triton-ascend-elementwise

Optimizing elementwise operations on Ascend involves writing Triton kernels with tl.jit, flattening tensors to 1D, and applying blocking strategies to ensure contiguous memory access and reduce latency.

The standard pattern for pure elementwise operators uses a 1D flattened tensor to guarantee contiguous memory access, applies kernel fusion to reduce memory traffic, and supports broadcasting for real-world models.

Yes, Triton-Ascend inference kernels support broadcasting for per-element computations and allow kernel fusion to combine multiple elementwise steps, effectively minimizing memory traffic during execution.

No, optimizing elementwise kernels applies strictly to pure elementwise operators with no cross-element dependencies, ensuring operations like cast and relu execute independently across the tensor.

The 1D flattened tensor approach focuses on pure elementwise and broadcasting operations without cross-element dependencies, limiting its use for complex computations requiring inter-element data access.