qualcomm Write xtensor code
Use this skill when prototyping tensor manipulation logic. xtensor provides NumPy-like C++ syntax for rapid development. The resulting code can later be converted to direct QNN buffer operations using /convert-xtensor-to-qnn.
Core reference
| Operation | PyTorch/NumPy | xtensor |
|---|---|---|
| Zeros | np.zeros((3,4)) |
xt::zeros<double>({3, 4}) |
| Ones | np.ones((3,4)) |
xt::ones<double>({3, 4}) |
| From data | np.array([[1,2],[3,4]]) |
xt::xarray<int>{{1,2},{3,4}} |
| Range | np.arange(0,10,2) |
xt::arange<int>(0, 10, 2) |
| Linspace | np.linspace(0,1,5) |
xt::linspace<double>(0, 1, 5) |
| Element access | a[0,1] |
a(0, 1) |
| Slice | a[1:3, :] |
xt::view(a, xt::range(1,3), xt::all()) |
| Boolean index | a[a > 0] |
xt::filter(a, a > 0) |
| Matmul | np.dot(a, b) |
xt::linalg::dot(a, b) |
| Reshape | a.reshape(2,3) |
xt::reshape_view(a, {2, 3}) |
| Transpose | a.T |
xt::transpose(a) |
| Flatten | a.flatten() |
xt::flatten(a) |
| Concat | np.concatenate([a,b], 0) |
xt::concatenate(xt::xtuple(a, b), 0) |
| Stack | np.stack([a,b]) |
xt::stack(xt::xtuple(a, b)) |
| Cast | a.astype(float) |
xt::cast<double>(a) |
| Sum | np.sum(a) |
xt::sum(a) |
| Mean | np.mean(a) |
xt::mean(a) |
| Max/Min | np.max(a) / np.min(a) |
xt::amax(a) / xt::amin(a) |
| Argmax | np.argmax(a) |
xt::argmax(a) |
| Row select | a[indices] |
xt::view(a, xt::keep(indices), xt::all()) |
std::vector conversions
// vector → xarray
xt::adapt(vec) // 1D (shape inferred)
xt::adapt(vec, shape) // explicit shape
// xarray → vector
std::vector<T> vec(arr.begin(), arr.end());
Critical pitfalls
xt::split— never useautofor element access:auto chunks = xt::split(arr, 2, 0); xt::xarray<float> chunk0 = chunks[0]; // explicit type requiredxt::concatenate— never self-assign:a = xt::concatenate(xt::xtuple(a, b), 0); // WRONG xt::xarray<float> c = xt::concatenate(xt::xtuple(a, b), 0); // correctxt::filter— usext::equalfor comparisons:xt::filter(a, xt::equal(a, b)) += 1; // correct xt::filter(a, a == b) += 1; // WRONGxt::interp— requires monotonically increasing x. Flip descending data first, interpolate, then flip back.Lazy evaluation — xtensor is lazy by default. Use
xt::eval(expr)to force materialization when needed.Shape type — uses
std::vector<size_t>for shape arguments.Print shape — use
xt::adapt(arr.shape())notarr.shape()directly.
Includes
#include <xtensor/xarray.hpp>
#include <xtensor/xview.hpp>
#include <xtensor/xio.hpp>
#include <xtensor/xadapt.hpp>
// For linalg:
#include <xtensor-blas/xlinalg.hpp>