frost(sdpa): bound the SM80 backward q-loop by the sliding window (6-12x on gpt_oss-style SWA) + window-aware deterministic relay - #866
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Included review availability: Your plan provides up to 12 included reviews per hour; 9 remain after this review. 📝 WalkthroughWalkthroughSM80 SDPA backward now trims sliding-window query iterations, adjusts deterministic dQ semaphore relay turns, and supports bottom-right causal and non-causal window references. Tests cover long sequences, THD inputs, and deterministic execution. ChangesSM80 sliding-window backward
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to This localized change bounds sliding-window backward attention work and updates its deterministic relay behavior without any supplied merge-blocking correctness, deployment, security, or availability risk; no actionable merge-blocking risk remains beyond normal checks and review. Sequence Diagram(s)sequenceDiagram
participant SDPA backward tests
participant _ref_grads
participant bprop_f16_sm80
participant dQ semaphore
SDPA backward tests->>_ref_grads: Build the sliding-window reference
SDPA backward tests->>bprop_f16_sm80: Run SM80 backward
bprop_f16_sm80->>bprop_f16_sm80: Limit q-loop to attended tiles
bprop_f16_sm80->>dQ semaphore: Use adjusted relay_turn
dQ semaphore-->>bprop_f16_sm80: Continue ordered dQ relay
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Full details: Description checkExplanation The description clearly explains the problem, implementation, performance impact, compatibility context, related issue, and test coverage. It does not reproduce the template headings or pre-submission checklist, and it does not state the affected area or API impact under dedicated headings, but the required technical information is mostly present.
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
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…ow-aware deterministic relay The SM80 backward bounded each kv-tile's q-loop only from below (the causal skip) and applied the sliding window purely as a per-element mask, so every kv-tile swept all q-tiles to the end of the sequence: O(S^2) work for an O(S*W) problem. The A100 attention_training dashboard showed it on the gpt_oss config (W=128, d=64): cudnn_oss backward 5x slower than the cuDNN backend at S=2k, 19x at 8k, 75x at 32k, while the forward already trims its kv range by the window (kv_left). Add the upper bound: a kv-tile [kv_base, kv_base+tile_kv) is attended by no q with anchor(q) > kv_base + tile_kv - 1 + W (anchor = q, or q + causal_diag under bottom-right), so n_iters is capped at ceil((kv_base + tile_kv + W - diag) / tile_q) - q_lo_tile; fully-masked tiles run 0 iters and store dK = dV = 0. THD uses in-sequence indices, so the bound is per packed sequence. Deterministic path: the dQ relay spun until counter == kv_tile, which assumes every lower kv-tile visited the q-tile. A high-end cut breaks that (q_hi grows with kv_tile), so the relay now counts turns from the q-tile's first visitor, kv_first = max((q_row0 + diag - W) // tile_kv, 0) — the inverse of the clamp — for both the acquire target and the release value (SM120 precedent: bprop_f16_sm120.py relay_turn). Without a window it folds to kv_tile. A100, b=2 h=16 d=64 W=128 bf16 causal, backward median (cuDNN backend ref): non-det S=4k: 3.111 -> 0.507 ms (ref 0.425) S=8k: 12.041 -> 0.980 ms (ref 0.802) det S=4k: 3.463 ms (ref 2.609) S=8k: 10.876 ms (ref 9.457) Tests: test_sm80_bwd_swa_long_seq (top-left causal+window, window-only, bottom-right causal+window with s_q != s_kv) at S >> W against the dense reference (which gains bottom-right + window support), test_sm80_bwd_thd_swa (per-sequence bound), test_sm80_bwd_swa_deterministic (bitwise repeatable, matches non-deterministic and the reference, TL and BR). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Inline comments:
In `@test/python/fe_api/sdpa/test_sdpa_bwd_sm80.py`:
- Around line 561-568: Move the long-sequence parameterized test
test_sm80_bwd_swa_long_seq from L0 to an appropriate higher test level, and do
the same for the repeated deterministic 2048-token sweep at
test/python/fe_api/sdpa/test_sdpa_bwd_sm80.py lines 622-625. Update both
affected test declarations while preserving their parameters and behavior.
- Line 665: Update the _ref_grads assignment in the affected test to bind the
unused dk_ref and dv_ref results to _, while retaining dq_ref for the existing
assertion and preserving all other test behavior.
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python/cudnn/sdpa/bwd/kernels/bprop_f16_sm80.pytest/python/fe_api/sdpa/test_sdpa_bwd_sm80.py
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… grads CodeRabbit on NVIDIA#866: keep L0 fast. The 2048-token sliding-window sweeps (three window geometries; deterministic TL+BR with three backward passes each) move to L1, and one representative case of each stays at L0 as a smoke test — the same smoke/sweep pattern this file already uses for the flavor x mask x GQA x dtype sweep — so the kernel change keeps a default regression check (whole-file L0: 12 passed in 27 s). Also bind the unused dk_ref/dv_ref in the deterministic test to `_` (RUF059). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Drop the history and perf narrative ("without this ... 5x-75x") and state
what the code maintains: the window's q upper bound, and the relay turn as
a kv-tile's rank among a q-tile's contiguous visitor set.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Problem
The A100
attention_trainingdashboard (results CSVs, 2026-08-26) shows the SM80 FROST backward on the gpt_oss config (sliding window W=128, d=64) running 5× slower than the cuDNN backend at S=2k, 19× at 8k and 75× at 32k — super-linear, while the forward is at parity or faster.Root cause:
bprop_f16_sm80.pybounded each kv-tile's q-loop only from below (the causal skip,q_lo_tile) and applied the window purely as a per-element mask in_mask_p, so every kv-tile swept all q-tiles to the end of the sequence — O(S²) work for an O(S·W) problem. The forward kernel already trims its kv range by the window (kv_left); the backward never got the mirror.Change
[kv_base, kv_base+tile_kv)is attended by no q withanchor(q) > kv_base + tile_kv − 1 + W(anchor = q, orq + causal_diagunder bottom-right — exactly_mask_p's semantics), son_itersis capped atceil((kv_base + tile_kv + W − diag) / tile_q) − q_lo_tile. Fully-masked tiles run 0 iterations and the epilogue stores dK = dV = 0. THD uses in-sequence indices, so the bound is per packed sequence.counter == kv_tile, which assumes every lower kv-tile visited the q-tile; a high-end cut breaks that (q_higrows withkv_tile), so a naive bound would hang the relay. The relay now counts turns from the q-tile's first visitor,kv_first = max((q_row0 + diag − W) // tile_kv, 0)— the algebraic inverse of the clamp — for both the acquire target and theatomic_exchrelease value. Folds tokv_tilewithout a window. Same shape as the SM120 kernel'srelay_turn.Numbers (A100, b=2 h=16 d=64 W=128 bf16 causal, backward median; cuDNN backend in parentheses)
Non-deterministic: 6.1× / 12.3× faster, now scaling linearly and within ~1.2× of the backend. Deterministic is bounded by the serialized relay on both implementations; it's now within ~1.15–1.3× of the backend's deterministic path (the dashboard had it at ~2.3×).
Tests (
test/python/fe_api/sdpa/test_sdpa_bwd_sm80.py, A100)test_sm80_bwd_swa_long_seq[causal_swa_tl | swa_only | causal_swa_br]— S ≫ W (2048/W=128, BR with s_q=1536 ≠ s_kv=2048) against the dense reference, which gains bottom-right + window support mirroring_mask_p.test_sm80_bwd_thd_swa— per-sequence bound with sequences shorter and longer than the window.test_sm80_bwd_swa_deterministic[tl | br]— bitwise repeatable, agrees with the non-deterministic path and the reference (a wrong first-visitor would hang or mis-order).Cross-arch note: SM120 already had both the clamp and the relay adjustment; this brings SM80 to parity. Project-board assignment pending (see #863 discussion).
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Summary by CodeRabbit
Bug Fixes
Tests