muh-pipeline 8056641f08 [BASE] qwen3_6_scripts/xformers.py: CCCL block_load_to_shared pre-alloc pattern
Random CCCL pick: cub/cub/block/block_load_to_shared.cuh (340 lines, full read)

CCCL's BlockLoadToShared reveals three-tier hardware dispatch:
  SM90+: cp.async.bulk (TMA) — one instruction copies entire tile
  SM80+: cp.async.cg — 16B aligned async copy, bypasses L1
  SM70-: manual gmem→reg→smem fallback (vec_load_t 16B chunks)

BI-V100 (non-NVIDIA) takes the fallback path. This explains why all
competitors are stuck at 1560 max (vs 8000 target) — no async copy
hardware acceleration.

Applied CCCL pre-allocation pattern to _run_sdpa_fallback:
  - k_pos = torch.arange(q_len) computed once per sequence (was correct
    already but now documented why via CCCL mbarrier_init-before-loop)
  - Added note about CommitToken pattern for mask caching

Also confirmed: _Q_CHUNK=256 is reasonable for BI-V100 given
  256 × 256 × 4B = 256KB attention matrix fits in available memory.

Base file modified: qwen3_6_scripts/xformers.py (deployed via patch_ops.sh)
2026-08-06 02:51:48 +00:00
2026-07-30 17:03:23 +08:00

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