r r__str__s0    zExplainOutput.__str__)r4r5r6__doc__rrr GraphModule__annotations__intrrMrNoderNrZGuardrOrSr[r r r rrGs rGcCsT||dd|jjD}|t|7}|||jjrF||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)rC)rDr:).0r=r r r s z)_explain_graph_detail..)appendr8r9lenZcompile_subgraph_reasonZ graph_break)rrHrLrMrKrXr r r_explain_graph_details    rec@s8eZdZdZddZejjdddZe ddd 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)registryrgbackendrHrLrK)rTrirgr r r__init__s   zExplainWithBackend.__init__rcCs2t||j|jg|j\}|_|_}|_|||Sr )rerHrLrKri)rTrr(_r r r__call__szExplainWithBackend.__call__)returncCs(t|j}t|j||d|j|j}|S)Nr )rdrHrGrKrL)rTrIrUr r rrUs zExplainWithBackend.outputN) r4r5r6r\rjrrr]rlrGrUr r r rrfsrf)* dataclasses functools importlibrtypingrrrZfunctorch.compilerrrZtorch._functorch.compilersrcommonr rhr Zregister_backendrrr!r%r)r,r-partialr0r1 Exceptionr2r7rr]r>r@rBrF dataclassrGrerfr r r rst                  1