ggml-org/llama.cpp b8179

on GitHub

CUDA: add CDNA3 MFMA support for flash attention MMA kernel (#19806)

• CUDA: add CDNA3 MFMA support for flash attention MMA kernel

Add MI300X (gfx942) MFMA tensor core flash attention using
v_mfma_f32_16x16x16_f16 (FP16 in, FP32 accumulate).

• Add FATTN_WARP_SIZE=64 for CDNA wavefront64
• Add CDNA config for head sizes 64, 80, 96, 112, 128
• Add FP16 MFMA intrinsic path in mma.cuh
• Add manual V transpose load for MFMA register layout
• Route CDNA to MMA for prompt processing, VEC for token generation
• Fix Q loading and combine stride granularity for non-power-of-2 heads

Benchmarks (Qwen2.5-1.5B Q4_K_M, MI300X):
pp512 +7%, pp1024 +13%, pp2048 +23%, pp4096 +39%
tg128 -10% (FA overhead, VEC used for both)

All 2480 flash attention tests pass.

Ref: #17917

• address review: replace FATTN_WARP_SIZE with constexpr, improve dispatch

• Replace #define FATTN_WARP_SIZE with constexpr int warp_size =
  ggml_cuda_get_physical_warp_size() in each device function
• Use ne[1]*gqa_ratio threshold for MMA vs tile dispatch. Benchmarked
  crossover on MI300X @ d32768 with power-of-2 GQA models:
  hsk=64 (Llama 1B, gqa=4): MMA wins at eff >= 128 (+11%)
  hsk=128 (Llama 3B, gqa=4): MMA wins at eff >= 128 (+4%)
  Unified threshold: eff_nq >= 128 for all head sizes.
• Remove VEC fallback; small batches fall through to tile kernel

• Update ggml/src/ggml-cuda/fattn.cu

• use ggml_cuda_info().devices warp_size instead of hardcoded check

Co-authored-by: Johannes Gäßler johannesg@5d6.de