fused attention

**Fused attention** is the **combined-kernel execution of key attention substeps such as score computation, masking, softmax, and value aggregation** - it minimizes intermediate tensor materialization and improves sequence processing efficiency. **What Is Fused attention?** - **Definition**: Attention implementation that merges multiple stages of scaled dot-product attention into fewer GPU kernels. - **Pipeline Scope**: Commonly fuses QK matmul scaling, mask application, softmax normalization, and weighted value accumulation. - **Memory Objective**: Keeps blocks on-chip where possible instead of writing full score matrices to HBM. - **Algorithm Family**: Includes FlashAttention-like methods and framework-specific fused kernels. **Why Fused attention Matters** - **Long-Sequence Performance**: Attention dominates runtime and memory at larger context lengths. - **Bandwidth Reduction**: Avoiding score-matrix writes removes major memory bottlenecks. - **Higher Throughput**: Fewer launches and improved locality increase tokens-per-second. - **Better Scaling**: Enables larger batch or context settings under the same memory budget. - **Serving Benefits**: Reduces latency and memory overhead in autoregressive decoding paths. **How It Is Used in Practice** - **Kernel Selection**: Dispatch fused kernels based on head dimension, causal mode, and precision. - **Profile Comparison**: Benchmark fused versus unfused attention under representative sequence lengths. - **Stability Checks**: Validate numerical parity and masking correctness across edge cases. Fused attention is **one of the most important optimizations in modern transformer systems** - combining attention stages into efficient kernels is essential for high-context performance.