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fix(attention): handle extreme negative logits in masked softmax - #4401
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📝 WalkthroughWalkthroughChangesThe finite Extreme-logit attention handling
Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: 🟡 Moderate · up to The PR fixes extreme-negative-logit handling in the covered attention paths, but an FP8 API path can reject a valid explicit BF16 output and fully masked FA3 rows can still produce NaN; an affected regression test may also fail on older CUDA architectures. These are concrete merge-readiness risks that should be fixed or explicitly accepted before merging. Possibly related PRs
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In `@tests/attention/test_extreme_logits_regression.py`:
- Around line 279-303: Update the direct-execution runner for the extreme-logit
tests so any failure produces a nonzero process status. Remove the manual
try/except wrappers or re-raise each caught exception after logging, and avoid
printing the overall success message when any test fails.
- Around line 189-205: Update the masking test around q, k, and v to use
deterministic token-level values instead of random Q/K tensors. Construct at
least one masked key whose logit exceeds every valid key while keeping values
distinguishable, then verify the flashinfer output matches the reference and is
unaffected by that masked key.
- Around line 43-50: The reference causal masking in the test’s score
computation must match single_prefill_with_kv_cache’s default NHD layout and
causal=True behavior. Update the mask construction around scores, seq_len, and
kv_len to use the API’s expected head/length dimensions and any required padding
or offset, while preserving unmasked behavior when causal is false.
- Around line 63-65: Update the Q/K generation in the extreme-logits regression
setup so inputs account for FA2’s sm_scale of 1/sqrt(head_dim): construct keys
such that the maximum scaled score equals target_logit, while all other pairs
produce a lower scaled score and the designated pair remains the slight maximum.
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Pull request overview
This PR addresses FA2 masked-softmax correctness when all valid logits in a row are extremely negative, by replacing the finite “-inf sentinel” with true IEEE infinity, and adds a regression test intended to prevent recurrence.
Changes:
- Switch
flashinfer::math::inffrom a finite constant (5e4) to IEEE+infinityto prevent running-max clamping in online softmax. - Add a new attention regression test file targeting extreme-negative-logit regimes and masking behavior.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
| include/flashinfer/math.cuh | Updates the infinity sentinel used by attention kernels to true IEEE infinity. |
| tests/attention/test_extreme_logits_regression.py | Adds regression coverage for extreme negative logits and masked semantics (but needs fixes for reference correctness/robustness). |
Suppressed comments (7)
tests/attention/test_extreme_logits_regression.py:37
- The reference implementation is computing scores with
torch.matmul(q_fp64, k_fp64.transpose(-2, -1)), which broadcasts to the wrong shape (it produces a[kv_len, H, 1]-like result for the current inputs). This makeso_refa different shape than FlashInfer’s output and the test can silently pass/fail due to broadcasting in the error computation. Rework the reference to explicitly match FlashInfer’s[qo_len, num_qo_heads, head_dim]output shape and (optionally) GQA broadcasting.
# Convert to FP64 for numerically stable computation
q_fp64 = q.float().to(torch.float64)
k_fp64 = k.float().to(torch.float64)
v_fp64 = v.float().to(torch.float64)
tests/attention/test_extreme_logits_regression.py:88
create_test_case_with_target_logit()currently sets K elements totarget_logit / head_dim, but FlashInfer appliessm_scale(default1/sqrt(head_dim)). As a result, the scaled attention scores are much less extreme thantarget_logit, so the test may not actually exercise the -5e4 failure regime. Construct K so that the scaled dot-product score is approximatelytarget_logitunder the default scale.
# K produces target_logit for all positions, except position 0 which produces
# logit = target_logit + 5000 (slightly higher to be the row maximum)
k = torch.full((kv_len, 1, head_dim), target_logit / head_dim, dtype=dtype_t, device=device)
k[0] = (target_logit + 5000.0) / head_dim
tests/attention/test_extreme_logits_regression.py:123
rel_errordivides byo_ref.norm()without casting; sinceo_refis returned inq.dtype(fp16/bf16), the norm can underflow to 0 and produceinf/nanrelative errors. Compute the denominator in float and add a small epsilon.
# Compute relative error
rel_error = (o_flashinfer.float() - o_ref.float()).norm() / o_ref.norm()
tests/attention/test_extreme_logits_regression.py:164
- Same issue as above:
o_ref.norm()is computed in fp16/bf16 and can underflow to 0, makingrel_errormeaningless. Use a float norm + epsilon.
# Compute relative error
rel_error = (o_flashinfer.float() - o_ref.float()).norm() / o_ref.norm()
tests/attention/test_extreme_logits_regression.py:207
- Same issue as above: compute the relative-error denominator in float to avoid underflow when
o_refis bf16/fp16.
o_ref = reference_prefill_attention(q, k, v, causal=True)
rel_error = (o_flashinfer.float() - o_ref.float()).norm() / o_ref.norm()
tests/attention/test_extreme_logits_regression.py:195
- This test claims “half the positions masked”, but
qis length-1 whilek/vare lengthseq_len. With FlashInfer’s prefill causal semantics (supports kv_len != qo_len), a single query token typically has no future positions to mask here, so this isn’t validating masked behavior. Makeqlengthseq_len(or use an explicitcustom_mask) and choose K values consistent with the defaultsm_scale.
q = torch.randn(1, num_heads, head_dim, dtype=torch_dtype_map[dtype], device=device)
# Scale K to produce extreme negative logits
k_scale = -1e5 / head_dim
k = torch.full((seq_len, 1, head_dim), k_scale, dtype=torch_dtype_map[dtype], device=device)
tests/attention/test_extreme_logits_regression.py:267
- Same issue as above:
o_ref.norm()is computed in bf16/fp16 and can underflow to 0. Compute the denominator in float and add a small epsilon.
# Verify correctness
rel_error = (o_flashinfer.float() - o_ref.float()).norm() / o_ref.norm()
tolerance = 1e-2
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| #include <cmath> | ||
| #include <cstdint> | ||
| #include <limits> | ||
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| // that legitimately fall below any fixed finite threshold. | ||
| // Masked positions use -inf, and fully-masked rows are detected | ||
| // via equality checks (m == -math::inf) in output normalization. | ||
| constexpr float inf = std::numeric_limits<float>::infinity(); |
This was referenced Aug 10, 2026
Closed
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I ran the three missing regression surfaces on an NVIDIA B200 against the exact PR head
So the shared true-infinity change fixes the classic paged-decode case, but the tested head still needs additional handling for the ragged valid-row update and for scheduled empty rows. The final valid row in #4452 remains correct ( The linked issues now include full deterministic scripts, raw outputs, public source permalinks, and untrimmed |
Fixes #4267, #4450, #4451, #4452 The FA2 attention kernels masked logits with a finite sentinel (5e4). Because the online-softmax running maximum is tracked over the raw QK dot product, a valid logit below -5e4 could never raise the initialized maximum: every probability underflowed to zero and valid rows returned zero output with a sentinel LSE. Representing masked logits as IEEE -inf fixes that, but is not sufficient on its own. Online-softmax updates evaluate exp2(m_prev - m) and exp2(s - m); a row whose every logit is masked (fully masked rows of a bottom-right aligned causal prefill, a kv warp slice owning no valid column, or an empty split-KV chunk) computes (-inf) - (-inf) = NaN, which reaches the output and LSE because the final (m != -inf) guard cannot recover from NaN * 0. Clamp every exponent difference at -inf before exp2. m is the running maximum, so all clamped differences are <= 0 for finite inputs and finite-input numerics are unchanged; the only reachable NaN input maps to exp2(-inf) = 0, so a fully masked tile contributes nothing. A fully masked row ends with m = -inf, d = 0, o = 0 and normalizes to output 0 and LSE -inf (state_t::normalize skips the division when d == 0), matching the empty-row semantics of the MLA kernels. The same clamp is applied to every consumer of the sentinel: FA2 prefill (fp32 and fp16-reduction paths, VO-split), FA2 decode (vector and MLA variants), split-KV state merging, mla.cuh and mla_hopper.cuh. Add regression tests covering extreme negative logits for single prefill, a masked key that outranks every valid key, the ragged one-key fixture (#4451), fully masked rows (#4452), the split-KV empty chunk of multi-token causal prefill, and classic paged decode (#4450).
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Actionable comments posted: 2
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Inline comments:
In `@flashinfer/jit/attention/variants.py`:
- Around line 41-43: Update the FA3 finalization logic around the scale and
d_new calculations to clamp fully masked rows before subtracting -inf values,
matching FA2 behavior and preventing NaN propagation when sink contains -inf.
Add a deterministic regression test covering a fully masked row with an -inf
sink and verify the finalized output remains valid.
In `@flashinfer/prefill.py`:
- Around line 3861-3867: In the ragged output handling around the cached
_cached_o_data_type, compute one effective output dtype that maps the
FP8/default 8-bit case to torch.bfloat16, then reuse it for both omitted-output
allocation and explicit out validation. Update the validation path near the
existing out checks to compare against this effective dtype, and add a
regression test covering explicit BF16 out with FP8 input.
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…to pr-4401 # Conflicts: # flashinfer/prefill.py
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[SUCCESS] Pipeline #63397947: 16/16 executed test jobs passed |
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…ytorch#553) Summary: Bottom-right causal/local attention with `seqlen_q > seqlen_k` (dense, or any sequence in a varlen batch) leaves the first `seqlen_q - seqlen_k` query rows with no visible keys. The Blackwell CUTLASS FMHA kernels mishandled these rows in four ways: 1. Fwd NaN output: such a row ends with `row_sum = 0`, and the epilogue scale `scale_output / row_sum` turned O = 0 into `0 * inf = NaN`. The scale is now 0 for such rows. LSE was already `-inf`, which is correct. 2. Bwd NaN in dQ/dK/dV: the bwd rebuilds P as `exp2(scale * S + scaled_lse)` with `scaled_lse = -log2(e) * LSE`, which is `+inf` for such rows, so P = `exp2(-inf + inf) = NaN`. The NaN reaches dK/dV for every key in the KV tile. The `sum_OdO` prep kernel now emits `scaled_lse = -inf` for them, so P = 0. (The MLA bwd uses the same prep kernel.) 3. Fwd hang: if a whole 256-row Q tile has no visible keys, the trip count is <= 0. The load and MMA warps always process one KV tile while the softmax warps process none, so the pipelines deadlock. `correction_empty` was only used for `seqlen_k == 0`. Every warp role now sends tiles with no KV work (`has_no_kv_tiles`) through `correction_empty`, which writes O = 0 and LSE = `-inf`. This also covers top-left local windows that end past the last key. 4. Fwd wrong output: `CausalMask<false>::get_masked_trip_count` took a negative `%` remainder of the offset when `seqlen_q > seqlen_k`. When the offset is not a multiple of 128 it masks one KV tile too few, so some rows attended to future keys. It now uses a non-negative remainder. Found by checking whether the empty-row bugs fixed in flashinfer-ai/flashinfer#4401 also affect these kernels. Differential Revision: D123858765
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Summary: Pull Request resolved: #553 Bottom-right causal/local attention with `seqlen_q > seqlen_k` (dense, or any sequence in a varlen batch) leaves the first `seqlen_q - seqlen_k` query rows with no visible keys. The Blackwell CUTLASS FMHA kernels mishandled these rows in four ways: 1. Fwd NaN output: such a row ends with `row_sum = 0`, and the epilogue scale `scale_output / row_sum` turned O = 0 into `0 * inf = NaN`. The scale is now 0 for such rows. LSE was already `-inf`, which is correct. 2. Bwd NaN in dQ/dK/dV: the bwd rebuilds P as `exp2(scale * S + scaled_lse)` with `scaled_lse = -log2(e) * LSE`, which is `+inf` for such rows, so P = `exp2(-inf + inf) = NaN`. The NaN reaches dK/dV for every key in the KV tile. The `sum_OdO` prep kernel now emits `scaled_lse = -inf` for them, so P = 0. (The MLA bwd uses the same prep kernel.) 3. Fwd hang: if a whole 256-row Q tile has no visible keys, the trip count is <= 0. The load and MMA warps always process one KV tile while the softmax warps process none, so the pipelines deadlock. `correction_empty` was only used for `seqlen_k == 0`. Every warp role now sends tiles with no KV work (`has_no_kv_tiles`) through `correction_empty`, which writes O = 0 and LSE = `-inf`. This also covers top-left local windows that end past the last key. 4. Fwd wrong output: `CausalMask<false>::get_masked_trip_count` took a negative `%` remainder of the offset when `seqlen_q > seqlen_k`. When the offset is not a multiple of 128 it masks one KV tile too few, so some rows attended to future keys. It now uses a non-negative remainder. Found by checking whether the empty-row bugs fixed in flashinfer-ai/flashinfer#4401 also affect these kernels. Reviewed By: cthi Differential Revision: D123858765 fbshipit-source-id: 15ee6ba6d20047562dd7d98e647c8f68d0f51860
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📌 Description
The FA2 attention kernels masked logits with a finite sentinel (
math::inf = 5e4). Because the online-softmax running maximum is tracked over the raw QK dot product, a valid logit below-5e4could never raise the initialized maximum: every probability underflowed to zero and valid rows returned zero output with a sentinel LSE (#4267, #4450).Replacing the sentinel with IEEE
-infis necessary but not sufficient. The online-softmax updates evaluateexp2(m_prev - m)andexp2(s - m); a row whose every logit is masked computes(-inf) - (-inf) = NaN, which reaches the output and LSE because the existing final(m != -inf) ? rcp(d) : 0guard cannot recover fromNaN * 0. Three surfaces hit this (#4451, #4452, plus multi-token split-KV decode):qo_len > kv_len),state_t::merge/normalize.Fix
Clamp every exponent difference at
-infbeforeexp2(max(diff, -math::inf);maxdrops the NaN operand). Sincemis the running maximum, every clamped difference is already<= 0for finite inputs, so finite-input numerics are unchanged; the only reachable NaN input maps toexp2(-inf) = 0, i.e. a fully masked tile contributes nothing. A fully masked row now ends withm = -inf, d = 0, o = 0, normalizes to output 0 and LSE = -inf (state_t::normalizeskips the division whend == 0), matching the empty-row semantics the MLA kernels already use.The clamp is applied to every consumer of the sentinel: FA2 prefill
update_mdo_states(fp32 and fp16-reduction paths),threadblock_sync_mdo_states, the VO-split path, FA2 decode (vector + MLA update sites), split-KVstate_t::merge,mla.cuh, andmla_hopper.cuh.math::infnow usescuda::std::numeric_limitsfor consistency with the rest of the codebase.Verification (RTX 4090 / SM89, CUDA 13.3, torch 2.5.1)
All three deterministic fixtures from the linked issues reproduce locally and now pass:
v0.6.16.post3/main0, sentinel LSE1, LSE-66854.859)0, sentinel LSE1, LSE-66855.859, bit-exact)0, LSE-inf)test_cuda_graph_uniform_multi_token_decode_with_paged_kv_cache, 16 cases)Test suites (SM89):
test_extreme_logits_regression.py24 passed;test_batch_decode_kernels.py+test_decode_prefill_lse.py+test_batch_prefill.py3093 passed;test_deepseek_mla.py6127 passed;test_single_prefill.py+test_decode_prefill_lse.py191 passed.mla_hopper.cuhchanges compile-verified forsm_90a(no SM90 GPU available here — CI coverage requested). The regression tests were confirmed to fail on the pre-fix revision (reverted-kernel run: 4 failures on the #4451/#4452 fixtures).Performance (RTX 4090, bf16, median of 50 iters)
No regression beyond run-to-run noise (the added
maxis onemax.f32perex2).🔍 Related Issues
Fixes #4267
Fixes #4450
Fixes #4451
Fixes #4452
🚀 Pull Request Checklist
✅ Pre-commit Checks
pre-commitby runningpip install pre-commit(or used your preferred method).pre-commit install.pre-commit run --all-filesand fixed any reported issues.🧪 Tests
unittest, etc.).Reviewer Notes
mla_hopper.cuh),tests/attention/test_cute_dsl_mla_dcp.py, and the expectations in [Bug] Ragged FA2 prefill returns zeros for a one-key finite row below the -5e4 sentinel #4451/[Bug] Paged causal prefill returns finite LSE for fully masked rows when qo_len > kv_len #4452. Onmainthey returned a finite sentinel LSE.hopper/attention_updater.cuh,OnlineSoftmax::fill_value) shares the sentinel but was left unchanged: it needs its own empty-row handling including the output rescale (0/0), which I cannot exercise on SM89. Happy to follow up separately if desired.25533329→ad2475b0) to add the empty-row/split-KV handling requested in review and to fix the commit author; the earlier head is superseded.Summary by CodeRabbit
Bug Fixes
Tests