/*
* This function executes all the standard NumPy reduction function
* boilerplate code, just calling assign_identity and the appropriate
* inner loop function where necessary.
*
* operand : The array to be reduced.
* out : NULL, or the array into which to place the result.
* wheremask : NOT YET SUPPORTED, but this parameter is placed here
* so that support can be added in the future without breaking
* API compatibility. Pass in NULL.
* operand_dtype : The dtype the inner loop expects for the operand.
* result_dtype : The dtype the inner loop expects for the result.
* casting : The casting rule to apply to the operands.
* axis_flags : Flags indicating the reduction axes of 'operand'.
* reorderable : If True, the reduction being done is reorderable, which
* means specifying multiple axes of reduction at once is ok,
* and the reduction code may calculate the reduction in an
* arbitrary order. The calculation may be reordered because
* of cache behavior or multithreading requirements.
* keepdims : If true, leaves the reduction dimensions in the result
* with size one.
* subok : If true, the result uses the subclass of operand, otherwise
* it is always a base class ndarray.
* assign_identity : If NULL, PyArray_InitializeReduceResult is used, otherwise
* this function is called to initialize the result to
* the reduction's unit.
* loop : The loop which does the reduction.
* data : Data which is passed to assign_identity and the inner loop.
* buffersize : Buffer size for the iterator. For the default, pass in 0.
* funcname : The name of the reduction function, for error messages.
* errormask : forwarded from _get_bufsize_errmask
*
* TODO FIXME: if you squint, this is essentially an second independent
* implementation of generalized ufuncs with signature (i)->(), plus a few
* extra bells and whistles. (Indeed, as far as I can tell, it was originally
* split out to support a fancy version of count_nonzero... which is not
* actually a reduction function at all, it's just a (i)->() function!) So
* probably these two implementation should be merged into one. (In fact it
* would be quite nice to support axis= and keepdims etc. for arbitrary
* generalized ufuncs!)
*/
NPY_NO_EXPORT PyArrayObject *
PyUFunc_ReduceWrapper(PyArrayObject *operand, PyArrayObject *out,
PyArrayObject *wheremask,
PyArray_Descr *operand_dtype,
PyArray_Descr *result_dtype,
NPY_CASTING casting,
npy_bool *axis_flags, int reorderable,
int keepdims,
int subok,
PyArray_AssignReduceIdentityFunc *assign_identity,
PyArray_ReduceLoopFunc *loop,
void *data, npy_intp buffersize, const char *funcname,
int errormask)
{
PyArrayObject *result = NULL, *op_view = NULL;
npy_intp skip_first_count = 0;
/* Iterator parameters */
NpyIter *iter = NULL;
PyArrayObject *op[2];
PyArray_Descr *op_dtypes[2];
npy_uint32 flags, op_flags[2];
/* Validate that the parameters for future expansion are NULL */
if (wheremask != NULL) {
PyErr_SetString(PyExc_RuntimeError,
"Reduce operations in NumPy do not yet support "
"a where mask");
return NULL;
}
/*
* This either conforms 'out' to the ndim of 'operand', or allocates
* a new array appropriate for this reduction.
*
* A new array with WRITEBACKIFCOPY is allocated if operand and out have memory
* overlap.
*/
Py_INCREF(result_dtype);
result = PyArray_CreateReduceResult(operand, out,
result_dtype, axis_flags,
keepdims, subok, funcname);
if (result == NULL) {
goto fail;
}
/*
* Initialize the result to the reduction unit if possible,
* otherwise copy the initial values and get a view to the rest.
*/
if (assign_identity != NULL) {
/*
* If this reduction is non-reorderable, make sure there are
* only 0 or 1 axes in axis_flags.
*/
if (!reorderable && check_nonreorderable_axes(PyArray_NDIM(operand),
axis_flags, funcname) < 0) {
goto fail;
}
if (assign_identity(result, data) < 0) {
goto fail;
}
op_view = operand;
Py_INCREF(op_view);
}
else {
op_view = PyArray_InitializeReduceResult(result, operand,
axis_flags, reorderable,
&skip_first_count, funcname);
if (op_view == NULL) {
goto fail;
}
/* empty op_view signals no reduction; but 0-d arrays cannot be empty */
if ((PyArray_SIZE(op_view) == 0) || (PyArray_NDIM(operand) == 0)) {
Py_DECREF(op_view);
op_view = NULL;
goto finish;
}
}
/* Set up the iterator */
op[0] = result;
op[1] = op_view;
op_dtypes[0] = result_dtype;
op_dtypes[1] = operand_dtype;
flags = NPY_ITER_BUFFERED |
NPY_ITER_EXTERNAL_LOOP |
NPY_ITER_GROWINNER |
NPY_ITER_DONT_NEGATE_STRIDES |
NPY_ITER_ZEROSIZE_OK |
NPY_ITER_REDUCE_OK |
NPY_ITER_REFS_OK;
op_flags[0] = NPY_ITER_READWRITE |
NPY_ITER_ALIGNED |
NPY_ITER_NO_SUBTYPE;
op_flags[1] = NPY_ITER_READONLY |
NPY_ITER_ALIGNED;
iter = NpyIter_AdvancedNew(2, op, flags,
NPY_KEEPORDER, casting,
op_flags,
op_dtypes,
-1, NULL, NULL, buffersize);
if (iter == NULL) {
goto fail;
}
/* Start with the floating-point exception flags cleared */
PyUFunc_clearfperr();
if (NpyIter_GetIterSize(iter) != 0) {
NpyIter_IterNextFunc *iternext;
char **dataptr;
npy_intp *strideptr;
npy_intp *countptr;
int needs_api;
iternext = NpyIter_GetIterNext(iter, NULL);
if (iternext == NULL) {
goto fail;
}
dataptr = NpyIter_GetDataPtrArray(iter);
strideptr = NpyIter_GetInnerStrideArray(iter);
countptr = NpyIter_GetInnerLoopSizePtr(iter);
needs_api = NpyIter_IterationNeedsAPI(iter);
/* Straightforward reduction */
if (loop == NULL) {
PyErr_Format(PyExc_RuntimeError,
"reduction operation %s did not supply an "
"inner loop function", funcname);
goto fail;
}
if (loop(iter, dataptr, strideptr, countptr,
iternext, needs_api, skip_first_count, data) < 0) {
goto fail;
}
}
/* Check whether any errors occurred during the loop */
if (PyErr_Occurred() ||
_check_ufunc_fperr(errormask, NULL, "reduce") < 0) {
goto fail;
}
NpyIter_Deallocate(iter);
Py_DECREF(op_view);
finish:
/* Strip out the extra 'one' dimensions in the result */
if (out == NULL) {
if (!keepdims) {
PyArray_RemoveAxesInPlace(result, axis_flags);
}
}
else {
PyArray_ResolveWritebackIfCopy(result); /* prevent spurious warnings */
Py_DECREF(result);
result = out;
Py_INCREF(result);
}
return result;
fail:
PyArray_ResolveWritebackIfCopy(result); /* prevent spurious warnings */
Py_XDECREF(result);
Py_XDECREF(op_view);
if (iter != NULL) {
NpyIter_Deallocate(iter);
}
return NULL;
}
static PyObject *
PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out,
int axis, int otype)
{
PyArrayObject *op[2];
PyArray_Descr *op_dtypes[2] = {NULL, NULL};
int op_axes_arrays[2][NPY_MAXDIMS];
int *op_axes[2] = {op_axes_arrays[0], op_axes_arrays[1]};
npy_uint32 op_flags[2];
int idim, ndim, otype_final;
int needs_api, need_outer_iterator;
NpyIter *iter = NULL, *iter_inner = NULL;
/* The selected inner loop */
PyUFuncGenericFunction innerloop = NULL;
void *innerloopdata = NULL;
const char *ufunc_name = ufunc->name ? ufunc->name : "(unknown)";
/* These parameters come from extobj= or from a TLS global */
int buffersize = 0, errormask = 0;
NPY_BEGIN_THREADS_DEF;
NPY_UF_DBG_PRINT1("\nEvaluating ufunc %s.accumulate\n", ufunc_name);
#if 0
printf("Doing %s.accumulate on array with dtype : ", ufunc_name);
PyObject_Print((PyObject *)PyArray_DESCR(arr), stdout, 0);
printf("\n");
#endif
if (_get_bufsize_errmask(NULL, "accumulate", &buffersize, &errormask) < 0) {
return NULL;
}
/* Take a reference to out for later returning */
Py_XINCREF(out);
otype_final = otype;
if (get_binary_op_function(ufunc, &otype_final,
&innerloop, &innerloopdata) < 0) {
PyArray_Descr *dtype = PyArray_DescrFromType(otype);
PyErr_Format(PyExc_ValueError,
"could not find a matching type for %s.accumulate, "
"requested type has type code '%c'",
ufunc_name, dtype ? dtype->type : '-');
Py_XDECREF(dtype);
goto fail;
}
ndim = PyArray_NDIM(arr);
/*
* Set up the output data type, using the input's exact
* data type if the type number didn't change to preserve
* metadata
*/
if (PyArray_DESCR(arr)->type_num == otype_final) {
if (PyArray_ISNBO(PyArray_DESCR(arr)->byteorder)) {
op_dtypes[0] = PyArray_DESCR(arr);
Py_INCREF(op_dtypes[0]);
}
else {
op_dtypes[0] = PyArray_DescrNewByteorder(PyArray_DESCR(arr),
NPY_NATIVE);
}
}
else {
op_dtypes[0] = PyArray_DescrFromType(otype_final);
}
if (op_dtypes[0] == NULL) {
goto fail;
}
#if NPY_UF_DBG_TRACING
printf("Found %s.accumulate inner loop with dtype : ", ufunc_name);
PyObject_Print((PyObject *)op_dtypes[0], stdout, 0);
printf("\n");
#endif
/* Set up the op_axes for the outer loop */
for (idim = 0; idim < ndim; ++idim) {
op_axes_arrays[0][idim] = idim;
op_axes_arrays[1][idim] = idim;
}
/* The per-operand flags for the outer loop */
op_flags[0] = NPY_ITER_READWRITE |
NPY_ITER_NO_BROADCAST |
NPY_ITER_ALLOCATE |
NPY_ITER_NO_SUBTYPE;
op_flags[1] = NPY_ITER_READONLY;
op[0] = out;
op[1] = arr;
need_outer_iterator = (ndim > 1);
/* We can't buffer, so must do UPDATEIFCOPY */
if (!PyArray_ISALIGNED(arr) || (out && !PyArray_ISALIGNED(out)) ||
!PyArray_EquivTypes(op_dtypes[0], PyArray_DESCR(arr)) ||
(out &&
!PyArray_EquivTypes(op_dtypes[0], PyArray_DESCR(out)))) {
need_outer_iterator = 1;
}
if (need_outer_iterator) {
int ndim_iter = 0;
npy_uint32 flags = NPY_ITER_ZEROSIZE_OK|
NPY_ITER_REFS_OK;
PyArray_Descr **op_dtypes_param = NULL;
/*
* The way accumulate is set up, we can't do buffering,
* so make a copy instead when necessary.
*/
ndim_iter = ndim;
flags |= NPY_ITER_MULTI_INDEX;
/* Add some more flags */
op_flags[0] |= NPY_ITER_UPDATEIFCOPY|NPY_ITER_ALIGNED;
op_flags[1] |= NPY_ITER_COPY|NPY_ITER_ALIGNED;
op_dtypes_param = op_dtypes;
op_dtypes[1] = op_dtypes[0];
NPY_UF_DBG_PRINT("Allocating outer iterator\n");
iter = NpyIter_AdvancedNew(2, op, flags,
NPY_KEEPORDER, NPY_UNSAFE_CASTING,
op_flags,
op_dtypes_param,
ndim_iter, op_axes, NULL, 0);
if (iter == NULL) {
goto fail;
}
/* In case COPY or UPDATEIFCOPY occurred */
op[0] = NpyIter_GetOperandArray(iter)[0];
op[1] = NpyIter_GetOperandArray(iter)[1];
if (PyArray_SIZE(op[0]) == 0) {
if (out == NULL) {
out = op[0];
Py_INCREF(out);
}
goto finish;
}
if (NpyIter_RemoveAxis(iter, axis) != NPY_SUCCEED) {
goto fail;
}
if (NpyIter_RemoveMultiIndex(iter) != NPY_SUCCEED) {
goto fail;
}
}
/* Get the output */
if (out == NULL) {
if (iter) {
op[0] = out = NpyIter_GetOperandArray(iter)[0];
Py_INCREF(out);
}
else {
PyArray_Descr *dtype = op_dtypes[0];
Py_INCREF(dtype);
op[0] = out = (PyArrayObject *)PyArray_NewFromDescr(
&PyArray_Type, dtype,
ndim, PyArray_DIMS(op[1]), NULL, NULL,
0, NULL);
if (out == NULL) {
goto fail;
}
}
}
/*
* If the reduction axis has size zero, either return the reduction
* unit for UFUNC_REDUCE, or return the zero-sized output array
* for UFUNC_ACCUMULATE.
*/
if (PyArray_DIM(op[1], axis) == 0) {
goto finish;
}
else if (PyArray_SIZE(op[0]) == 0) {
goto finish;
}
if (iter && NpyIter_GetIterSize(iter) != 0) {
char *dataptr_copy[3];
npy_intp stride_copy[3];
npy_intp count_m1, stride0, stride1;
NpyIter_IterNextFunc *iternext;
char **dataptr;
int itemsize = op_dtypes[0]->elsize;
/* Get the variables needed for the loop */
iternext = NpyIter_GetIterNext(iter, NULL);
if (iternext == NULL) {
goto fail;
}
dataptr = NpyIter_GetDataPtrArray(iter);
/* Execute the loop with just the outer iterator */
count_m1 = PyArray_DIM(op[1], axis)-1;
stride0 = 0, stride1 = PyArray_STRIDE(op[1], axis);
NPY_UF_DBG_PRINT("UFunc: Reduce loop with just outer iterator\n");
stride0 = PyArray_STRIDE(op[0], axis);
stride_copy[0] = stride0;
stride_copy[1] = stride1;
stride_copy[2] = stride0;
needs_api = NpyIter_IterationNeedsAPI(iter);
NPY_BEGIN_THREADS_NDITER(iter);
do {
dataptr_copy[0] = dataptr[0];
dataptr_copy[1] = dataptr[1];
dataptr_copy[2] = dataptr[0];
/*
* Copy the first element to start the reduction.
*
* Output (dataptr[0]) and input (dataptr[1]) may point to
* the same memory, e.g. np.add.accumulate(a, out=a).
*/
if (otype == NPY_OBJECT) {
/*
* Incref before decref to avoid the possibility of the
* reference count being zero temporarily.
*/
Py_XINCREF(*(PyObject **)dataptr_copy[1]);
Py_XDECREF(*(PyObject **)dataptr_copy[0]);
*(PyObject **)dataptr_copy[0] =
*(PyObject **)dataptr_copy[1];
}
else {
memmove(dataptr_copy[0], dataptr_copy[1], itemsize);
}
if (count_m1 > 0) {
/* Turn the two items into three for the inner loop */
dataptr_copy[1] += stride1;
dataptr_copy[2] += stride0;
NPY_UF_DBG_PRINT1("iterator loop count %d\n",
(int)count_m1);
innerloop(dataptr_copy, &count_m1,
stride_copy, innerloopdata);
}
} while (iternext(iter));
NPY_END_THREADS;
}
else if (iter == NULL) {
char *dataptr_copy[3];
npy_intp stride_copy[3];
int itemsize = op_dtypes[0]->elsize;
/* Execute the loop with no iterators */
npy_intp count = PyArray_DIM(op[1], axis);
npy_intp stride0 = 0, stride1 = PyArray_STRIDE(op[1], axis);
NPY_UF_DBG_PRINT("UFunc: Reduce loop with no iterators\n");
if (PyArray_NDIM(op[0]) != PyArray_NDIM(op[1]) ||
!PyArray_CompareLists(PyArray_DIMS(op[0]),
PyArray_DIMS(op[1]),
PyArray_NDIM(op[0]))) {
PyErr_SetString(PyExc_ValueError,
"provided out is the wrong size "
"for the reduction");
goto fail;
}
stride0 = PyArray_STRIDE(op[0], axis);
stride_copy[0] = stride0;
stride_copy[1] = stride1;
stride_copy[2] = stride0;
/* Turn the two items into three for the inner loop */
dataptr_copy[0] = PyArray_BYTES(op[0]);
dataptr_copy[1] = PyArray_BYTES(op[1]);
dataptr_copy[2] = PyArray_BYTES(op[0]);
/*
* Copy the first element to start the reduction.
*
* Output (dataptr[0]) and input (dataptr[1]) may point to the
* same memory, e.g. np.add.accumulate(a, out=a).
*/
if (otype == NPY_OBJECT) {
/*
* Incref before decref to avoid the possibility of the
* reference count being zero temporarily.
*/
Py_XINCREF(*(PyObject **)dataptr_copy[1]);
Py_XDECREF(*(PyObject **)dataptr_copy[0]);
*(PyObject **)dataptr_copy[0] =
*(PyObject **)dataptr_copy[1];
}
else {
memmove(dataptr_copy[0], dataptr_copy[1], itemsize);
}
if (count > 1) {
--count;
dataptr_copy[1] += stride1;
dataptr_copy[2] += stride0;
NPY_UF_DBG_PRINT1("iterator loop count %d\n", (int)count);
needs_api = PyDataType_REFCHK(op_dtypes[0]);
if (!needs_api) {
NPY_BEGIN_THREADS_THRESHOLDED(count);
}
innerloop(dataptr_copy, &count,
stride_copy, innerloopdata);
NPY_END_THREADS;
}
}
finish:
Py_XDECREF(op_dtypes[0]);
NpyIter_Deallocate(iter);
NpyIter_Deallocate(iter_inner);
return (PyObject *)out;
fail:
Py_XDECREF(out);
Py_XDECREF(op_dtypes[0]);
NpyIter_Deallocate(iter);
NpyIter_Deallocate(iter_inner);
return NULL;
}
