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/**
* @brief Pure CPython bindings for SimSIMD.
* @file lib.c
* @author Ash Vardanian
* @date January 1, 2023
* @copyright Copyright (c) 2023
*/
#include <math.h>
#if __linux__
#define SIMSIMD_TARGET_ARM_NEON 1
#define SIMSIMD_TARGET_ARM_SVE 1
#define SIMSIMD_TARGET_X86_AVX2 1
#define SIMSIMD_TARGET_X86_AVX512 1
#include <omp.h>
#elif defined(_MSC_VER)
#define SIMSIMD_TARGET_ARM_NEON 0
#define SIMSIMD_TARGET_ARM_SVE 0
#define SIMSIMD_TARGET_X86_AVX2 0
#define SIMSIMD_TARGET_X86_AVX512 0
#elif defined(__APPLE__)
#define SIMSIMD_TARGET_ARM_NEON 1
#define SIMSIMD_TARGET_ARM_SVE 0
#define SIMSIMD_TARGET_X86_AVX2 1
#define SIMSIMD_TARGET_X86_AVX512 0
#endif
#define SIMSIMD_RSQRT(x) (1 / sqrtf(x))
#define SIMSIMD_LOG(x) (logf(x))
#include <simsimd/simsimd.h>
#define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <numpy/arrayobject.h>
typedef struct parsed_vector_or_matrix_t {
char* start;
size_t dimensions;
size_t count;
size_t stride;
int is_flat;
simsimd_datatype_t datatype;
} parsed_vector_or_matrix_t;
/// @brief Global variable that caches the CPU capabilities, and is computed just onc, when the module is loaded.
simsimd_capability_t static_capabilities = simsimd_cap_serial_k;
int same_string(char const* a, char const* b) { return strcmp(a, b) == 0; }
simsimd_datatype_t numpy_string_to_datatype(char const* name) {
// https://docs.python.org/3/library/struct.html#format-characters
if (same_string(name, "f") || same_string(name, "<f") || same_string(name, "f4") || same_string(name, "<f4"))
return simsimd_datatype_f32_k;
else if (same_string(name, "e") || same_string(name, "<e") || same_string(name, "f2") || same_string(name, "<f2"))
return simsimd_datatype_f16_k;
else if (same_string(name, "b") || same_string(name, "<b") || same_string(name, "i1") || same_string(name, "|i1"))
return simsimd_datatype_i8_k;
else if (same_string(name, "B") || same_string(name, "<B") || same_string(name, "u1") || same_string(name, "|u1"))
return simsimd_datatype_b8_k;
else if (same_string(name, "d") || same_string(name, "<d") || same_string(name, "i8") || same_string(name, "<i8"))
return simsimd_datatype_f64_k;
else
return simsimd_datatype_unknown_k;
}
simsimd_datatype_t python_string_to_datatype(char const* name) {
if (same_string(name, "f") || same_string(name, "f32"))
return simsimd_datatype_f32_k;
else if (same_string(name, "h") || same_string(name, "f16"))
return simsimd_datatype_f16_k;
else if (same_string(name, "c") || same_string(name, "i8"))
return simsimd_datatype_i8_k;
else if (same_string(name, "b") || same_string(name, "b8"))
return simsimd_datatype_b8_k;
else if (same_string(name, "d") || same_string(name, "f64"))
return simsimd_datatype_f64_k;
else
return simsimd_datatype_unknown_k;
}
simsimd_metric_kind_t python_string_to_metric_kind(char const* name) {
if (same_string(name, "sqeuclidean"))
return simsimd_metric_sqeuclidean_k;
else if (same_string(name, "inner"))
return simsimd_metric_inner_k;
else if (same_string(name, "cosine"))
return simsimd_metric_cosine_k;
else if (same_string(name, "hamming"))
return simsimd_metric_hamming_k;
else if (same_string(name, "jaccard"))
return simsimd_metric_jaccard_k;
else if (same_string(name, "kullbackleibler") || same_string(name, "kl"))
return simsimd_metric_kl_k;
else if (same_string(name, "jensenshannon") || same_string(name, "js"))
return simsimd_metric_js_k;
else if (same_string(name, "jaccard"))
return simsimd_metric_jaccard_k;
else
return simsimd_metric_unknown_k;
}
static PyObject* api_get_capabilities(PyObject* self) {
simsimd_capability_t caps = static_capabilities;
PyObject* cap_dict = PyDict_New();
if (!cap_dict)
return NULL;
#define ADD_CAP(name) PyDict_SetItemString(cap_dict, #name, PyBool_FromLong(caps& simsimd_cap_##name##_k))
ADD_CAP(serial);
ADD_CAP(arm_neon);
ADD_CAP(arm_sve);
ADD_CAP(arm_sve2);
ADD_CAP(x86_avx2);
ADD_CAP(x86_avx512);
ADD_CAP(x86_avx2fp16);
ADD_CAP(x86_avx512fp16);
ADD_CAP(x86_avx512vpopcntdq);
ADD_CAP(x86_avx512vnni);
#undef ADD_CAP
return cap_dict;
}
int parse_tensor(PyObject* tensor, Py_buffer* buffer, parsed_vector_or_matrix_t* parsed) {
if (PyObject_GetBuffer(tensor, buffer, PyBUF_STRIDES | PyBUF_FORMAT) != 0) {
PyErr_SetString(PyExc_TypeError, "arguments must support buffer protocol");
return -1;
}
parsed->start = buffer->buf;
parsed->datatype = numpy_string_to_datatype(buffer->format);
if (buffer->ndim == 1) {
if (buffer->strides[0] > buffer->itemsize) {
PyErr_SetString(PyExc_ValueError, "input vectors must be contiguous");
PyBuffer_Release(buffer);
return -1;
}
parsed->is_flat = 1;
parsed->dimensions = buffer->shape[0];
parsed->count = 1;
parsed->stride = 0;
} else if (buffer->ndim == 2) {
if (buffer->strides[1] > buffer->itemsize) {
PyErr_SetString(PyExc_ValueError, "input vectors must be contiguous");
PyBuffer_Release(buffer);
return -1;
}
parsed->is_flat = 0;
parsed->dimensions = buffer->shape[1];
parsed->count = buffer->shape[0];
parsed->stride = buffer->strides[0];
} else {
PyErr_SetString(PyExc_ValueError, "input tensors must be 1D or 2D");
PyBuffer_Release(buffer);
return -1;
}
return 0;
}
void free_capsule(void *capsule) {
void* obj = PyCapsule_GetPointer(capsule, PyCapsule_GetName(capsule));
free(obj);
};
static PyObject* impl_metric(simsimd_metric_kind_t metric_kind, PyObject* const* args, Py_ssize_t nargs) {
if (nargs != 2) {
PyErr_SetString(PyExc_TypeError, "function expects exactly 2 arguments");
return NULL;
}
PyObject* output = NULL;
PyObject* input_tensor_a = args[0];
PyObject* input_tensor_b = args[1];
Py_buffer buffer_a, buffer_b;
parsed_vector_or_matrix_t parsed_a, parsed_b;
if (parse_tensor(input_tensor_a, &buffer_a, &parsed_a) != 0 ||
parse_tensor(input_tensor_b, &buffer_b, &parsed_b) != 0) {
return NULL; // Error already set by parse_tensor
}
// Check dimensions
if (parsed_a.dimensions != parsed_b.dimensions) {
PyErr_SetString(PyExc_ValueError, "vector dimensions don't match");
goto cleanup;
}
if (parsed_a.count == 0 || parsed_b.count == 0) {
PyErr_SetString(PyExc_ValueError, "collections can't be empty");
goto cleanup;
}
if (parsed_a.count > 1 && parsed_b.count > 1 && parsed_a.count != parsed_b.count) {
PyErr_SetString(PyExc_ValueError, "collections must have the same number of elements or just one element");
goto cleanup;
}
// Check data types
if (parsed_a.datatype != parsed_b.datatype && parsed_a.datatype != simsimd_datatype_unknown_k &&
parsed_b.datatype != simsimd_datatype_unknown_k) {
PyErr_SetString(PyExc_ValueError, "input tensors must have matching and supported datatypes");
goto cleanup;
}
simsimd_metric_punned_t metric = NULL;
simsimd_capability_t capability = simsimd_cap_serial_k;
simsimd_datatype_t datatype = parsed_a.datatype;
simsimd_find_metric_punned(metric_kind, datatype, static_capabilities, simsimd_cap_any_k, &metric, &capability);
if (!metric) {
PyErr_SetString(PyExc_ValueError, "unsupported metric and datatype combination");
goto cleanup;
}
// If the distance is computed between two vectors, rather than matrices, return a scalar
if (parsed_a.is_flat && parsed_b.is_flat) {
output = PyFloat_FromDouble(metric(parsed_a.start, parsed_b.start, parsed_a.dimensions, parsed_b.dimensions));
} else {
// In some batch requests we may be computing the distance from multiple vectors to one,
// so the stride must be set to zero avoid illegal memory access
if (parsed_a.count == 1)
parsed_a.stride = 0;
if (parsed_b.count == 1)
parsed_b.stride = 0;
size_t count_max = parsed_a.count > parsed_b.count ? parsed_a.count : parsed_b.count;
// Compute the distances
float* distances = malloc(count_max * sizeof(float));
for (size_t i = 0; i < count_max; ++i)
distances[i] = metric( //
parsed_a.start + i * parsed_a.stride, //
parsed_b.start + i * parsed_b.stride, //
parsed_a.dimensions, //
parsed_b.dimensions);
// Create a new PyArray object for the output
npy_intp dims[1] = {count_max};
PyArray_Descr* descr = PyArray_DescrFromType(NPY_FLOAT32);
PyArrayObject* output_array = (PyArrayObject*)PyArray_NewFromDescr( //
&PyArray_Type, descr, 1, dims, NULL, distances, NPY_ARRAY_WRITEABLE, NULL);
if (!output_array) {
free(distances);
goto cleanup;
}
PyObject *wrapper = PyCapsule_New(distances, "wrapper", (PyCapsule_Destructor)&free_capsule);
if (!wrapper) {
free(distances);
Py_DECREF(output_array);
goto cleanup;
}
if (PyArray_SetBaseObject((PyArrayObject *)output_array, wrapper) < 0) {
free(distances);
Py_DECREF(output_array);
Py_DECREF(wrapper);
goto cleanup;
}
output = output_array;
}
cleanup:
PyBuffer_Release(&buffer_a);
PyBuffer_Release(&buffer_b);
return output;
}
static PyObject* impl_cdist( //
PyObject* input_tensor_a, PyObject* input_tensor_b, //
simsimd_metric_kind_t metric_kind, size_t threads) {
PyObject* output = NULL;
Py_buffer buffer_a, buffer_b;
parsed_vector_or_matrix_t parsed_a, parsed_b;
if (parse_tensor(input_tensor_a, &buffer_a, &parsed_a) != 0 ||
parse_tensor(input_tensor_b, &buffer_b, &parsed_b) != 0) {
return NULL; // Error already set by parse_tensor
}
// Check dimensions
if (parsed_a.dimensions != parsed_b.dimensions) {
PyErr_SetString(PyExc_ValueError, "vector dimensions don't match");
goto cleanup;
}
if (parsed_a.count == 0 || parsed_b.count == 0) {
PyErr_SetString(PyExc_ValueError, "collections can't be empty");
goto cleanup;
}
// Check data types
if (parsed_a.datatype != parsed_b.datatype && parsed_a.datatype != simsimd_datatype_unknown_k &&
parsed_b.datatype != simsimd_datatype_unknown_k) {
PyErr_SetString(PyExc_ValueError, "input tensors must have matching and supported datatypes");
goto cleanup;
}
simsimd_metric_punned_t metric = NULL;
simsimd_capability_t capability = simsimd_cap_serial_k;
simsimd_datatype_t datatype = parsed_a.datatype;
simsimd_find_metric_punned(metric_kind, datatype, static_capabilities, simsimd_cap_any_k, &metric, &capability);
if (!metric) {
PyErr_SetString(PyExc_ValueError, "unsupported metric and datatype combination");
goto cleanup;
}
// If the distance is computed between two vectors, rather than matrices, return a scalar
if (parsed_a.is_flat && parsed_b.is_flat) {
output = PyFloat_FromDouble(metric(parsed_a.start, parsed_b.start, parsed_a.dimensions, parsed_b.dimensions));
} else {
#ifdef __linux__
#ifdef _OPENMP
if (threads == 0)
threads = omp_get_num_procs();
omp_set_num_threads(threads);
#endif
#endif
// Compute the distances
float* distances = malloc(parsed_a.count * parsed_b.count * sizeof(float));
#pragma omp parallel for collapse(2)
for (size_t i = 0; i < parsed_a.count; ++i)
for (size_t j = 0; j < parsed_b.count; ++j)
distances[i * parsed_b.count + j] = metric( //
parsed_a.start + i * parsed_a.stride, //
parsed_b.start + j * parsed_b.stride, //
parsed_a.dimensions, //
parsed_b.dimensions);
// Create a new PyArray object for the output
npy_intp dims[2] = {parsed_a.count, parsed_b.count};
PyArray_Descr* descr = PyArray_DescrFromType(NPY_FLOAT32);
PyArrayObject* output_array = (PyArrayObject*)PyArray_NewFromDescr( //
&PyArray_Type, descr, 2, dims, NULL, distances, NPY_ARRAY_WRITEABLE, NULL);
if (!output_array) {
free(distances);
goto cleanup;
}
PyObject *wrapper = PyCapsule_New(distances, "wrapper", (PyCapsule_Destructor)&free_capsule);
if (!wrapper) {
free(distances);
Py_DECREF(output_array);
goto cleanup;
}
if (PyArray_SetBaseObject((PyArrayObject *)output_array, wrapper) < 0) {
free(distances);
Py_DECREF(output_array);
Py_DECREF(wrapper);
goto cleanup;
}
output = output_array;
}
cleanup:
PyBuffer_Release(&buffer_a);
PyBuffer_Release(&buffer_b);
return output;
}
static PyObject* impl_pointer(simsimd_metric_kind_t metric_kind, PyObject* args) {
char const* type_name = PyUnicode_AsUTF8(PyTuple_GetItem(args, 0));
if (!type_name) {
PyErr_SetString(PyExc_ValueError, "Invalid type name");
return NULL;
}
simsimd_datatype_t datatype = python_string_to_datatype(type_name);
if (!type_name) {
PyErr_SetString(PyExc_ValueError, "Unsupported type");
return NULL;
}
simsimd_metric_punned_t metric = NULL;
simsimd_capability_t capability = simsimd_cap_serial_k;
simsimd_find_metric_punned(metric_kind, datatype, static_capabilities, simsimd_cap_any_k, &metric, &capability);
if (metric == NULL) {
PyErr_SetString(PyExc_ValueError, "No such metric");
return NULL;
}
return PyLong_FromUnsignedLongLong((unsigned long long)metric);
}
static PyObject* api_cdist(PyObject* self, PyObject* args, PyObject* kwargs) {
PyObject *input_tensor_a, *input_tensor_b;
PyObject* metric_obj = NULL;
PyObject* threads_obj = NULL;
if (!PyTuple_Check(args) || PyTuple_Size(args) < 2) {
PyErr_SetString(PyExc_TypeError, "function expects at least 2 positional arguments");
return NULL;
}
input_tensor_a = PyTuple_GetItem(args, 0);
input_tensor_b = PyTuple_GetItem(args, 1);
if (PyTuple_Size(args) > 2)
metric_obj = PyTuple_GetItem(args, 2);
if (PyTuple_Size(args) > 3)
threads_obj = PyTuple_GetItem(args, 3);
// Checking for named arguments in kwargs
if (kwargs) {
if (!metric_obj) {
metric_obj = PyDict_GetItemString(kwargs, "metric");
} else if (PyDict_GetItemString(kwargs, "metric")) {
PyErr_SetString(PyExc_TypeError, "Duplicate argument for 'metric'");
return NULL;
}
if (!threads_obj) {
threads_obj = PyDict_GetItemString(kwargs, "threads");
} else if (PyDict_GetItemString(kwargs, "threads")) {
PyErr_SetString(PyExc_TypeError, "Duplicate argument for 'threads'");
return NULL;
}
}
// Process the PyObject values
simsimd_metric_kind_t metric_kind = simsimd_metric_l2sq_k;
if (metric_obj) {
char const* metric_str = PyUnicode_AsUTF8(metric_obj);
if (!metric_str && PyErr_Occurred()) {
PyErr_SetString(PyExc_TypeError, "Expected 'metric' to be a string");
return NULL;
}
metric_kind = python_string_to_metric_kind(metric_str);
if (metric_kind == simsimd_metric_unknown_k) {
PyErr_SetString(PyExc_ValueError, "Unsupported metric");
return NULL;
}
}
size_t threads = 1;
if (threads_obj)
threads = PyLong_AsSize_t(threads_obj);
if (PyErr_Occurred()) {
PyErr_SetString(PyExc_TypeError, "Expected 'threads' to be an unsigned integer");
return NULL;
}
return impl_cdist(input_tensor_a, input_tensor_b, metric_kind, threads);
}
static PyObject* api_l2sq_pointer(PyObject* self, PyObject* args) { return impl_pointer(simsimd_metric_l2sq_k, args); }
static PyObject* api_cos_pointer(PyObject* self, PyObject* args) { return impl_pointer(simsimd_metric_cos_k, args); }
static PyObject* api_ip_pointer(PyObject* self, PyObject* args) { return impl_pointer(simsimd_metric_ip_k, args); }
static PyObject* api_kl_pointer(PyObject* self, PyObject* args) { return impl_pointer(simsimd_metric_kl_k, args); }
static PyObject* api_js_pointer(PyObject* self, PyObject* args) { return impl_pointer(simsimd_metric_js_k, args); }
static PyObject* api_hamming_pointer(PyObject* self, PyObject* args) {
return impl_pointer(simsimd_metric_hamming_k, args);
}
static PyObject* api_jaccard_pointer(PyObject* self, PyObject* args) {
return impl_pointer(simsimd_metric_jaccard_k, args);
}
static PyObject* api_l2sq(PyObject* self, PyObject* const* args, Py_ssize_t nargs) {
return impl_metric(simsimd_metric_l2sq_k, args, nargs);
}
static PyObject* api_cos(PyObject* self, PyObject* const* args, Py_ssize_t nargs) {
return impl_metric(simsimd_metric_cos_k, args, nargs);
}
static PyObject* api_ip(PyObject* self, PyObject* const* args, Py_ssize_t nargs) {
return impl_metric(simsimd_metric_ip_k, args, nargs);
}
static PyObject* api_kl(PyObject* self, PyObject* const* args, Py_ssize_t nargs) {
return impl_metric(simsimd_metric_kl_k, args, nargs);
}
static PyObject* api_js(PyObject* self, PyObject* const* args, Py_ssize_t nargs) {
return impl_metric(simsimd_metric_js_k, args, nargs);
}
static PyObject* api_hamming(PyObject* self, PyObject* const* args, Py_ssize_t nargs) {
return impl_metric(simsimd_metric_hamming_k, args, nargs);
}
static PyObject* api_jaccard(PyObject* self, PyObject* const* args, Py_ssize_t nargs) {
return impl_metric(simsimd_metric_jaccard_k, args, nargs);
}
static PyMethodDef simsimd_methods[] = {
// Introspecting library and hardware capabilities
{"get_capabilities", api_get_capabilities, METH_NOARGS, "Get hardware capabilities"},
// NumPy and SciPy compatible interfaces (two matrix or vector arguments)
{"sqeuclidean", api_l2sq, METH_FASTCALL, "L2sq (Sq. Euclidean) distances between a pair of matrices"},
{"cosine", api_cos, METH_FASTCALL, "Cosine (Angular) distances between a pair of matrices"},
{"inner", api_ip, METH_FASTCALL, "Inner (Dot) Product distances between a pair of matrices"},
{"hamming", api_hamming, METH_FASTCALL, "Hamming distances between a pair of matrices"},
{"jaccard", api_jaccard, METH_FASTCALL, "Jaccard (Bitwise Tanimoto) distances between a pair of matrices"},
{"kullbackleibler", api_kl, METH_FASTCALL, "Kullback-Leibler divergence between probability distributions"},
{"jensenshannon", api_js, METH_FASTCALL, "Jensen-Shannon divergence between probability distributions"},
// Conventional `cdist` and `pdist` insterfaces with third string argument, and optional `threads` arg
{"cdist", api_cdist, METH_VARARGS | METH_KEYWORDS,
"Compute distance between each pair of the two collections of inputs"},
// Exposing underlying API for USearch
{"pointer_to_sqeuclidean", api_l2sq_pointer, METH_VARARGS, "L2sq (Sq. Euclidean) function pointer as `int`"},
{"pointer_to_cosine", api_cos_pointer, METH_VARARGS, "Cosine (Angular) function pointer as `int`"},
{"pointer_to_inner", api_ip_pointer, METH_VARARGS, "Inner (Dot) Product function pointer as `int`"},
{"pointer_to_kullbackleibler", api_ip_pointer, METH_VARARGS, "Kullback-Leibler function pointer as `int`"},
{"pointer_to_jensenshannon", api_ip_pointer, METH_VARARGS, "Jensen-Shannon function pointer as `int`"},
// Sentinel
{NULL, NULL, 0, NULL}};
static PyModuleDef simsimd_module = {
PyModuleDef_HEAD_INIT,
.m_name = "SimSIMD",
.m_doc = "Fastest SIMD-Accelerated Vector Similarity Functions for x86 and Arm",
.m_size = -1,
.m_methods = simsimd_methods,
};
PyMODINIT_FUNC PyInit_simsimd(void) {
import_array();
PyObject* module = PyModule_Create(&simsimd_module);
if (module) {
char version_str[50];
sprintf(version_str, "%d.%d.%d", SIMSIMD_VERSION_MAJOR, SIMSIMD_VERSION_MINOR, SIMSIMD_VERSION_PATCH);
PyModule_AddStringConstant(module, "__version__", version_str);
}
static_capabilities = simsimd_capabilities();
return module;
}
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