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# Source code for torch.cuda.memory

    import collections
    import contextlib
    import ctypes
    import pickle
    import sys
    import warnings
    from inspect import signature

    from typing import Any, Dict, Optional, Tuple, Union

    import torch
    from torch import _C

    from torch.types import Device
    from . import _get_device_index, _get_nvml_device_index, _lazy_init, is_initialized

    from ._memory_viz import memory as _memory, segments as _segments
    from ._utils import _dummy_type

    __all__ = [
        "caching_allocator_alloc",
        "caching_allocator_delete",
        "set_per_process_memory_fraction",
        "empty_cache",
        "memory_stats",
        "memory_stats_as_nested_dict",
        "reset_accumulated_memory_stats",
        "reset_peak_memory_stats",
        "reset_max_memory_allocated",
        "reset_max_memory_cached",
        "memory_allocated",
        "max_memory_allocated",
        "memory_reserved",
        "max_memory_reserved",
        "memory_cached",
        "max_memory_cached",
        "memory_snapshot",
        "memory_summary",
        "list_gpu_processes",
        "mem_get_info",
        "get_allocator_backend",
        "CUDAPluggableAllocator",
        "change_current_allocator",
    ]

    if not hasattr(torch._C, "_cuda_CUDAAllocator"):
        # Define dummy base classes
        torch._C.__dict__["_cuda_CUDAAllocator"] = _dummy_type("_cuda_CUDAAllocator")

    def _host_allocator():
        _lazy_init()
        return torch._C._cuda_cudaHostAllocator()

    @contextlib.contextmanager
    def _free_mutex():
        torch._C._cuda_lock_mutex()
        try:
            yield
        finally:
            torch._C._cuda_unlock_mutex()

    def caching_allocator_alloc(size, device: Union[Device, int] = None, stream=None):
        r"""Performs 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.
        """
        if device is None:
            device = torch.cuda.current_device()
        device = _get_device_index(device)
        if stream is None:
            stream = torch.cuda.current_stream(device)
        if isinstance(stream, torch.cuda.streams.Stream):
            stream = stream.cuda_stream
        if not isinstance(stream, int):
            raise TypeError(
                "Invalid type for stream argument, must be "
                "`torch.cuda.Stream` or `int` representing a pointer "
                "to a existing stream"
            )
        with torch.cuda.device(device):
            return torch._C._cuda_cudaCachingAllocator_raw_alloc(size, stream)

    def caching_allocator_delete(mem_ptr):
        r"""Deletes 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.
        """
        torch._C._cuda_cudaCachingAllocator_raw_delete(mem_ptr)

    def set_per_process_memory_fraction(
        fraction, device: Union[Device, int] = None
    ) -> None:
        r"""Set 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.
        """
        _lazy_init()
        if device is None:
            device = torch.cuda.current_device()
        device = _get_device_index(device)
        if not isinstance(fraction, float):
            raise TypeError("Invalid type for fraction argument, must be `float`")
        if fraction < 0 or fraction > 1:
            raise ValueError(f"Invalid fraction value: {fraction}. Allowed range: 0~1")

        torch._C._cuda_setMemoryFraction(fraction, device)

    def empty_cache() -> None:
        r"""Releases 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.
        """
        if is_initialized():
            torch._C._cuda_emptyCache()

    def memory_stats(device: Union[Device, int] = None) -> Dict[str, Any]:
        r"""Returns 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<cuda-memory-envvars>`, some stats are not
            meaningful, and are always reported as zero.
        """
        result = []

        def _recurse_add_to_result(prefix, obj):
            if isinstance(obj, dict):
                if len(prefix) > 0:
                    prefix += "."
                for k, v in obj.items():
                    _recurse_add_to_result(prefix + k, v)
            else:
                result.append((prefix, obj))

        stats = memory_stats_as_nested_dict(device=device)
        _recurse_add_to_result("", stats)
        result.sort()

        return collections.OrderedDict(result)

    def memory_stats_as_nested_dict(device: Union[Device, int] = None) -> Dict[str, Any]:
        r"""Returns the result of :func:`~torch.cuda.memory_stats` as a nested dictionary."""
        if not is_initialized():
            return {}
        device = _get_device_index(device, optional=True)
        return torch._C._cuda_memoryStats(device)

    def reset_accumulated_memory_stats(device: Union[Device, int] = None) -> None:
        r"""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.
        """
        device = _get_device_index(device, optional=True)
        return torch._C._cuda_resetAccumulatedMemoryStats(device)

    def reset_peak_memory_stats(device: Union[Device, int] = None) -> None:
        r"""Resets 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.
        """
        device = _get_device_index(device, optional=True)
        return torch._C._cuda_resetPeakMemoryStats(device)

    def reset_max_memory_allocated(device: Union[Device, int] = None) -> None:
        r"""Resets 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.
        """
        warnings.warn(
            "torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, "
            "which resets /all/ peak memory stats.",
            FutureWarning,
        )
        return reset_peak_memory_stats(device=device)

    def reset_max_memory_cached(device: Union[Device, int] = None) -> None:
        r"""Resets 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.
        """
        warnings.warn(
            "torch.cuda.reset_max_memory_cached now calls torch.cuda.reset_peak_memory_stats, "
            "which resets /all/ peak memory stats.",
            FutureWarning,
        )
        return reset_peak_memory_stats(device=device)

    def memory_allocated(device: Union[Device, int] = None) -> int:
        r"""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.
        """
        return memory_stats(device=device).get("allocated_bytes.all.current", 0)

    def max_memory_allocated(device: Union[Device, int] = None) -> int:
        r"""Returns 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.
        """
        return memory_stats(device=device).get("allocated_bytes.all.peak", 0)

    def memory_reserved(device: Union[Device, int] = None) -> int:
        r"""Returns 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.
        """
        return memory_stats(device=device).get("reserved_bytes.all.current", 0)

    def max_memory_reserved(device: Union[Device, int] = None) -> int:
        r"""Returns 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.
        """
        return memory_stats(device=device).get("reserved_bytes.all.peak", 0)

    def memory_cached(device: Union[Device, int] = None) -> int:
        r"""Deprecated; see :func:`~torch.cuda.memory_reserved`."""
        warnings.warn(
            "torch.cuda.memory_cached has been renamed to torch.cuda.memory_reserved",
            FutureWarning,
        )
        return memory_reserved(device=device)

    def max_memory_cached(device: Union[Device, int] = None) -> int:
        r"""Deprecated; see :func:`~torch.cuda.max_memory_reserved`."""
        warnings.warn(
            "torch.cuda.max_memory_cached has been renamed to torch.cuda.max_memory_reserved",
            FutureWarning,
        )
        return max_memory_reserved(device=device)

    def memory_snapshot():
        r"""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.
        """
        return torch._C._cuda_memorySnapshot()["segments"]

    def memory_summary(device: Union[Device, int] = None, abbreviated: bool = False) -> str:
        r"""Returns 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.
        """
        device = _get_device_index(device, optional=True)
        stats = memory_stats(device=device)

        def _format_size(sz, pref_sz):
            prefixes = ["B  ", "KiB", "MiB", "GiB", "TiB", "PiB"]
            prefix = prefixes[0]
            for new_prefix in prefixes[1:]:
                if pref_sz < 768 * 1024:
                    break
                prefix = new_prefix
                sz //= 1024
                pref_sz /= 1024
            return f"{sz:6d} {prefix}"

        def _format_count(cnt, pref_cnt):
            prefixes = [" ", "K", "M"]
            prefix = prefixes[0]
            for new_prefix in prefixes[1:]:
                if pref_cnt < 750 * 1000:
                    break
                prefix = new_prefix
                cnt //= 1000
                pref_cnt /= 1000
            return f"{cnt:7d} {prefix} "

        metrics_to_display = [
            ("allocated_bytes", "Allocated memory", _format_size),
            ("active_bytes", "Active memory", _format_size),
            ("requested_bytes", "Requested memory", _format_size),
            ("reserved_bytes", "GPU reserved memory", _format_size),
            ("inactive_split_bytes", "Non-releasable memory", _format_size),
            ("allocation", "Allocations", _format_count),
            ("active", "Active allocs", _format_count),
            ("segment", "GPU reserved segments", _format_count),
            ("inactive_split", "Non-releasable allocs", _format_count),
        ]

        lines = []
        lines.append("=" * 75)
        lines.append(" {_:16} PyTorch CUDA memory summary, device ID {device:<17d} ")
        lines.append("-" * 75)
        lines.append(
            "  {_:9} CUDA OOMs: {num_ooms:<12d} | {_:6} cudaMalloc retries: {num_alloc_retries:<8d}  "
        )
        lines.append("=" * 75)
        lines.append(
            "        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  "
        )

        for metric_key, metric_name, formatter in metrics_to_display:
            lines.append("-" * 75)
            submetrics = [("all", metric_name)]
            if not abbreviated:
                submetrics.append(("large_pool", "      from large pool"))
                submetrics.append(("small_pool", "      from small pool"))

            current_prefval, peak_prefval, allocated_prefval, freed_prefval = (
                None,
                None,
                None,
                None,
            )

            for submetric_key, submetric_name in submetrics:
                prefix = metric_key + "." + submetric_key + "."

                current = stats[prefix + "current"]
                peak = stats[prefix + "peak"]
                allocated = stats[prefix + "allocated"]
                freed = stats[prefix + "freed"]

                if current_prefval is None:
                    current_prefval = current
                    peak_prefval = peak
                    allocated_prefval = allocated
                    freed_prefval = freed

                lines.append(
                    " {:<21} | {} | {} | {} | {} ".format(
                        submetric_name,
                        formatter(current, current_prefval),
                        formatter(peak, peak_prefval),
                        formatter(allocated, allocated_prefval),
                        formatter(freed, freed_prefval),
                    ),
                )

        metrics_to_display = [
            ("oversize_allocations", "Oversize allocations", _format_count),
            ("oversize_segments", "Oversize GPU segments", _format_count),
        ]

        for metric_key, metric_name, formatter in metrics_to_display:
            lines.append("-" * 75)

            prefix = metric_key + "."

            current = stats[prefix + "current"]
            peak = stats[prefix + "peak"]
            allocated = stats[prefix + "allocated"]
            freed = stats[prefix + "freed"]

            lines.append(
                " {:<21} | {} | {} | {} | {} ".format(
                    metric_name,
                    formatter(current, current),
                    formatter(peak, peak),
                    formatter(allocated, allocated),
                    formatter(freed, freed),
                ),
            )

        lines.append("=" * 75)

        fmt_dict = {"_": "", "device": device}
        for k, v in stats.items():
            fmt_dict[k.replace(".", "-")] = v
        return "|" + "|\n|".join(lines).format(**fmt_dict) + "|\n"

    def list_gpu_processes(device: Union[Device, int] = None) -> str:
        r"""Returns 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).
        """

        try:
            import pynvml  # type: ignore[import]
        except ModuleNotFoundError:
            return "pynvml module not found, please install pynvml"
        from pynvml import NVMLError_DriverNotLoaded

        try:
            pynvml.nvmlInit()
        except NVMLError_DriverNotLoaded:
            return "cuda driver can't be loaded, is cuda enabled?"
        device = _get_nvml_device_index(device)
        handle = pynvml.nvmlDeviceGetHandleByIndex(device)
        procs = pynvml.nvmlDeviceGetComputeRunningProcesses(handle)
        lines = []
        lines.append(f"GPU:{device}")
        if len(procs) == 0:
            lines.append("no processes are running")
        for p in procs:
            mem = p.usedGpuMemory / (1024 * 1024)
            lines.append(f"process {p.pid:>10d} uses {mem:>12.3f} MB GPU memory")
        return "\n".join(lines)

    def mem_get_info(device: Union[Device, int] = None) -> Tuple[int, int]:
        r"""Returns 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.
        """
        if device is None:
            device = torch.cuda.current_device()
        device = _get_device_index(device)
        return torch.cuda.cudart().cudaMemGetInfo(device)

    def _record_memory_history_legacy(
        enabled: bool,
        record_context=True,
        trace_alloc_max_entries=1,
        trace_alloc_record_context=False,
        device: Union[Device, int] = None,
        record_context_cpp=False,
    ):
        _C._cuda_record_memory_history_legacy(
            enabled,
            record_context,
            record_context_cpp,
            trace_alloc_max_entries,
            trace_alloc_record_context,
        )

    def _record_memory_history(enabled="all", *args, **kwargs):
        """
        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.
        """

        if isinstance(enabled, bool):
            return _record_memory_history_legacy(enabled, *args, **kwargs)
        else:
            return _record_memory_history_impl(enabled, *args, **kwargs)

    def _record_memory_history_impl(
        enabled: Optional[str] = "all",
        context: Optional[str] = "all",
        stacks: str = "all",
        max_entries: int = sys.maxsize,
        device: Union[Device, int] = None,
    ):
        _C._cuda_record_memory_history(enabled, context, stacks, max_entries)

    _record_memory_history.__signature__ = signature(_record_memory_history_impl)  # type: ignore[attr-defined]

    def _snapshot(device: Union[Device, int] = None):
        """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
        """
        return _C._cuda_memorySnapshot()

    def _dump_snapshot(filename="dump_snapshot.pickle"):
        """
        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. Defaults to "dump_snapshot.pickle".
        """
        s = _snapshot()
        with open(filename, "wb") as f:
            pickle.dump(s, f)

    def _save_segment_usage(filename="output.svg", snapshot=None):
        if snapshot is None:
            snapshot = _snapshot()
        with open(filename, "w") as f:
            f.write(_segments(snapshot))

    def _save_memory_usage(filename="output.svg", snapshot=None):
        if snapshot is None:
            snapshot = _snapshot()
        with open(filename, "w") as f:
            f.write(_memory(snapshot))

    def _set_allocator_settings(env: str):
        return torch._C._cuda_cudaCachingAllocator_set_allocator_settings(env)

    def get_allocator_backend() -> str:
        r"""Returns 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.
        """
        return torch._C._cuda_getAllocatorBackend()

    class _CUDAAllocator:
        r"""Wrapper over internal CUDA memory allocators."""

        def __init__(self, allocator: torch._C._cuda_CUDAAllocator):
            self._allocator = allocator

        def allocator(self):
            return self._allocator

    class CUDAPluggableAllocator(_CUDAAllocator):
        r"""CUDA memory allocator loaded from a so file.

        Memory 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
        """

        def __init__(self, path_to_so_file: str, alloc_fn_name: str, free_fn_name: str):
            allocator = ctypes.CDLL(path_to_so_file)
            alloc_fn = ctypes.cast(getattr(allocator, alloc_fn_name), ctypes.c_void_p).value
            free_fn = ctypes.cast(getattr(allocator, free_fn_name), ctypes.c_void_p).value
            assert alloc_fn is not None
            assert free_fn is not None
            self._allocator = torch._C._cuda_customAllocator(alloc_fn, free_fn)

    def change_current_allocator(allocator: _CUDAAllocator) -> None:
        r"""Changes 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
        """
        torch._C._cuda_changeCurrentAllocator(allocator.allocator())

    def _get_current_allocator() -> _CUDAAllocator:
        r"""Returns the allocator being currently used.

        .. note::
            See :ref:`cuda-memory-management` for details on creating and using a custom allocator
        """
        return _CUDAAllocator(torch._C._cuda_getAllocator())

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