o h\@s.ddlZddlmZmZddlZddlmZddlmZm Z eee e dfZ dddZ dd e ddfd d Zdd e ddfd d Zdd e dee effddZdd e dee effddZdd e de fddZdd e de fddZdd e de fddZdd e de fddZdd e dee e ffddZgdZdS)N)AnyUnion)Device)_get_device_indexis_initializedreturncCstr tjdSdS)aZRelease all unoccupied cached memory currently held by the caching allocator so that those can be used in other XPU application. .. note:: :func:`~torch.xpu.empty_cache` doesn't increase the amount of XPU memory available for PyTorch. However, it may help reduce fragmentation of XPU memory in certain cases. N)rtorch_C_xpu_emptyCacher r d/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/xpu/memory.py empty_cache s rdevicecCt|dd}tj|S)aReset the "peak" stats tracked by the XPU memory allocator. See :func:`~torch.xpu.memory_stats` for details. Peak stats correspond to the `"peak"` key in each individual stat dict. Args: device (torch.device or int or str, optional): selected device. Returns statistic for the current device, given by :func:`~torch.xpu.current_device`, if :attr:`device` is ``None`` (default). Toptional)rr r _xpu_resetPeakMemoryStatsrr r r reset_peak_memory_stats rcCr)aReset the "accumulated" (historical) stats tracked by the XPU memory allocator. See :func:`~torch.xpu.memory_stats` for details. Accumulated stats correspond to the `"allocated"` and `"freed"` keys in each individual stat dict. Args: device (torch.device or int or str, optional): selected device. Returns statistic for the current device, given by :func:`~torch.xpu.current_device`, if :attr:`device` is ``None`` (default). Tr)rr r _xpu_resetAccumulatedMemoryStatsrr r r reset_accumulated_memory_stats)rrcCs"tsiSt|dd}tj|S)zLReturn the result of :func:`~torch.xpu.memory_stats` as a nested dictionary.Tr)rrr r _xpu_memoryStatsrr r r memory_stats_as_nested_dict8s  rcsFgdtdtddffdd t|d}d|tS) a@Return a dictionary of XPU 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_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: amount of allocated memory. - ``"reserved_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: amount of reserved memory. - ``"active_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``: amount of active memory. - ``"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. 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 (for size >= 1MB allocations). - ``small_pool``: statistics for the small allocation pool (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. Args: device (torch.device or int or str, optional): selected device. Returns statistics for the current device, given by :func:`~torch.xpu.current_device`, if :attr:`device` is ``None`` (default). prefixobjrNcsTt|tr!t|dkr|d7}|D] \}}|||qdS||fdS)Nr.) isinstancedictlenitemsappend)rrkv_recurse_add_to_resultresultr r r&hs  z,memory_stats.._recurse_add_to_resultr)strrrsort collections OrderedDict)rstatsr r%r memory_stats@s &  r.cCt|dddS)aReturn the current GPU memory occupied by tensors in bytes for a given device. Args: device (torch.device or int or str, optional): selected device. Returns statistic for the current device, given by :func:`~torch.xpu.current_device`, if :attr:`device` is ``None`` (default). .. note:: This is likely less than the amount shown in `xpu-smi` since some unused memory can be held by the caching allocator and some context needs to be created on GPU. rzallocated_bytes.all.currentrr.getrr r r memory_allocatedxs r2cCr/)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.xpu.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 or str, optional): selected device. Returns statistic for the current device, given by :func:`~torch.xpu.current_device`, if :attr:`device` is ``None`` (default). rzallocated_bytes.all.peakrr0rr r r max_memory_allocatedr3cCr/)aJReturn the current GPU memory managed by the caching allocator in bytes for a given device. Args: device (torch.device or int or str, optional): selected device. Returns statistic for the current device, given by :func:`~torch.xpu.current_device`, if :attr:`device` is ``None`` (default). rzreserved_bytes.all.currentrr0rr r r memory_reservedsr5cCr/)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.xpu.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 or str, optional): selected device. Returns statistic for the current device, given by :func:`~torch.xpu.current_device`, if :attr:`device` is ``None`` (default). rzreserved_bytes.all.peakrr0rr r r max_memory_reservedr4r6cCr)aReturn the global free and total GPU memory for a given device. Args: device (torch.device or int or str, optional): selected device. Returns statistic for the current device, given by :func:`~torch.xpu.current_device`, if :attr:`device` is ``None`` (default). Returns: int: the memory available on the device in units of bytes. int: the total memory on the device in units of bytes Tr)rr r _xpu_getMemoryInforr r r mem_get_infos r8) rr3r6r8r2r5r.rrr)rN)N)r+typingrrr torch.typesrr(rrr)int _device_trrrrrr.r2r3r5r6tupler8__all__r r r r s"   8