Memory-Efficient Attention Kernels

Step 1: Select representative workloads#

• Sequence lengths covering short, medium, and long contexts.
• Batch sizes matching production traffic (microbatch and full batch).
• Mix of precision modes (FP16, BF16, FP8) and hardware targets (data center GPUs, edge accelerators).

Step 2: Establish baseline#

• Use widely trusted kernels (e.g., vendor-maintained references) with deterministic settings.
• Record throughput (tokens/sec), latency, peak memory, and numerical parity metrics.

Step 3: Run controlled experiments#

• Toggle one variable at a time: new kernel, precision change, scheduling tweaks.
• Use multiple seeds to detect variance.
• Capture system-level telemetry (SM occupancy, memory bandwidth) to explain results.

Step 4: Audit correctness#

• Compare outputs to baseline using strict tolerances. Inspect attention weights and downstream logits.
• Run gradient checks during training scenarios to ensure stability.