Wired up two suggestions from the FlashAttention-4 blog + attention-gym:
1. `--fa4_prefill` flag: `BLOCK_SIZE=(256, 128)` + `BACKEND="FLASH"` on the
prefill create_block_mask, pad to 256-row Q tile. Confirmed FA4 kernel
fires on Blackwell (torch 2.11 + flash-attn CuTeDSL). Output is coherent
but 4617 tok/s vs 5744 baseline at batch 64 + LoRA.
Root cause: our prefill mask is document_causal, which evaluates
`docs[q_idx] == docs[kv_idx]`. The FA4 CuTe kernel's known limitation
(documented in attention-gym/examples/flex_flash_attention.py) is that
"Indexing by kv_idx is a large perf hit". The doc mask hits that
slow path directly. To benefit from FA4 on prefill we would need to
refactor the mask so the per-kv lookup goes away, which is non-trivial
given the document-boundary + causal combo.
2. flex_autotune_replay.py: new script that drives the pattern from
attention-gym/examples/flex_autotune_replay.py -- sets
`TORCHINDUCTOR_FLEX_ATTENTION_LOGGING_FILE` + runs with
`mode="max-autotune-no-cudagraphs"`, parses the JSON log (handling
symbolic dims like `s40`), picks the decode-shape entry (Q_LEN=1),
and writes best fwd_* kernel options as JSON.
Inductor's best for the decode shape: `fwd_num_warps=4, fwd_num_stages=3,
fwd_BLOCK_M=64, fwd_BLOCK_N=64, fwd_USE_TMA=False`. Applied end-to-end:
4827 tok/s vs 5744 manual baseline. The per-call time-minimum Inductor
uses doesn't track the cumulative register-spill / L1 effects across
the 36-layer stack.
Kept `--fa4_prefill` and flex_autotune_replay.py in-tree -- they are
useful scaffolding for anyone who wants to push further (refactor the mask,
run the 144-config exhaustive fwd sweep from attention-gym/examples/flex_grid_sweep.py,
etc.). Default config is unchanged.
Also documented the run-to-run variance: over 10 rounds at batch 64 + LoRA,
median 4192 and best 5660 tok/s; the spread is GPU clock throttling +
variable prompt-length distributions. The 5744 "baseline" we report is
best-of-N, matching the prior harness, but steady-state median is closer
to 75 % of that.
Writeup update in scripts/benchmarks/results/flex_vs_vllm.md.
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