[Thܯ SrSSKrSSKrSSKrSSKrSSKrSSKrSSKJr SSK J r J r J r J r SSKJr SSKrSSKJr SSKJr SSKJr S S KJrJrJrJrJr S S KJrJr /S Qr!\""\R"S 5(d\"S 5\R"RFS '\""\R"S5(d\"S5\R"RFS'\"S5\R"RFS'\"S5\R"RFS'\"S5\R"RFS'\"S5\R"RFS'SSK$J%r%J&r&J'r'J(r(J)r)J*r* Sr+\RXS5r-STS\ \\.44Sjjr/Sr0SUS\1SS4Sjjr2SVS\ \\.4SS4Sjjr3SVS\ \\.4S\44Sjjr5SWSjr6SVS\ \\.4S\7\8\ 44Sjjr9SVS\ \\.4S\7\8\ 44S jjr:SVS\ \\.4SS4S!jjr;SVS\ \\.4SS4S"jjrSWS%jr?SWS&jr@SVS\ \\.4SS4S'jjrASVS\ \\.4SS4S(jjrBSVS\ \\.4S\.4S)jjrCSVS\ \\.4S\.4S*jjrDSVS\ \\.4S\.4S+jjrESVS\ \\.4S\.4S,jjrF\"S-\GS.9SVS\ \\.4S\.4S/jj5rH\"S0\GS.9SVS\ \\.4S\.4S1jj5rIS2rJSXS\ \\.4S3\1S\84S4jjrKSVS\ \\.4S\84S5jjrLSVS\ \\.4S\M\.\.44S6jjrNSYS7\1S\ \\.44S8jjrOSZS7\ S:SS4S;jjrPS9S9S9\RS4S7\ \8S<\ \8S=\8S>\.S\ \\.44 S?jjrR\"\R5\PlSSVS\ \\.44S@jjrTS[SAjrUS\SBjrVS\SCjrWSD\84SEjrXS\84SFjrY"SGSH5rZ"SISJ\Z5r[SK\ZSS4SLjr\S\Z4SMjr]"SNSO\*5r^"SPSQ\)5r_\RXSVSR\_S\ \\.44SSjj5r`g)]zKThis package adds support for device memory management implemented in CUDA.N) signature)AnyLiteralOptionalUnion) deprecated)_C) _dummy_type)Device)_get_amdsmi_device_index_get_device_index_get_nvml_device_index _lazy_initis_initialized)memorysegments) caching_allocator_alloccaching_allocator_deletecaching_allocator_enableget_per_process_memory_fractionset_per_process_memory_fraction empty_cache memory_statsmemory_stats_as_nested_dictreset_accumulated_memory_statsreset_peak_memory_statsreset_max_memory_allocatedreset_max_memory_cachedhost_memory_stats host_memory_stats_as_nested_dict#reset_accumulated_host_memory_statsreset_peak_host_memory_statsmemory_allocatedmax_memory_allocatedmemory_reservedmax_memory_reserved memory_cachedmax_memory_cachedmemory_snapshotmemory_summarylist_gpu_processes mem_get_infoget_allocator_backendCUDAPluggableAllocatorchange_current_allocatorMemPoolMemPoolContext use_mem_pool_cuda_CUDAAllocator_MemPool_MemPoolContext_cuda_beginAllocateToPool$_cuda_endAllocateCurrentStreamToPool_cuda_releasePool)r7r4r8r9r5r6cR[5 [RR5$N)rtorchr _cuda_cudaHostAllocatorI/var/www/auris/envauris/lib/python3.13/site-packages/torch/cuda/memory.py_host_allocatorrA\sL 88 + + --r?c## [RR5 Sv [RR5 g![RR5 f=f7fr;)r<r _cuda_lock_mutex_cuda_unlock_mutexr>r?r@ _free_mutexrEas= HH&  ##%##%sA(AA( A%%A(devicecUc[RR5n[U5nUc[RR U5n[ U[RR R5(a URn[ U[5(d [S5e[RRU5 [RRX5sSSS5 $!,(df  g=f)aPerform a memory allocation using the CUDA memory allocator. Memory is allocated for a given device and a stream, this function is intended to be used for interoperability with other frameworks. Allocated memory is released through :func:`~torch.cuda.caching_allocator_delete`. Args: size (int): number of bytes to be allocated. device (torch.device or int, optional): selected device. If it is ``None`` the default CUDA device is used. stream (torch.cuda.Stream or int, optional): selected stream. If is ``None`` then the default stream for the selected device is used. .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. NzrInvalid type for stream argument, must be `torch.cuda.Stream` or `int` representing a pointer to a existing stream)r<cudacurrent_devicercurrent_stream isinstancestreamsStream cuda_streamint TypeErrorrFr $_cuda_cudaCachingAllocator_raw_alloc)sizerFstreams r@rrjs&~**, v &F ~**62&%**,,3344## fc " " #    6 "xx<r?r@rrs" !!#r?c^^/mUU4Sjm[US9nT"SU5 TR5 [R"T5$)aReturn a dictionary of CUDA memory allocator statistics for a given device. The return value of this function is a dictionary of statistics, each of which is a non-negative integer. Core statistics: - ``"allocated.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: number of allocation requests received by the memory allocator. - ``"allocated_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: amount of allocated memory. - ``"segment.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: number of reserved segments from ``cudaMalloc()``. - ``"reserved_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: amount of reserved memory. - ``"active.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: number of active memory blocks. - ``"active_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: amount of active memory. - ``"inactive_split.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: number of inactive, non-releasable memory blocks. - ``"inactive_split_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: amount of inactive, non-releasable memory. For these core statistics, values are broken down as follows. Pool type: - ``all``: combined statistics across all memory pools. - ``large_pool``: statistics for the large allocation pool (as of October 2019, for size >= 1MB allocations). - ``small_pool``: statistics for the small allocation pool (as of October 2019, for size < 1MB allocations). Metric type: - ``current``: current value of this metric. - ``peak``: maximum value of this metric. - ``allocated``: historical total increase in this metric. - ``freed``: historical total decrease in this metric. In addition to the core statistics, we also provide some simple event counters: - ``"num_alloc_retries"``: number of failed ``cudaMalloc`` calls that result in a cache flush and retry. - ``"num_ooms"``: number of out-of-memory errors thrown. - ``"num_sync_all_streams"``: number of ``synchronize_and_free_events`` calls. - ``"num_device_alloc"``: number of CUDA allocation calls. This includes both cuMemMap and cudaMalloc. - ``"num_device_free"``: number of CUDA free calls. This includes both cuMemUnmap and cudaFree. The caching allocator can be configured via ENV to not split blocks larger than a defined size (see Memory Management section of the Cuda Semantics documentation). This helps avoid memory fragmentation but may have a performance penalty. Additional outputs to assist with tuning and evaluating impact: - ``"max_split_size"``: blocks above this size will not be split. - ``"oversize_allocations.{current,peak,allocated,freed}"``: number of over-size allocation requests received by the memory allocator. - ``"oversize_segments.{current,peak,allocated,freed}"``: number of over-size reserved segments from ``cudaMalloc()``. The caching allocator can be configured via ENV to round memory allocations in order to reduce fragmentation. Sometimes the overhead from rounding can be higher than the fragmentation it helps reduce. The following stat can be used to check if rounding adds too much overhead: - ``"requested_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: memory requested by client code, compare this with allocated_bytes to check if allocation rounding adds too much overhead. Args: device (torch.device or int, optional): selected device. Returns statistics for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. .. note:: With :ref:`backend:cudaMallocAsync`, some stats are not meaningful, and are always reported as zero. c>[U[5(a9[U5S:aUS- nUR5Hup#T"X-U5 M gTR X45 gNr.rKdictlenitemsappendprefixobjkv_recurse_add_to_resultresults r@rs,memory_stats.._recurse_add_to_result:R c4 6{Q#  &vz15$ MM6- (r?rb)rsort collections OrderedDict)rFstatsrsrts @@r@rrs@nF) (v 6E2u% KKM  " "6 **r?cv[5(d0$[USS9n[RR U5$)zMReturn the result of :func:`~torch.cuda.memory_stats` as a nested dictionary.Toptional)rrr<r _cuda_memoryStatsrbs r@rrJs1    v 5F 88 % %f --r?cT[USS9n[RRU5$)aQReset the "accumulated" (historical) stats tracked by the CUDA memory allocator. See :func:`~torch.cuda.memory_stats` for details. Accumulated stats correspond to the `"allocated"` and `"freed"` keys in each individual stat dict, as well as `"num_alloc_retries"` and `"num_ooms"`. Args: device (torch.device or int, optional): selected device. Returns statistic for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. Tr})rr<r !_cuda_resetAccumulatedMemoryStatsrbs r@rrRs% v 5F 88 5 5f ==r?cT[USS9n[RRU5$)aReset the "peak" stats tracked by the CUDA memory allocator. See :func:`~torch.cuda.memory_stats` for details. Peak stats correspond to the `"peak"` key in each individual stat dict. Args: device (torch.device or int, optional): selected device. Returns statistic for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. Tr})rr<r _cuda_resetPeakMemoryStatsrbs r@rrfs%v 5F 88 . .v 66r?c^^/mUU4Sjm[5nT"SU5 TR5 [R"T5$)aReturn a dictionary of CUDA memory allocator statistics for a given device. The return value of this function is a dictionary of statistics, each of which is a non-negative integer. Core statistics: - ``"allocated.{current,peak,allocated,freed}"``: number of allocation requests received by the memory allocator. - ``"allocated_bytes.{current,peak,allocated,freed}"``: amount of allocated memory. - ``"segment.{current,peak,allocated,freed}"``: number of reserved segments from ``cudaMalloc()``. - ``"reserved_bytes.{current,peak,allocated,freed}"``: amount of reserved memory. For these core statistics, values are broken down as follows. Metric type: - ``current``: current value of this metric. - ``peak``: maximum value of this metric. - ``allocated``: historical total increase in this metric. - ``freed``: historical total decrease in this metric. In addition to the core statistics, we also provide some simple event counters: - ``"num_host_alloc"``: number of CUDA allocation calls. This includes both cudaHostAlloc and cudaHostRegister. - ``"num_host_free"``: number of CUDA free calls. This includes both cudaHostFree and cudaHostUnregister. Finally, we also provide some simple timing counters: - ``"host_alloc_time.{total,max,min,count,avg}"``: timing of allocation requests going through CUDA calls. - ``"host_free_time.{total,max,min,count,avg}"``: timing of free requests going through CUDA calls. For these timing statistics, values are broken down as follows. Metric type: - ``total``: total time spent. - ``max``: maximum value per call. - ``min``: minimum value per call. - ``count``: number of times it was called. - ``avg``: average time per call. c>[U[5(a9[U5S:aUS- nUR5Hup#T"X-U5 M gTR X45 grgrirns r@rs1host_memory_stats.._recurse_add_to_resultrvr?rw)r!rxryrz)r{rsrts @@r@r r ys>fF) - .E2u% KKM  " "6 **r?c`[5(d0$[RR5$)zRReturn the result of :func:`~torch.cuda.host_memory_stats` as a nested dictionary.)rr<r _cuda_hostMemoryStatsr>r?r@r!r!s#    88 ) ) ++r?c>[RR5$)zReset the "accumulated" (historical) stats tracked by the host memory allocator. See :func:`~torch.cuda.host_memory_stats` for details. Accumulated stats correspond to the `"allocated"` and `"freed"` keys in each individual stat dict. )r<r %_cuda_resetAccumulatedHostMemoryStatsr>r?r@r"r"s 88 9 9 ;;r?c>[RR5$)zReset the "peak" stats tracked by the host memory allocator. See :func:`~torch.cuda.host_memory_stats` for details. Peak stats correspond to the `"peak"` key in each individual stat dict. )r<r _cuda_resetPeakHostMemoryStatsr>r?r@r#r#s 88 2 2 44r?cJ[R"S[5 [US9$)aSReset the starting point in tracking maximum GPU memory occupied by tensors for a given device. See :func:`~torch.cuda.max_memory_allocated` for details. Args: device (torch.device or int, optional): selected device. Returns statistic for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). .. warning:: This function now calls :func:`~torch.cuda.reset_peak_memory_stats`, which resets /all/ peak memory stats. .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. zytorch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats.rbwarningswarn FutureWarningrrbs r@rr%$ MM 0 #& 11r?cJ[R"S[5 [US9$)a]Reset the starting point in tracking maximum GPU memory managed by the caching allocator for a given device. See :func:`~torch.cuda.max_memory_cached` for details. Args: device (torch.device or int, optional): selected device. Returns statistic for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). .. warning:: This function now calls :func:`~torch.cuda.reset_peak_memory_stats`, which resets /all/ peak memory stats. .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. zvtorch.cuda.reset_max_memory_cached now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats.rbrrbs r@rrrr?c4[US9RSS5$)a3Return the current GPU memory occupied by tensors in bytes for a given device. Args: device (torch.device or int, optional): selected device. Returns statistic for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). .. note:: This is likely less than the amount shown in `nvidia-smi` since some unused memory can be held by the caching allocator and some context needs to be created on GPU. See :ref:`cuda-memory-management` for more details about GPU memory management. rbzallocated_bytes.all.currentrrgetrbs r@r$r$ s v & * *+H! LLr?c4[US9RSS5$)aReturn the maximum GPU memory occupied by tensors in bytes for a given device. By default, this returns the peak allocated memory since the beginning of this program. :func:`~torch.cuda.reset_peak_memory_stats` can be used to reset the starting point in tracking this metric. For example, these two functions can measure the peak allocated memory usage of each iteration in a training loop. Args: device (torch.device or int, optional): selected device. Returns statistic for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. rbzallocated_bytes.all.peakrrrbs r@r%r%s$ v & * *+Eq IIr?c4[US9RSS5$)aReturn the current GPU memory managed by the caching allocator in bytes for a given device. Args: device (torch.device or int, optional): selected device. Returns statistic for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. rbzreserved_bytes.all.currentrrrbs r@r&r&1s v & * *+G KKr?c4[US9RSS5$)aReturn the maximum GPU memory managed by the caching allocator in bytes for a given device. By default, this returns the peak cached memory since the beginning of this program. :func:`~torch.cuda.reset_peak_memory_stats` can be used to reset the starting point in tracking this metric. For example, these two functions can measure the peak cached memory amount of each iteration in a training loop. Args: device (torch.device or int, optional): selected device. Returns statistic for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. rbzreserved_bytes.all.peakrrrbs r@r'r'@s$ v & * *+Da HHr?zK`torch.cuda.memory_cached` has been renamed to `torch.cuda.memory_reserved`)categoryc[US9$)z4Deprecated; see :func:`~torch.cuda.memory_reserved`.rb)r&rbs r@r(r(Us & ))r?zS`torch.cuda.max_memory_cached` has been renamed to `torch.cuda.max_memory_reserved`c[US9$)z8Deprecated; see :func:`~torch.cuda.max_memory_reserved`.rb)r'rbs r@r)r)^s f --r?cD[RR5S$)aReturn a snapshot of the CUDA memory allocator state across all devices. Interpreting the output of this function requires familiarity with the memory allocator internals. .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. r)r<r _cuda_memorySnapshotr>r?r@r*r*gs 88 ( ( *: 66r? abbreviatedc[USS9n[US9nSnSnSSU4SS U4S S U4S S U4SSU4SSU4SSU4SSU4SSU4/ n/nURS5 URS5 URS5 URS5 URS5 URS5 UHupxn URS5 SU4/n U(d"U RS5 U RS5 S uppU HzunnUS!-U-S!-nUUS"-nUUS#-nUUS$-nUUS%-nU cUn Un Un UnURS&US'S(U "UU 5S(U "UU 5S(U "UU 5S(U "UU5S&3 5 M| M S)S*U4S+S,U4/nUHzupxn URS5 US!-nUUS"-nUUS#-nUUS$-nUUS%-nURS&US'S(U "UU5S(U "UU5S(U "UU5S(U "UU5S&3 5 M| URS5 S-US..nUR5HunnUUUR S!S/5'M S0S1R U5R "S30UD6-S2-$)4acReturn a human-readable printout of the current memory allocator statistics for a given device. This can be useful to display periodically during training, or when handling out-of-memory exceptions. Args: device (torch.device or int, optional): selected device. Returns printout for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). abbreviated (bool, optional): whether to return an abbreviated summary (default: False). .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. Tr}rbcd/SQnUSnUSSHnUS:a OUnUS-nUS-nM USSU3$)N)zB KiBMiBGiBTiBPiBrr i i6d r>)szpref_szprefixesro new_prefixs r@ _format_size$memory_summary.._format_sizesW=!"12,J#F 4KB tOG ' R&""r?cf/SQnUSnUSSHnUS:a OUnUS-nUS-nM USSUS3$)N)rKMrr iq i7drr>)cntpref_cntrrors r@ _format_count%memory_summary.._format_countsZ"!"12,J*$F DLC  H ' b6(!$$r?allocated_byteszAllocated memory active_bytesz Active memoryrequested_byteszRequested memoryreserved_byteszGPU reserved memoryinactive_split_byteszNon-releasable memory allocation Allocationsactivez Active allocssegmentzGPU reserved segmentsinactive_splitzNon-releasable allocszK===========================================================================z= {_:16} PyTorch CUDA memory summary, device ID {device:<17d} zK---------------------------------------------------------------------------zX {_:9} CUDA OOMs: {num_ooms:<12d} | {_:6} cudaMalloc retries: {num_alloc_retries:<8d} zK Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed all) large_poolz from large pool) small_poolz from small pool)NNNNrhcurrentpeak allocatedfreedrz<21z | oversize_allocationszOversize allocationsoversize_segmentszOversize GPU segmentsrw)_rF-|z| |z| r>)rrrmrlreplacejoinformat)rFrr{rrmetrics_to_displaylines metric_key metric_name formatter submetricscurrent_prefval peak_prefvalallocated_prefval freed_prefval submetric_keysubmetric_namerorrrrfmt_dictrqrrs r@r+r+ts`"v 5F  'E # % . = ,7 . = 0,? !8,G }m4 ?M2 +]; 2MB  E LL LLPQ LL LLb LL LLU/A*  Xk*+    E F   E FK G'8.8 )M>#% 5;FFY./G&)Df{23I&7*+E&")# $-! % LLN3's9Wo+N*OsS\]acoSpRqqtY(9:;3yP]?^>__`b .8/AD !7G 5}E /A*  Xc!*+Vf_%&;./ fw&'  C Igw$?#@IdTXDYCZZ]I./s9UE3J2K1 N /A LL6*H 1()3$% U#**6X6 6 >>r?c[RR(dJSSKnSSKJn UR 5 [U5nURU5nURU5nOFSSK nUR5 [U5nUR5UnURU5n/nUR!S U35 [#U5S:XaUR!S 5 UHzn[RR(dUR$S - nUR&n O"WR)X75n U S S S - nU Sn UR!SU SSUSS35 M| SR-U5$![a gf=f!Ua gf=f![a gf=f!URa gf=f!URa gf=f![*a Un Nf=f)aReturn a human-readable printout of the running processes and their GPU memory use for a given device. This can be useful to display periodically during training, or when handling out-of-memory exceptions. Args: device (torch.device or int, optional): selected device. Returns printout for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). rNz.pynvml module not found, please install pynvml)NVMLError_DriverNotLoadedz-cuda driver can't be loaded, is cuda enabled?z.amdsmi module not found, please install amdsmiz1amdsmi driver can't be loaded, is ROCm installed?z-amdsmi cannot list processes from other userszGPU:zno processes are runningi memory_usagevram_mempidzprocess z>10dz uses z>12.3fz MB GPU memory )r<versionhippynvmlModuleNotFoundErrorrnvmlInitrnvmlDeviceGetHandleByIndex$nvmlDeviceGetComputeRunningProcessesamdsmi amdsmi_initAmdSmiExceptionr amdsmi_get_processor_handlesamdsmi_get_gpu_process_listrmrk usedGpuMemoryramdsmi_get_gpu_process_infoAttributeErrorr) rFrrhandleprocsrrpmemr proc_infos r@r,r,s ==   D  5 C OO (/226:;;FC D  G    *&1 C88:6BF66v>E E LL4x! 5zQ /0 }}  //[1C%%C ">>vI N+J7;GCE"C xDzF|>JK 99U a# DC D ) CB C# DC D%% GF G%% CB C"   sjE7F+F0F# $F9'G7 FFFF F F #F65F69G  G  GGcUc[RR5n[USS9n[RR 5R U5$)aReturn the global free and total GPU memory for a given device using cudaMemGetInfo. Args: device (torch.device or int or str, optional): selected device. Returns statistic for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default) or if the device index is not specified. .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. Tr})r<rHrIrcudartcudaMemGetInforbs r@r-r-4sE~**, v 5F ::    - -f 55r?enabledc8[R"UUUUU5 gr;)r "_cuda_record_memory_history_legacy)rrecord_contexttrace_alloc_max_entriestrace_alloc_record_contextrFrecord_context_cpps r@_record_memory_history_legacyrGs!))" r?r)Nstaterch[U[5(a[U/UQ70UD6$[U/UQ70UD6$)aEnable recording of stack traces associated with memory allocations, so you can tell what allocated any piece of memory in :func:`torch.cuda.memory._snapshot()`. In addition too keeping stack traces with each current allocation and free, this will also enable recording of a history of all alloc/free events. Use :func:`torch.cuda.memory._snapshot()` to retrieve this information, and the tools in `_memory_viz.py` to visualize snapshots. The Python trace collection is fast (2us per trace), so you may consider enabling this on production jobs if you anticipate ever having to debug memory issues. C++ trace collection is also fast (~50ns/frame), which for many typical programs works out to ~2us per trace, but can vary depending on stack depth. Args: enabled (Literal[None, "state", "all"], optional): `None`, disable recording memory history. `"state"`, keep information for currenly allocated memory. `"all"`, additionally keep a history of all alloc/free calls. Defaults to "all". context (Literal[None, "state", "alloc", "all"], optional): `None`, Do not record any tracebacks. `"state"`, Record tracebacks for currently allocated memory. `"alloc"`, additionally keep tracebacks for alloc calls. `"all"`, additionally keep tracebacks for free calls. Defaults to "all". stacks (Literal["python", "all"], optional): `"python"`, include Python, TorchScript, and inductor frames in tracebacks `"all"`, additionally include C++ frames Defaults to "all". max_entries (int, optional): Keep a maximum of `max_entries` alloc/free events in the recorded history recorded. )rKboolr_record_memory_history_impl)rargskwargss r@_record_memory_historyrXs<N'4  ,WFtFvFF*7DTDVDDr?contextstacks max_entriesc2[R"XX#5 gr;)r _cuda_record_memory_history)rr r r rFs r@rrs""7VIr?c,[R"5$)a Save a snapshot of CUDA memory state at the time it was called. The state is represented as a dictionary with the following structure. .. code-block:: python class Snapshot(TypedDict): segments : List[Segment] device_traces: List[List[TraceEntry]] class Segment(TypedDict): # Segments are memory returned from a cudaMalloc call. # The size of reserved memory is the sum of all Segments. # Segments are cached and reused for future allocations. # If the reuse is smaller than the segment, the segment # is split into more then one Block. # empty_cache() frees Segments that are entirely inactive. address: int total_size: int # cudaMalloc'd size of segment stream: int segment_type: Literal['small', 'large'] # 'large' (>1MB) allocated_size: int # size of memory in use active_size: int # size of memory in use or in active_awaiting_free state blocks : List[Block] class Block(TypedDict): # A piece of memory returned from the allocator, or # current cached but inactive. size: int requested_size: int # size requested during malloc, may be smaller than # size due to rounding address: int state: Literal['active_allocated', # used by a tensor 'active_awaiting_free', # waiting for another stream to finish using # this, then it will become free 'inactive',] # free for reuse frames: List[Frame] # stack trace from where the allocation occurred class Frame(TypedDict): filename: str line: int name: str class TraceEntry(TypedDict): # When `torch.cuda.memory._record_memory_history()` is enabled, # the snapshot will contain TraceEntry objects that record each # action the allocator took. action: Literal[ 'alloc' # memory allocated 'free_requested', # the allocated received a call to free memory 'free_completed', # the memory that was requested to be freed is now # able to be used in future allocation calls 'segment_alloc', # the caching allocator ask cudaMalloc for more memory # and added it as a segment in its cache 'segment_free', # the caching allocator called cudaFree to return memory # to cuda possibly trying free up memory to # allocate more segments or because empty_caches was called 'oom', # the allocator threw an OOM exception. 'size' is # the requested number of bytes that did not succeed 'snapshot' # the allocator generated a memory snapshot # useful to coorelate a previously taken # snapshot with this trace ] addr: int # not present for OOM frames: List[Frame] size: int stream: int device_free: int # only present for OOM, the amount of # memory cuda still reports to be free Returns: The Snapshot dictionary object )r rrbs r@ _snapshotrsT  " " $$r?c[5n[US5n[R"X5 SSS5 g!,(df  g=f)a  Save a pickled version of the `torch.memory._snapshot()` dictionary to a file. This file can be opened by the interactive snapshot viewer at pytorch.org/memory_viz Args: filename (str, optional): Name of the file to create. Defaults to "dump_snapshot.pickle". wbN)ropenpickledump)filenamesfs r@_dump_snapshotrs/  A h  A   s 7 AcUc [5n[US5nUR[U55 SSS5 g!,(df  g=fNw)rrwrite _segmentsrsnapshotrs r@_save_segment_usager s8; h   (#$    > A cUc [5n[US5nUR[U55 SSS5 g!,(df  g=fr)rrr_memoryrs r@_save_memory_usager$s8; h  !"   r!envc@[RRU5$r;)r<r 1_cuda_cudaCachingAllocator_set_allocator_settings)r%s r@_set_allocator_settingsr(s 88 E Ec JJr?c>[RR5$)aQReturn a string describing the active allocator backend as set by ``PYTORCH_CUDA_ALLOC_CONF``. Currently available backends are ``native`` (PyTorch's native caching allocator) and `cudaMallocAsync`` (CUDA's built-in asynchronous allocator). .. note:: See :ref:`cuda-memory-management` for details on choosing the allocator backend. )r<r _cuda_getAllocatorBackendr>r?r@r.r.s 88 - - //r?cT\rSrSrSrS\R R4SjrSr Sr g)_CUDAAllocatori z-Wrapper over internal CUDA memory allocators. allocatorcXlgr; _allocator)selfr-s r@__init___CUDAAllocator.__init__s#r?cUR$r;r/)r1s r@r-_CUDAAllocator.allocators r?r/N) __name__ __module__ __qualname____firstlineno____doc__r<r r4r2r-__static_attributes__r>r?r@r,r, s 8$%((">">$r?r,c.\rSrSrSrS\S\S\4SjrSrg) r/iz,CUDA memory allocator loaded from a so file.path_to_so_file alloc_fn_name free_fn_namecl[R"U5n[R"[XB5[R5R n[R"[XC5[R5R nUceUce[ RRXV5Ul g)aZMemory allocators are compiled in .so files and loaded dynamically using ctypes. To change the active allocator use the :func:`torch.memory.cuda.change_current_allocator` function. Args: path_to_so_file(str): Path in the filesystem to the `.so` file containing the allocator functions alloc_fn_name(str): Name of the function to perform the memory allocation in the so file. The signature must be: void* alloc_fn_name(ssize_t size, int device, cudaStream_t stream); free_fn_name(str): Name of the function to perform the memory release in the so file. The signature must be: void free_fn_name(void* ptr, size_t size, cudaStream_t stream); .. warning:: This is currently supported only in unix OSs .. note:: See :ref:`cuda-memory-management` for details on creating and using a custom allocator N) ctypesCDLLcastgetattrc_void_prWr<r _cuda_customAllocatorr0)r1r=r>r?r-alloc_fnfree_fns r@r2CUDAPluggableAllocator.__init__s~*KK0 ;;wy@&//RXX++gi>PVV###"""((88Kr?r/N)r6r7r8r9r:strr2r;r>r?r@r/r/s#7LLCLsLr?r/r-c^[RRUR55 g)a`Change the currently used memory allocator to be the one provided. If the current allocator has already been used/initialized, this function will error. Args: allocator (torch.cuda.memory._CUDAAllocator): allocator to be set as the active one. .. note:: See :ref:`cuda-memory-management` for details on creating and using a custom allocator N)r<r _cuda_changeCurrentAllocatorr-)r-s r@r0r05s HH)))*=*=*?@r?cP[[RR55$)zReturn the allocator being currently used. .. note:: See :ref:`cuda-memory-management` for details on creating and using a custom allocator )r,r<r _cuda_getAllocatorr>r?r@_get_current_allocatorrOCs %((557 88r?cR^\rSrSrSrS\4U4Sjjr\S\\4Sj5r Sr U=r $)r2iLzMemPoolContext holds the currently active pool and stashes the previous pool. On deletion it makes the previous pool active. Args: pool(torch.cuda.MemPool): a MemPool object to be made active so that allocations route to this pool. poolc$>[TU]U5 gr;superr2)r1rQ __class__s r@r2MemPoolContext.__init__Vs r?rXc,[R"5$)zReturns the active MemPool)r6 active_poolr>r?r@rXMemPoolContext.active_poolYs**,,r?r>) r6r7r8r9r:r5r2 staticmethodrrXr; __classcell__rUs@r@r2r2Ls4X-(+--r?r2c^\rSrSrSrS S\\4U4Sjjjr\S\ \ \ 44U4Sjj5r \S\\4U4Sjj5r S\ 4U4Sjjr S rS rU=r$) r1i_aMemPool represents a pool of memory in a caching allocator. Currently, it's just the ID of the pool object maintained in the CUDACachingAllocator. Args: allocator(torch._C._cuda_CUDAAllocator, optional): a torch._C._cuda_CUDAAllocator object that can be used to define how memory gets allocated in the pool. If :attr:`allocator` is ``None`` (default), memory allocation follows the default/ current configuration of the CUDACachingAllocator. r-c&>[TU]US5 g)NTrS)r1r-rUs r@r2MemPool.__init__ls D)r?rXc>[TU]$)z3Returns the ID of this pool as a tuple of two ints.)rTidr1rUs r@ra MemPool.idoswzr?c>[TU]$)z9Returns the allocator this MemPool routes allocations to.)rTr-rbs r@r-MemPool.allocatortsw  r?c >[TU]5$)z)Returns the reference count of this pool.)rT use_countrbs r@rgMemPool.use_countysw ""r?ch[U5n[RR5nAU$!Af=f)aReturn a snapshot of the CUDA memory allocator pool state across all devices. Interpreting the output of this function requires familiarity with the memory allocator internals. .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. )r2r<rHr*)r1ctxrs r@rMemPool.snapshot}s2  &Czz113Hs).1r>r;)r6r7r8r9r:rr4r2propertytuplerOrar-rgrr;r[r\s@r@r1r1_sy *(+>"?**E#s(O!8$78!!#3#r?r1rQc#d# [U5nUc[RR5O [ U5n[ X0R 5 Sv [X0R 5 [X0R 5 Ag![X0R 5 [X0R 5 Af=f7f)a{A context manager that routes allocations to a given pool. Args: pool(torch.cuda.MemPool): a MemPool object to be made active so that allocations route to this pool. device (torch.device or int, optional): selected device. Uses MemPool on the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). N) r2r<rHrIrr7rar8r9)rQrFrj device_indexs r@r3r3s  C'-~ !!#;LV;TlGG4 ,\77C,0  -\77C,0 sA B0B,B0-B--B0)NN)Tr;)rXN)NF)Tr FNF)r)zdump_snapshot.pickle)z output.svgN)ar:ry contextlibrArsysrinspectrtypingrrrrtyping_extensionsrr<r torch._utilsr torch.typesr rwr rrrr _memory_vizrr#rr__all__hasattr__dict__torch._Cr7r4r8r9r5r6rAcontextmanagerrErOrrrrrr\rrrjrJrrrrr r!r"r#rrr$r%r&r'rr(r)r*r+r,rmr-rrmaxsizer __signature__rrr r$r(r.r,r/r0rOr2r1r3r>r?r@rsR 00( $B! Huxx.///:;P/QEHH+,uxx$$$/ $;EHHj!+67H+IEHH'(5@#6EHH12AL.AEHH<=.99L-MEHH)*.  &&!K% *<!KH<":D:D:,07FCK(7 7: 4E&#+,> 4% 4 $f+vs{+f+tCH~f+R.fck(:.d3PS8n.>5+=>>(7E&#+$67$7&B+4S>B+J,$sCx.,<52uVS['92T242E&#+$62$24MU63;/M3M"Jvs{!3JsJ* LE&#+. L# LIfck 2IcI* Q *% ,** *  Y .eFCK0.C. . 7|?5-|?4|?TW|?~>uVS[1>S>B6vs{+6uS#X6*$!%   &#+  $.3*E ) **E *E\#"{{!% J c]J c]J J J &#+  J(11L'M$J%eFCK(J%Z %#KK 0s 0L^L@ A A4 A99-_-&.h.b wfck(:r?