refactor: remove devicearray code to reduce complexity

numba_cuda/numba/cuda/cext/_dispatcher.cpp

@@ -12,7 +12,6 @@
 #include "frameobject.h"
 #include "traceback.h"
 #include "typeconv.hpp"
-#include "_devicearray.h"
 
 /*
  * Notes on the C_TRACE macro:
@@ -940,37 +939,6 @@ Dispatcher_cuda_call(Dispatcher *self, PyObject *args, PyObject *kws)
     return retval;
 }
 
-static int
-import_devicearray(void)
-{
-    PyObject *devicearray = PyImport_ImportModule(NUMBA_DEVICEARRAY_IMPORT_NAME);
-    if (devicearray == NULL) {
-        return -1;
-    }
-
-    PyObject *d = PyModule_GetDict(devicearray);
-    if (d == NULL) {
-      Py_DECREF(devicearray);
-      return -1;
-    }
-
-    PyObject *key = PyUnicode_FromString("_DEVICEARRAY_API");
-    PyObject *c_api = PyDict_GetItemWithError(d, key);
-    int retcode = 0;
-    if (PyCapsule_IsValid(c_api, NUMBA_DEVICEARRAY_IMPORT_NAME "._DEVICEARRAY_API")) {
-      DeviceArray_API = (void**)PyCapsule_GetPointer(c_api, NUMBA_DEVICEARRAY_IMPORT_NAME "._DEVICEARRAY_API");
-      if (DeviceArray_API == NULL) {
-        retcode = -1;
-      }
-    } else {
-      retcode = -1;
-    }
-
-    Py_DECREF(key);
-    Py_DECREF(devicearray);
-    return retcode;
-}
-
 static PyMethodDef Dispatcher_methods[] = {
     { "_clear", (PyCFunction)Dispatcher_clear, METH_NOARGS, NULL },
     { "_insert", (PyCFunction)Dispatcher_Insert, METH_VARARGS | METH_KEYWORDS,
@@ -1076,12 +1044,6 @@ static PyMethodDef ext_methods[] = {
 
 
 MOD_INIT(_dispatcher) {
-    if (import_devicearray() < 0) {
-      PyErr_Print();
-      PyErr_SetString(PyExc_ImportError, NUMBA_DEVICEARRAY_IMPORT_NAME " failed to import");
-      return MOD_ERROR_VAL;
-    }
-
     PyObject *m;
     MOD_DEF(m, "_dispatcher", "No docs", ext_methods)
     if (m == NULL)

numba_cuda/numba/cuda/cext/_typeof.cpp

@@ -9,7 +9,6 @@
 
 #include "_typeof.h"
 #include "_hashtable.h"
-#include "_devicearray.h"
 #include "pyerrors.h"
 
 #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
@@ -56,9 +55,6 @@ 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.
  */
@@ -857,109 +853,6 @@ int typecode_arrayscalar(PyObject *dispatcher, PyObject* 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)
 {
@@ -994,10 +887,6 @@ typeof_typecode(PyObject *dispatcher, PyObject *val)
     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