[TRTLLM-15204][fix] Initialize KDA dt_bias and re-enable the prefill parity suite on B200 - #17793
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Included review availability: Your plan includes up to 12 reviews per rolling hour; 9 remain after this review. WalkthroughThe Kimi KDA mixer now initializes ChangesKimi KDA initialization and validation
Estimated code review effort: 2 (Simple) | ~10 minutes Merge Risk: ⚪ Minimal · up to The PR initializes KDA dt_bias to prevent nondeterministic NaN parity failures and re-enables the B200 prefill tests; no actionable merge-blocking risk remains beyond normal checks and review. Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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In `@tensorrt_llm/_torch/modules/kimi_kda/kimi_kda_mixer.py`:
- Around line 245-258: The dt_bias initialization in the Kimi KDA mixer uses
log(dt) instead of the exact inverse-softplus expression used by FLA. Update the
initialization around self.dt_bias to either use the exact inverse-softplus form
while preserving MetaInitMode compatibility, or document the intentional
approximation and its resulting deviation; add a focused test if choosing the
exact behavior.
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…parity suite on B200 The KDA prefill parity suite failed on B200 with cosine=nan while passing on GB300. Root cause: KimiKDALinearAttention.dt_bias was nn.Parameter(torch.empty(...)) with no initialization. The parity tests construct modules from random init, so dt_bias carried recycled heap memory; whenever that memory contained NaN/Inf bit patterns, BOTH the optimized CuTe DSL path and the FLA reference produced all-NaN outputs and the cosine check returned nan. The B200-vs-GB300 split was just the different allocation history of each CI test process, not a hardware or kernel difference: with dt_bias initialized, all shapes (including the 8191-token varlen cases) are parity-clean on B200 across repeated runs, and a control run reproduces the NaN exactly when and only when the uninitialized parameter contains non-finite garbage. The init mirrors FLA's KDA dt_bias scheme (inverse-softplus of dt ~ LogUniform[1e-3, 1e-1], fla/layers/kda.py) in its small-dt regime, expressed as a single uniform_ so module construction stays on MetaInitMode's random-init allowlist. Checkpoint loading overwrites the value either way, so production behavior is unchanged. Re-wires test_kda_prefill_op.py into l0_b200.yml, closing the gap left when the suite was unwired pending this investigation. Signed-off-by: Brian Nguyen <brnguyen@nvidia.com>
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/bot skip --comment "Rebased onto latest main. The dt_bias fix in this PR is byte-identical to the commit that last passed a full green pipeline (PR_Github #66763 / L0_MergeRequest_PR #54368, commit c874bd1, SUCCESS). The only delta since that green run is dropping two waives.txt lines, which were re-homed with real tracking bugs to PR #17847 (merged, commit bec64fa) and now live on main. No source or test-logic change since green CI, so skipping the pipeline." |
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4 participants
Description
The KDA prefill parity suite (
tests/unittest/_torch/modules/kimi_kda/test_kda_prefill_op.py) failed on B200 withcosine = nanwhile passing on GB300 (TRTLLM-15204), and was unwired froml0_b200.ymlpending investigation.Root cause — not a kernel or hardware issue.
KimiKDALinearAttention.dt_biaswas declared asnn.Parameter(torch.empty(...))and never initialized. The parity tests construct modules from random init, sodt_biascarried whatever was in the recycled allocation. Whenever that memory contained NaN/Inf bit patterns, both the optimized CuTe DSL path and the FLA Triton reference produced all-NaN outputs, andcosine_similarity(nan, nan) = nanfailed the check. The B200-vs-GB300 split was an artifact of each CI process's allocation history (first module in a fresh process tends to get zeroed pages; later constructions get heap garbage), not a numeric difference between the chips.Evidence (B200, 8xB200 node):
dt_biasexplicitly initialized, 0 failures across repeated runs of every failing shape, including the 8191-token varlen cases.dt_biasleft uninitialized, failures correlate exactly with non-finite values observed insidedt_biasitself (e.g. 293/2/15 non-finite elements → all-NaN outputs; a finite-garbage draw passed).Fix. Initialize
dt_biasfollowing FLA's KDA scheme (inverse-softplus ofdt ~ LogUniform[1e-3, 1e-1],fla/layers/kda.py), approximated by a singleuniform_(log(1e-3), log(1e-1))— equal to the exact form within ~5% in this dt range — so module construction stays onMetaInitMode's random-init allowlist (exp/expm1/clampwould raiseMetaInitExceptionand force the full-CPU-init fallback). Checkpoint loading overwrites the parameter either way, so production behavior is unchanged.Also re-wires
test_kda_prefill_op.pyintol0_b200.yml.Note:
test_kda_decode_op.pyshares the module-construction pattern and is covered by the same module-level fix.Test Coverage
tests/unittest/_torch/modules/kimi_kda/test_kda_prefill_op.py(re-enabled on B200; 7/7 pass at this commit, plus a 75-pair stress replay with 0 failures — see validation comment)tests/unittest/_torch/modules/kimi_kda/test_kda_decode_op.pyPR Checklist
Dev Engineer Review
dt_biasnow uses a finite inverse-softplus initialization overlog(1e-3)tolog(1e-1).test_kda_prefill_op.pyis enabled intests/integration/test_lists/test-db/l0_b200.yml.0.999985.PR_Github#66763and `L0_MergeRequest_PR `#54368pipelines passed.QA Engineer Review
tests/integration/test_lists/test-db/l0_b200.yml.test_kda_prefill_op.pyto the B200 pre-merge test list.