U 9%e@sddlZddlZddlZddlZddlZddlZddlmZddlm Z m Z m Z m Z m Z ddlZddlmZddlmZddlmZmZmZmZddlmZmZdd lmZd d d d ddddddddddddddddddd gZeejd!sed!ejj d!<d"d#Z!ej"d$d%Z#d^e ee$fd&d'd Z%d(d Z&d_e ee$fdd)d*d Z'dd+d,d Z(d`e ee$fe e)e fd)d-dZ*dae ee$fe e)e fd)d.dZ+dbe ee$fdd)d/dZ,dce ee$fdd)d0dZ-dde ee$fdd)d1dZ.dee ee$fdd)d2dZ/dfe ee$fe$d)d3dZ0dge ee$fe$d)d4dZ1dhe ee$fe$d)d5dZ2die ee$fe$d)d6dZ3dje ee$fe$d)d7dZ4dke ee$fe$d)d8dZ5d9dZ6dle ee$fe7e)d;ddZ:doe7e ee$fd@dAdBZ;dpdDdEZee>e<_?dqe ee$fd&dIdJZ@drdLdMZAdsdOdPZBdtdQdRZCe)dSdTdUZDe)d+dVdZEGdWdXdXZFGdYddeFZGeFddZd[d ZHeFd+d\d]ZIdS)uN) signature)AnyDictOptionalTupleUnion)_C)Device)_get_device_index_get_nvml_device_index _lazy_initis_initialized)memorysegments) _dummy_typecaching_allocator_alloccaching_allocator_deleteset_per_process_memory_fraction empty_cache memory_statsmemory_stats_as_nested_dictreset_accumulated_memory_statsreset_peak_memory_statsreset_max_memory_allocatedreset_max_memory_cachedmemory_allocatedmax_memory_allocatedmemory_reservedmax_memory_reserved memory_cachedmax_memory_cachedmemory_snapshotmemory_summarylist_gpu_processes mem_get_infoget_allocator_backendCUDAPluggableAllocatorchange_current_allocator_cuda_CUDAAllocatorcCsttjSN)r torchrZ_cuda_cudaHostAllocatorr,r,P/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/cuda/memory.py_host_allocator4sr.ccs&tjz dVW5tjXdSr*)r+rZ_cuda_lock_mutexZ_cuda_unlock_mutexr,r,r,r- _free_mutex9s  r/devicec Cs|dkrtj}t|}|dkr.tj|}t|tjjjrD|j}t|t sVt dtj |tj ||W5QRSQRXdS)aPerforms 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_devicer Zcurrent_stream isinstancestreamsZStreamZ cuda_streamint TypeErrorr1rZ$_cuda_cudaCachingAllocator_raw_alloc)sizer1streamr,r,r-rBs   cCstj|dS)aDeletes memory allocated using the CUDA memory allocator. Memory allocated with :func:`~torch.cuda.caching_allocator_alloc`. is freed here. The associated device and stream are tracked inside the allocator. Args: mem_ptr (int): memory address to be freed by the allocator. .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. N)r+rZ%_cuda_cudaCachingAllocator_raw_delete)Zmem_ptrr,r,r-rfs)r1returncCsdt|dkrtj}t|}t|ts2td|dksB|dkrRtd|dtj ||dS)aSet memory fraction for a process. The fraction is used to limit an caching allocator to allocated memory on a CUDA device. The allowed value equals the total visible memory multiplied fraction. If trying to allocate more than the allowed value in a process, will raise an out of memory error in allocator. Args: fraction(float): Range: 0~1. Allowed memory equals total_memory * fraction. device (torch.device or int, optional): selected device. If it is ``None`` the default CUDA device is used. .. note:: In general, the total available free memory is less than the total capacity. Nz3Invalid type for fraction argument, must be `float`rr zInvalid fraction value: z. Allowed range: 0~1) r r+r2r3r r4floatr7 ValueErrorrZ_cuda_setMemoryFraction)fractionr1r,r,r-rws  )r:cCstrtjdS)aReleases all unoccupied cached memory currently held by the caching allocator so that those can be used in other GPU application and visible in `nvidia-smi`. .. note:: :func:`~torch.cuda.empty_cache` doesn't increase the amount of GPU memory available for PyTorch. However, it may help reduce fragmentation of GPU memory in certain cases. See :ref:`cuda-memory-management` for more details about GPU memory management. N)rr+rZ_cuda_emptyCacher,r,r,r-rs cs8gfddt|d}d|tS)aReturns 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. 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. csRt|tr@t|dkr|d7}|D]\}}|||q&n||fdS)Nr.)r4dictlenitemsappend)prefixobjkv_recurse_add_to_resultresultr,r-rHs   z,memory_stats.._recurse_add_to_resultr0)rsort collections OrderedDict)r1statsr,rGr-rs S  cCs"ts iSt|dd}tj|S)zNReturns the result of :func:`~torch.cuda.memory_stats` as a nested dictionary.Toptional)rr r+rZ_cuda_memoryStatsr0r,r,r-rs cCst|dd}tj|S)a~Resets 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. TrO)r r+rZ!_cuda_resetAccumulatedMemoryStatsr0r,r,r-rs cCst|dd}tj|S)aResets 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. TrO)r r+rZ_cuda_resetPeakMemoryStatsr0r,r,r-r#s cCstdtt|dS)aResets 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.r0warningswarn FutureWarningrr0r,r,r-r6s cCstdtt|dS)aResets 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.r0rQr0r,r,r-rQs cCst|dddS)a`Returns 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. r0zallocated_bytes.all.currentrrgetr0r,r,r-rlscCst|dddS)aReturns 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. r0zallocated_bytes.all.peakrrUr0r,r,r-r~scCst|dddS)aReturns 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. r0zreserved_bytes.all.currentrrUr0r,r,r-rs cCst|dddS)aReturns 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. r0zreserved_bytes.all.peakrrUr0r,r,r-rscCstdtt|dS)z4Deprecated; see :func:`~torch.cuda.memory_reserved`.zGtorch.cuda.memory_cached has been renamed to torch.cuda.memory_reservedr0)rRrSrTrr0r,r,r-r s cCstdtt|dS)z8Deprecated; see :func:`~torch.cuda.max_memory_reserved`.zOtorch.cuda.max_memory_cached has been renamed to torch.cuda.max_memory_reservedr0)rRrSrTrr0r,r,r-r!s cCstjdS)a)Returns 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"s F)r1 abbreviatedr:c Cs|t|dd}t|d}dd}dd}dd |fd d |fd d |fdd|fdd|fdd|fdd|fdd|fdd|fg }g}|d|d|d|d|d|d|D]\}}} |dd|fg} |s| d | d!d"\} } } }| D]\}}|d#|d#}||d$}||d%}||d&}||d'}| d(kr^|} |} |} |}|d)|| || | || | || | ||qqd*d+|fd,d-|fg}|D]~\}}} |d|d#}||d$}||d%}||d&}||d'}|d)|| ||| ||| ||| ||q|dd.|d/}|D]\}}|||d#d0<qFd1d2|jf|d3S)4aReturns a human-readable printout of the current memory allocator statistics for a given device. This can be useful to display periodically during training, or when handling out-of-memory exceptions. Args: device (torch.device or int, optional): selected device. Returns printout for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). abbreviated (bool, optional): whether to return an abbreviated summary (default: False). .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. TrOr0cSsZddddddg}|d}|ddD]$}|d kr4qJ|}|d }|d }q$|d d |S) NzB KiBMiBGiBTiBPiBrr i iZ6d r,)szZpref_szprefixesrC new_prefixr,r,r- _format_sizes z$memory_summary.._format_sizecSsVdddg}|d}|ddD]$}|dkr.qD|}|d}|d}q|dd|dS) Nr^KMrr iq iZ7dr,)ZcntZpref_cntr`rCrar,r,r- _format_counts  z%memory_summary.._format_countZallocated_byteszAllocated memoryZ active_bytesz Active memoryZrequested_byteszRequested memoryZreserved_byteszGPU reserved memoryZinactive_split_byteszNon-releasable memoryZ allocationZ Allocationsactivez Active allocssegmentzGPU reserved segmentsZinactive_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)Z large_poolz from large pool)Z small_poolz from small pool)NNNNr>currentpeak allocatedfreedNz {:<21} | {} | {} | {} | {} Zoversize_allocationszOversize allocationsZoversize_segmentszOversize GPU segmentsrJ)_r1-|z| |z| )r rrBformatrAreplacejoin)r1rXrNrbreZmetrics_to_displaylinesZ metric_keyZ metric_name formatterZ submetricsZcurrent_prefvalZ peak_prefvalZallocated_prefvalZ freed_prefvalZ submetric_keyZsubmetric_namerCrirjrkrlZfmt_dictrErFr,r,r-r#s                            cCsz ddl}Wntk r"YdSXddlm}z |Wn|k rRYdSXt|}||}||}g}|d|t|dkr|d|D],}|j d}|d |j d d |d d qd |S)aReturns 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?zGPU:zno processes are runningizprocess z>10dz uses z>12.3fz MB GPU memory ) pynvmlModuleNotFoundErrorruZnvmlInitr ZnvmlDeviceGetHandleByIndexZ$nvmlDeviceGetComputeRunningProcessesrBr@Z usedGpuMemorypidrr)r1rwruhandleZprocsrspZmemr,r,r-r$cs(          cCs*|dkrtj}t|}tj|S)aReturns the global free and total GPU memory for a given device using cudaMemGetInfo. 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. N)r+r2r3r ZcudartZcudaMemGetInfor0r,r,r-r%s  T)enabledr1cCst|||||dSr*)rZ"_cuda_record_memory_history_legacy)r|Zrecord_contextZtrace_alloc_max_entriesZtrace_alloc_record_contextr1Zrecord_context_cppr,r,r-_record_memory_history_legacysr}rhcOs.t|trt|f||St|f||SdS)a Enables 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. N)r4boolr}_record_memory_history_impl)r|argskwargsr,r,r-_record_memory_historys' rr|contextstacksZ max_entriesr1cCst||||dSr*)rZ_cuda_record_memory_historyrr,r,r-rsrcCstS)a Saves 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 )rrWr0r,r,r- _snapshotsIrdump_snapshot.picklec Cs,t}t|d}t||W5QRXdS)a Saves 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. 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