o h@sddlZddlZddlZddlZddlZddlmZmZddlm Z ddl m Z ddl m Z ddlmZmZmZddlmZddlmZddlZddlmmZdd lmZdd lmZmZm Z m!Z!m"Z"dd l#m$Z$dd l%m&Z&m'Z'dd l(m)Z)m*Z*gdZ+dZ,Gdddej-Z.ddZ/GdddeZ0GdddZ1Gddde Z2ddddde3de3de3de3de3d e3d!efd"d#Z4d$e3d!e2fd%d&Z5 'd1d(e6d)ee6d*e7fd+d,Z8Gd-d.d.e1Z9Gd/d0d0e0Z:dS)2N)ABCabstractmethod)Iterable)Enum)partial)AnyCallableOptional)Self)warn)_get_privateuse1_backend_name)_add_execution_trace_observer!_disable_execution_trace_observer _enable_execution_trace_observer_ExperimentalConfig _remove_execution_trace_observer) is_fbcode)kineto_availableProfilerActivity) MemoryProfileMemoryProfileTimeline)supported_activitiesProfilerActionscheduletensorboard_trace_handlerprofileExecutionTraceObserver ProfilerStepc@seZdZdZddZdS) _NumpyEncoderz~ Json encoder for numpy types (np.int, np.float, np.array etc.) Returns default encoder if numpy is not available cCsxzddl}Wntytj||YSwt||jr!t|St||jr+t |St||j r5| Stj||S)zEncode NumPy types to JSONrN) numpy ImportErrorjson JSONEncoderdefault isinstanceintegerintfloatingfloatndarraytolist)selfobjnpr.k/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/profiler/profiler.pyr#/s     z_NumpyEncoder.defaultN)__name__ __module__ __qualname____doc__r#r.r.r.r/r)s rcCs tjS)a Returns a set of supported profiler tracing activities. 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This satisfies 3 methods: start, stop and cleanupcCdSNr.r+r.r.r/startQz_ITraceObserver.startcCr8r9r.r:r.r.r/stopUr<z_ITraceObserver.stopcCr8r9r.r:r.r.r/cleanupYr<z_ITraceObserver.cleanupN)r0r1r2r3rr;r=r>r.r.r.r/r7Ms  r7c@sdeZdZdZddddddddddd deeedededed ed ed eed ee d edee ge ffddZ ddZ ddZddZddZddZde fddZd=de de fd d!Zd"edeefd#d$Z  % d>d&ed'ed(efd)d*Zd+d,Zd-e d.e fd/d0Zd-e d.e fd1d2Zd-e d.e fd3d4Zd5d6Zd7efd8d9Zd?de d:ee d7dfd;d<ZdS)@_KinetoProfilea Low-level profiler wrap the autograd profile Args: activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values: ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``, ``torch.profiler.ProfilerActivity.XPU``. Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA or (when available) ProfilerActivity.XPU. record_shapes (bool): save information about operator's input shapes. profile_memory (bool): track tensor memory allocation/deallocation (see ``export_memory_timeline`` for more details). with_stack (bool): record source information (file and line number) for the ops. with_flops (bool): use formula to estimate the FLOPS of specific operators (matrix multiplication and 2D convolution). with_modules (bool): record module hierarchy (including function names) corresponding to the callstack of the op. e.g. If module A's forward call's module B's forward which contains an aten::add op, then aten::add's module hierarchy is A.B Note that this support exist, at the moment, only for TorchScript models and not eager mode models. experimental_config (_ExperimentalConfig) : A set of experimental options used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed. execution_trace_observer (ExecutionTraceObserver) : A PyTorch Execution Trace Observer object. `PyTorch Execution Traces `__ offer a graph based representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators. When this argument is included the observer start() and stop() will be called for the same time window as PyTorch profiler. acc_events (bool): Enable the accumulation of FunctionEvents across multiple profiling cycles .. note:: This API is experimental and subject to change in the future. Enabling shape and stack tracing results in additional overhead. When record_shapes=True is specified, profiler will temporarily hold references to the tensors; that may further prevent certain optimizations that depend on the reference count and introduce extra tensor copies. NF activities record_shapesprofile_memory with_stack with_flops with_modulesexperimental_configexecution_trace_observer acc_eventscustom_trace_id_callbackrArBrCrDrErFrGrHrIrJc Cs|rt|nt|_||_||_||_||_||_||_||_ | |_ | |_ d|_ d|_ d|_tj|jvr8d|_n(tj|jvrBd|_ntj|jvrLd|_ntj|jvrVd|_n tj|jvr`t|_i|_dS)Ncudaxpumtiahpu)setrrArBrErCrDrFrGrHrIrJprofilermem_tl use_devicerCUDAXPUMTIAHPU PrivateUse1r preset_metadata) r+rArBrCrDrErFrGrHrIrJr.r.r/__init__s0      z_KinetoProfile.__init__cCs||dSr9) prepare_trace start_tracer:r.r.r/r;s z_KinetoProfile.startcCs |dSr9) stop_tracer:r.r.r/r=s z_KinetoProfile.stopc CsZ|jdus|js&tjtj|jv|j|j|j |j |j |j d|j |j|jd |_|jdS)NT) use_cpurRrBrErCrDrF use_kinetorGrIrJ)rPrIprofrrCPUrArRrBrErCrDrFrGrJ_prepare_tracer:r.r.r/rZs  z_KinetoProfile.prepare_tracecCs|jr|j|jdusJ|j|jr|dd|jr&|dd|jr/|dd|jr8|dd|j rA|ddt r| }|rU|dt j |tdttd rwd dlmm}|jjrwdtjd <|d dd tjd <|jD] \}}|||q|dSdS)NrC1rDrBrFrEdistributedInfocls _inductorrDISABLE_CUPTI_LAZY_REINIT0TEARDOWN_CUPTI)rHr;rP _start_tracerCadd_metadata_jsonrDrBrFrEr_get_distributed_infor!dumpsrhasattrr4torch._inductor.configrfconfigtriton cudagraphsosenvironrXitems)r+ dist_infoinductor_configkvr.r.r/r[s<           z_KinetoProfile.start_tracecCs2|jr|j|jdusJ|jddddSr9)rHr=rP__exit__r:r.r.r/r\s z_KinetoProfile.stop_tracepathc Cs|jsJ|drVtjdddd}||j|j}t|jd#}t|d }| |Wdn1s:wYWdn1sIwYt |j|S|j|S) z Exports the collected trace in Chrome JSON format. If kineto is enabled, only last cycle in schedule is exported. .gzw+b.jsonFsuffixdeleterbwbN) rPendswithtempfileNamedTemporaryFilecloseexport_chrome_tracenameopengzip writelinesrsremove)r+r{fpretvaluefinfoutr.r.r/rs     z"_KinetoProfile.export_chrome_traceself_cpu_time_totalmetriccCs|jsJ|j||S)zSave stack traces to a file Args: path (str): save stacks file to this location; metric (str): metric to use: "self_cpu_time_total" or "self_cuda_time_total" )rP export_stacks)r+r{rr.r.r/rs z_KinetoProfile.export_stacksenablecCs|jsdS|j||dS)aToggle collection of activities on/off at any point of collection. Currently supports toggling Torch Ops (CPU) and CUDA activity supported in Kineto Args: activities (iterable): list of activity groups to use in profiling, supported values: ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA`` Examples: .. code-block:: python with torch.profiler.profile( activities=[ torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA, ] ) as p: code_to_profile_0() // turn off collection of all CUDA activity p.toggle_collection_dynamic(False, [torch.profiler.ProfilerActivity.CUDA]) code_to_profile_1() // turn on collection of all CUDA activity p.toggle_collection_dynamic(True, [torch.profiler.ProfilerActivity.CUDA]) code_to_profile_2() print(p.key_averages().table( sort_by="self_cuda_time_total", row_limit=-1)) N)rPtoggle_collection_dynamic)r+rrAr.r.r/rsz(_KinetoProfile.toggle_collection_dynamicrgroup_by_input_shapegroup_by_stack_ngroup_by_overload_namecCs|jsJ|j|||S)aAverages events, grouping them by operator name and (optionally) input shapes, stack and overload name. .. note:: To use shape/stack functionality make sure to set record_shapes/with_stack when creating profiler context manager. )rP key_averages)r+rrrr.r.r/r2s z_KinetoProfile.key_averagescCs|jsJ|jjS)z Returns the list of unaggregated profiler events, to be used in the trace callback or after the profiling is finished )rPfunction_eventsr:r.r.r/eventsDs z_KinetoProfile.eventskeyvaluecCs&d|ddd}tj||dS)zo Adds a user defined metadata with a string key and a string value into the trace file "z\"N)replacer4r5_add_metadata_json)r+rr wrapped_valuer.r.r/ add_metadataLsz_KinetoProfile.add_metadatacCstj||dS)zs Adds a user defined metadata with a string key and a valid json value into the trace file N)r4r5rr+rrr.r.r/rkTsz _KinetoProfile.add_metadata_jsoncCs||j|<dS)z Preset a user defined metadata when the profiler is not started and added into the trace file later. 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ValueErrorrrPkineto_resultsr)r+requiredmissingr.r:r/_memory_profilevs  z_KinetoProfile._memory_profiledevicec Cs0|dur|jr|jdkr|jd}n tjrdnd}t||_|dr0|j||dS|drt j dd d d }| |d rO|j |j |n|j|j |t|j #}t|d  }||Wdn1sswYWdn1swYt|j dS|j||dS)aoExport memory event information from the profiler collected tree for a given device, and export a timeline plot. There are 3 exportable files using ``export_memory_timeline``, each controlled by the ``path``'s suffix. - For an HTML compatible plot, use the suffix ``.html``, and a memory timeline plot will be embedded as a PNG file in the HTML file. - For plot points consisting of ``[times, [sizes by category]]``, where ``times`` are timestamps and ``sizes`` are memory usage for each category. The memory timeline plot will be saved a JSON (``.json``) or gzipped JSON (``.json.gz``) depending on the suffix. - For raw memory points, use the suffix ``.raw.json.gz``. Each raw memory event will consist of ``(timestamp, action, numbytes, category)``, where ``action`` is one of ``[PREEXISTING, CREATE, INCREMENT_VERSION, DESTROY]``, and ``category`` is one of the enums from ``torch.profiler._memory_profiler.Category``. Output: Memory timeline written as gzipped JSON, JSON, or HTML. NrKz:0zcuda:0cpuz.htmlr|w+tr~Frz raw.json.gzwt)rRr4rKrrrrQrexport_memory_timeline_htmlrrrexport_memory_timeline_rawrexport_memory_timelinerrrrsr)r+r{rrrrr.r.r/rs*      z%_KinetoProfile.export_memory_timeline)r)FrFr9)r0r1r2r3r rrboolrr7rrrYr;r=rZr[r\rrrr&rrrrkrrlrrrr.r.r.r/r?^s|*     )(  #  r?c@s eZdZdZdZdZdZdZdS)rzG Profiler actions that can be taken at the specified intervals rN)r0r1r2r3NONEWARMUPRECORDRECORD_AND_SAVEr.r.r.r/rs r)repeat skip_firstskip_first_waitwaitwarmupactiverrrrcsddtdtffdd }dkr$dkr$dkr$dkr$dks(Jddkr0td|S)a  Returns a callable that can be used as profiler ``schedule`` argument. The profiler will skip the first ``skip_first`` steps, then wait for ``wait`` steps, then do the warmup for the next ``warmup`` steps, then do the active recording for the next ``active`` steps and then repeat the cycle starting with ``wait`` steps. The optional number of cycles is specified with the ``repeat`` parameter, the zero value means that the cycles will continue until the profiling is finished. The ``skip_first_wait`` parameter controls whether the first ``wait`` stage should be skipped. This can be useful if a user wants to wait longer than ``skip_first`` between cycles, but not for the first profile. For example, if ``skip_first`` is 10 and ``wait`` is 20, the first cycle will wait 10 + 20 = 30 steps before warmup if ``skip_first_wait`` is zero, but will wait only 10 steps if ``skip_first_wait`` is non-zero. All subsequent cycles will then wait 20 steps between the last active and warmup. steprcs|dksJ|kr tjS|8}dkr|7}}dkr,||kr,tjS||}|kr7tjS|kr@tjS||dkrItjStjS)Nrr)rrrrr)r num_stepsmod_steprrrrrrr.r/ schedule_fns&    zschedule..schedule_fnrz#Invalid profiler schedule argumentsz>Profiler won't be using warmup, this can skew profiler results)r&rr )rrrrrrrr.rr/rs *r_cCstjS)zy Default profiler behavior - immediately starts recording the events, keeps doing it on every profiler step. )rr)rr.r.r/_default_schedule_fnrFdir_name worker_nameuse_gzipcs4ddlddlddldfdd }|S)a  Outputs tracing files to directory of ``dir_name``, then that directory can be directly delivered to tensorboard as logdir. ``worker_name`` should be unique for each worker in distributed scenario, it will be set to '[hostname]_[pid]' by default. rNrc sjs#z jddWnty"}ztd|d}~wws0ddd}r@|d}|j |dS)NT)exist_okzCan't create directory: rrz.pt.trace.jsonr|) r{isdirmakedirs Exception RuntimeError gethostnamegetpidtime_nsrr)r_e file_namerrssockettimerrr.r/ handler_fn s z-tensorboard_trace_handler..handler_fnrN)rsrr)rrrrr.rr/rs  rcs eZdZdZdddddddddddddd deeedeeege fdeede fd e d e d e d e d e dee dee de dee deegefffddZddZddZddZddZddZdd Zd!d"Zd#d$Zd%d&Zd'eejfd(d)ZZS)*ra0Profiler context manager. Args: activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values: ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``, ``torch.profiler.ProfilerActivity.XPU``. Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA or (when available) ProfilerActivity.XPU. schedule (Callable): callable that takes step (int) as a single parameter and returns ``ProfilerAction`` value that specifies the profiler action to perform at each step. on_trace_ready (Callable): callable that is called at each step when ``schedule`` returns ``ProfilerAction.RECORD_AND_SAVE`` during the profiling. record_shapes (bool): save information about operator's input shapes. profile_memory (bool): track tensor memory allocation/deallocation. with_stack (bool): record source information (file and line number) for the ops. with_flops (bool): use formula to estimate the FLOPs (floating point operations) of specific operators (matrix multiplication and 2D convolution). with_modules (bool): record module hierarchy (including function names) corresponding to the callstack of the op. e.g. If module A's forward call's module B's forward which contains an aten::add op, then aten::add's module hierarchy is A.B Note that this support exist, at the moment, only for TorchScript models and not eager mode models. experimental_config (_ExperimentalConfig) : A set of experimental options used for Kineto library features. Note, backward compatibility is not guaranteed. execution_trace_observer (ExecutionTraceObserver) : A PyTorch Execution Trace Observer object. `PyTorch Execution Traces `__ offer a graph based representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators. When this argument is included the observer start() and stop() will be called for the same time window as PyTorch profiler. See the examples section below for a code sample. acc_events (bool): Enable the accumulation of FunctionEvents across multiple profiling cycles use_cuda (bool): .. deprecated:: 1.8.1 use ``activities`` instead. .. note:: Use :func:`~torch.profiler.schedule` to generate the callable schedule. Non-default schedules are useful when profiling long training jobs and allow the user to obtain multiple traces at the different iterations of the training process. The default schedule simply records all the events continuously for the duration of the context manager. .. note:: Use :func:`~torch.profiler.tensorboard_trace_handler` to generate result files for TensorBoard: ``on_trace_ready=torch.profiler.tensorboard_trace_handler(dir_name)`` After profiling, result files can be found in the specified directory. Use the command: ``tensorboard --logdir dir_name`` to see the results in TensorBoard. For more information, see `PyTorch Profiler TensorBoard Plugin `__ .. note:: Enabling shape and stack tracing results in additional overhead. When record_shapes=True is specified, profiler will temporarily hold references to the tensors; that may further prevent certain optimizations that depend on the reference count and introduce extra tensor copies. Examples: .. code-block:: python with torch.profiler.profile( activities=[ torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA, ] ) as p: code_to_profile() print(p.key_averages().table( sort_by="self_cuda_time_total", row_limit=-1)) Using the profiler's ``schedule``, ``on_trace_ready`` and ``step`` functions: .. code-block:: python # Non-default profiler schedule allows user to turn profiler on and off # on different iterations of the training loop; # trace_handler is called every time a new trace becomes available def trace_handler(prof): print(prof.key_averages().table( sort_by="self_cuda_time_total", row_limit=-1)) # prof.export_chrome_trace("/tmp/test_trace_" + str(prof.step_num) + ".json") with torch.profiler.profile( activities=[ torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA, ], # In this example with wait=1, warmup=1, active=2, repeat=1, # profiler will skip the first step/iteration, # start warming up on the second, record # the third and the forth iterations, # after which the trace will become available # and on_trace_ready (when set) is called; # the cycle repeats starting with the next step schedule=torch.profiler.schedule( wait=1, warmup=1, active=2, repeat=1), on_trace_ready=trace_handler # on_trace_ready=torch.profiler.tensorboard_trace_handler('./log') # used when outputting for tensorboard ) as p: for iter in range(N): code_iteration_to_profile(iter) # send a signal to the profiler that the next iteration has started p.step() The following sample shows how to setup up an Execution Trace Observer (`execution_trace_observer`) .. code-block:: python with torch.profiler.profile( ... execution_trace_observer=( ExecutionTraceObserver().register_callback("./execution_trace.json") ), ) as p: for iter in range(N): code_iteration_to_profile(iter) p.step() You can also refer to test_execution_trace_with_kineto() in tests/profiler/test_profiler.py. 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The data will be written to output_file_path. rcSstjdddd}||jS)Nr}r~Fr)rrrr)rr.r.r/get_temp_uncompressed_fileszLExecutionTraceObserver.register_callback..get_temp_uncompressed_filer|)rr"r&rr'r )r+r&r6r.r.r/r0s  z(ExecutionTraceObserver.register_callbackFcCs:|jsdS|jr |jStj|j|d}|sdS||_|jS)a Generates the resources directory for the generated kernels, or index tensor data or any other metadata that is required to complete the Execution Trace content. The directory is created right where the ET file is being output. Only works if the observer has called set_extra_resource_collection(val=True). Returns None if the observer is not configured with extra resource collection. N) create_dir)r$r%rget_resources_dir_for_et_pathr&)r+r3generated_pathr.r.r/r4s z(ExecutionTraceObserver.get_resources_dirr7cCsztj|\}}tj|tj|dd}tj|s;|r9zt|W|Sty8td|YdSwdS|S)Nr _resourcesz(Execution trace exception when creating ) rsr{splitrsplitextexistsmkdirrr ) trace_pathr7work_dirr resource_dirr.r.r/r8s   z4ExecutionTraceObserver.get_resources_dir_for_et_pathc sd fdd }dtdtddfdd}jrMz|Wnty7}z td |WYd}~nd}~wwtjd rH|jjd _dSdS) zE Removes ET observer from record function callbacks. rNc sz}Wnty }ztd|WYd}~dSd}~ww|s%dSddlm}dd|jD}|D]}|durExecution trace exception when generating resource directory: r) PyCodeCachecSs"g|] }t|dddur|jqS)__file__N)rrCrr.r.r/rs z\ExecutionTraceObserver.unregister_callback.._save_triton_kernels..) r4rr torch._inductor.codecacherBmodulesrsr{basenamershutilcopyfile)rArrB kernel_files kernel_filerdstr:r.r/_save_triton_kernelss,   zHExecutionTraceObserver.unregister_callback.._save_triton_kernelsuncompressed_file output_filec Sstd|d|t|d#}t|d }||Wdn1s&wYWdn1s5wYt|dS)NzExecution Trace: compressing z to rr)printrrrrsr)rMrNrrr.r.r/ _save_gz_files  zAExecutionTraceObserver.unregister_callback.._save_gz_filez(Execution trace failed to save kernels: gzFr) rr"r=rr rr&rr')r+rLrPrr.r:r/r+s   z*ExecutionTraceObserver.unregister_callbackcC|jS)z^ Returns True if the execution trace observer is registered, otherwise False. )r"r:r.r.r/ is_registered)rz$ExecutionTraceObserver.is_registeredcCrR)zK Returns True if the observer is running, otherwise False. )r#r:r.r.r/ is_running0sz!ExecutionTraceObserver.is_runningcCs,|jr|jstd|_|dSdSdS)z$ Starts to capture. TN)r"r#r_record_pg_configr:r.r.r/r;6s  zExecutionTraceObserver.startcCs|jr td|_dSdS)z# Stops to capture. FN)r#rr:r.r.r/r=?s zExecutionTraceObserver.stopcCr(r)r*r:r.r.r/r>Gr-zExecutionTraceObserver.cleanupcCs|jr|jSdS)z7 Returns the output file name or None. N)r&r:r.r.r/get_output_file_pathMsz+ExecutionTraceObserver.get_output_file_pathcCsN|jr!tjr#tjr%tjjjj}tj dt j |t ddSdSdSdS)Nz## process_group:init ##rd) rSr4rrrr_worldpg_config_infor5 _record_function_with_args_enterr!rmr)r+rXr.r.r/rUVs   z(ExecutionTraceObserver._record_pg_configr)F)r0r1r2r3rYr, staticmethodr rr1rr r0r4rr8r+propertyrSrTr;r=r>rVrUr.r.r.r/rjs2     5   r)NF);rr!rsrGrabcrrcollections.abcrenumr functoolsrtypingrrr typing_extensionsr warningsr r4torch.autograd.profilerr5rPr_torch._Cr torch._C._profilerr rrrrtorch._environmentrtorch.autogradrrtorch.profiler._memory_profilerrr__all__rr"rrr7r?rr&rrrrrrrr.r.r.r/sv       V 8  P