_typeof.cpp

#include "_pymodule.h"

#include <string.h>
#include <time.h>
#include <assert.h>

#include "_numba_common.h"
#include "_typeof.h"
#include "_hashtable.h"
#include "_devicearray.h"
#include "pyerrors.h"

#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include <numpy/ndarrayobject.h>
#if NPY_ABI_VERSION >= 0x02000000
    #include <numpy/npy_2_compat.h>
#endif

#if (PY_MAJOR_VERSION >= 3) && (PY_MINOR_VERSION == 13)
    #ifndef Py_BUILD_CORE
        #define Py_BUILD_CORE 1
    #endif
    #include "internal/pycore_setobject.h"  // _PySet_NextEntry()
#endif


/* Cached typecodes for basic scalar types */
static int tc_int8;
static int tc_int16;
static int tc_int32;
static int tc_int64;
static int tc_uint8;
static int tc_uint16;
static int tc_uint32;
static int tc_uint64;
static int tc_float32;
static int tc_float64;
static int tc_complex64;
static int tc_complex128;
static int BASIC_TYPECODES[12];

static int tc_intp;

/* The type object for the numba .dispatcher.OmittedArg class
 * that wraps omitted arguments.
 */
static PyObject *omittedarg_type;

static PyObject *typecache;
static PyObject *ndarray_typecache;
static PyObject *structured_dtypes;

static PyObject *str_typeof_pyval = NULL;
static PyObject *str_value = NULL;
static PyObject *str_numba_type = NULL;

/* CUDA device array API */
void **DeviceArray_API;

/*
 * Type fingerprint computation.
 */

typedef struct {
    /* A buffer the fingerprint will be written to */
    char *buf;
    size_t n;
    size_t allocated;
    /* A preallocated buffer, sufficient to fit the fingerprint for most types */
    char static_buf[40];
} string_writer_t;

static void
string_writer_init(string_writer_t *w)
{
    w->buf = w->static_buf;
    w->n = 0;
    w->allocated = sizeof(w->static_buf) / sizeof(unsigned char);
}

static void
string_writer_clear(string_writer_t *w)
{
    if (w->buf != w->static_buf)
        free(w->buf);
}

static void
string_writer_move(string_writer_t *dest, const string_writer_t *src)
{
    dest->n = src->n;
    dest->allocated = src->allocated;
    if (src->buf == src->static_buf) {
        dest->buf = dest->static_buf;
        memcpy(dest->buf, src->buf, src->n);
    }
    else {
        dest->buf = src->buf;
    }
}

/* Ensure at least *bytes* can be appended to the string writer's buffer. */
static int
string_writer_ensure(string_writer_t *w, size_t bytes)
{
    size_t newsize;
    bytes += w->n;
    if (bytes <= w->allocated)
        return 0;
    newsize = (w->allocated << 2) + 1;
    if (newsize < bytes)
        newsize = bytes;
    if (w->buf == w->static_buf) {
        w->buf = (char *) malloc(newsize);
        memcpy(w->buf, w->static_buf, w->allocated);
    }
    else
        w->buf = (char *) realloc(w->buf, newsize);
    if (w->buf) {
        w->allocated = newsize;
        return 0;
    }
    else {
        PyErr_NoMemory();
        return -1;
    }
}

static int
string_writer_put_char(string_writer_t *w, unsigned char c)
{
    if (string_writer_ensure(w, 1))
        return -1;
    w->buf[w->n++] = c;
    return 0;
}

static int
string_writer_put_int32(string_writer_t *w, unsigned int v)
{
    if (string_writer_ensure(w, 4))
        return -1;
    w->buf[w->n] = v & 0xff;
    w->buf[w->n + 1] = (v >> 8) & 0xff;
    w->buf[w->n + 2] = (v >> 16) & 0xff;
    w->buf[w->n + 3] = (v >> 24) & 0xff;
    w->n += 4;
    return 0;
}

static int
string_writer_put_intp(string_writer_t *w, npy_intp v)
{
    if (string_writer_ensure(w, NPY_SIZEOF_PY_INTPTR_T))
        return -1;
    w->buf[w->n] = v & 0xff;
    w->buf[w->n + 1] = (v >> 8) & 0xff;
    w->buf[w->n + 2] = (v >> 16) & 0xff;
    w->buf[w->n + 3] = (v >> 24) & 0xff;
#if NPY_SIZEOF_PY_INTPTR_T == 8
    w->buf[w->n + 4] = (v >> 32) & 0xff;
    w->buf[w->n + 5] = (v >> 40) & 0xff;
    w->buf[w->n + 6] = (v >> 48) & 0xff;
    w->buf[w->n + 7] = (v >> 56) & 0xff;
#endif
    w->n += NPY_SIZEOF_PY_INTPTR_T;
    return 0;
}

static int
string_writer_put_string(string_writer_t *w, const char *s)
{
    if (s == NULL) {
        return string_writer_put_char(w, 0);
    }
    else {
        size_t N = strlen(s) + 1;
        if (string_writer_ensure(w, N))
            return -1;
        memcpy(w->buf + w->n, s, N);
        w->n += N;
        return 0;
    }
}

enum opcode {
    OP_START_TUPLE = '(',
    OP_END_TUPLE = ')',
    OP_INT = 'i',
    OP_FLOAT = 'f',
    OP_COMPLEX = 'c',
    OP_BOOL = '?',
    OP_OMITTED = '!',

    OP_BYTEARRAY = 'a',
    OP_BYTES = 'b',
    OP_NONE = 'n',
    OP_LIST = '[',
    OP_SET = '{',

    OP_BUFFER = 'B',
    OP_NP_SCALAR = 'S',
    OP_NP_ARRAY = 'A',
    OP_NP_DTYPE = 'D'
};

#define TRY(func, w, arg) \
    do { \
        if (func(w, arg)) return -1; \
    } while (0)


static int
fingerprint_unrecognized(void)
{
    PyErr_SetString(PyExc_NotImplementedError,
                    "cannot compute type fingerprint for value");
    return -1;
}

static int
compute_dtype_fingerprint(string_writer_t *w, PyArray_Descr *descr)
{
    int typenum = descr->type_num;
    if (typenum < NPY_OBJECT)
        return string_writer_put_char(w, (char) typenum);
    if (typenum == NPY_VOID) {
        /* Structured dtype: serialize the dtype pointer.  Unfortunately,
         * some structured dtypes can be ephemeral, so we have to
         * intern them to avoid pointer reuse and fingerprint collisions.
         * (e.g. np.recarray(dtype=some_dtype) creates a new dtype
         *  equal to some_dtype)
         */
        PyObject *interned = PyDict_GetItem(structured_dtypes,
                                            (PyObject *) descr);
        if (interned == NULL) {
            interned = (PyObject *) descr;
            if (PyDict_SetItem(structured_dtypes, interned, interned))
                return -1;
        }
        TRY(string_writer_put_char, w, (char) typenum);
        return string_writer_put_intp(w, (npy_intp) interned);
    }
#if NPY_API_VERSION >= 0x00000007
    if (PyTypeNum_ISDATETIME(typenum)) {
        PyArray_DatetimeMetaData *md;
#if NPY_ABI_VERSION >= 0x02000000
        md = &(((PyArray_DatetimeDTypeMetaData *)PyDataType_C_METADATA(descr))->meta);
#else
        md = &(((PyArray_DatetimeDTypeMetaData *)descr->c_metadata)->meta);
#endif
        TRY(string_writer_put_char, w, (char) typenum);
        TRY(string_writer_put_char, w, (char) md->base);
        return string_writer_put_int32(w, (char) md->num);
    }
#endif

    return fingerprint_unrecognized();
}

static int
compute_fingerprint(string_writer_t *w, PyObject *val)
{
    /*
     * Implementation note: for performance, we start with common
     * types that can be tested with fast checks.
     */
    if (val == Py_None)
        return string_writer_put_char(w, OP_NONE);
    if (PyBool_Check(val))
        return string_writer_put_char(w, OP_BOOL);
    /* Note we avoid matching int subclasses such as IntEnum */
    if (PyInt_CheckExact(val) || PyLong_CheckExact(val))
        return string_writer_put_char(w, OP_INT);
    if (PyFloat_Check(val))
        return string_writer_put_char(w, OP_FLOAT);
    if (PyComplex_CheckExact(val))
        return string_writer_put_char(w, OP_COMPLEX);
    if (PyTuple_Check(val)) {
        if(PyTuple_CheckExact(val)) {
            Py_ssize_t i, n;
            n = PyTuple_GET_SIZE(val);
            TRY(string_writer_put_char, w, OP_START_TUPLE);
            for (i = 0; i < n; i++)
                TRY(compute_fingerprint, w, PyTuple_GET_ITEM(val, i));
            TRY(string_writer_put_char, w, OP_END_TUPLE);
            return 0;
        }
        /* as per typeof.py, check "_asdict" for namedtuple. */
        else if(PyObject_HasAttrString(val, "_asdict"))
        {
            /*
             * This encodes the class name and field names of a namedtuple into
             * the fingerprint on the condition that the number of fields is
             * small (<10) and that the class name and field names are encodable
             * as ASCII.
             */
            PyObject * clazz = NULL;
            PyObject * name = NULL;
            PyObject * _fields =  PyObject_GetAttrString(val, "_fields");
            PyObject * field = NULL;
            PyObject * ascii_str = NULL;
            Py_ssize_t i, n, j, flen;
            char * buf = NULL;
            int ret;

            clazz = PyObject_GetAttrString(val, "__class__");
            if (clazz == NULL)
                return -1;

            name = PyObject_GetAttrString(clazz, "__name__");
            Py_DECREF(clazz);
            if (name == NULL)
                return -1;

            ascii_str = PyUnicode_AsEncodedString(name, "ascii", "ignore");
            Py_DECREF(name);
            if (ascii_str == NULL)
                return -1;
            ret = PyBytes_AsStringAndSize(ascii_str, &buf, &flen);

            if (ret == -1)
                return -1;
            for(j = 0; j < flen; j++) {
                TRY(string_writer_put_char, w, buf[j]);
            }
            Py_DECREF(ascii_str);

            if (_fields == NULL)
                return -1;

            n = PyTuple_GET_SIZE(val);

            TRY(string_writer_put_char, w, OP_START_TUPLE);
            for (i = 0; i < n; i++) {
                field = PyTuple_GET_ITEM(_fields, i);
                if (field == NULL)
                    return -1;
                ascii_str = PyUnicode_AsEncodedString(field, "ascii", "ignore");
                if (ascii_str == NULL)
                    return -1;
                ret = PyBytes_AsStringAndSize(ascii_str, &buf, &flen);
                if (ret == -1)
                    return -1;
                for(j = 0; j < flen; j++) {
                    TRY(string_writer_put_char, w, buf[j]);
                }
                Py_DECREF(ascii_str);
                TRY(compute_fingerprint, w, PyTuple_GET_ITEM(val, i));
            }
            TRY(string_writer_put_char, w, OP_END_TUPLE);
            Py_DECREF(_fields);
            return 0;
        }
    }
    if (PyBytes_Check(val))
        return string_writer_put_char(w, OP_BYTES);
    if (PyByteArray_Check(val))
        return string_writer_put_char(w, OP_BYTEARRAY);
    if ((PyObject *) Py_TYPE(val) == omittedarg_type) {
        PyObject *default_val = PyObject_GetAttr(val, str_value);
        if (default_val == NULL)
            return -1;
        TRY(string_writer_put_char, w, OP_OMITTED);
        TRY(compute_fingerprint, w, default_val);
        Py_DECREF(default_val);
        return 0;
    }
    if (PyArray_IsScalar(val, Generic)) {
        /* Note: PyArray_DescrFromScalar() may be a bit slow on
           non-trivial types. */
        PyArray_Descr *descr = PyArray_DescrFromScalar(val);
        if (descr == NULL)
            return -1;
        TRY(string_writer_put_char, w, OP_NP_SCALAR);
        TRY(compute_dtype_fingerprint, w, descr);
        Py_DECREF(descr);
        return 0;
    }
    if (PyArray_Check(val)) {
        PyArrayObject *ary = (PyArrayObject *) val;
        int ndim = PyArray_NDIM(ary);

        TRY(string_writer_put_char, w, OP_NP_ARRAY);
        TRY(string_writer_put_int32, w, ndim);
        if (PyArray_IS_C_CONTIGUOUS(ary))
            TRY(string_writer_put_char, w, 'C');
        else if (PyArray_IS_F_CONTIGUOUS(ary))
            TRY(string_writer_put_char, w, 'F');
        else
            TRY(string_writer_put_char, w, 'A');
        if (PyArray_ISWRITEABLE(ary))
            TRY(string_writer_put_char, w, 'W');
        else
            TRY(string_writer_put_char, w, 'R');
        return compute_dtype_fingerprint(w, PyArray_DESCR(ary));
    }
    if (PyList_Check(val)) {
        Py_ssize_t n = PyList_GET_SIZE(val);
        if (n == 0) {
            PyErr_SetString(PyExc_ValueError,
                            "cannot compute fingerprint of empty list");
            return -1;
        }
        /* Only the first item is considered, as in typeof.py */
        TRY(string_writer_put_char, w, OP_LIST);
        TRY(compute_fingerprint, w, PyList_GET_ITEM(val, 0));
        return 0;
    }
    /* Note we only accept sets, not frozensets */
    if (Py_TYPE(val) == &PySet_Type) {
        Py_hash_t h;
        PyObject *item;
        Py_ssize_t pos = 0;
        /* Only one item is considered, as in typeof.py */
        if (!_PySet_NextEntry(val, &pos, &item, &h)) {
            /* Empty set */
            PyErr_SetString(PyExc_ValueError,
                            "cannot compute fingerprint of empty set");
            return -1;
        }
        TRY(string_writer_put_char, w, OP_SET);
        TRY(compute_fingerprint, w, item);
        return 0;
    }
    if (PyObject_CheckBuffer(val)) {
        Py_buffer buf;
        int flags = PyBUF_ND | PyBUF_STRIDES | PyBUF_FORMAT;
        char contig;
        int ndim;
        char readonly;

        /* Attempt to get a writable buffer, then fallback on read-only */
        if (PyObject_GetBuffer(val, &buf, flags | PyBUF_WRITABLE)) {
            PyErr_Clear();
            if (PyObject_GetBuffer(val, &buf, flags))
                goto _unrecognized;
        }
        if (PyBuffer_IsContiguous(&buf, 'C'))
            contig = 'C';
        else if (PyBuffer_IsContiguous(&buf, 'F'))
            contig = 'F';
        else
            contig = 'A';
        ndim = buf.ndim;
        readonly = buf.readonly ? 'R' : 'W';
        if (string_writer_put_char(w, OP_BUFFER) ||
            string_writer_put_int32(w, ndim) ||
            string_writer_put_char(w, contig) ||
            string_writer_put_char(w, readonly) ||
            string_writer_put_string(w, buf.format) ||
            /* We serialize the object's Python type as well, to
               distinguish between types which have Numba specializations
               (e.g. array.array() vs. memoryview)
            */
            string_writer_put_intp(w, (npy_intp) Py_TYPE(val))) {
            PyBuffer_Release(&buf);
            return -1;
        }
        PyBuffer_Release(&buf);
        return 0;
    }
    if (NUMBA_PyArray_DescrCheck(val)) {
        TRY(string_writer_put_char, w, OP_NP_DTYPE);
        return compute_dtype_fingerprint(w, (PyArray_Descr *) val);
    }

_unrecognized:
    /* Type not recognized */
    return fingerprint_unrecognized();
}

PyObject *
typeof_compute_fingerprint(PyObject *val)
{
    PyObject *res;
    string_writer_t w;

    string_writer_init(&w);

    if (compute_fingerprint(&w, val))
        goto error;
    res = PyBytes_FromStringAndSize(w.buf, w.n);

    string_writer_clear(&w);
    return res;

error:
    string_writer_clear(&w);
    return NULL;
}

/*
 * Getting the typecode from a Type object.
 */
static int
_typecode_from_type_object(PyObject *tyobj) {
    int typecode;
    PyObject *tmpcode = PyObject_GetAttrString(tyobj, "_code");
    if (tmpcode == NULL) {
        return -1;
    }
    typecode = PyLong_AsLong(tmpcode);
    Py_DECREF(tmpcode);
    return typecode;
}

/* When we want to cache the type's typecode for later lookup, we need to
   keep a reference to the returned type object so that it cannot be
   deleted. This is because of the following events occurring when first
   using a @jit function for a given set of types:

    1. typecode_fallback requests a new typecode for an arbitrary Python value;
       this implies creating a Numba type object (on the first dispatcher call);
       the typecode cache is then populated.
    2. matching of the typecode list in _dispatcherimpl.cpp fails, since the
       typecode is new.
    3. we have to compile: compile_and_invoke() is called, it will invoke
       Dispatcher_Insert to register the new signature.

   The reference to the Numba type object returned in step 1 is deleted as
   soon as we call Py_DECREF() on it, since we are holding the only
   reference. If this happens and we use the typecode we got to populate the
   cache, then the cache won't ever return the correct typecode, and the
   dispatcher will never successfully match the typecodes with those of
   some already-compiled instance. So we need to make sure that we don't
   call Py_DECREF() on objects whose typecode will be used to populate the
   cache. This is ensured by calling _typecode_fallback with
   retain_reference == 0.

   Note that technically we are leaking the reference, since we do not continue
   to hold a pointer to the type object that we get back from typeof_pyval.
   However, we don't need to refer to it again, we just need to make sure that
   it is never deleted.
*/
static int
_typecode_fallback(PyObject *dispatcher, PyObject *val,
                   int retain_reference) {
    PyObject *numba_type;
    int typecode;

    /*
     * For values that define "_numba_type_", which holds a numba Type
     * instance that should be used as the type of the value.
     * Note this is done here, not in typeof_typecode(), so that
     * some values can still benefit from fingerprint caching.
     */
    if (PyObject_HasAttr(val, str_numba_type)) {
        numba_type = PyObject_GetAttrString(val, "_numba_type_");
        if (!numba_type)
            return -1;
    }
    else {
        // Go back to the interpreter
        numba_type = PyObject_CallMethodObjArgs((PyObject *) dispatcher,
                                                str_typeof_pyval, val, NULL);
    }
    if (!numba_type)
        return -1;
    typecode = _typecode_from_type_object(numba_type);
    if (!retain_reference)
        Py_DECREF(numba_type);
    return typecode;
}

/* Variations on _typecode_fallback for convenience */

static
int typecode_fallback(PyObject *dispatcher, PyObject *val) {
    return _typecode_fallback(dispatcher, val, 0);
}

static
int typecode_fallback_keep_ref(PyObject *dispatcher, PyObject *val) {
    return _typecode_fallback(dispatcher, val, 1);
}


/* A cache mapping fingerprints (string_writer_t *) to typecodes (int). */
static _Numba_hashtable_t *fingerprint_hashtable = NULL;

static Py_uhash_t
hash_writer(const void *key)
{
    string_writer_t *writer = (string_writer_t *) key;
    Py_uhash_t x = 0;

    /* The old FNV algorithm used by Python 2 */
    if (writer->n > 0) {
        unsigned char *p = (unsigned char *) writer->buf;
        Py_ssize_t len = writer->n;
        x ^= *p << 7;
        while (--len >= 0)
            x = (1000003*x) ^ *p++;
        x ^= writer->n;
        if (x == (Py_uhash_t) -1)
            x = -2;
    }
    return x;
}

static int
compare_writer(const void *key, const _Numba_hashtable_entry_t *entry)
{
    string_writer_t *v = (string_writer_t *) key;
    string_writer_t *w = (string_writer_t *) entry->key;
    if (v->n != w->n)
        return 0;
    return memcmp(v->buf, w->buf, v->n) == 0;
}

/* Try to compute *val*'s typecode using its fingerprint and the
 * fingerprint->typecode cache.
 */
static int
typecode_using_fingerprint(PyObject *dispatcher, PyObject *val)
{
    int typecode;
    string_writer_t w;

    string_writer_init(&w);

    if (compute_fingerprint(&w, val)) {
        string_writer_clear(&w);
        if (PyErr_ExceptionMatches(PyExc_NotImplementedError)) {
            /* Can't compute a type fingerprint for the given value,
               fall back on typeof() without caching. */
            PyErr_Clear();
            return typecode_fallback(dispatcher, val);
        }
        return -1;
    }
    if (_Numba_HASHTABLE_GET(fingerprint_hashtable, &w, typecode) > 0) {
        /* Cache hit */
        string_writer_clear(&w);
        return typecode;
    }

    /* Not found in cache: invoke pure Python typeof() and cache result.
     * Note we have to keep the type alive forever as explained
     * above in _typecode_fallback().
     */
    typecode = typecode_fallback_keep_ref(dispatcher, val);
    if (typecode >= 0) {
        string_writer_t *key = (string_writer_t *) malloc(sizeof(string_writer_t));
        if (key == NULL) {
            string_writer_clear(&w);
            PyErr_NoMemory();
            return -1;
        }
        /* Ownership of the string writer's buffer will be transferred
         * to the hash table.
         */
        string_writer_move(key, &w);
        if (_Numba_HASHTABLE_SET(fingerprint_hashtable, key, typecode)) {
            string_writer_clear(&w);
            PyErr_NoMemory();
            return -1;
        }
    }
    return typecode;
}


/*
 * Direct lookup table for extra-fast typecode resolution of simple array types.
 */

#define N_DTYPES 12
#define N_NDIM 5    /* Fast path for up to 5D array */
#define N_LAYOUT 3
static int cached_arycode[N_NDIM][N_LAYOUT][N_DTYPES];

/* Convert a Numpy dtype number to an internal index into cached_arycode.
   The returned value must also be a valid index into BASIC_TYPECODES. */
static int dtype_num_to_typecode(int type_num) {
    int dtype;
    switch(type_num) {
    case NPY_INT8:
        dtype = 0;
        break;
    case NPY_INT16:
        dtype = 1;
        break;
    case NPY_INT32:
        dtype = 2;
        break;
    case NPY_INT64:
        dtype = 3;
        break;
    case NPY_UINT8:
        dtype = 4;
        break;
    case NPY_UINT16:
        dtype = 5;
        break;
    case NPY_UINT32:
        dtype = 6;
        break;
    case NPY_UINT64:
        dtype = 7;
        break;
    case NPY_FLOAT32:
        dtype = 8;
        break;
    case NPY_FLOAT64:
        dtype = 9;
        break;
    case NPY_COMPLEX64:
        dtype = 10;
        break;
    case NPY_COMPLEX128:
        dtype = 11;
        break;
    default:
        /* Type not included in the global lookup table */
        dtype = -1;
    }
    return dtype;
}

static
int get_cached_typecode(PyArray_Descr* descr) {
    PyObject* tmpobject = PyDict_GetItem(typecache, (PyObject*)descr);
    if (tmpobject == NULL)
        return -1;

    return PyLong_AsLong(tmpobject);
}

static
void cache_typecode(PyArray_Descr* descr, int typecode) {
    PyObject* value = PyLong_FromLong(typecode);
    PyDict_SetItem(typecache, (PyObject*)descr, value);
    Py_DECREF(value);
}

static
PyObject* ndarray_key(int ndim, int layout, int readonly, PyArray_Descr* descr) {
    PyObject* tmpndim = PyLong_FromLong(ndim);
    PyObject* tmplayout = PyLong_FromLong(layout);
    PyObject* tmpreadonly = PyLong_FromLong(readonly);
    PyObject* key = PyTuple_Pack(4, tmpndim, tmplayout, tmpreadonly, descr);
    Py_DECREF(tmpndim);
    Py_DECREF(tmplayout);
    Py_DECREF(tmpreadonly);
    return key;
}

static
int get_cached_ndarray_typecode(int ndim, int layout, int readonly, PyArray_Descr* descr) {
    PyObject* key = ndarray_key(ndim, layout, readonly, descr);
    PyObject *tmpobject = PyDict_GetItem(ndarray_typecache, key);
    if (tmpobject == NULL)
        return -1;

    Py_DECREF(key);
    return PyLong_AsLong(tmpobject);
}

static
void cache_ndarray_typecode(int ndim, int layout, int readonly, PyArray_Descr* descr,
                            int typecode) {
    PyObject* key = ndarray_key(ndim, layout, readonly, descr);
    PyObject* value = PyLong_FromLong(typecode);
    PyDict_SetItem(ndarray_typecache, key, value);
    Py_DECREF(key);
    Py_DECREF(value);
}

static
int typecode_ndarray(PyObject *dispatcher, PyArrayObject *ary) {
    int typecode;
    int dtype;
    int ndim = PyArray_NDIM(ary);
    int layout = 0;
    int readonly = 0;

    /* The order in which we check for the right contiguous-ness is important.
       The order must match the order by numba.numpy_support.map_layout.
       Further, only *contiguous-ness* is checked, not alignment, byte order or
       write permissions.
    */
    if (PyArray_IS_C_CONTIGUOUS(ary)){
        layout = 1;
    } else if (PyArray_IS_F_CONTIGUOUS(ary)) {
        layout = 2;
    }

    /* the typecode cache by convention is for "behaved" arrays (aligned and
     * writeable), all others must be forced to the fall back */
    if (!PyArray_ISBEHAVED(ary)) goto FALLBACK;

    if (ndim <= 0 || ndim > N_NDIM) goto FALLBACK;

    dtype = dtype_num_to_typecode(PyArray_TYPE(ary));
    if (dtype == -1) goto FALLBACK;

    /* Fast path, using direct table lookup */
    assert(layout < N_LAYOUT);
    assert(ndim <= N_NDIM);
    assert(dtype < N_DTYPES);

    typecode = cached_arycode[ndim - 1][layout][dtype];
    if (typecode == -1) {
        /* First use of this table entry, so it requires populating */
        typecode = typecode_fallback_keep_ref(dispatcher, (PyObject*)ary);
        cached_arycode[ndim - 1][layout][dtype] = typecode;
    }
    return typecode;

FALLBACK:
    /* Slower path, for non-trivial array types */

    /* If this isn't a structured array then we can't use the cache */
    if (PyArray_TYPE(ary) != NPY_VOID)
        return typecode_using_fingerprint(dispatcher, (PyObject *) ary);

    /* Check type cache */
    readonly = !PyArray_ISWRITEABLE(ary);
    typecode = get_cached_ndarray_typecode(ndim, layout, readonly, PyArray_DESCR(ary));
    if (typecode == -1) {
        /* First use of this type, use fallback and populate the cache */
        typecode = typecode_fallback_keep_ref(dispatcher, (PyObject*)ary);
        cache_ndarray_typecode(ndim, layout, readonly, PyArray_DESCR(ary), typecode);
    }
    return typecode;
}

static
int typecode_arrayscalar(PyObject *dispatcher, PyObject* aryscalar) {
    int typecode;
    PyArray_Descr *descr;
    descr = PyArray_DescrFromScalar(aryscalar);
    if (!descr)
        return typecode_using_fingerprint(dispatcher, aryscalar);

    /* Is it a structured scalar? */
    if (descr->type_num == NPY_VOID) {
        typecode = get_cached_typecode(descr);
        if (typecode == -1) {
            /* Resolve through fallback then populate cache */
            typecode = typecode_fallback_keep_ref(dispatcher, aryscalar);
            cache_typecode(descr, typecode);
        }
        Py_DECREF(descr);
        return typecode;
    }

    /* Is it one of the well-known basic types? */
    typecode = dtype_num_to_typecode(descr->type_num);
    Py_DECREF(descr);
    if (typecode == -1)
        return typecode_using_fingerprint(dispatcher, aryscalar);
    return BASIC_TYPECODES[typecode];
}

static
int typecode_devicendarray(PyObject *dispatcher, PyObject *ary)
{
    int typecode;
    int dtype;
    int ndim;
    int layout = 0;
    PyObject *ndim_obj = nullptr;
    PyObject *num_obj = nullptr;
    PyObject *dtype_obj = nullptr;
    int dtype_num = 0;

    PyObject* flags = PyObject_GetAttrString(ary, "flags");
    if (flags == NULL)
    {
        PyErr_Clear();
        goto FALLBACK;
    }

    if (PyDict_GetItemString(flags, "C_CONTIGUOUS") == Py_True) {
        layout = 1;
    } else if (PyDict_GetItemString(flags, "F_CONTIGUOUS") == Py_True) {
        layout = 2;
    }

    Py_DECREF(flags);

    ndim_obj = PyObject_GetAttrString(ary, "ndim");
    if (ndim_obj == NULL) {
        /* If there's no ndim, try to proceed by clearing the error and using the
         * fallback. */
        PyErr_Clear();
        goto FALLBACK;
    }

    ndim = PyLong_AsLong(ndim_obj);
    Py_DECREF(ndim_obj);

    if (PyErr_Occurred()) {
        /* ndim wasn't an integer for some reason - unlikely to happen, but try
         * the fallback. */
        PyErr_Clear();
        goto FALLBACK;
    }

    if (ndim <= 0 || ndim > N_NDIM)
        goto FALLBACK;

    dtype_obj = PyObject_GetAttrString(ary, "dtype");
    if (dtype_obj == NULL) {
        /* No dtype: try the fallback. */
        PyErr_Clear();
        goto FALLBACK;
    }

    num_obj = PyObject_GetAttrString(dtype_obj, "num");
    Py_DECREF(dtype_obj);

    if (num_obj == NULL) {
        /* This strange dtype has no num - try the fallback. */
        PyErr_Clear();
        goto FALLBACK;
    }

    dtype_num = PyLong_AsLong(num_obj);
    Py_DECREF(num_obj);

    if (PyErr_Occurred()) {
        /* num wasn't an integer for some reason - unlikely to happen, but try
         * the fallback. */
        PyErr_Clear();
        goto FALLBACK;
    }

    dtype = dtype_num_to_typecode(dtype_num);
    if (dtype == -1) {
        /* Not a dtype we have in the global lookup table. */
        goto FALLBACK;
    }

    /* Fast path, using direct table lookup */
    assert(layout < N_LAYOUT);
    assert(ndim <= N_NDIM);
    assert(dtype < N_DTYPES);
    typecode = cached_arycode[ndim - 1][layout][dtype];

    if (typecode == -1) {
        /* First use of this table entry, so it requires populating */
        typecode = typecode_fallback_keep_ref(dispatcher, (PyObject*)ary);
        cached_arycode[ndim - 1][layout][dtype] = typecode;
    }

    return typecode;

FALLBACK:
    /* Slower path, for non-trivial array types. At present this always uses
       the fingerprinting to get the typecode. Future optimization might
       implement a cache, but this would require some fast equivalent of
       PyArray_DESCR for a device array. */

    return typecode_using_fingerprint(dispatcher, (PyObject *) ary);
}

extern "C" int
typeof_typecode(PyObject *dispatcher, PyObject *val)
{
    PyTypeObject *tyobj = Py_TYPE(val);
    int subtype_attr;
    /* This needs to be kept in sync with Dispatcher.typeof_pyval(),
     * otherwise funny things may happen.
     */
    if (tyobj == &PyInt_Type || tyobj == &PyLong_Type) {
#if SIZEOF_VOID_P < 8
        /* On 32-bit platforms, choose between tc_intp (32-bit) and tc_int64 */
        PY_LONG_LONG ll = PyLong_AsLongLong(val);
        if (ll == -1 && PyErr_Occurred()) {
            /* The integer is too large, let us truncate it */
            PyErr_Clear();
            return tc_int64;
        }
        if ((ll & 0xffffffff) != ll)
            return tc_int64;
#endif
        return tc_intp;
    }
    else if (tyobj == &PyFloat_Type)
        return tc_float64;
    else if (tyobj == &PyComplex_Type)
        return tc_complex128;
    /* Array scalar handling */
    else if (PyArray_CheckScalar(val)) {
        return typecode_arrayscalar(dispatcher, val);
    }
    /* Array handling */
    else if (tyobj == &PyArray_Type) {
        return typecode_ndarray(dispatcher, (PyArrayObject*)val);
    }
    /* Subtype of CUDA device array */
    else if (PyType_IsSubtype(tyobj, &DeviceArrayType)) {
        return typecode_devicendarray(dispatcher, val);
    }
    /* Subtypes of Array handling */
    else if (PyType_IsSubtype(tyobj, &PyArray_Type)) {
        /* By default, Numba will treat all numpy.ndarray subtypes as if they
           were the base numpy.ndarray type.  In this way, ndarray subtypes
           can easily use all of the support that Numba has for ndarray
           methods.
           EXPERIMENTAL: There may be cases where a programmer would NOT want
           ndarray subtypes to be treated exactly like the base numpy.ndarray.
           For this purpose, a currently experimental feature allows a
           programmer to add an attribute named
           __numba_array_subtype_dispatch__ to their ndarray subtype.  This
           attribute can have any value as Numba only checks for the presence
           of the attribute and not its value.  When present, a ndarray subtype
           will NOT be typed by Numba as a regular ndarray but this code will
           fallthrough to the typecode_using_fingerprint call, which will
           create a new unique Numba typecode for this ndarray subtype.  This
           behavior has several significant effects.  First, since this
           ndarray subtype will be treated as a different type by Numba,
           the Numba dispatcher would then specialize on this type.  So, if
           there was a function that had several parameters that were
           expected to be either numpy.ndarray or a subtype of ndarray, then
           Numba would compile a custom version of this function for each
           combination of base and subtypes that were actually passed to the
           function.  Second, because this subtype would now be treated as
           a totally separate type, it will cease to function in Numba unless
           an implementation of that type is provided to Numba through the
           Numba type extension mechanisms (e.g., overload).  This would
           typically start with defining a Numba type corresponding to the
           ndarray subtype. This is the same concept as how Numba has a
           corollary of numpy.ndarray in its type system as types.Array.
           Next, one would typically defining boxing and unboxing routines
           and the associated memory model.  Then, overloads for NumPy
           functions on that type would be created.  However,
           if the same default array memory model is used then there are tricks
           one can do to look at Numba's internal types.Array registries and
           to quickly apply those to the subtype as well.  In this manner,
           only those cases where the base ndarray and the ndarray subtype
           behavior differ would new custom functions need to be written for
           the subtype. Finally,
           after adding support for the new type, you would have a separate
           ndarray subtype that could operate with other objects of the same
           subtype but would not support interoperation with regular NumPy
           ndarrays.  In standard Python, this interoperation is provided
           through the __array_ufunc__ magic method in the ndarray subtype
           class and in that case the function operates on ndarrays or their
           subtypes.  This idea is extended into Numba such that
           __array_ufunc__ can be present in a Numba array type object.
           In this case, this function is consulted during Numba typing and
           so the arguments to __array_ufunc__ are Numba types instead of
           ndarray subtypes.  The array type __array_ufunc__ returns the
           type of the output of the given ufunc.
         */
        subtype_attr = PyObject_HasAttrString(val, "__numba_array_subtype_dispatch__");
        if (!subtype_attr) {
            return typecode_ndarray(dispatcher, (PyArrayObject*)val);
        }
    }

    return typecode_using_fingerprint(dispatcher, val);
}


static
void* wrap_import_array(void) {
    import_array(); /* import array returns NULL on failure */
    return (void*)1;
}


static
int init_numpy(void) {
    return wrap_import_array() != NULL;
}


/*
 * typeof_init(omittedarg_type, typecode_dict)
 * (called from dispatcher.py to fill in missing information)
 */
extern "C" PyObject *
typeof_init(PyObject *self, PyObject *args)
{
    PyObject *tmpobj;
    PyObject *dict;
    int index = 0;

    if (!PyArg_ParseTuple(args, "O!O!:typeof_init",
                          &PyType_Type, &omittedarg_type,
                          &PyDict_Type, &dict))
        return NULL;

    /* Initialize Numpy API */
    if ( ! init_numpy() ) {
        return NULL;
    }

    #define UNWRAP_TYPE(S)                                              \
        if(!(tmpobj = PyDict_GetItemString(dict, #S))) return NULL;     \
        else {  tc_##S = PyLong_AsLong(tmpobj);                         \
                BASIC_TYPECODES[index++] = tc_##S;  }

    UNWRAP_TYPE(int8)
    UNWRAP_TYPE(int16)
    UNWRAP_TYPE(int32)
    UNWRAP_TYPE(int64)

    UNWRAP_TYPE(uint8)
    UNWRAP_TYPE(uint16)
    UNWRAP_TYPE(uint32)
    UNWRAP_TYPE(uint64)

    UNWRAP_TYPE(float32)
    UNWRAP_TYPE(float64)

    UNWRAP_TYPE(complex64)
    UNWRAP_TYPE(complex128)

    switch(sizeof(void*)) {
    case 4:
        tc_intp = tc_int32;
        break;
    case 8:
        tc_intp = tc_int64;
        break;
    default:
        PyErr_SetString(PyExc_AssertionError, "sizeof(void*) != {4, 8}");
        return NULL;
    }

    #undef UNWRAP_TYPE

    typecache = PyDict_New();
    ndarray_typecache = PyDict_New();
    structured_dtypes = PyDict_New();
    if (typecache == NULL || ndarray_typecache == NULL ||
        structured_dtypes == NULL) {
        PyErr_SetString(PyExc_RuntimeError, "failed to create type cache");
        return NULL;
    }

    fingerprint_hashtable = _Numba_hashtable_new(sizeof(int),
                                              hash_writer,
                                              compare_writer);
    if (fingerprint_hashtable == NULL) {
        PyErr_NoMemory();
        return NULL;
    }

    /* initialize cached_arycode to all ones (in bits) */
    memset(cached_arycode, 0xFF, sizeof(cached_arycode));

    str_typeof_pyval = PyString_InternFromString("typeof_pyval");
    str_value = PyString_InternFromString("value");
    str_numba_type = PyString_InternFromString("_numba_type_");
    if (!str_value || !str_typeof_pyval || !str_numba_type)
        return NULL;

    Py_RETURN_NONE;
}