# Memory management in NumPy

The `numpy.ndarray` is a python class. It requires additional memory allocations to hold `numpy.ndarray.strides`, `numpy.ndarray.shape` and `numpy.ndarray.data` attributes. These attributes are specially allocated after creating the python object in __new__. The `strides` and `shape` are stored in a piece of memory allocated internally.

The `data` allocation used to store the actual array values (which could be pointers in the case of `object` arrays) can be very large, so NumPy has provided interfaces to manage its allocation and release. This document details how those interfaces work.

## Historical overview

Since version 1.7.0, NumPy has exposed a set of `PyDataMem_*` functions (`PyDataMem_NEW`, `PyDataMem_FREE`, `PyDataMem_RENEW`) which are backed by alloc, free, realloc respectively. In that version NumPy also exposed the PyDataMem_EventHook function (now deprecated) described below, which wrap the OS-level calls.

Since those early days, Python also improved its memory management capabilities, and began providing various management policies beginning in version 3.4. These routines are divided into a set of domains, each domain has a `PyMemAllocatorEx` structure of routines for memory management. Python also added a `tracemalloc` module to trace calls to the various routines. These tracking hooks were added to the NumPy `PyDataMem_*` routines.

NumPy added a small cache of allocated memory in its internal `npy_alloc_cache`, `npy_alloc_cache_zero`, and `npy_free_cache` functions. These wrap `alloc`, `alloc-and-memset(0)` and `free` respectively, but when `npy_free_cache` is called, it adds the pointer to a short list of available blocks marked by size. These blocks can be re-used by subsequent calls to `npy_alloc*`, avoiding memory thrashing.

## Configurable memory routines in NumPy (NEP 49)

Users may wish to override the internal data memory routines with ones of their own. Since NumPy does not use the Python domain strategy to manage data memory, it provides an alternative set of C-APIs to change memory routines. There are no Python domain-wide strategies for large chunks of object data, so those are less suited to NumPy’s needs. User who wish to change the NumPy data memory management routines can use `PyDataMem_SetHandler`, which uses a `PyDataMem_Handler` structure to hold pointers to functions used to manage the data memory. The calls are still wrapped by internal routines to call `PyTraceMalloc_Track`, `PyTraceMalloc_Untrack`, and will use the deprecated `PyDataMem_EventHookFunc` mechanism. Since the functions may change during the lifetime of the process, each `ndarray` carries with it the functions used at the time of its instantiation, and these will be used to reallocate or free the data memory of the instance.

type PyDataMem_Handler

  A struct to hold function pointers used to manipulate memory

      typedef struct {
          char name[127];  /* multiple of 64 to keep the struct aligned */
          uint8_t version; /* currently 1 */
          PyDataMemAllocator allocator;
      } PyDataMem_Handler;

  where the allocator structure is

      /* The declaration of free differs from PyMemAllocatorEx */
      typedef struct {
          void *ctx;
          void* (*malloc) (void *ctx, size_t size);
          void* (*calloc) (void *ctx, size_t nelem, size_t elsize);
          void* (*realloc) (void *ctx, void *ptr, size_t new_size);
          void (*free) (void *ctx, void *ptr, size_t size);
      } PyDataMemAllocator;

PyObject *PyDataMem_SetHandler(PyObject *handler)

  Set a new allocation policy. If the input value is `NULL`, will reset the policy to the default. Return the previous policy, or return `NULL` if an error has occurred. We wrap the user-provided functions so they will still call the python and numpy memory management callback hooks.

PyObject *PyDataMem_GetHandler()

  Return the current policy that will be used to allocate data for the next `PyArrayObject`. On failure, return `NULL`.

For an example of setting up and using the PyDataMem_Handler, see the test in `numpy/core/tests/test_mem_policy.py`

void PyDataMem_EventHookFunc(void *inp, void *outp, size_t size, void *user_data);

  This function will be called during data memory manipulation

PyDataMem_EventHookFunc *PyDataMem_SetEventHook(PyDataMem_EventHookFunc *newhook, void *user_data, void **old_data)

  Sets the allocation event hook for numpy array data.

  Returns a pointer to the previous hook or `NULL`. If old_data is non-`NULL`, the previous user_data pointer will be copied to it.

  If not `NULL`, hook will be called at the end of each `PyDataMem_NEW/FREE/RENEW`:

      result = PyDataMem_NEW(size)        -> (*hook)(NULL, result, size, user_data)
      PyDataMem_FREE(ptr)                 -> (*hook)(ptr, NULL, 0, user_data)
      result = PyDataMem_RENEW(ptr, size) -> (*hook)(ptr, result, size, user_data)

  When the hook is called, the GIL will be held by the calling thread. The hook should be written to be reentrant, if it performs operations that might cause new allocation events (such as the creation/destruction numpy objects, or creating/destroying Python objects which might cause a gc).

  Deprecated in v1.23

## What happens when deallocating if there is no policy set

A rare but useful technique is to allocate a buffer outside NumPy, use `PyArray_NewFromDescr` to wrap the buffer in a `ndarray`, then switch the `OWNDATA` flag to true. When the `ndarray` is released, the appropriate function from the `ndarray`’s `PyDataMem_Handler` should be called to free the buffer. But the `PyDataMem_Handler` field was never set, it will be `NULL`. For backward compatibility, NumPy will call `free()` to release the buffer. If `NUMPY_WARN_IF_NO_MEM_POLICY` is set to `1`, a warning will be emitted. The current default is not to emit a warning, this may change in a future version of NumPy.

A better technique would be to use a `PyCapsule` as a base object:

    /* define a PyCapsule_Destructor, using the correct deallocator for buff */
    void free_wrap(void *capsule){
        void * obj = PyCapsule_GetPointer(capsule, PyCapsule_GetName(capsule));
        free(obj);
    };

    /* then inside the function that creates arr from buff */
    ...
    arr = PyArray_NewFromDescr(... buf, ...);
    if (arr == NULL) {
        return NULL;
    }
    capsule = PyCapsule_New(buf, "my_wrapped_buffer",
                            (PyCapsule_Destructor)&free_wrap);
    if (PyArray_SetBaseObject(arr, capsule) == -1) {
        Py_DECREF(arr);
        return NULL;
    }
    ...

## Example of memory tracing with `np.lib.tracemalloc_domain`

Note that since Python 3.6 (or newer), the builtin `tracemalloc` module can be used to track allocations inside NumPy. NumPy places its CPU memory allocations into the `np.lib.tracemalloc_domain` domain. For additional information, check: https://docs.python.org/3/library/tracemalloc.html.

Here is an example on how to use `np.lib.tracemalloc_domain`:

    """
       The goal of this example is to show how to trace memory
       from an application that has NumPy and non-NumPy sections.
       We only select the sections using NumPy related calls.
    """

    import tracemalloc
    import numpy as np

    # Flag to determine if we select NumPy domain
    use_np_domain = True

    nx = 300
    ny = 500

    # Start to trace memory
    tracemalloc.start()

    # Section 1
    # ---------

    # NumPy related call
    a = np.zeros((nx,ny))

    # non-NumPy related call
    b = [i**2 for i in range(nx*ny)]

    snapshot1 = tracemalloc.take_snapshot()
    # We filter the snapshot to only select NumPy related calls
    np_domain = np.lib.tracemalloc_domain
    dom_filter = tracemalloc.DomainFilter(inclusive=use_np_domain,
                                          domain=np_domain)
    snapshot1 = snapshot1.filter_traces([dom_filter])
    top_stats1 = snapshot1.statistics('traceback')

    print("================ SNAPSHOT 1 =================")
    for stat in top_stats1:
        print(f"{stat.count} memory blocks: {stat.size / 1024:.1f} KiB")
        print(stat.traceback.format()[-1])

    # Clear traces of memory blocks allocated by Python
    # before moving to the next section.
    tracemalloc.clear_traces()

    # Section 2
    #----------

    # We are only using NumPy
    c = np.sum(a*a)

    snapshot2 = tracemalloc.take_snapshot()
    top_stats2 = snapshot2.statistics('traceback')

    print()
    print("================ SNAPSHOT 2 =================")
    for stat in top_stats2:
        print(f"{stat.count} memory blocks: {stat.size / 1024:.1f} KiB")
        print(stat.traceback.format()[-1])

    tracemalloc.stop()

    print()
    print("============================================")
    print("\nTracing Status : ", tracemalloc.is_tracing())

    try:
        print("\nTrying to Take Snapshot After Tracing is Stopped.")
        snap = tracemalloc.take_snapshot()
    except Exception as e:
        print("Exception : ", e)