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#include "_pymodule.h"
#include <string.h>
#include <time.h>
#include <assert.h>
#include "_numba_common.h"
#include "_typeof.h"
#include "_hashtable.h"
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include <numpy/ndarrayobject.h>
/* 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;
/*
* 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 = malloc(newsize);
else
w->buf = 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;
md = &(((PyArray_DatetimeDTypeMetaData *)descr->c_metadata)->meta);
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, PyArray_Descr* descr) {
PyObject* tmpndim = PyLong_FromLong(ndim);
PyObject* tmplayout = PyLong_FromLong(layout);
PyObject* key = PyTuple_Pack(3, tmpndim, tmplayout, descr);
Py_DECREF(tmpndim);
Py_DECREF(tmplayout);
return key;
}
static
int get_cached_ndarray_typecode(int ndim, int layout, PyArray_Descr* descr) {
PyObject* key = ndarray_key(ndim, layout, 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, PyArray_Descr* descr,
int typecode) {
PyObject* key = ndarray_key(ndim, layout, 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;
/* 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 */
typecode = get_cached_ndarray_typecode(ndim, layout, 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, 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];
}
int
typeof_typecode(PyObject *dispatcher, PyObject *val)
{
PyTypeObject *tyobj = Py_TYPE(val);
/* 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 (PyType_IsSubtype(tyobj, &PyArray_Type)) {
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)
*/
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;
}
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