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test_ndarray.cpp
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test_ndarray.cpp
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#include <nanobind/nanobind.h>
#include <nanobind/ndarray.h>
#include <algorithm>
#include <vector>
namespace nb = nanobind;
using namespace nb::literals;
int destruct_count = 0;
static float f_global[] { 1, 2, 3, 4, 5, 6, 7, 8 };
static int i_global[] { 1, 2, 3, 4, 5, 6, 7, 8 };
#if defined(__aarch64__)
namespace nanobind {
template <> struct ndarray_traits<__fp16> {
static constexpr bool is_float = true;
static constexpr bool is_bool = false;
static constexpr bool is_int = false;
static constexpr bool is_signed = true;
};
};
#endif
NB_MODULE(test_ndarray_ext, m) {
m.def("get_shape", [](const nb::ndarray<nb::ro> &t) {
nb::list l;
for (size_t i = 0; i < t.ndim(); ++i)
l.append(t.shape(i));
return l;
}, "array"_a.noconvert());
m.def("get_size", [](const nb::ndarray<> &t) {
return t.size();
}, "array"_a.noconvert());
m.def("get_itemsize", [](const nb::ndarray<> &t) {
return t.itemsize();
}, "array"_a.noconvert());
m.def("get_nbytes", [](const nb::ndarray<> &t) {
return t.nbytes();
}, "array"_a.noconvert());
m.def("check_shape_ptr", [](const nb::ndarray<> &t) {
std::vector<int64_t> shape(t.ndim());
std::copy(t.shape_ptr(), t.shape_ptr() + t.ndim(), shape.begin());
for (size_t i = 0; i < t.ndim(); ++i)
if (shape[i] != (int64_t) t.shape(i))
return false;
return true;
});
m.def("check_stride_ptr", [](const nb::ndarray<> &t) {
std::vector<int64_t> stride(t.ndim());
std::copy(t.stride_ptr(), t.stride_ptr() + t.ndim(), stride.begin());
for (size_t i = 0; i < t.ndim(); ++i)
if (stride[i] != (int64_t) t.stride(i))
return false;
return true;
});
m.def("check_float", [](const nb::ndarray<> &t) {
return t.dtype() == nb::dtype<float>();
});
m.def("check_bool", [](const nb::ndarray<> &t) {
return t.dtype() == nb::dtype<bool>();
});
m.def("pass_float32", [](const nb::ndarray<float> &) { }, "array"_a.noconvert());
m.def("pass_uint32", [](const nb::ndarray<uint32_t> &) { }, "array"_a.noconvert());
m.def("pass_bool", [](const nb::ndarray<bool> &) { }, "array"_a.noconvert());
m.def("pass_float32_shaped",
[](const nb::ndarray<float, nb::shape<3, nb::any, 4>> &) {}, "array"_a.noconvert());
m.def("pass_float32_shaped_ordered",
[](const nb::ndarray<float, nb::c_contig,
nb::shape<nb::any, nb::any, 4>> &) {}, "array"_a.noconvert());
m.def("check_order", [](nb::ndarray<nb::c_contig>) -> char { return 'C'; });
m.def("check_order", [](nb::ndarray<nb::f_contig>) -> char { return 'F'; });
m.def("check_order", [](nb::ndarray<>) -> char { return '?'; });
m.def("make_contig", [](nb::ndarray<nb::c_contig> a) { return a; });
m.def("check_device", [](nb::ndarray<nb::device::cpu>) -> const char * { return "cpu"; });
m.def("check_device", [](nb::ndarray<nb::device::cuda>) -> const char * { return "cuda"; });
m.def("initialize",
[](nb::ndarray<float, nb::shape<10>, nb::device::cpu> &t) {
for (size_t i = 0; i < 10; ++i)
t(i) = (float) i;
});
m.def("initialize",
[](nb::ndarray<float, nb::shape<10, nb::any>, nb::device::cpu> &t) {
int k = 0;
for (size_t i = 0; i < 10; ++i)
for (size_t j = 0; j < t.shape(1); ++j)
t(i, j) = (float) k++;
});
m.def(
"noimplicit",
[](nb::ndarray<float, nb::c_contig, nb::shape<2, 2>>) { return 0; },
"array"_a.noconvert());
m.def(
"implicit",
[](nb::ndarray<float, nb::c_contig, nb::shape<2, 2>>) { return 0; },
"array"_a);
m.def("inspect_ndarray", [](nb::ndarray<> ndarray) {
printf("Tensor data pointer : %p\n", ndarray.data());
printf("Tensor dimension : %zu\n", ndarray.ndim());
for (size_t i = 0; i < ndarray.ndim(); ++i) {
printf("Tensor dimension [%zu] : %zu\n", i, ndarray.shape(i));
printf("Tensor stride [%zu] : %zu\n", i, (size_t) ndarray.stride(i));
}
printf("Tensor is on CPU? %i\n", ndarray.device_type() == nb::device::cpu::value);
printf("Device ID = %u\n", ndarray.device_id());
printf("Tensor dtype check: int16=%i, uint32=%i, float32=%i\n",
ndarray.dtype() == nb::dtype<int16_t>(),
ndarray.dtype() == nb::dtype<uint32_t>(),
ndarray.dtype() == nb::dtype<float>()
);
});
m.def("process", [](nb::ndarray<uint8_t, nb::shape<nb::any, nb::any, 3>,
nb::c_contig, nb::device::cpu> ndarray) {
// Double brightness of the MxNx3 RGB image
for (size_t y = 0; y < ndarray.shape(0); ++y)
for (size_t x = 0; x < ndarray.shape(1); ++x)
for (size_t ch = 0; ch < 3; ++ch)
ndarray(y, x, ch) = (uint8_t) std::min(255, ndarray(y, x, ch) * 2);
});
m.def("destruct_count", []() { return destruct_count; });
m.def("return_dlpack", []() {
float *f = new float[8] { 1, 2, 3, 4, 5, 6, 7, 8 };
size_t shape[2] = { 2, 4 };
nb::capsule deleter(f, [](void *data) noexcept {
destruct_count++;
delete[] (float *) data;
});
return nb::ndarray<float, nb::shape<2, 4>>(f, 2, shape, deleter);
});
m.def("passthrough", [](nb::ndarray<> a) { return a; }, nb::rv_policy::none);
m.def("passthrough_copy", [](nb::ndarray<> a) { return a; }, nb::rv_policy::copy);
m.def("ret_numpy", []() {
float *f = new float[8] { 1, 2, 3, 4, 5, 6, 7, 8 };
size_t shape[2] = { 2, 4 };
nb::capsule deleter(f, [](void *data) noexcept {
destruct_count++;
delete[] (float *) data;
});
return nb::ndarray<nb::numpy, float, nb::shape<2, 4>>(f, 2, shape,
deleter);
});
m.def("ret_numpy_const_ref", []() {
size_t shape[2] = { 2, 4 };
return nb::ndarray<nb::numpy, const float, nb::shape<2, 4>>(f_global, 2, shape);
}, nb::rv_policy::reference);
m.def("ret_numpy_const", []() {
return nb::ndarray<nb::numpy, const float, nb::shape<2, 4>>(f_global, { 2, 4 });
});
m.def("ret_pytorch", []() {
float *f = new float[8] { 1, 2, 3, 4, 5, 6, 7, 8 };
size_t shape[2] = { 2, 4 };
nb::capsule deleter(f, [](void *data) noexcept {
destruct_count++;
delete[] (float *) data;
});
return nb::ndarray<nb::pytorch, float, nb::shape<2, 4>>(f, 2, shape,
deleter);
});
m.def("ret_array_scalar", []() {
float* f = new float[1] { 1 };
size_t shape[1] = {};
nb::capsule deleter(f, [](void* data) noexcept {
destruct_count++;
delete[] (float *) data;
});
return nb::ndarray<nb::numpy, float>(f, 0, shape, deleter);
});
m.def("noop_3d_c_contig",
[](nb::ndarray<float, nb::ndim<3>, nb::c_contig>) { return; });
m.def("noop_2d_f_contig",
[](nb::ndarray<float, nb::ndim<2>, nb::f_contig>) { return; });
m.def("accept_rw", [](nb::ndarray<float, nb::shape<2>> a) { return a(0); });
m.def("accept_ro", [](nb::ndarray<const float, nb::shape<2>> a) { return a(0); });
m.def("check", [](nb::handle h) { return nb::ndarray_check(h); });
struct Cls {
auto f1() { return nb::ndarray<nb::numpy, float>(data, { 10 }); }
auto f2() { return nb::ndarray<nb::numpy, float>(data, { 10 }, nb::cast(this, nb::rv_policy::none)); }
auto f3(nb::handle owner) { return nb::ndarray<nb::numpy, float>(data, { 10 }, owner); }
~Cls() {
destruct_count++;
}
float data [10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
};
nb::class_<Cls>(m, "Cls")
.def(nb::init<>())
.def("f1", &Cls::f1)
.def("f2", &Cls::f2)
.def("f1_ri", &Cls::f1, nb::rv_policy::reference_internal)
.def("f2_ri", &Cls::f2, nb::rv_policy::reference_internal)
.def("f3_ri", &Cls::f3, nb::rv_policy::reference_internal);
#if defined(__aarch64__)
m.def("ret_numpy_half", []() {
__fp16 *f = new __fp16[8] { 1, 2, 3, 4, 5, 6, 7, 8 };
size_t shape[2] = { 2, 4 };
nb::capsule deleter(f, [](void *data) noexcept {
destruct_count++;
delete[] (__fp16*) data;
});
return nb::ndarray<nb::numpy, __fp16, nb::shape<2, 4>>(f, 2, shape,
deleter);
});
#endif
}