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/**
* @brief MaxSim late-interaction operations for NumKong Python bindings.
* @file python/maxsim.c
* @author Ash Vardanian
* @date March 9, 2026
*
* This module owns:
* - `MaxSimPackedMatrix`: opaque pre-packed matrix for MaxSim scoring.
* - Packing API: `maxsim_pack()`.
* - Packed scoring API: `maxsim_packed()`.
* - Convenience API: `maxsim()` (pack + compute).
*/
#include "maxsim.h"
#include "tensor.h"
#include <numkong/maxsim.h>
static void MaxSimPackedMatrix_dealloc(PyObject *self) { Py_TYPE(self)->tp_free(self); }
static size_t maxsim_packed_matrix_nbytes(MaxSimPackedMatrix *mm) {
nk_dots_packed_size_punned_t size_fn = NULL;
nk_capability_t cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_maxsim_packed_size_k, mm->dtype, static_capabilities,
(nk_kernel_punned_t *)&size_fn, &cap);
if (!size_fn || !cap) return 0;
return size_fn(mm->vector_count, mm->depth);
}
static PyObject *MaxSimPackedMatrix_repr(PyObject *self) {
MaxSimPackedMatrix *mm = (MaxSimPackedMatrix *)self;
size_t packed_size = maxsim_packed_matrix_nbytes(mm);
return PyUnicode_FromFormat("<MaxSimPackedMatrix vector_count=%zu depth=%zu dtype='%s' nbytes=%zu>",
(size_t)mm->vector_count, (size_t)mm->depth, dtype_to_string(mm->dtype), packed_size);
}
static PyObject *MaxSimPackedMatrix_get_vector_count(PyObject *self, void *closure) {
nk_unused_(closure);
return PyLong_FromSize_t(((MaxSimPackedMatrix *)self)->vector_count);
}
static PyObject *MaxSimPackedMatrix_get_depth(PyObject *self, void *closure) {
nk_unused_(closure);
return PyLong_FromSize_t(((MaxSimPackedMatrix *)self)->depth);
}
static PyObject *MaxSimPackedMatrix_get_dtype(PyObject *self, void *closure) {
nk_unused_(closure);
return PyUnicode_FromString(dtype_to_string(((MaxSimPackedMatrix *)self)->dtype));
}
static PyObject *MaxSimPackedMatrix_get_nbytes(PyObject *self, void *closure) {
nk_unused_(closure);
return PyLong_FromSize_t(maxsim_packed_matrix_nbytes((MaxSimPackedMatrix *)self));
}
static PyGetSetDef MaxSimPackedMatrix_getset[] = {
{"vector_count", MaxSimPackedMatrix_get_vector_count, NULL, "Number of vectors", NULL},
{"depth", MaxSimPackedMatrix_get_depth, NULL, "Number of dimensions per vector (depth)", NULL},
{"dtype", MaxSimPackedMatrix_get_dtype, NULL, "Data type of the packed vectors", NULL},
{"nbytes", MaxSimPackedMatrix_get_nbytes, NULL, "Size of the packed buffer in bytes", NULL},
{NULL, NULL, NULL, NULL, NULL},
};
static PyObject *MaxSimPackedMatrix_packed_size(PyObject *cls, PyObject *const *args, Py_ssize_t nargs,
PyObject *kwnames) {
nk_unused_(cls);
PyObject *vector_count_obj = NULL, *depth_obj = NULL, *dtype_obj = NULL;
Py_ssize_t nkw = kwnames ? PyTuple_Size(kwnames) : 0;
Py_ssize_t total = nargs + nkw;
if (nargs < 2 || total > 3 || nargs > 3) {
PyErr_SetString(PyExc_TypeError, "packed_size(vector_count, depth, /, dtype='bf16')");
return NULL;
}
vector_count_obj = args[0];
depth_obj = args[1];
if (nargs >= 3) dtype_obj = args[2];
for (Py_ssize_t i = 0; i < nkw; i++) {
PyObject *name = PyTuple_GET_ITEM(kwnames, i);
PyObject *value = args[nargs + i];
if (PyUnicode_CompareWithASCIIString(name, "dtype") == 0) {
if (dtype_obj) {
PyErr_SetString(PyExc_TypeError, "packed_size() got multiple values for argument 'dtype'");
return NULL;
}
dtype_obj = value;
}
else {
PyErr_Format(PyExc_TypeError, "packed_size() got unexpected keyword argument '%S'", name);
return NULL;
}
}
if (!dtype_obj) {
PyErr_SetString(PyExc_TypeError, "packed_size() requires 'dtype' argument");
return NULL;
}
nk_size_t vector_count = (nk_size_t)PyLong_AsSize_t(vector_count_obj);
if (vector_count == (nk_size_t)-1 && PyErr_Occurred()) return NULL;
nk_size_t depth = (nk_size_t)PyLong_AsSize_t(depth_obj);
if (depth == (nk_size_t)-1 && PyErr_Occurred()) return NULL;
char const *dtype_str = PyUnicode_AsUTF8(dtype_obj);
if (!dtype_str) return NULL;
nk_dtype_t dtype = python_string_to_dtype(dtype_str);
if (dtype == nk_dtype_unknown_k) {
PyErr_Format(PyExc_ValueError, "Unknown dtype: '%s'", dtype_str);
return NULL;
}
nk_dots_packed_size_punned_t size_fn = NULL;
nk_capability_t cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_maxsim_packed_size_k, dtype, static_capabilities, (nk_kernel_punned_t *)&size_fn,
&cap);
if (!size_fn || !cap) {
PyErr_Format(PyExc_LookupError, "No maxsim packed_size kernel for dtype '%s'", dtype_str);
return NULL;
}
return PyLong_FromSize_t(size_fn(vector_count, depth));
}
static PyMethodDef MaxSimPackedMatrix_methods[] = {
{"packed_size", (PyCFunction)MaxSimPackedMatrix_packed_size, METH_CLASS | METH_FASTCALL | METH_KEYWORDS,
"Return packed buffer size in bytes for given dimensions and dtype."},
{NULL, NULL, 0, NULL},
};
PyTypeObject MaxSimPackedMatrixType = {
PyVarObject_HEAD_INIT(NULL, 0).tp_name = "numkong.MaxSimPackedMatrix",
.tp_doc = "Opaque pre-packed matrix for MaxSim late-interaction scoring",
.tp_basicsize = sizeof(MaxSimPackedMatrix),
.tp_itemsize = sizeof(char),
.tp_dealloc = MaxSimPackedMatrix_dealloc,
.tp_flags = Py_TPFLAGS_DEFAULT,
.tp_getset = MaxSimPackedMatrix_getset,
.tp_methods = MaxSimPackedMatrix_methods,
.tp_repr = MaxSimPackedMatrix_repr,
};
char const doc_maxsim_pack[] = //
"maxsim_pack(b, /, dtype='bf16') -> MaxSimPackedMatrix\n\n" //
"Pack a 2D matrix for MaxSim late-interaction scoring.\n\n" //
"Parameters:\n" //
" b (array_like): Source matrix with shape (vector_count, depth).\n" //
" dtype (str, optional): Packing dtype. Default: 'bf16'.\n" //
" Supported values: 'bf16', 'f16', 'f32'.\n\n" //
"Returns:\n" //
" MaxSimPackedMatrix: Opaque packed matrix for maxsim_packed().\n\n" //
"Signature:\n" //
" >>> def maxsim_pack(b, /, dtype='bf16') -> MaxSimPackedMatrix: ...";
PyObject *api_maxsim_pack(PyObject *self, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames) {
nk_unused_(self);
PyObject *b_obj = NULL;
char const *dtype_str = "bf16";
Py_ssize_t nkw = kwnames ? PyTuple_Size(kwnames) : 0;
Py_ssize_t total = nargs + nkw;
if (nargs < 1 || total > 2) {
PyErr_SetString(PyExc_TypeError, "maxsim_pack() requires 1-2 arguments: b, dtype='bf16'");
return NULL;
}
b_obj = args[0];
for (Py_ssize_t i = 0; i < nkw; i++) {
PyObject *name = PyTuple_GET_ITEM(kwnames, i);
if (PyUnicode_CompareWithASCIIString(name, "dtype") == 0) {
if (nargs >= 2) {
PyErr_SetString(PyExc_TypeError, "maxsim_pack() got multiple values for argument 'dtype'");
return NULL;
}
PyObject *val = args[nargs + i];
if (!PyUnicode_Check(val)) {
PyErr_SetString(PyExc_TypeError, "dtype must be a string");
return NULL;
}
dtype_str = PyUnicode_AsUTF8(val);
}
else {
char const *name_str = PyUnicode_AsUTF8(name);
PyErr_Format(PyExc_TypeError, "maxsim_pack() got unexpected keyword argument '%s'", name_str);
return NULL;
}
}
if (nargs >= 2) {
if (!PyUnicode_Check(args[1])) {
PyErr_SetString(PyExc_TypeError, "dtype must be a string");
return NULL;
}
dtype_str = PyUnicode_AsUTF8(args[1]);
}
nk_dtype_t target_dtype = python_string_to_dtype(dtype_str);
if (target_dtype == nk_dtype_unknown_k) {
PyErr_Format(PyExc_ValueError, "Unsupported dtype '%s'", dtype_str);
return NULL;
}
if (target_dtype != nk_bf16_k && target_dtype != nk_f16_k && target_dtype != nk_f32_k) {
PyErr_Format(PyExc_ValueError, "maxsim_pack() only supports 'bf16', 'f16', 'f32'; got '%s'", dtype_str);
return NULL;
}
Py_buffer b_buffer;
nk_buffer_backing_t b_backing;
if (!nk_get_buffer(b_obj, &b_buffer, PyBUF_STRIDES | PyBUF_FORMAT, &b_backing)) {
PyErr_SetString(PyExc_TypeError, "b must support buffer protocol or __array_interface__");
return NULL;
}
if (b_buffer.ndim != 2) {
PyBuffer_Release(&b_buffer);
PyErr_SetString(PyExc_ValueError, "b must be a 2D matrix");
return NULL;
}
nk_dtype_t src_dtype = dtype_from_buffer(&b_buffer);
if (src_dtype == nk_dtype_unknown_k) {
PyErr_Format(PyExc_TypeError, "Unsupported buffer format '%s'", b_buffer.format);
PyBuffer_Release(&b_buffer);
return NULL;
}
if (b_buffer.strides[0] < 0 || b_buffer.strides[1] < 0) {
PyBuffer_Release(&b_buffer);
PyErr_SetString(PyExc_ValueError, "maxsim packing does not support negative strides");
return NULL;
}
nk_size_t vector_count = (nk_size_t)b_buffer.shape[0];
nk_size_t depth = (nk_size_t)b_buffer.shape[1];
nk_size_t row_stride = (nk_size_t)b_buffer.strides[0];
nk_size_t col_stride = (nk_size_t)b_buffer.strides[1];
if (src_dtype != target_dtype) {
PyBuffer_Release(&b_buffer);
PyErr_Format(PyExc_TypeError, "Input dtype '%s' does not match target dtype '%s'. Cast the input first.",
dtype_to_python_string(src_dtype), dtype_to_python_string(target_dtype));
return NULL;
}
if (col_stride != (nk_size_t)bytes_per_dtype(target_dtype)) {
PyBuffer_Release(&b_buffer);
PyErr_SetString(PyExc_ValueError, "Input matrix must be row-contiguous");
return NULL;
}
nk_dots_packed_size_punned_t size_fn = NULL;
nk_capability_t cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_maxsim_packed_size_k, target_dtype, static_capabilities,
(nk_kernel_punned_t *)&size_fn, &cap);
if (!size_fn || !cap) {
PyBuffer_Release(&b_buffer);
PyErr_Format(PyExc_LookupError, "No maxsim packed_size kernel for dtype '%s'",
dtype_to_python_string(target_dtype));
return NULL;
}
nk_size_t packed_size = size_fn(vector_count, depth);
MaxSimPackedMatrix *packed = PyObject_NewVar(MaxSimPackedMatrix, &MaxSimPackedMatrixType, packed_size);
if (!packed) {
PyBuffer_Release(&b_buffer);
PyErr_NoMemory();
return NULL;
}
packed->dtype = target_dtype;
packed->vector_count = vector_count;
packed->depth = depth;
nk_dots_pack_punned_t pack_fn = NULL;
cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_maxsim_pack_k, target_dtype, static_capabilities, (nk_kernel_punned_t *)&pack_fn,
&cap);
if (!pack_fn || !cap) {
Py_DECREF(packed);
PyBuffer_Release(&b_buffer);
PyErr_Format(PyExc_LookupError, "No maxsim pack kernel for dtype '%s'", dtype_to_python_string(target_dtype));
return NULL;
}
{
PyThreadState *save = PyEval_SaveThread();
pack_fn(b_buffer.buf, vector_count, depth, row_stride, packed->start);
PyEval_RestoreThread(save);
}
PyBuffer_Release(&b_buffer);
return (PyObject *)packed;
}
char const doc_maxsim_packed[] = //
"maxsim_packed(queries, documents, /) -> float\n\n" //
"Compute MaxSim late-interaction score between two packed matrices.\n\n" //
"Parameters:\n" //
" queries (MaxSimPackedMatrix): Packed query vectors.\n" //
" documents (MaxSimPackedMatrix): Packed document vectors.\n\n" //
"Returns:\n" //
" float: Sum of per-query minimum angular distances.\n\n" //
"Signature:\n" //
" >>> def maxsim_packed(queries, documents, /) -> float: ...";
static PyObject *maxsim_result_to_py_number( //
nk_maxsim_packed_punned_t kernel, nk_dtype_t input_dtype, //
void const *queries, void const *documents, //
nk_size_t query_count, nk_size_t document_count, nk_size_t depth) {
nk_dtype_t out_dtype = nk_kernel_output_dtype(nk_kernel_maxsim_packed_k, input_dtype);
if (out_dtype == nk_dtype_unknown_k) {
PyErr_Format(PyExc_ValueError, "Cannot determine output dtype for maxsim_packed('%s')",
dtype_to_python_string(input_dtype));
return NULL;
}
nk_scalar_buffer_t result = {0};
PyThreadState *save = PyEval_SaveThread();
switch (out_dtype) {
case nk_f64_k: kernel(queries, documents, query_count, document_count, depth, &result.f64); break;
case nk_f32_k: kernel(queries, documents, query_count, document_count, depth, &result.f32); break;
default:
PyEval_RestoreThread(save);
PyErr_Format(PyExc_ValueError, "Unsupported maxsim_packed output dtype '%s'",
dtype_to_python_string(out_dtype));
return NULL;
}
PyEval_RestoreThread(save);
return scalar_to_py_number(&result, out_dtype);
}
PyObject *api_maxsim_packed(PyObject *self, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames) {
nk_unused_(self);
if (nargs != 2 || (kwnames && PyTuple_Size(kwnames) > 0)) {
PyErr_SetString(PyExc_TypeError, "maxsim_packed() requires exactly 2 positional arguments: queries, documents");
return NULL;
}
if (!PyObject_TypeCheck(args[0], &MaxSimPackedMatrixType)) {
PyErr_SetString(PyExc_TypeError, "queries must be a MaxSimPackedMatrix (use maxsim_pack() first)");
return NULL;
}
if (!PyObject_TypeCheck(args[1], &MaxSimPackedMatrixType)) {
PyErr_SetString(PyExc_TypeError, "documents must be a MaxSimPackedMatrix (use maxsim_pack() first)");
return NULL;
}
MaxSimPackedMatrix *queries = (MaxSimPackedMatrix *)args[0];
MaxSimPackedMatrix *documents = (MaxSimPackedMatrix *)args[1];
if (queries->dtype != documents->dtype) {
PyErr_Format(PyExc_TypeError, "dtype mismatch: queries is '%s' but documents is '%s'",
dtype_to_python_string(queries->dtype), dtype_to_python_string(documents->dtype));
return NULL;
}
if (queries->depth != documents->depth) {
PyErr_Format(PyExc_ValueError, "depth mismatch: queries have depth=%zu but documents have depth=%zu",
(size_t)queries->depth, (size_t)documents->depth);
return NULL;
}
nk_maxsim_packed_punned_t kernel = NULL;
nk_capability_t cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_maxsim_packed_k, queries->dtype, static_capabilities, (nk_kernel_punned_t *)&kernel,
&cap);
if (!kernel || !cap) {
PyErr_Format(PyExc_LookupError, "No maxsim_packed kernel for dtype '%s'",
dtype_to_python_string(queries->dtype));
return NULL;
}
return maxsim_result_to_py_number(kernel, queries->dtype, queries->start, documents->start, queries->vector_count,
documents->vector_count, queries->depth);
}
char const doc_maxsim[] = //
"maxsim(queries, documents, /, dtype='bf16') -> float\n\n" //
"Convenience MaxSim: pack both matrices and compute in one call.\n\n" //
"Parameters:\n" //
" queries (array_like): Query matrix with shape (query_count, depth).\n" //
" documents (array_like): Document matrix with shape (document_count, depth).\n" //
" dtype (str, optional): Packing dtype. Default: 'bf16'.\n" //
" Supported values: 'bf16', 'f16', 'f32'.\n\n" //
"Returns:\n" //
" float: Sum of per-query minimum angular distances.\n\n" //
"Signature:\n" //
" >>> def maxsim(queries, documents, /, dtype='bf16') -> float: ...";
PyObject *api_maxsim(PyObject *self, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames) {
nk_unused_(self);
PyObject *queries_obj = NULL, *documents_obj = NULL;
char const *dtype_str = "bf16";
Py_ssize_t nkw = kwnames ? PyTuple_Size(kwnames) : 0;
Py_ssize_t total = nargs + nkw;
if (nargs < 2 || total > 3) {
PyErr_SetString(PyExc_TypeError, "maxsim() requires 2-3 arguments: queries, documents, dtype='bf16'");
return NULL;
}
queries_obj = args[0];
documents_obj = args[1];
for (Py_ssize_t i = 0; i < nkw; i++) {
PyObject *name = PyTuple_GET_ITEM(kwnames, i);
if (PyUnicode_CompareWithASCIIString(name, "dtype") == 0) {
if (nargs >= 3) {
PyErr_SetString(PyExc_TypeError, "maxsim() got multiple values for argument 'dtype'");
return NULL;
}
PyObject *val = args[nargs + i];
if (!PyUnicode_Check(val)) {
PyErr_SetString(PyExc_TypeError, "dtype must be a string");
return NULL;
}
dtype_str = PyUnicode_AsUTF8(val);
}
else {
char const *name_str = PyUnicode_AsUTF8(name);
PyErr_Format(PyExc_TypeError, "maxsim() got unexpected keyword argument '%s'", name_str);
return NULL;
}
}
if (nargs >= 3) {
if (!PyUnicode_Check(args[2])) {
PyErr_SetString(PyExc_TypeError, "dtype must be a string");
return NULL;
}
dtype_str = PyUnicode_AsUTF8(args[2]);
}
nk_dtype_t target_dtype = python_string_to_dtype(dtype_str);
if (target_dtype == nk_dtype_unknown_k) {
PyErr_Format(PyExc_ValueError, "Unsupported dtype '%s'", dtype_str);
return NULL;
}
if (target_dtype != nk_bf16_k && target_dtype != nk_f16_k && target_dtype != nk_f32_k) {
PyErr_Format(PyExc_ValueError, "maxsim() only supports 'bf16', 'f16', 'f32'; got '%s'", dtype_str);
return NULL;
}
Py_buffer queries_buffer, documents_buffer;
nk_buffer_backing_t queries_backing, documents_backing;
int have_queries = 0, have_documents = 0;
PyObject *return_obj = NULL;
MaxSimPackedMatrix *q_packed = NULL, *d_packed = NULL;
if (!nk_get_buffer(queries_obj, &queries_buffer, PyBUF_STRIDES | PyBUF_FORMAT, &queries_backing)) {
PyErr_SetString(PyExc_TypeError, "queries must support buffer protocol or __array_interface__");
return NULL;
}
have_queries = 1;
if (!nk_get_buffer(documents_obj, &documents_buffer, PyBUF_STRIDES | PyBUF_FORMAT, &documents_backing)) {
PyErr_SetString(PyExc_TypeError, "documents must support buffer protocol or __array_interface__");
goto cleanup;
}
have_documents = 1;
if (queries_buffer.ndim != 2 || documents_buffer.ndim != 2) {
PyErr_SetString(PyExc_ValueError, "queries and documents must be 2D matrices");
goto cleanup;
}
{
nk_dtype_t queries_dtype = dtype_from_buffer(&queries_buffer);
if (queries_dtype == nk_dtype_unknown_k) {
PyErr_Format(PyExc_TypeError, "Unsupported buffer format '%s'", queries_buffer.format);
goto cleanup;
}
nk_dtype_t documents_dtype = dtype_from_buffer(&documents_buffer);
if (documents_dtype == nk_dtype_unknown_k) {
PyErr_Format(PyExc_TypeError, "Unsupported buffer format '%s'", documents_buffer.format);
goto cleanup;
}
if (queries_dtype != target_dtype) {
PyErr_Format(PyExc_TypeError, "queries dtype '%s' does not match target dtype '%s'",
dtype_to_python_string(queries_dtype), dtype_to_python_string(target_dtype));
goto cleanup;
}
if (documents_dtype != target_dtype) {
PyErr_Format(PyExc_TypeError, "documents dtype '%s' does not match target dtype '%s'",
dtype_to_python_string(documents_dtype), dtype_to_python_string(target_dtype));
goto cleanup;
}
}
if (queries_buffer.strides[1] != (Py_ssize_t)bytes_per_dtype(target_dtype) ||
documents_buffer.strides[1] != (Py_ssize_t)bytes_per_dtype(target_dtype)) {
PyErr_SetString(PyExc_ValueError, "Input matrices must be row-contiguous");
goto cleanup;
}
{
nk_size_t query_count = (nk_size_t)queries_buffer.shape[0], query_depth = (nk_size_t)queries_buffer.shape[1];
nk_size_t document_count = (nk_size_t)documents_buffer.shape[0],
document_depth = (nk_size_t)documents_buffer.shape[1];
nk_size_t query_stride = (nk_size_t)queries_buffer.strides[0];
nk_size_t document_stride = (nk_size_t)documents_buffer.strides[0];
if (query_depth != document_depth) {
PyErr_Format(PyExc_ValueError, "Depth mismatch: queries have depth=%zu but documents have depth=%zu",
(size_t)query_depth, (size_t)document_depth);
goto cleanup;
}
nk_dots_packed_size_punned_t size_fn = NULL;
nk_capability_t cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_maxsim_packed_size_k, target_dtype, static_capabilities,
(nk_kernel_punned_t *)&size_fn, &cap);
if (!size_fn || !cap) {
PyErr_Format(PyExc_LookupError, "No maxsim packed_size kernel for dtype '%s'", dtype_str);
goto cleanup;
}
nk_dots_pack_punned_t pack_fn = NULL;
cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_maxsim_pack_k, target_dtype, static_capabilities,
(nk_kernel_punned_t *)&pack_fn, &cap);
if (!pack_fn || !cap) {
PyErr_Format(PyExc_LookupError, "No maxsim pack kernel for dtype '%s'", dtype_str);
goto cleanup;
}
nk_maxsim_packed_punned_t kernel = NULL;
cap = nk_cap_serial_k;
nk_find_kernel_punned(nk_kernel_maxsim_packed_k, target_dtype, static_capabilities,
(nk_kernel_punned_t *)&kernel, &cap);
if (!kernel || !cap) {
PyErr_Format(PyExc_LookupError, "No maxsim_packed kernel for dtype '%s'", dtype_str);
goto cleanup;
}
nk_size_t q_packed_size = size_fn(query_count, query_depth);
nk_size_t d_packed_size = size_fn(document_count, document_depth);
q_packed = PyObject_NewVar(MaxSimPackedMatrix, &MaxSimPackedMatrixType, q_packed_size);
if (!q_packed) {
PyErr_NoMemory();
goto cleanup;
}
q_packed->dtype = target_dtype;
q_packed->vector_count = query_count;
q_packed->depth = query_depth;
d_packed = PyObject_NewVar(MaxSimPackedMatrix, &MaxSimPackedMatrixType, d_packed_size);
if (!d_packed) {
PyErr_NoMemory();
goto cleanup;
}
d_packed->dtype = target_dtype;
d_packed->vector_count = document_count;
d_packed->depth = document_depth;
{
PyThreadState *save = PyEval_SaveThread();
pack_fn(queries_buffer.buf, query_count, query_depth, query_stride, q_packed->start);
pack_fn(documents_buffer.buf, document_count, document_depth, document_stride, d_packed->start);
PyEval_RestoreThread(save);
}
return_obj = maxsim_result_to_py_number(kernel, target_dtype, q_packed->start, d_packed->start, query_count,
document_count, query_depth);
}
cleanup:
if (have_queries) PyBuffer_Release(&queries_buffer);
if (have_documents) PyBuffer_Release(&documents_buffer);
Py_XDECREF(q_packed);
Py_XDECREF(d_packed);
return return_obj;
}
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