840b8a37d9
Wire SpargeAttn CPU reference into test_sparge: build the block_map on host via sparge::build_block_map_meansim and cross-check against the GPU-produced map; self-check the VSA delta-LUT (valid count + reachable kb indices); split PASS/FAIL into separate block_map / LUT / attention-output lines for clearer diagnosis. Set sparge_tool::SpargeParams::BLKQ default to 64 to match SpargeAttn SM90 convention (cite upstream qk_int_sv_f8_cuda_sm90.cu:143-144); tighten bf16 tolerance back to the dense FMHA baseline (4e-2 atol, 1e-2 rtol). Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
Sparge Attention (Composable Kernel)
A Composable Kernel port of SpargeAttn for AMD GPU. Both the block-map pipeline (mean-pool → cosine sim → pooled QK → top-k LUT) and the sparse FMHA stage run on-GPU. Two attention backends are exposed via -pipeline=vsa (default, faster) and -pipeline=jenga (async K/V load variant).
Status vs Upstream
Implemented:
- per-block mean-pool, cosine similarity, pooled QK
- top-k /
cdfthreshdblock selection, BlockMap LUT - sparse FMHA (both
vsaandjengabackends) - per-head
topk/simthreshd1/cdfthreshd
Not yet ported (upstream pinned to commit ae5b629):
- K smoothing — pre-pool
k -= km; required for diffusion / video checkpoints (CogVideoX, Mochi-1, Flux, OpenSora, SD 3.5) (spas_sage_attn/core.py:L53) - is_causal mask in pooled score — required for causal-LM prefill (Llama, Qwen) (spas_sage_attn/utils.py:L338)
- attention_sink — column 0 forced ON; upstream is hard-wired to
Trueat inference (spas_sage_attn/autotune.py:L355) - pv_threshold per-Q-tile skip in attn kernel — pure perf, ~5–15% on the dominant attention slice (spas_sage_attn/core.py:L265)
- Sort-based top-k selection — replaces our O(N_k^2) iterative argmax; matters at long seqlen (s ≥ 16k) (spas_sage_attn/utils.py:L345)
- Q/K int8 quant fusion in pool kernel — enables a downstream int8 GEMM0 in the attn kernel (spas_sage_attn/utils.py:L371)
Performance
At b=2 h=32 s=16384 fp16, sparge (vsa backend) reaches 1.78× FMHA throughput at topk=0.4 and 5.04× at topk=0.1, and stays above 1.0× across the full topk range.
Speedup vs FMHA, b=2 h=32 s=16384 d=128 fp16. Shape chosen to match Fig. 10 of the SpargeAttn paper (arXiv:2502.18137; Mochi-1, 22K context, head_dim=128); s=16384 is the closest grid point. Gray-outlined points have >30% inter-rep spread.
BlockMap (_pre) stacked on attention (_attn), b=2 h=32 d=128 fp16 topk=0.4. BlockMap is roughly 17% of total at s=16384.
Usage
ninja tile_example_sparge
./bin/tile_example_sparge -pipeline=vsa -b=2 -h=32 -s=16384 -d=128 -topk=0.4 -simthreshd1=0.001
Add -v=1 for CPU validation; use a small shape (-b=1 -h=2 -s=512), since full-shape CPU reference scales O(s²) and runs 30+ minutes at s=8k, hours at s=16k.