qualcomm Convert xtensor to QNN tensors
Convert xtensor-based prototype code to direct QNN buffer operations for production. This eliminates the extra memory copy that xtensor introduces.
Conversion patterns
| xtensor | QNN equivalent |
|---|---|
xt::xarray<T> |
Raw pointer + shape metadata |
xt::view(arr, range) |
Offset calculation into shared buffer |
xt::zeros<T>({...}) |
memset(ptr, 0, size) |
xt::ones<T>({...}) |
Loop initialization |
xt::adapt(ptr, shape) |
Direct pointer use (already zero-copy) |
xt::eval(expr) |
Materialize into pre-allocated buffer |
QNN buffer access
// Read from graph output
float* output_ptr = static_cast<float*>(graph.outputPtr("tensor_name"));
// Write to graph input
float* input_ptr = static_cast<float*>(graph.inputPtr("tensor_name"));
// Using Graph API (handles dtype conversion: float32, float16, ufixed8/16, int32)
graph.write("tensor_name", float_data_ptr);
graph.read("tensor_name", float_output_ptr);
Memory layout
QNN uses row-major (C-style) layout, same as xtensor default.
For 2D tensor X[rows][cols]:
X[0,0], X[0,1], ..., X[0,cols-1], X[1,0], X[1,1], ...
Shared buffers
ARN (prefill) and AR1 (decode) graphs share input/output buffers since they never run concurrently. Modifying one graph's input buffer affects the other.
Reshape operations
Expand (2x2 → 2x3): Process last row to first (move backward), zero-pad new positions.
Shrink (2x3 → 2x2): Process first row to last (move forward), compact memory.
When to convert
| Scenario | Use |
|---|---|
| Initial prototyping | xtensor |
| Debugging complex logic | xtensor |
| Production deployment | QNN tensors |
| Performance-critical paths | QNN tensors |
| One-off preprocessing | xtensor (acceptable overhead) |
Workflow
- Read the xtensor code to understand the tensor operations
- Identify buffer pointers from
graph.inputPtr()/graph.outputPtr() - Replace xtensor operations with direct pointer arithmetic
- Ensure correct stride calculations for multi-dimensional access
- Use
memsetfor zero-initialization,memcpyfor block copies - Verify correctness by comparing output values against the xtensor version