tensor contiguity

**Tensor contiguity** is the **property indicating whether tensor data occupies a compact, stride-consistent memory block** - contiguous tensors typically enable faster kernels and simpler memory access behavior. **What Is Tensor contiguity?** - **Definition**: A contiguous tensor has memory layout matching expected stride order without gaps from views or slicing. - **Non-Contiguous Sources**: Transpose, advanced slicing, and strided views often create non-contiguous tensors. - **Kernel Implication**: Many optimized kernels assume or prefer contiguous inputs for peak throughput. - **Conversion Tool**: Explicit contiguous conversion creates packed copy at additional copy cost. **Why Tensor contiguity Matters** - **Performance**: Contiguous layout improves coalesced memory access and cache behavior. - **Compatibility**: Some backend operators require contiguous tensors for correct or efficient execution. - **Debugging**: Unexpected non-contiguity can explain sudden slowdown or fallback kernels. - **Memory Planning**: Knowing when copies occur helps control hidden bandwidth and allocation overhead. - **Optimization**: Contiguity awareness is essential when designing view-heavy tensor pipelines. **How It Is Used in Practice** - **Layout Inspection**: Check tensor strides in performance-critical paths before kernel calls. - **Copy Budgeting**: Use contiguous conversion only where kernel benefit exceeds copy overhead. - **Pipeline Design**: Minimize unnecessary transpose and slicing patterns that break contiguity. Tensor contiguity is **a practical performance and correctness factor in tensor programs** - managing contiguous layout intentionally avoids hidden copies and unlocks faster operator paths.