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Remove some code duplication in bindings #416

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Merged
merged 2 commits into from
Sep 20, 2022
Merged

Remove some code duplication in bindings #416

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00b434f
Remove some code duplication in bindings
dyashuni Sep 18, 2022
97e7009
Refactoring
dyashuni Sep 19, 2022
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141 changes: 63 additions & 78 deletions python_bindings/bindings.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@ inline void ParallelFor(size_t start, size_t end, size_t numThreads, Function fn
while (true) {
size_t id = current.fetch_add(1);

if ((id >= end)) {
if (id >= end) {
break;
}

Expand Down Expand Up @@ -79,6 +79,54 @@ inline void assert_true(bool expr, const std::string & msg) {
}


inline void get_input_array_shapes(const py::buffer_info& buffer, size_t* rows, size_t* features) {
if (buffer.ndim != 2 && buffer.ndim != 1) {
char msg[256];
snprintf(msg, sizeof(msg),
"Input vector data wrong shape. Number of dimensions %d. Data must be a 1D or 2D array.",
buffer.ndim);
throw std::runtime_error(msg);
}
if (buffer.ndim == 2) {
*rows = buffer.shape[0];
*features = buffer.shape[1];
} else {
*rows = 1;
*features = buffer.shape[0];
}
}


inline std::vector<size_t> get_input_ids_and_check_shapes(const py::object& ids_, size_t feature_rows) {
std::vector<size_t> ids;
if (!ids_.is_none()) {
py::array_t < size_t, py::array::c_style | py::array::forcecast > items(ids_);
auto ids_numpy = items.request();
// check shapes
if (!((ids_numpy.ndim == 1 && ids_numpy.shape[0] == feature_rows) ||
(ids_numpy.ndim == 0 && feature_rows == 1))) {
char msg[256];
snprintf(msg, sizeof(msg),
"The input label shape %d does not match the input data vector shape %d",
ids_numpy.ndim, feature_rows);
throw std::runtime_error(msg);
}
// extract data
if (ids_numpy.ndim == 1) {
std::vector<size_t> ids1(ids_numpy.shape[0]);
for (size_t i = 0; i < ids1.size(); i++) {
ids1[i] = items.data()[i];
}
ids.swap(ids1);
} else if (ids_numpy.ndim == 0) {
ids.push_back(*items.data());
}
}

return ids;
}


template<typename dist_t, typename data_t = float>
class Index {
public:
Expand Down Expand Up @@ -146,7 +194,7 @@ class Index {
void set_ef(size_t ef) {
default_ef = ef;
if (appr_alg)
appr_alg->ef_ = ef;
appr_alg->ef_ = ef;
}


Expand Down Expand Up @@ -188,41 +236,17 @@ class Index {
num_threads = num_threads_default;

size_t rows, features;

if (buffer.ndim != 2 && buffer.ndim != 1) throw std::runtime_error("data must be a 1d/2d array");
if (buffer.ndim == 2) {
rows = buffer.shape[0];
features = buffer.shape[1];
} else {
rows = 1;
features = buffer.shape[0];
}
get_input_array_shapes(buffer, &rows, &features);

if (features != dim)
throw std::runtime_error("wrong dimensionality of the vectors");
throw std::runtime_error("Wrong dimensionality of the vectors");

// avoid using threads when the number of additions is small:
if (rows <= num_threads * 4) {
num_threads = 1;
}

std::vector<size_t> ids;

if (!ids_.is_none()) {
py::array_t < size_t, py::array::c_style | py::array::forcecast > items(ids_);
auto ids_numpy = items.request();
if (ids_numpy.ndim == 1 && ids_numpy.shape[0] == rows) {
std::vector<size_t> ids1(ids_numpy.shape[0]);
for (size_t i = 0; i < ids1.size(); i++) {
ids1[i] = items.data()[i];
}
ids.swap(ids1);
} else if (ids_numpy.ndim == 0 && rows == 1) {
ids.push_back(*items.data());
} else {
throw std::runtime_error("wrong dimensionality of the labels");
}
}
std::vector<size_t> ids = get_input_ids_and_check_shapes(ids_, rows);

{
int start = 0;
Expand Down Expand Up @@ -503,7 +527,7 @@ class Index {

for (size_t i = 0; i < appr_alg->cur_element_count; i++) {
if (label_lookup_val_npy.data()[i] < 0) {
throw std::runtime_error("internal id cannot be negative!");
throw std::runtime_error("Internal id cannot be negative!");
} else {
appr_alg->label_lookup_.insert(std::make_pair(label_lookup_key_npy.data()[i], label_lookup_val_npy.data()[i]));
}
Expand Down Expand Up @@ -561,15 +585,7 @@ class Index {

{
py::gil_scoped_release l;

if (buffer.ndim != 2 && buffer.ndim != 1) throw std::runtime_error("data must be a 1d/2d array");
if (buffer.ndim == 2) {
rows = buffer.shape[0];
features = buffer.shape[1];
} else {
rows = 1;
features = buffer.shape[0];
}
get_input_array_shapes(buffer, &rows, &features);

// avoid using threads when the number of searches is small:
if (rows <= num_threads * 4) {
Expand Down Expand Up @@ -725,36 +741,12 @@ class BFIndex {
py::array_t < dist_t, py::array::c_style | py::array::forcecast > items(input);
auto buffer = items.request();
size_t rows, features;

if (buffer.ndim != 2 && buffer.ndim != 1) throw std::runtime_error("data must be a 1d/2d array");
if (buffer.ndim == 2) {
rows = buffer.shape[0];
features = buffer.shape[1];
} else {
rows = 1;
features = buffer.shape[0];
}
get_input_array_shapes(buffer, &rows, &features);

if (features != dim)
throw std::runtime_error("wrong dimensionality of the vectors");
throw std::runtime_error("Wrong dimensionality of the vectors");

std::vector<size_t> ids;

if (!ids_.is_none()) {
py::array_t < size_t, py::array::c_style | py::array::forcecast > items(ids_);
auto ids_numpy = items.request();
if (ids_numpy.ndim == 1 && ids_numpy.shape[0] == rows) {
std::vector<size_t> ids1(ids_numpy.shape[0]);
for (size_t i = 0; i < ids1.size(); i++) {
ids1[i] = items.data()[i];
}
ids.swap(ids1);
} else if (ids_numpy.ndim == 0 && rows == 1) {
ids.push_back(*items.data());
} else {
throw std::runtime_error("wrong dimensionality of the labels");
}
}
std::vector<size_t> ids = get_input_ids_and_check_shapes(ids_, rows);

{
for (size_t row = 0; row < rows; row++) {
Expand Down Expand Up @@ -802,14 +794,7 @@ class BFIndex {
{
py::gil_scoped_release l;

if (buffer.ndim != 2 && buffer.ndim != 1) throw std::runtime_error("data must be a 1d/2d array");
if (buffer.ndim == 2) {
rows = buffer.shape[0];
features = buffer.shape[1];
} else {
rows = 1;
features = buffer.shape[0];
}
get_input_array_shapes(buffer, &rows, &features);

data_numpy_l = new hnswlib::labeltype[rows * k];
data_numpy_d = new dist_t[rows * k];
Expand All @@ -836,14 +821,14 @@ class BFIndex {

return py::make_tuple(
py::array_t<hnswlib::labeltype>(
{rows, k}, // shape
{k * sizeof(hnswlib::labeltype),
{ rows, k }, // shape
{ k * sizeof(hnswlib::labeltype),
sizeof(hnswlib::labeltype)}, // C-style contiguous strides for each index
data_numpy_l, // the data pointer
free_when_done_l),
py::array_t<dist_t>(
{rows, k}, // shape
{k * sizeof(dist_t), sizeof(dist_t)}, // C-style contiguous strides for each index
{ rows, k }, // shape
{ k * sizeof(dist_t), sizeof(dist_t) }, // C-style contiguous strides for each index
data_numpy_d, // the data pointer
free_when_done_d));
}
Expand Down