U 9%ed@sddlZddlZddlZddlZddlZddlmZmZddlm Z ddl m Z m Z m Z mZddlmZeeZejZdZeGdddZeGd d d ZeZead d ejd d d Zdddddddd d d d d d d d d d d d d d d d dde ee ee ee ee ee ee eeeeeeeeeeeeeeeeee e eeeeffdddZ ddZ!ddZ"dFddZ#ddZ$dZ%dZ&dd'e(e%e(e&gdZ)ddZ*d d!Z+d"d#Z,dGd$d%Z-dHd&d'Z.ej/d(d)Z0d*d+Z1d,d-Z2d.d/Z3Gd0d1d1ej4Z5e5Z6d2d3Z7eZ8d4d5Z9d6d7Z:d8d9Z;d:d;Zd?Z>dId@dAZ?e/ddBdCZ@GdDdEdEZAddlBZCddlDmEZFdS)JN) dataclassfield) __import__)DictOptionalSetUnion)WeakSetZ TORCH_LOGSc@seZdZUeedZeeefed<ee dZ e eed<ee dZ e eed<ee dZ e eed<ee dZe eed<eedZeeefed<ee dZe eed<eedZeeejfed <d d Zd d ZddZddZddZddZddZddZddZddZdS) LogRegistrydefault_factorylog_alias_to_log_qnameartifact_log_qnameschild_log_qnamesartifact_namesvisible_artifactsartifact_descriptionsoff_by_default_artifact_namesartifact_log_formatterscCs ||jkSNrselfnamerW/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/_logging/_internal.py is_artifact7szLogRegistry.is_artifactcCs ||jkSrr )raliasrrris_log:szLogRegistry.is_logcCs||j|<dSrr)rr log_qnamerrr register_log>szLogRegistry.register_logcCsR|j||r|j|||j|<|r6|j||dk rNt||j|<dSr)raddrrrlogging Formatterr)rr descriptionvisibleoff_by_default log_formatrrrregister_artifact_nameBs    z"LogRegistry.register_artifact_namecCs|j|dSr)rr")rartifact_log_qnamerrrregister_artifact_logUsz!LogRegistry.register_artifact_logcCs|j|dSr)rr")rr rrrregister_child_logXszLogRegistry.register_child_logcCst|jSr)setr valuesrrrrget_log_qnames[szLogRegistry.get_log_qnamescCs t|jSr)r-rr/rrrget_artifact_log_qnames^sz#LogRegistry.get_artifact_log_qnamescCs t|jSr)r-rr/rrrget_child_log_qnamesasz LogRegistry.get_child_log_qnamescCs ||jkSr)r)rZartifact_qnamerrris_off_by_defaultdszLogRegistry.is_off_by_defaultN)__name__ __module__ __qualname__rdictr rstr__annotations__r-rrrrrrrrr#r$rrr!r)r+r,r0r1r2r3rrrrr s$ r c@sfeZdZUeedZeeefed<ee dZ e eed<ddZ ddZ dd Zd d Zd d ZdS)LogStater log_qname_to_levelrcCs|j|dSr)rr")r artifact_namerrrenable_artifactpszLogState.enable_artifactcCs ||jkSrrrrrris_artifact_enabledsszLogState.is_artifact_enabledcCs||j|<dSr)r;)rr log_levelrrr enable_logvszLogState.enable_logcCs |jSr)r;itemsr/rrrget_log_level_pairsyszLogState.get_log_level_pairscCs|j|jdSr)r;clearrr/rrrrC|s zLogState.clearN)r4r5r6rr7r;rr8r9r-rrr=r>r@rBrCrrrrr:hs r:T) graph_breaks recompilesdynamicguards trace_sourceF)alldynamoaotrFinductor distributedonnxbytecode aot_graphsaot_joint_graph ddp_graphsgraph graph_coderD graph_sizesrGrErH trace_call output_codeschedule perf_hintsonnx_diagnosticsmodulescsnttjkrtddStp&ifdd}||||||||| | || | | ||||||||||ddS)aB Sets the log level for individual components and toggles individual log artifact types. .. warning:: This feature is a prototype and may have compatibility breaking changes in the future. .. note:: The ``TORCH_LOGS`` environment variable has complete precedence over this function, so if it was set, this function does nothing. A component is a set of related features in PyTorch. All of the log messages emitted from a given component have their own log levels. If the log level of a particular message has priority greater than or equal to its component's log level setting, it is emitted. Otherwise, it is supressed. This allows you to, for instance, silence large groups of log messages that are not relevant to you and increase verbosity of logs for components that are relevant. The expected log level values, ordered from highest to lowest priority, are: * ``logging.CRITICAL`` * ``logging.ERROR`` * ``logging.WARNING`` * ``logging.INFO`` * ``logging.DEBUG`` * ``logging.NOTSET`` See documentation for the Python ``logging`` module for more information on log levels: ``_ An artifact is a particular type of log message. Each artifact is assigned to a parent component. A component can emit many different kinds of artifacts. In general, an artifact is emitted if either its corresponding setting in the argument list below is turned on or if its parent component is set to a log level less than or equal to the log level of the artifact. Keyword args: all (:class:`Optional[int]`): The default log level for all components. Default: ``logging.WARN`` dynamo (:class:`Optional[int]`): The log level for the TorchDynamo component. Default: ``logging.WARN`` aot (:class:`Optional[int]`): The log level for the AOTAutograd component. Default: ``logging.WARN`` inductor (:class:`Optional[int]`): The log level for the TorchInductor component. Default: ``logging.WARN`` dynamic (:class:`Optional[int]`): The log level for dynamic shapes. Default: ``logging.WARN`` distributed (:class:`Optional[int]`): Whether to log communication operations and other debug info from pytorch distributed components. Default: ``logging.WARN`` onnx (:class:`Optional[int]`): The log level for the ONNX exporter component. Default: ``logging.WARN`` bytecode (:class:`bool`): Whether to emit the original and generated bytecode from TorchDynamo. Default: ``False`` aot_graphs (:class:`bool`): Whether to emit the graphs generated by AOTAutograd. Default: ``False`` aot_joint_graph (:class:`bool`): Whether to emit the joint forward-backward graph generated by AOTAutograd. Default: ``False`` ddp_graphs (:class:`bool`): Whether to emit graphs generated by DDPOptimizer. Default: ``False`` graph (:class:`bool`): Whether to emit the graph captured by TorchDynamo in tabular format. Default: ``False`` graph_code (:class:`bool`): Whether to emit the python source of the graph captured by TorchDynamo. Default: ``False`` graph_breaks (:class:`bool`): Whether to emit the graph breaks encountered by TorchDynamo. Default: ``False`` graph_sizes (:class:`bool`): Whether to emit tensor sizes of the graph captured by TorchDynamo. Default: ``False`` guards (:class:`bool`): Whether to emit the guards generated by TorchDynamo for each compiled function. Default: ``False`` recompiles (:class:`bool`): Whether to emit a guard failure reason and message every time TorchDynamo recompiles a function. Default: ``False`` trace_source (:class:`bool`): Whether to emit when TorchDynamo begins tracing a new line. Default: ``False`` trace_call (:class:`bool`): Whether to emit detailed line location when TorchDynamo creates an FX node corresponding to function call. Python 3.11+ only. Default: ``False`` output_code (:class:`bool`): Whether to emit the TorchInductor output code. Default: ``False`` schedule (:class:`bool`): Whether to emit the TorchInductor schedule. Default: ``False`` perf_hints (:class:`bool`): Whether to emit the TorchInductor perf hints. Default: ``False`` onnx_diagnostics (:class:`bool`): Whether to emit the ONNX exporter diagnostics in logging. Default: ``False`` modules (dict): This argument provides an alternate way to specify the above log component and artifact settings, in the format of a keyword args dictionary given as a single argument. There are two cases where this is useful (1) if a new log component or artifact has been registered but a keyword argument for it has not been added to this function and (2) if the log level for an unregistered module needs to be set. This can be done by providing the fully-qualified module name as the key, with the log level as the value. Default: ``None`` Example:: >>> # xdoctest: +SKIP >>> import logging # The following changes the "dynamo" component to emit DEBUG-level # logs, and to emit "graph_code" artifacts. >>> torch._logging.set_logs(dynamo=logging.DEBUG, graph_code=True) # The following enables the logs for a different module >>> torch._logging.set_logs(modules={"unregistered.module.name": logging.DEBUG}) zQUsing TORCH_LOGS environment variable for log settings, ignoring call to set_logsNc s\|dd}|rf|tjkrDtd|ddddtjDtjD]}||krN|||<qNnt}t | D]\}}|dkr|}t |rt |tstd|d||rt|q~t|s|tjkr4|tjkrtd |d |ddd dtjDttj|||q~|dkrBq~q~td |q~tdS) NrIz&Unrecognized log level for kwarg all: z, valid level values are: ,cSsg|] }t|qSrr8.0krrr Dsz/set_logs.._set_logs..z!Expected bool to enable artifact z , received zUnrecognized log level for log z: cSsg|] }t|qSrr]r^rrrra^sz6Unrecognized log or artifact name passed to set_logs: )popr# _levelToName ValueErrorjoinkeys log_registryr DEFAULT_LOG_LEVEL itertoolschainrAr isinstancebool log_stater=rrr@get _init_logs)kwargsZ default_levelrvalr[rr _set_logs?sF  $     *  zset_logs.._set_logs)rIrJrKrLrFrOrPrQrRrMrSrTrDrUrGrErHrVrWrXrYrNrZ) LOG_ENV_VARosenvironlogwarningrmrC)rIrJrKrFrLrMrNrOrPrQrRrSrTrDrUrGrErHrVrWrXrYrZr[rsrrrrset_logssD(  /rycCsddtDS)z3 Returns: a list of all registered loggers cSsg|]}t|qSr)r# getLogger)r_qnamerrrraszget_loggers..)rgr0rrrr get_loggerssr|cCst||dS)z Enables a log to be controlled by the env var and user API with the setting_name Args: setting_name: the shorthand name used in the env var and user API log_name: the log name that the setting_name is associated with N)rgr!) setting_nameZlog_namerrrr!sr!cCst|||||dS)a Enables an artifact to be controlled by the env var and user API with name Args: setting_name: the shorthand name used in the env var and user API description: A description of what this outputs visible: Whether it gets suggested to users by default off_by_default: whether this artifact should be logged when the ancestor loggers are enabled at level DEBUG N)rgr))r}r%r&r'r(rrrregister_artifacts r~cCs\|tjkr(tdt|dt|d|d|}t|}||_t|t||S)NzArtifact name: z. not registered,please call register_artifact(z") in torch._logging.registrations.z.__) rgrrdreprr#rzr<r+configure_artifact_log)Z module_qnamer<r{rwrrrgetArtifactLoggers   r+-(|z?)cCs4t|jrd|_t|jr0|tjd|_dSNFT) rgr3r< propagatermr>setLevelr#DEBUG)rwrrrrs    rcCs tdS)Nz$((\+|-)?[\w\.]+,\s*)*(\+|-)?[\w\.]+?)recompilerrrr_gen_settings_regexsrcCstt|dk Sr)r fullmatchr)settingsrrr_validate_settingssrcs~ddd|rtj}ntj}|r&d}nd}dgttjfdd|D}d d |}d }d |d |d|d}|S)NcSs$t||kst|d|t|S)N )lenAssertionError)slengthrrrpad_toszhelp_message..pad_tozAll registered namesz?Visible registered names (use TORCH_LOGS='+help' for full list)rIcs$g|]}|dtj|qS) )rgr)r_rrrrrasz help_message..z z aD Examples: TORCH_LOGS="+dynamo,aot" will set the log level of TorchDynamo to logging.DEBUG and AOT to logging.INFO TORCH_LOGS="-dynamo,+inductor" will set the log level of TorchDynamo to logging.ERROR and TorchInductor to logging.DEBUG TORCH_LOGS="aot_graphs" will enable the aot_graphs artifact TORCH_LOGS="+dynamo,schedule" will enable set the log level of TorchDynamo to logging.DEBUG and enable the schedule artifact TORCH_LOGS="+some.random.module,schedule" will set the log level of some.random.module to logging.DEBUG and enable the schedule artifact z TORCH_LOGS Info z  )r)rgrrlistr rfre)verboseZprinted_artifactsheadinglinesZ setting_infoZexamplesmsgrrr help_messages2   rcCs:ddgttjttj}d|d|d}|S)Nz, rIz Invalid log settings: z, must be a comma separated list of fully qualified module names, registered log names or registered artifact names. For more info on various settings, try TORCH_LOGS="help" Valid settings: r)rerrgr rfr)rrZvalid_settingsrrrr_invalid_settings_err_msg s rcCsb|dkrtS|dkr&ttddn|dkr.get_name_level_pairrI)r7rdrrrrsubsplitr:rgr0r@rrr rr=_is_valid_module_has_registered_parentr!r,)rZ log_namesrrmrrr rrr_parse_log_settingssD               rcCs*zt|WdStk r$YdSXdSNTF)r ImportError)r{rrrrTs rcCs"tjtd}|dk rt|adSr)rurvrnrtrrm)Z log_settingrrr_update_log_state_from_env\srcCs2t|}t}|jr.|j|kr&dS|j}qdSr)r#rzrgr0parentr)r Zcur_logZregistered_log_qnamesrrrrcs  rc@seZdZddZdS)TorchLogsFormatterc stt|jdd}|dk rsz,TorchLogsFormatter.format..)getattrr#rzrrgrrnformat getMessagemessage formatTimedatefmtasctimerZ rankprefixdistZ is_availableZis_initializedZget_rankZ compileidtorchZ_guardsZCompileContextZcurrent_compile_id levelnamere)rrecordr<Zartifact_formatterrZ compile_idrrrrrs*   ,zTorchLogsFormatter.formatN)r4r5r6rrrrrrqsrcCs.t|}|t|tj||dSr)_track_handler setFormatterDEFAULT_FORMATTERrr#r addHandler)Zcreate_handler_fnrwZ debug_handlerrrr_setup_handlerss   rcCst||Sr)handlersr"handlerrrrrs rcCs|tkSr)rrrrr_is_torch_handlersrcCs(dd|jD}|D]}||qdS)NcSsg|]}t|r|qSr)r)r_rrrrrasz#_clear_handlers..)r removeHandler)rwZ to_removerrrr_clear_handlerssrcCsltD](}t|}|tjd|_t|qt t t D] }t|}|tj d|_qFdSr) rgr0r#rzrWARNINGrrrirjr1r2NOTSET)r rwr*rrr _reset_logss      rcCstSrrmrrrr_get_log_statesrcCs|adSrr)staterrr_set_log_statesrcstttD]\}}t|}||qtD]4}t|}t tj |dk r:t fdd|q:t D]}t|}t |qxdS)Ncs tSr)r# FileHandlerr log_file_namerrz_init_logs..) rrrmrBr#rzrrgr0r StreamHandlerr1r)rr rrwr*rrrros&       rocOs|j||dS)a This function is similar to `logger.warning()`, but will emit the warning with the same message only once Note: The cache is for the function arguments, so 2 different callers using the same arguments will hit the cache. The assumption here is that all warning messages are unique across the code. If they aren't then need to switch to another type of cache that includes the caller frame information in the hashing function. N)rx)Z logger_objargsrprrr warning_oncesrc@seZdZddZddZdS) LazyStringcOs||_||_||_dSrfuncrrp)rrrrprrr__init__szLazyString.__init__cCs|j|j|jSrrr/rrr__str__szLazyString.__str__N)r4r5r6rrrrrrrsr)FFN)F)F)N)G functoolsrir#rur dataclassesrr importlibrtypingrrrrweakrefr rzr4rwWARNrhrtr r:rgrmrZDEFAULT_LOGGINGintrlr8ryr|r!r~rrrreescapeZVERBOSITY_REGEXrrrrr lru_cacherrrrr$rrrrrrrrrrrorrZ torch._guardsrZtorch.distributedrMrrrrrs   V  |  1  :