DSA backward SM90: fix three top-k and attention-sink boundary failures

[SuperGoodGame]

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Affected area

FE OSS kernels or CuTeDSL

Summary

Three independent failures in python/cudnn/deepseek_sparse_attention/sparse_attention_backward/dsa_bwd_sm90.py, each reachable from documented inputs, plus regression coverage for all of them.

1. A nonpositive topk_length corrupts memory or hangs. n_block_max is 0 and n_block is -1, but WG0 still runs its unconditional first n_block, so the KV gather indexes topk_idxs[-64 + row] out of bounds and dereferences the result as a KV row. WG1 meanwhile runs zero mainloop iterations, leaving WG0 to wait alone on G4_half_ready and sdS_consumed, both 256-thread named barriers. Whichever lands first decides whether the symptom is cudaErrorIllegalAddress or a hang. WG1's acc_dQ_2/3 are also never zero-initialised in this case (zero_init=first_iter runs only on the first mainloop iteration), so the epilogue TMA-stores stale registers into the caller's dq.

2. Padded top-k columns are never masked. The gather zero-fills their KV row in SMEM, so their score is exactly 0 rather than -inf and their probability comes out as exp2(-LSE). For a sufficiently negative LSE that overflows to +inf, and GEMM4 multiplies it by the zeroed KV row, so inf * 0 = NaN propagates across the entire dQ tile. Both padding layouts are affected: a compact topk_length whose tail does not fill the 64-row tile, and the non-compact layout where -1 marks padding.

3. A saturating attn_sink NaNs every gradient. Folding the sink into the LSE uses a max-shifted logaddexp; once fmax(lse_log2, sink_log2) is infinite the shift evaluates inf - inf. attn_sink does not have to be infinite — the log2(e) rescale saturates for any finite |sink| > 3.4e38 / log2(e) ≈ 2.36e38. This hits both the d_sink weight and the LSE handed to the main kernel, so all of dQ, dKV and d_sink become NaN.

Why

1 — a symmetric guard rather than an early exit. topk_length is CTA-uniform: both warpgroups read mTopkLength[batch_idx, seq_idx] for the same scheduler tile, so guarding both sides on topK > 0 leaves every cross-warpgroup barrier unarrived on both sides instead of one side waiting alone. The mainloop is structurally untouched, and the existing TMA epilogue is reused to write the zero tile, so no raw mdQ has to be plumbed into the kernel and the zero store inherits the same addressing, row predication and d=576 tail handling as the normal path. Zeroing the four dQ accumulators in the guarded branch also removes the stale-register store described above — that is the same defect's other half, not a separate change.

A second-order hazard had to be handled. Guarding only the barriers leaves WG0's Q/dO TMA in flight, and it lands in sQ, which WG1's epilogue writes. On the mainloop path the sP_ready/sdS_ready handshake transitively orders that load ahead of both epilogues; with the mainloop skipped there is no such ordering, and the in-flight load overwrote the zeros WG1 had already stored to sQ[256:] — dq[256:576] came back holding q, nondeterministically. The guard therefore covers WG0's whole prologue; an empty row needs no Q, dO, LSE or dP_sum.

2 — mask in the softmax, where the padding is already known. A column's KV coordinate comes from a loop-invariant identity tensor built once per CTA, mirroring the existing pattern in scatter_dkv_atomic. The compact-tail test is emitted only for the peeled first n_block (is_first is a trace-time constant), so it costs nothing in the steady-state loop. The negative-index test sits under const_expr(not self.have_topk_length) and is not emitted at all when topk_length is supplied, matching the contract documented at dsa_bwd_sm100.py:321 ("None means non-compact (use full topk, -1 entries in topk_idxs)").

3 — keep the value finite at the two sites that saturate. p_sink becomes a sigmoid, algebraically identical to exp2(sink - logaddexp2(lse, sink)) but finite when either term saturates. The LSE-with-sink logaddexp now shifts by its maximum only while that maximum is finite; an infinite maximum is already the answer.

Related issues

Related to #676. This addresses the empty-row hang, the padded/invalid top-k NaN and the saturating-sink NaN. The remaining two items in that issue are analysed under "Not addressed" below and deliberately left unpatched.

API and compatibility impact

• No public API, signature or kernel-parameter change.

• dq and dkv are bit-identical to develop on ordinary inputs, verified by dumping both builds on the same inputs and comparing.

• d_sink moves by at most 2.5e-6 relative on ordinary inputs — the sigmoid rewrite of an algebraically identical expression, well inside the 5e-2 tolerance the existing tests use.

• Performance (H200 / SM90, whole sparse_attention_backward_wrapper wall clock, same-GPU interleaved A/B, best of 5 reps × 30 iterations, d=576 h=64):

  case	develop	this PR	delta
  compact, topk=1024 (16 n_blocks)	2.1681 ms	2.1667 ms	−0.07% (noise)
  compact, topk=64 (1 n_block, worst case for the tail mask)	2.1660 ms	2.1736 ms	+0.35%
  non-compact, topk=1024 (no topk_length)	2.3829 ms	2.4037 ms	+0.87%

  The tail-mask cost amortises as 1/n_block_max, hence nothing measurable at topk=1024 and +0.35% at topk=64. The +0.87% is the per-column topk_idxs read and applies only to the non-compact layout; that code is not emitted when topk_length is supplied. Hoisting the read out of the row loop was implemented and measured no better (2.4058 ms vs 2.4037 ms), so the simpler form was kept. Happy to split the negative-index half into its own PR if that trade is not wanted here.

Testing

cd test/python
pytest fe_api/dsa/ -q          # 71 passed, 61 skipped, 14 deselected   (H200, SM90)

pre-commit run --files \
  python/cudnn/deepseek_sparse_attention/sparse_attention_backward/dsa_bwd_sm90.py \
  test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py            # clean

Eleven test cases newly execute on SM90 — four from widening the existing zero-top-k test, seven new. Ten of the eleven fail on develop; the eleventh (saturating_attn_sink[neg-inf]) passes there and is a companion guard for the negative side, which fmax never sends through the NaN path.

• test_DSA_sparse_attention_backward_zero_topk_length — the SM100 test from DSA backward SM100: support zero-length top-k rows in the kernel #439, generalised rather than duplicated: renamed off sm100_, gate widened from major*10+minor < 100 to major < 9 (matching api.py's own "requires SM90+"), and one d576-mixed parameter added so empty and non-empty rows land in adjacent CTAs, which is what catches a warpgroup-asymmetric skip. On develop all four parameters die with cudaErrorIllegalAddress.
• ..._sm90_padded_topk_columns_contribute_zero — 4 parameters over {d512-h64, d576-h32} × {compact, non-compact}, asserting finiteness and agreement with the reference. Self-validating: it asserts lse.max() < -80, so it cannot silently stop exercising the overflow. On develop all four fail on assert torch.isfinite(dq).all().
• ..._sm90_saturating_attn_sink — 3 parameters {2.4e38, +inf, -inf}. For a positive saturating sink it also pins the limit: the sink takes the whole denominator, so dq == 0, dkv == 0 and d_sink == -sum(dP_sum).

Beyond the suite:

• 800 launches of the empty-row path in one process (400 all-empty + 400 mixed, d=576), 0 failures. The sQ overwrite described above was nondeterministic, so process-level repeats alone were not sufficient evidence.
• The saturating-sink limits were checked numerically: with sink = +inf the kernel returns dq == 0, dkv == 0 and d_sink == -sum(dP_sum) to 1.2e-7.

Not tested: SM100 / SM100-h16, no such GPU available here — see below.

Alternatives considered

• DSA backward SM100: support zero-length top-k rows in the kernel #439's shape (early exit plus a hand-written zero store) was not ported. SM90 splits dQ by column across the two warpgroups and writes it with a TMA epilogue, so exiting before that epilogue would skip the dQ store entirely and leave the caller's buffer untouched — exactly what DSA backward SM100: support zero-length top-k rows in the kernel #439's own NaN-filled-buffer assertion is designed to catch. It would also have required plumbing the raw gmem mdQ into the kernel, since the parameters named mdQ/mdQ_64 are the TMA tensors. Reusing the epilogue avoids both. (For the record, SM90's tile loop is not persistent — SingleTileScheduler.advance_to_next_work() only clears _is_first_block — so an early exit would not have dropped tiles; the dQ store is the reason, not tile loss.)
• SM100's pre-negated scaled_lse (dsa_bwd_sm100.py:713) does not help here. It is a representation choice that pairs with fma_packed_f32x2, and the negation happens two lines after the NaN is produced, so it fixes neither failure; on SM90 a*b - c already maps to a single FFMA with a negated addend.
• No guard was added for a negative topk_idxs entry inside [0, topK) when topk_length is supplied. Per dsa_bwd_sm100.py:321 that layout is compact and every entry in range is valid, so the check would sit on the hot path guarding against out-of-contract input. Glad to add it if the contract is meant to be looser.

Not addressed (context for #676)

• d_sink precision — not a formula bug; the precision is lost upstream, in the forward. Against an fp64 reference the kernel reproduces the fp64 result recomputed from the BF16-stored out to 7.06e-9, and that BF16 quantisation of out is the whole of the 5.33e-5 gap against an fp64-exact out. So the kernel implements the standard FlashAttention delta = O · dO identity exactly, and the reduction at :317 is already FP32 — nothing in the backward can recover precision the forward discarded before out was handed over.

  There is real accuracy on the table, though. Feeding two different delta values into an otherwise-fp64 backward (d=576, h=32, topk=128; max relative error vs fp64):

  delta source	delta	dq	dK	d_sink
  BF16_O · dO — what the kernel does	1.4e-3	3.1e-4	1.4e-4	1.1e-3
  sum_j P_j · dP_j accumulated in FP32	5.6e-7	1.4e-7	3.0e-8	1.6e-7

  The second form needs every P_j·dP_j before any dS_j can be formed, so it costs an extra GEMM1+GEMM2 pass over the top-k (~40% of the mainloop MACs by K/N extent, plus a second KV gather); caching P/dP instead would be tens of GB. That is why no FlashAttention implementation does it, and it is not something to slip into a bug-fix PR. The cheap exact fix is forward-side: FlashMLA already holds O in FP32 accumulators and could emit delta (s_q × h floats) directly, after which the backward just reads it. Happy to open a separate issue for that if it is useful.

• Online-softmax / running-maximum rescale — the premise does not hold for this kernel, and the clamp suggested in the issue is unreachable after fix 2. This kernel has neither an online softmax nor a running maximum; the only fmax uses are the two sink logaddexp sites fixed here. Once padded columns are masked, S*scale - lse <= 0 holds by construction for every live column (the sink only raises the denominator), so the overflow is no longer reachable for self-consistent inputs, and a clamp would cost an fmin on the hottest instruction in the kernel.

• SM100 / SM100-h16 appear to share fixes 2 and 3. The logaddexp expression is byte-identical at dsa_bwd_sm100.py:710-712 and dsa_bwd_sm100_h16.py:678-680, and I see no column mask before the exp2 at dsa_bwd_sm100.py:1908-1909. I have no SM100 GPU, so I left them alone rather than ship untested changes. Glad to port both if someone can run the tests.

Summary by CodeRabbit

• Bug Fixes

  • Improved sparse-attention backward stability for infinite or saturated attention values.
  • Correctly handles empty top-k rows, padded entries, and tail blocks without producing invalid gradients.
  • Prevented non-finite results when masked scores interact with infinite attention sinks.
  • Ensured valid zero gradients for queries with no selected top-k entries.

• Documentation

  • Clarified sparse top-k padding semantics, valid index ranges, and length requirements.

• Tests

  • Updated regression coverage for empty selections, padding, overflow, and infinite attention sinks.

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📝 Walkthrough

Walkthrough

The SM90 sparse attention backward path handles infinite sink and LSE operands, empty top-k rows, padded top-k columns, and invalid sparse indices. Documentation and regression tests define and validate these cases.

Changes

Sparse backward edge cases

Layer / File(s)	Summary
Saturation-safe sink numerics python/cudnn/deepseek_sparse_attention/sparse_attention_backward/dsa_bwd_sm90.py, test/python/fe_api/dsa/dsa_reference.py, test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py	The backward path avoids invalid inf - inf operations for finite and infinite sink values. The reference masks invalid attention weights explicitly. Tests cover positive saturating sinks and zero gradients.
Top-k control and probability masking python/cudnn/deepseek_sparse_attention/sparse_attention_backward/dsa_bwd_sm90.py, python/cudnn/deepseek_sparse_attention/sparse_attention_backward/_interface_sm90.py	WG0 and WG1 skip mainloop work for nonpositive top-k lengths, zero dQ accumulators, peel tail blocks, and mask padded or negative sparse entries. The interface documents compact and non-compact padding semantics.
Edge-case regression coverage test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py	Tests cover empty rows, synchronization cases, padded top-k overflow, invalid indices, and SM90 platform behavior.

Estimated code review effort: 4 (Complex) | ~60 minutes

Merge Risk: ⚪ Minimal · up to 359e5

The PR fixes empty top-k rows, padded columns, and saturated attention sinks without changing the public interface or deployment behavior. No actionable merge-blocking risk remains after normal checks and review.

Sequence Diagram(s)

sequenceDiagram
  participant QueryMetadata
  participant WG0
  participant WG1
  participant dQEpilogue
  QueryMetadata->>WG0: provide top-k length and sparse indices
  QueryMetadata->>WG1: provide top-k length
  WG0->>dQEpilogue: zero dQ accumulators for empty rows
  WG1->>dQEpilogue: zero dQ accumulators for empty rows

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Check name	Status	Explanation
Title check	✅ Passed	The title clearly identifies the SM90 DSA backward fixes and the three top-k and attention-sink boundary failures.
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[SuperGoodGame]

Labels for this PR (I do not have permission to set them): cat-bug, mod-cutedsl, orig-external — the same set as #439, which is the closest precedent (external contributor, DSA backward, zero-length top-k). Milestone and Projects would also need to be set on your side; the merge-requirements check is currently red only for those two.

Two things I would specifically like a maintainer opinion on:

1. The +0.87% on the non-compact path (no topk_length) buys the negative-index column mask. If that trade is not wanted here I am happy to split it into its own PR and land the rest, which is free on the compact path.
2. SM100 / SM100-h16 appear to share fixes 2 and 3 (details in the PR body). I have no SM100 GPU so I left them untouched rather than ship untested changes — glad to port them if someone can run the tests.

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Inline comments:
In
`@python/cudnn/deepseek_sparse_attention/sparse_attention_backward/dsa_bwd_sm90.py`:
- Around line 1539-1549: Guard the mTopkIdxs_cur access in the
non-have_topk_length branch using the computed index against the valid top-k
length before reading it. For final partial blocks, set p to zero when the index
is out of range; otherwise preserve the existing negative-index masking
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[SuperGoodGame]

Good catch — this is real, and fixed in 3a1d8da.

The peeled tile spans all 64 columns even when it is the partial one, so with max_topk = 100 the load reaches index 127 against an extent of 100 (28 entries past the row); at max_topk = 70 it is 58 past. Confirmed by walking the index arithmetic on this host — no compute-sanitizer available here, so I could not get a fault report.

Worth recording that the results were unaffected: lanes at or past the tail are already zeroed by the tail mask immediately above, and this guard only ever zeroes, so whatever the stray entry held could not change p. It is a memory-safety defect only, which matches the "Stability & Availability" classification rather than correctness.

Fixed by clamping the index rather than gating the load behind the tail test, which keeps the inner loop free of nested dynamic control flow:

idx = n_block * self.tile_n + col
idx = idx if idx < self.max_topk else Int32(self.max_topk - 1)
if mTopkIdxs_cur[idx] < 0:
    p = Float32(0.0)

self.max_topk is topk_idxs.shape[-1], which is exactly the extent of mTopkIdxs_cur, so the index is in bounds by construction. The clamp is emitted only in the non-compact path, so the compact path is untouched.

The regression test now sizes the non-compact case's topk_idxs to n_valid + 18 = 118, i.e. a deliberate non-multiple of 64 with a 54-column tail, so the peeled tile addresses past the row unless the clamp holds. pytest fe_api/dsa/ -q is still 71 passed / 61 skipped on SM90, and pre-commit is clean.

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python/cudnn/deepseek_sparse_attention/sparse_attention_backward/dsa_bwd_sm90.py (1)

1186-1197: 🩺 Stability & Availability | 🟠 Major | 🏗️ Heavy lift

Synchronize both warpgroups after an empty tile.

When topK <= 0, WG0 and WG1 skip their mainloops and write zero dQ output. The 128-thread epilogue barriers do not synchronize the warpgroups. WG0 can issue the next tile’s Q TMA load into sQ while WG1’s TMA store still reads sQ, which can corrupt dQ.

Add a 256-thread named barrier after both dQ epilogues and before either warpgroup advances its scheduler.

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In
`@python/cudnn/deepseek_sparse_attention/sparse_attention_backward/dsa_bwd_sm90.py`
around lines 1186 - 1197, For the topK <= 0 empty-tile path in the dQ epilogues,
add a 256-thread named-barrier synchronization after both WG0 and WG1 epilogues
complete, before either warpgroup advances its scheduler or begins the next
tile’s Q TMA activity. Ensure both warpgroups participate in the barrier so WG0
cannot overwrite sQ while WG1’s TMA store is still reading it.

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Outside diff comments:
In
`@python/cudnn/deepseek_sparse_attention/sparse_attention_backward/dsa_bwd_sm90.py`:
- Around line 1186-1197: For the topK <= 0 empty-tile path in the dQ epilogues,
add a 256-thread named-barrier synchronization after both WG0 and WG1 epilogues
complete, before either warpgroup advances its scheduler or begins the next
tile’s Q TMA activity. Ensure both warpgroups participate in the barrier so WG0
cannot overwrite sQ while WG1’s TMA store is still reading it.

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[SuperGoodGame]

On the second finding — "Synchronize both warpgroups after an empty tile" — the reasoning is sound as a conditional, but the premise it rests on ("the next tile's Q TMA") does not hold for this kernel, and the shape it describes is not something this PR introduces. I would rather not add the barrier; happy to be overruled.

There is no next tile. SingleTileScheduler maps one CTA to exactly one tile, two independent ways:

• get_grid_shape() returns (num_block, num_head * num_splits, num_batch) — the entire tile space — and get_current_work() returns tile_idx straight from cute.arch.block_idx(). The mapping is a bijection, so no CTA has a second tile to advance to.
• advance_to_next_work() only does self._is_first_block = False, a plain Python attribute assignment, and is_valid_tile is that same Python bool. while work_tile.is_valid_tile: is therefore a trace-time loop that emits exactly one body. (Were it a real scf.while, a Python attribute mutation could not act as a loop-carried value and it would never terminate.)

The normal path has the identical shape. With topK > 0, WG0's last cross-warpgroup barrier for a tile is sdS_consumed before epilogue_dQ, and the first thing it would do on a hypothetical next tile is issue the Q TMA into sQ — with no 256-thread barrier between that and WG1's epilogue_dQ_wg1 TMA store. So if the scheduler ever became persistent, the hazard would exist on the ordinary path too, not just the empty one. It is a latent invariant of the current design rather than a regression here.

One barrier would not actually make the kernel persistence-safe, which is my main reason for leaving it out — it would read as assurance that is not there. Two of the named barriers already carry state across tiles today: KV_empty and sP_consumed each get K arrivals from WG1 but only K - 1 waits from WG0 per tile (WG0's arrive-and-wait at the top of the next n_block doubles as its arrival, and sP_consumed is skipped on the first n_block via dQ_accumulate). That leaves 128 unconsumed arrivals per tile. Harmless with one tile per CTA; under a persistent scheduler it would drift and release barriers early, independently of sQ.

For what it is worth, the sQ ordering is the thing this PR had to get right within a tile — guarding only the barriers left WG0's in-flight Q TMA overwriting the zeros WG1 had already stored to sQ[256:], which is why the guard covers WG0's whole prologue rather than just the barrier sites. That failure was reproducible (dq[256:576] came back holding q, nondeterministically) and is covered by the 800-launch stress described in the PR body.

If a maintainer would still like the invariant pinned down, I would suggest a comment at the scheduler construction site stating that the epilogues rely on one tile per CTA, rather than a barrier — and if the intent is to actually allow a persistent scheduler later, that is a larger change than this PR (the two barrier ledgers above would need fixing as well). Glad to do either.

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Inline comments:
In
`@python/cudnn/deepseek_sparse_attention/sparse_attention_backward/dsa_bwd_sm90.py`:
- Around line 343-344: Update the lse_row == Float32.inf fallback near the
lse_log2 == sink_log2 check so it does not overwrite p_sink when both values are
positive infinity; make the branches exclusive or guard the zero assignment with
sink_log2 != Float32.inf, preserving p_sink = Float32(0.5) for equal
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• python/cudnn/deepseek_sparse_attention/sparse_attention_backward/_interface_sm90.py
• python/cudnn/deepseek_sparse_attention/sparse_attention_backward/dsa_bwd_sm90.py
• test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py

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[SuperGoodGame]

_wg0_one_n_block dropped is_first. Develop already passed dQ_accumulate=False on the peeled first n_block and True on the rest, which was always the opposite of is_first, so the compact tail mask now uses not dQ_accumulate. num_valid_rows is unchanged. The only new parameter is tScS_mn (KV-column identity of each S accumulator lane).

[jiayus-nvidia]

@cudnn-ci-bot run oss

[cudnn-ci-bot]

🏁 Pipeline finished

SHA: 359e56a
Targets: oss
Branch: cudnn-gh/pr-785-359e56a
Pipeline: 65768960
Last updated: 2026-09-02 06:22 UTC

[SuperGoodGame]

@jiayus-nvidia @Anerudhan tks

[vedaanta]

I think these errors on sm90 are from this PR:

=========================== short test summary info ============================
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_sm90_saturating_attn_sink[pos-inf] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_qh32_uses_per_query_topk_without_padding - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_wrapper[False-512-512-512-16-acc_dtype0-dtype0] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_wrapper[False-512-512-512-64-acc_dtype0-dtype0] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_wrapper[False-512-576-512-16-acc_dtype0-dtype0] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_wrapper[False-512-576-512-32-acc_dtype0-dtype0] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_wrapper[True-512-512-512-16-acc_dtype0-dtype0] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_wrapper[True-512-512-512-64-acc_dtype0-dtype0] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_wrapper[True-512-576-512-16-acc_dtype0-dtype0] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_wrapper[True-512-576-512-32-acc_dtype0-dtype0] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_zero_topk_length[d512-mixed] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_zero_topk_length[d576-mixed] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_zero_topk_length[d576-h16-m128-boundaries] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_sm90_padded_topk_columns_contribute_zero[compact] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_sm90_padded_topk_columns_contribute_zero[non-compact] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
FAILED test/python/fe_api/dsa/test_DSA_sparse_attention_backward.py::test_DSA_sparse_attention_backward_sm90_saturating_attn_sink[finite-but-rescale-overflows] - AttributeError: module 'cutlass.cute.math' has no attribute 'isfinite'
===== 16 failed, 113 passed, 4477 skipped, 57 warnings in 72.41s (0:01:12) =====

[SuperGoodGame]

@vedaanta thanks — those SM90 failures are from cute.math.isfinite in the sink LSE fold. That symbol is a newer CuTeDSL API; this repo's cutedsl extra is nvidia-cutlass-dsl>=4.5.0 (not pinned) and the version CI installs does not have it, so compile raises AttributeError before any of the tests run.

Replaced it with the same ±Float32.inf compares this kernel already uses on develop (46a6d66c). Behavior is unchanged: an infinite max is already the logaddexp, and shifting it is inf-inf.

[vedaanta]

@cudnn-ci-bot run oss

[cudnn-ci-bot]

🏁 Pipeline finished

SHA: 46a6d66
Targets: oss
Branch: cudnn-gh/pr-785-46a6d66
Pipeline: 65792818
Last updated: 2026-09-02 06:51 UTC

[vedaanta]

@SuperGoodGame please feel free to merge after resolveing conflicts

[SuperGoodGame]

@vedaanta Conflicts with latest develop are resolved — rebased onto f12d052, keeping your new SM100 invalid-row tests and the d512-h128-two-cta param alongside the SM90 cases (that param skips via _require_exact_sm100() on SM90; the test itself now gates SM90+).

All checks are green, and the full fe_api/dsa/ suite passes locally on an SM90 host (70 passed / 76 skipped, SM100 cases skip as designed). GitHub doesn't offer me the merge button as an external contributor — could you merge when you get a chance? Thanks!