i?pddlZddlZddlZddlmZmZddlmZddlm Z m Z ddl m Z ddl Z ddlmZgdZGdd eZd Zd Zd Zd ZGddZGddZedgdZGddeZGddeZGddeZGddZdZdZdZ dZ!d#d Z" d$d!Z#d"Z$y)%N) defaultdict namedtuple) attrgetter)AnyOptional) deprecated) DeviceType) EventListFormattedTimesMixinIntervalKernel FunctionEventFunctionEventAvg StringTable MemRecordsAccceZdZdZfdZdZdZdZdZdZ e dZ dd Z d Z d Zd ed efdZ ddZdZxZS)r a> A list of profiling events with helper methods for analysis and visualization. EventList extends the standard Python list to provide specialized methods for working with profiling events (FunctionEvent or FunctionEventAvg objects). It includes utilities for aggregating statistics, formatting output tables, and exporting profiling data. This class is typically returned by profiler methods and should not be instantiated directly by users. Args: *args: Standard list arguments. use_device (str, optional): Device type for profiling ("cuda", "xpu", etc.). profile_memory (bool, optional): Whether memory profiling was enabled. Default: False. with_flops (bool, optional): Whether to include FLOP counts. Default: False. Attributes: _use_device (str): Device type being profiled. _profile_memory (bool): Whether memory profiling is enabled. _with_flops (bool): Whether FLOP counting is enabled. _tree_built (bool): Whether the event tree structure has been built. Key Methods: table(...): Format events as a table string for display. export_chrome_trace(path): Export to Chrome tracing format. export_stacks(path, metric): Export stack traces with metrics. key_averages(...): Compute averaged statistics grouped by operation name. total_average(): Compute aggregate totals across all events (sums, not averages). Properties: self_cpu_time_total: Sum of self CPU time across all events. Example:: import torch from torch.profiler import profile, ProfilerActivity with profile(activities=[ProfilerActivity.CPU]) as prof: x = torch.randn(100, 100) y = torch.matmul(x, x) # EventList is returned by prof.events() events = prof.events() # Display as formatted table print( events.table( sort_by="cpu_time_total", row_limit=20, top_level_events_only=False ) ) # Export to Chrome tracing format events.export_chrome_trace("trace.json") # Get averaged statistics avg_events = events.key_averages() print(avg_events.table()) # Export stack traces events.export_stacks("stacks.txt", "self_cpu_time_total") See Also: - :class:`FunctionEvent`: Individual profiling event - :class:`FunctionEventAvg`: Averaged profiling statistics - :meth:`table`: Format events as a readable table - :meth:`key_averages`: Aggregate events by operation name c|jdd}|jdd}|jdd}t||i|||_||_d|_||_y)N use_deviceprofile_memoryF with_flops)popsuper__init__ _use_device_profile_memory _tree_built _with_flops)selfargskwargsrrr __class__s V/var/www/html/engine/venv/lib/python3.12/site-packages/torch/autograd/profiler_util.pyrzEventList.__init___sfZZ d3 $4e<ZZ e4  $)&)%- %cr|j|j|jd|_y)NT)_populate_cpu_children_remove_dup_nodes_set_backward_stacktracesrrs r" _build_treezEventList._build_treejs. ##%   &&(r#c"|jSN)tabler(s r"__str__zEventList.__str__pszz|r#c t}tt|D]}||j||jj||jk(s=t||jj dk(sc||j ||j_||j ||j_||j D]}||j|_|j|t|dk(ryt|Dcgc] \}}||vs |}}}|j|j|Rcc}}w)Nr) setrangelen cpu_parentname cpu_childrenkernelsadd enumerateclearextend)r to_deleteidxchindevnew_evtss r"r&zEventList._remove_dup_nodesss2ISY' 'I((4S ,,11T#Y^^CDI00==>!C8z2EventList._populate_cpu_children..su||U]]&Cr#c\|jj|jj gSr+) time_rangestartendrGs r"rIz2EventList._populate_cpu_children..s&5#3#3#9#9E.bw_parents*{a  00r#z1Expected fwd_thread to be set for backward parent)stack sequence_nrrB fwd_threadrVget)r fwd_stacksrYtprfs @r"r'z#EventList._set_backward_stacktracess 1  .C~%#))*?__cjj1J&$'IIJqM  .  2C#A}<<'(K]]ALL1&NN1b1  2r#c&td|DS)Nc34K|]}|jywr+)self_cpu_time_total.0rHs r" z0EventList.self_cpu_time_total..s?5,,?)sumr(s r"rpzEventList.self_cpu_time_totals?$???r#c Vt||||||||j|j|| S)aPrint an EventList as a nicely formatted table. Args: sort_by (str, optional): Attribute used to sort entries. By default they are printed in the same order as they were registered. Valid keys include: ``cpu_time``, ``cuda_time``, ``xpu_time``, ``cpu_time_total``, ``cuda_time_total``, ``xpu_time_total``, ``cpu_memory_usage``, ``cuda_memory_usage``, ``xpu_memory_usage``, ``self_cpu_memory_usage``, ``self_cuda_memory_usage``, ``self_xpu_memory_usage``, ``count``. top_level_events_only(bool, optional): Boolean flag to determine the selection of events to display. If true, the profiler will only display events at top level like top-level invocation of python `lstm`, python `add` or other functions, nested events like low-level cpu/cuda/xpu ops events are omitted for profiler result readability. time_unit(str, optional): A time unit to be used for all values in the table. Valid options are: ``s``, ``ms`` and ``us``. Returns: A string containing the table. ) sort_by row_limitmax_src_column_widthmax_name_column_widthmax_shapes_column_widthheaderrrtop_level_events_only time_unit) _build_tablerr) rrwrxryrzr{r|r}r~s r"r,zEventList.tables?@ !5"7$;//''"7  r#c^ddl}|jsdn |j}t|d5}d}|jd|D]}|j|jdj |j|j j|j j|js |jnd|jd|jd |jD]P}|jd |jd |j jd |jd |d|d |dz }Rt|dkDr=|j|jdz |j |j#|jddddy#1swYyxYw)zExport an EventList as a Chrome tracing tools file. The checkpoint can be later loaded and inspected under ``chrome://tracing`` URL. Args: path (str): Path where the trace will be written. rNcudaw[zc{{"name": "{}", "ph": "X", "ts": {}, "dur": {}, "tid": {}, "pid": "CPU functions", "args": {{}}}}, z " node_id:z , thread_id:z "z {"name": "z", "ph": "s", "ts": z , "tid": z , "pid": "CPU functions", "id": z, "cat": "cpu_to_z", "args": {}}, r/])osropenwrite trace_nameformatrKrL elapsed_us is_remoterBrFr6r2seektellSEEK_SETtruncate)rpathr device_namefnext_idrY_s r"export_chrome_tracezEventList.export_chrome_traces $($4$4f$:J:J $_+ G GGCL! !>>)' (.v,,113"}} )#++l3::,bQ ( !AGG%cnn%56!!$!5!5 67""%**.!!( ***57( ( qLG!'! !D4y1}qvvx!|R[[1 GGCLW+ + + s E/F##F,c gdS)N)rpself_cuda_time_totalself_xpu_time_totalself_privateuse1_time_totalr(s r"supported_export_stacks_metricsz)EventList.supported_export_stacks_metricsUs   r#rmetricc ||jvr%tdt|jztjdd}t |d5}|D]}|j st |j dkDs)t||jddjddjd d}t|dkDstd }t|j D]}||j|z }|d z }|dd d ztt|z}|j|dz dddy#1swYyxYw)Nzmetric should be one of: z ; ____rrrdevicexpu privateuse1;rN  ) r ValueErrorstr maketransrrgr2getattrreplaceintreversed translater) rrrtranslate_tablerrY metric_value stack_strentrys r" export_stackszEventList.export_stacks]s> ==? ?+d::<=> --&9 $_ 2 299SYY!!3#*vx8 1 9 $L <(1,$& %-cii%8-E%)III%,I-%.crNS$83s.get_keysEIIEMM"E%%&EOO$E,,- C& 3,,-$ 3u1123!#u{{#4$455: r#rrrNr)rrVrrrrr7r valuesrrrrgrr)rrrrstatsravg_listrYs @r" key_averageszEventList.key_averagesus. G :EEU9V  38_ $ C .0@BX c#h   LLN''//''    'C "3#34CI(#% )$&!  ' r#cNt}|D]}||z }d|_d|_|S)a,Compute aggregate statistics across all events. Accumulates statistics from all events into a single FunctionEventAvg object. This is primarily useful for computing total metrics (total CPU time, total memory usage, etc.) across the entire profiling session, regardless of operation type. Note: This sums up times and counts across ALL different operations, so the "average" metrics (like cpu_time) represent the average time per operation call across the entire session, mixing all operation types together. For per-operation averages, use :meth:`key_averages` instead. Returns: FunctionEventAvg: A single aggregate object with key="Total" containing accumulated statistics. NTotal)rrD)r total_statrYs r" total_averagezEventList.total_averages;&&'  "C # J!JN "! r#)NdK7PNFN)FrF)__name__ __module__ __qualname____doc__rr)r-r&r%r'propertyrpr,rrrrrr __classcell__)r!s@r"r r sBH & "0D-L24@@  "#, \6p 2#2s24$$ BHr#r cNd}d}||k\r ||z ddS||k\r ||z ddS|ddS)+Define how to format time in FunctionEvent..A@@.3fsmsusr)time_us US_IN_SECONDUS_IN_MSs r" _format_timersU"LH,L(-Q//(H$S),,c]" r#cL|dk(r|dk7rtd|y|dz|z ddS)rrzExpected time_us == 0 but got NaNgY@.2f%)rV)r total_time_uss r"_format_time_sharers@ a< #A'!KL Lo -c 2! 44r#cd}d|z}d|z}t||k\r |dz|z ddSt||k\r |dz|z ddSt||k\r |dz|z ddSt|dzS)z&Return a formatted memory size string.i?rz GBz MBz KBz B)absr)nbytesKBMBGBs r"_format_memoryrs B B B 6{b3,#C(,, V 3,#C(,, V 3,#C(,,6{T!!r#c tfdS)Nc.tt|Sr+)rr)rr4s r"rIz!_attr_formatter..sgdD.A!Br#)rr4s`r"_attr_formatterrs B CCr#ceZdZdZedZedZedZedZedZ edZ e dZ e d Z e ed e d Zy )r z{Helpers for FunctionEvent and FunctionEventAvg. The subclass should define `*_time_total` and `count` attributes. cpu_time device_timecpu_time_totaldevice_time_totalrpself_device_time_totalc\|jdk(rdSd|jz|jz SNrgr)countrr(s r"rzFormattedTimesMixin.cpu_times+jjAosQ31D1D+Dtzz+QQr#c\|jdk(rdSd|jz|jz Sr)rrr(s r"rzFormattedTimesMixin.device_time s+jjAosT31G1G+G$**+TTr#z<`cuda_time` is deprecated, please use `device_time` instead.categoryc|jSr+)rr(s r" cuda_timezFormattedTimesMixin.cuda_times r#N)rrrrr cpu_time_strdevice_time_strcpu_time_total_strdevice_time_total_strself_cpu_time_total_strself_device_time_total_strrrrr FutureWarningrrr#r"r r s #:.L%m4O()9:+,?@-.CD!01I!J RRUUF   r#r ceZdZdZdZy)r c ||_||_yr+)rLrM)rrLrMs r"rzInterval.__init__s r#c4|j|jz S)z4 Returns the length of the interval )rMrLr(s r"rzInterval.elapsed_ussxx$**$$r#N)rrrrrrr#r"r r s %r#r r )r4rdurationc^eZdZdZddddddddddddej dddddddddfdZdZdZd Z e d Z e d Z e e d e dZe dZe dZe dZe e de dZe dZe e de dZe dZdZy)raProfiling information about a single function. FunctionEvent records the execution of a single operation during profiling. These events are obtained from the profiler/kineto and contain detailed timing and memory usage information. .. note:: FunctionEvent objects are typically created by the profiler/kineto and should not be instantiated directly by users. Access them through the profiler's output. Attributes: id (int): Unique identifier for this event. node_id (int): Node identifier for distributed profiling (-1 if not applicable). name (str): Name of the profiled function/operator. overload_name (str): Overload name for the operator (requires _ExperimentalConfig(capture_overload_names=True) set). trace_name (str): Same as name, just changes ProfilerStep* to ProfilerStep# time_range (Interval): Time interval containing start and end timestamps in microseconds. thread (int): Thread ID where the operation started. fwd_thread (int): Thread ID of the corresponding forward operation. kernels (List[Kernel]): List of device kernels launched by this operation. count (int): Number of times this event was called (usually 1). cpu_children (List[FunctionEvent]): Direct CPU child operations. cpu_parent (FunctionEvent): Direct CPU parent operation. input_shapes (Tuple[int, ...]): Shapes of input tensors (requires record_shapes=true). concrete_inputs (List[Any]): Concrete input values (requires record_shapes=true). kwinputs (Dict[str, Any]): Keyword arguments (requires record_shapes=true). stack (List[str]): Python stack trace where the operation was called (requires with_stack=true). scope (int): at::RecordScope identifier (0=forward, 1=backward, etc.). use_device (str): Device type being profiled ("cuda", "xpu", etc.). cpu_memory_usage (int): CPU memory allocated in bytes. device_memory_usage (int): Device memory allocated in bytes. is_async (bool): Whether this is an asynchronous operation. is_remote (bool): Whether this operation occurred on a remote node. sequence_nr (int): Sequence number for autograd operations. device_type (DeviceType): Type of device (CPU, CUDA, XPU, PrivateUse1, etc.). device_index (int): Index of the device (e.g., GPU 0, 1, 2). device_resource_id (int): Resource ID on the device (ie. stream ID). is_legacy (bool): Whether this is from the legacy profiler. flops (int): Estimated floating point operations. is_user_annotation (bool): Whether this is a user-annotated region. metadata_json (str): Additional metadata in JSON format. Properties: cpu_time_total (float): Total CPU time in microseconds. device_time_total (float): Total device (CUDA/XPU/etc) time in microseconds. self_cpu_time_total (float): CPU time excluding child operations. self_device_time_total (float): Device time excluding child operations. self_cpu_memory_usage (int): CPU memory usage excluding child operations. self_device_memory_usage (int): Device memory usage excluding child operations. cpu_time (float): Average CPU time per call. device_time (float): Average device time per call. key (str): Key used for grouping events (usually same as name). See Also: - :class:`torch.profiler.profile`: Context manager for profiling - :class:`EventList`: List container for FunctionEvent objects with helper methods - :class:`FunctionEventAvg`: Averaged statistics over multiple FunctionEvent objects NrFrNc||_||_||_||_||_t |||_||_||_g|_ d|_ g|_ d|_ ||_ ||_||_| |_| |_| |_| |_| |_||_||_||_||_||_||n||_||_||_||_d|_d|_d|_ ||_!y)Nr/rN)"idrFr4rrr rKrBrir6rr5r3rconcrete_inputskwinputsrgrercpu_memory_usagedevice_memory_usagerOrrhrP device_indexdevice_resource_idrflopsrself_cpu_percenttotal_cpu_percenttotal_device_percent metadata_json)rr r4rBstart_usend_usrrirrgrerrrrOrrhrFrPrrrrrrrrrs r"rzFunctionEvent.__init__ds<#  "/)$,Xv$>! )3%'  1337-9*9(0    )3%5(; & ( +'2!-(0F6H  )$) 2D "!#$&!*r#c|jtjk7r td|jj t |||y)NExpected device_type to be CPU)rPr rQrVr6rXr )rr4rr s r" append_kernelzFunctionEvent.append_kernels;   z~~ - !AB B F4:;r#c|jtjk7r tdt |t s td|jtjk7r td|j j|y)zAppend a CPU child of type FunctionEvent. One is supposed to append only direct children to the event to have correct self cpu time being reported. rz$Expected child to be a FunctionEventz$Expected child device_type to be CPUN)rPr rQrV isinstancerr5rX)rchilds r"rUzFunctionEvent.append_cpu_childsj   z~~ - !AB B%/ !GH H     . !GH H   'r#c|jtjk7r tdt |t s td|jtjk7r td||_y)a$Set the immediate CPU parent of type FunctionEvent. One profiling FunctionEvent should have only one CPU parent such that the child's range interval is completely inside the parent's. We use this connection to determine the event is from top-level op or not. rz%Expected parent to be a FunctionEventz%Expected parent device_type to be CPUN)rPr rQrVrrr3)rras r"rWzFunctionEvent.set_cpu_parents]   z~~ - !AB B&-0 !HI I    / !HI I r#c|js|jtjk7ry|jt d|j Dz S)Nrc34K|]}|jywr+)rrrr s r"rsz6FunctionEvent.self_cpu_memory_usage..s+ ',E " "+ rt)rOrPr rQrrur5r(s r"self_cpu_memory_usagez#FunctionEvent.self_cpu_memory_usagesJ ==D,, >$$s+ 040A0A+ (   r#c|js|jtjk7ry|jt d|j Dz S)Nrc34K|]}|jywr+)rr$s r"rsz9FunctionEvent.self_device_memory_usage..s. */E % %. rt)rOrPr rQrrur5r(s r"self_device_memory_usagez&FunctionEvent.self_device_memory_usagesJ ==D,, >''#. 373D3D. +   r#zO`self_cuda_memory_usage` is deprecated. Use `self_device_memory_usage` instead.rc|jSr+r(r(s r"self_cuda_memory_usagez$FunctionEvent.self_cuda_memory_usages ,,,r#cr|jtjk(r|jj Syr)rPr rQrKrr(s r"rzFunctionEvent.cpu_time_totals*   z~~ -??--/ /r#c|js|jtjk7ry|jt d|j Dz S)Nrc34K|]}|jywr+)rr$s r"rsz4FunctionEvent.self_cpu_time_total..s) %*E ) rt)rOrPr rQrrur5r(s r"rpz!FunctionEvent.self_cpu_time_totalsJ ==D,, >""S) .2.?.?) &   r#c(|js |jsy|jtjk(ra|j s9t d|jDt d|jDzSt d|jDS|jtjtjtjtjfvrtd|j|jjS)Nrc34K|]}|jywr+r rrkinfos r"rsz2FunctionEvent.device_time_total..De5>>Drtc34K|]}|jywr+r)rrr=s r"rsz2FunctionEvent.device_time_total..sK-/B((Krtc34K|]}|jywr+r1r2s r"rsz2FunctionEvent.device_time_total..r4rtDExpected device_type to be CUDA, PrivateUse1, MTIA, or HPU, but got )rOrrPr rQrrur6r5CUDA PrivateUse1MTIAHPUrVrKrr(s r"rzFunctionEvent.device_time_totals ==   z~~ ->>Dt||DDsK373D3DKH Dt||DDD&& ( %Z[_[k[kZlm??--/ /r#zA`cuda_time_total` is deprecated. Use `device_time_total` instead.c|jSr+r6r(s r"cuda_time_totalzFunctionEvent.cuda_time_totals %%%r#c|js |jsy|jtjk(r)|j t d|jDz S|jtjtjtjtjfvrtd|j|j S)Nrc34K|]}|jywr+r6r$s r"rsz7FunctionEvent.self_device_time_total..s0,1''0rtr8) rOrrPr rQrrur5r9r:r;r<rVr(s r"rz$FunctionEvent.self_device_time_totals ==   z~~ -))C0595F5F0- && ( %Z[_[k[kZlm)) )r#zK`self_cuda_time_total` is deprecated. Use `self_device_time_total` instead.c|jSr+rr(s r"rz"FunctionEvent.self_cuda_time_total,s ***r#c|jSr+rr(s r"rDzFunctionEvent.key4s yyr#c |j}|j}|j}djgd|jd|j d|j d|jd|jd|jd|jjd |jjd t|jDcgc]}|jc}d |d |d|j d |jdt|j d|j"d |d|d|j$d|j&d|j(d|j*dScc}w)Nrz)rrrjoinr r4rrPrFrrKrLrMrr5rBrrrOrrhr)rrrrr s r"__repr__zFunctionEvent.__repr__8soo ** "66 y y  y  y y { y/ y$J\J\I] y^ y++, y,5 y6:ll^ yCM yNRN_N_M` ya y--. y.6 y7;7J7J6K yL y t7H7H Ie IJK yLM yNYM yZ` yal_l ym yII;  y'  y(,{{m  y4B  yCFdFWFWBXAY  yZ  y !% 5 56  y 78  y 9D}  y ES  y TgRg  y h  y    y '2  y 37..1A  y BJ  y KOJZJZI[  y \g  y hlgugufv  y wx  y !JsF )rrrrr rQrrrUrWrr%r(rrr+rrprr>rrrDrLrr#r"rr(sl9DNN 9F+P< ( !"    Y-  -   000K&  &**&U+  +  r#rc*eZdZdZddZdZdZdZy)ra Averaged profiling statistics over multiple FunctionEvent objects. FunctionEventAvg aggregates statistics from multiple FunctionEvent objects with the same key (typically same operation name). This is useful for getting average performance metrics across multiple invocations of the same operation. This class is typically created by calling :meth:`EventList.key_averages()` on a profiler's event list. Attributes: key (str): Grouping key for the events (typically operation name). count (int): Total number of events aggregated. node_id (int): Node identifier for distributed profiling (-1 if not applicable). is_async (bool): Whether the operations are asynchronous. is_remote (bool): Whether the operations occurred on a remote node. use_device (str): Device type being profiled ("cuda", "xpu", etc.). cpu_time_total (int): Accumulated total CPU time in microseconds. device_time_total (int): Accumulated total device time in microseconds. self_cpu_time_total (int): Accumulated self CPU time (excluding children) in microseconds. self_device_time_total (int): Accumulated self device time (excluding children) in microseconds. input_shapes (List[List[int]]): Input tensor shapes (requires record_shapes=true). overload_name (str): Operator overload name (requires _ExperimentalConfig(capture_overload_names=True) set). stack (List[str]): Python stack trace where the operation was called (requires with_stack=true). scope (int): at::RecordScope identifier (0=forward, 1=backward, etc.). cpu_memory_usage (int): Accumulated CPU memory usage in bytes. device_memory_usage (int): Accumulated device memory usage in bytes. self_cpu_memory_usage (int): Accumulated self CPU memory usage in bytes. self_device_memory_usage (int): Accumulated self device memory usage in bytes. cpu_children (List[FunctionEvent]): CPU child events. cpu_parent (FunctionEvent): CPU parent event. device_type (DeviceType): Type of device (CPU, CUDA, XPU, PrivateUse1, etc.). is_legacy (bool): Whether from legacy profiler. flops (int): Total floating point operations. is_user_annotation (bool): Whether this is a user-annotated region. Properties: cpu_time (float): Average CPU time per invocation. device_time (float): Average device time per invocation. 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