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.