Gahow Wang
d67dda404e
Implement Flash Attention 2 forward kernel targeting SM120 (CC 12.0). FA4 requires TMEM (only on data-center Blackwell SM100), so FA2 is the correct target for consumer Blackwell GPUs like the RTX 5090. CUDA kernel (csrc/attention/flash_attention.cu): - Online softmax with tiled Q/K/V — O(1) extra memory, no S×S matrix - Tile sizes: BR=BC=64, head_dim up to 128 (runtime parameter) - BF16 input, FP32 accumulation, BF16 output - Native GQA: kv_head = q_head / (num_q_heads / num_kv_heads) - Causal mask with tile-level skip optimization - Shared memory: 32 KB (Q_tile 16KB + KV_tile 16KB, fits in 48KB default) - Grid: (q_tiles, batch × num_q_heads), Block: 128 threads Integration: - flash_attention() Rust wrapper in xserv-kernels with shape/dtype validation - Qwen3 forward_gpu_cache uses flash_attention directly (no repeat_kv_gpu) - Eliminates repeat_kv memory allocation + copy per layer per step - Naive attention() preserved for testing/comparison Validated on dash5 (RTX 5090, CUDA 12.9): - Correctness: 9/10 top-1 match vs HF (identical to pre-FA baseline) - Throughput: 12.9 tok/s (up from 10.3, +25% improvement) - Now at 35% of HF transformers baseline (up from 30%) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>