a dq@sTddlZddlZddlZddlZddlmZmZmZmZddl Z ddl m Z m Z m Z ddlmZddlmZmZddlmZddl mZgd Zee jd sed e jjd <d d Zejd dZdUeeefdddZddZ dVeeefddddZ!ddddZ"dWeeefee#efdddZ$dXeeefee#efdddZ%dYeeefddddZ&dZeeefddd d!Z'd[eeefddd"d#Z(d\eeefddd$d%Z)d]eeefedd&d'Z*d^eeefedd(d)Z+d_eeefedd*d+Z,d`eeefedd,d-Z-daeeefedd.d/Z.dbeeefedd0d1Z/d2d3Z0dceeefe1e#d5d6d7Z2ddeeefe#dd8d9Z3deeeefeeefdd:d;Z4dfe1eeefd=d>d?Z5dgeeefdd@dAZ6dhdCdDZ7didEdFZ8e#dGdHdIZ9e#ddJdKZ:GdLdMdMZ;GdNdOdOe;ZdS)jN)AnyDictUnionTuple)is_initialized_get_device_index _lazy_init) _dummy_type)segmentsmemory)Device)_C)caching_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)Z/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torch/cuda/memory.py_host_allocatorsr+ccs0tjzdVWtjn tj0dSr')r(rZ_cuda_lock_mutexZ_cuda_unlock_mutexr)r)r)r* _free_mutex"s r,devicecCs|durtj}t|}|dur.tj|}t|tjjjrD|j}t|t sVt dtj |tj ||WdS1s0YdS)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_devicerZcurrent_stream isinstanceZstreamsZStreamZ cuda_streamint TypeErrorr.rZ$_cuda_cudaCachingAllocator_raw_alloc)sizer.streamr)r)r*r+s   rcCstj|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*rMsr)r.returncCsbt|durtj}t|}t|ts2td|dksB|dkrPtd |tj ||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`rrz.Invalid fraction value: {}. Allowed range: 0~1) r r(r/r0rr1floatr3 ValueErrorformatrZ_cuda_setMemoryFraction)fractionr.r)r)r*r^s  r)r6cCstrtjdS)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*rys rcs8gfddt|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 overhed: - ``"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.)r1dictlenitemsappend)prefixobjkv_recurse_add_to_resultresultr)r*rEs   z,memory_stats.._recurse_add_to_resultr-)rsort collections OrderedDict)r.statsr)rDr*rs S  rcCs"ts iSt|dd}tj|S)zNReturns the result of :func:`~torch.cuda.memory_stats` as a nested dictionary.Toptional)rrr(rZ_cuda_memoryStatsr-r)r)r*rs rcCst|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. TrL)rr(rZ!_cuda_resetAccumulatedMemoryStatsr-r)r)r*rs rcCst|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. TrL)rr(rZ_cuda_resetPeakMemoryStatsr-r)r)r*r s rcCstdtt|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.r-warningswarn FutureWarningrr-r)r)r*rs rcCstdtt|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.r-rNr-r)r)r*r6s rcCst|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. r-zallocated_bytes.all.currentrrgetr-r)r)r*rPsrcCst|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. r-zallocated_bytes.all.peakrrRr-r)r)r*rbsrcCst|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. r-zreserved_bytes.all.currentrrRr-r)r)r*rxs rcCst|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. r-zreserved_bytes.all.peakrrRr-r)r)r*rsrcCstdtt|dS)z4Deprecated; see :func:`~torch.cuda.memory_reserved`.zGtorch.cuda.memory_cached has been renamed to torch.cuda.memory_reservedr-)rOrPrQrr-r)r)r*rs rcCstdtt|dS)z8Deprecated; see :func:`~torch.cuda.max_memory_reserved`.zOtorch.cuda.max_memory_cached has been renamed to torch.cuda.max_memory_reservedr-)rOrPrQrr-r)r)r*rs rcCstjdS)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*rs rF)r. abbreviatedr6c Cst|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(ur^|} |} |} |}|d)|| || | || | || | ||qqd*d+|fd,d-|fg}|D]~\}}} |d|d#}||d$}||d%}||d&}||d'}|d)|| ||| ||| ||| ||q|dd.|d/}|D]\}}|||d#d0<qFd1d2|jfi|d3S)4aReturns a human-readable printout of the current memory allocator statistics for a given device. This can be useful to display periodically during training, or when handling out-of-memory exceptions. Args: device (torch.device or int, optional): selected device. 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This can be useful to display periodically during training, or when handling out-of-memory exceptions. Args: device (torch.device or int, optional): selected device. Returns printout for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). rNz.pynvml module not found, please install pynvml)NVMLError_DriverNotLoadedz-cuda driver can't be loaded, is cuda enabled?TrLzGPU:zno processes are runningizprocess z>10dz uses z>12.3fz MB GPU memory ) pynvmlModuleNotFoundErrorrmZnvmlInitrZnvmlDeviceGetHandleByIndexZ$nvmlDeviceGetComputeRunningProcessesr?r=Z usedGpuMemorypidrj)r.rormhandleZprocsrkpZmemr)r)r*r!8s(             r!cCs*|durtj}t|}tj|S)aReturns the global free and total GPU memory occupied 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(r/r0rZcudartZcudaMemGetInfor-r)r)r*r"Zs  r"T)enabledr.cCsBtj|"t|||||Wdn1s40YdS)aEnables recording of Python stack traces to be associated with memory allocations, so you can tell what allocated any piece of memory in :func:`torch.memory_snapshot`. 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. .. warning: The :attr:`_enable_expensive_cpp` arguments lets you enable also collecting C++ stack traces. This collection is VERY SLOW and should only be used if you are debugging framework problems on a minified example. In principle, it should be possible to implement fast C++ stack trace collection; file an issue with us if you need it. N)r(r/r.rZ_cuda_recordMemoryHistory)rtZrecord_contextZtrace_alloc_max_entriesZtrace_alloc_record_contextr.Z_enable_expensive_cppr)r)r*_record_memory_historyls rucCs8tj|tWdS1s*0YdSr')r(r/r.rrTr-r)r)r* _snapshotsrv output.svgcCsJ|durt}t|d}|t|Wdn1s<0YdSNw)rvopenwrite _segmentsfilenameZsnapshotfr)r)r*_save_segment_usages rcCsJ|durt}t|d}|t|Wdn1s<0YdSrx)rvrzr{_memoryr}r)r)r*_save_memory_usages renvcCs tj|Sr')r(rZ1_cuda_cudaCachingAllocator_set_allocator_settingsrr)r)r*_set_allocator_settingssrcCs tjS)ajReturns a string describing the active allocator backend as set by ``PYTORCH_CUDA_ALLOC_CONF``. 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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 )path_to_so_file alloc_fn_name free_fn_namecCsbt|}tt||tjj}tt||tjj}|dusBJ|dusNJtj|||_ dSr') ctypesCDLLcastgetattrc_void_pvaluer(rZ_cuda_customAllocatorr)rrrrrZalloc_fnZfree_fnr)r)r*rs    zCUDAPluggableAllocator.__init__N)rrrrstrrr)r)r)r*r$sr$)rr6cCstj|dS)axChanges the currently used memory allocator to be the one provided. If the current allocator has already been used/initialized, this function will error. 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