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GLM k-pool selection now pads rows to 128 for the sparse forward path. Verify the reported SM90 alignment case and confirm the SM100 64-head kernel still handles its 64-row requirement.
KDA decode now uses is_kda_decode_supported(), which includes SM90. Prefill and verify retain their existing architecture gate.
The supplied validation reports H100 and B200 test results. MTP on Hopper and Blackwell end-to-end validation were not run. The reported performance and parity results are not independently verified here.
QA Engineer Review
The change modifies six test files and two CI test-list files. Coverage includes sparse-core behavior at widths 67 and 2051, KDA decode parity and dispatch, and BF16 state-pool decode eligibility. The changed KDA tests appear in the CPU, H100, B200, and GB300 CI lists as applicable; no list entry was found for the sparse-core test. Coverage verdict: needs follow-up, because Hopper MTP and Blackwell end-to-end validation were not run.
Per-File QA Perspective
tensorrt_llm/_torch/attention/backends/sparse/glm_kpool/backend.py — Verify that 128-row padding meets the SM90 sparse-kernel requirement and preserves behavior for SM100 kernels.
tensorrt_llm/_torch/modules/kimi_kda/_kda_kernels.py — Verify that is_kda_decode_supported() enables optimized decode on SM90 while prefill and verify keep their existing architecture gate.
tests/unittest/_torch/attention/sparse/glm_kpool/test_kernels.py — Extends sparse-core coverage to widths 67 and 2051. No CI or manual-QA test-list entry was found.
tests/unittest/_torch/modules/kimi_kda/test_kda_decode_op.py — Uses the decode-specific support check for GPU eligibility. Listed in the H100, B200, and GB300 CI lists.
tests/unittest/_torch/modules/kimi_kda/test_kda_kernel_dispatch.py — Covers architecture-based dispatch and the disabled optimized-decode option. Listed in the CPU CI list.
tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_bf16_state_pool.py — Uses the decode support check for CUDA-device eligibility. Listed in the B200 and GB300 multi-GPU CI lists.
tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_decode_state_parity.py — Uses the decode support check to gate Hopper and Blackwell state-parity coverage. Listed in the H100, B200, and GB300 CI lists.
tests/integration/test_lists/test-db/l0_cpu.yml — Adds the KDA dispatch test to the CPU CI list.
tests/integration/test_lists/test-db/l0_h100.yml — Adds KDA decode and decode-state-parity tests to the H100 CI list.
Description
GLM-5.3-Flash fails on Hopper in the first prefill (attention warmup):
glm_kpool pads the selected rows to a multiple of 64 before flash_mla_sparse_fwd. The SM90 kernel needs a multiple of 128, which is what the DeepSeek-V4 and CSA2 calls to the same function already pad to. GLM-5.3-Flash selects 2051 rows (512 pools of 4 plus a 3-token tail), so they were padded to 2112. This pads to 128 on every arch, which also covers the SM100 128-head kernel.
With that fixed, the 34 KDA layers still decode through the unfused FLA path on Hopper. trtllm::kda_decode runs on SM90 (its dispatcher picks the legacy kernels there, and tests/unittest/_torch/kernels/parallel/test_kda_decode.py already runs it on H100), but KDAKernelDispatch gated decode on the same predicate as the Blackwell-only CuTe DSL prefill and verify kernels. Decode now has its own predicate, is_kda_decode_supported(), which also accepts SM90. Prefill and verify keep their gate, and SM100/103/107 select the same KDA paths as before.
Official FP8 checkpoint (zai-org/GLM-5.3-Flash) on 4x H200, TP4 / EP4, BF16 KV, otherwise the B200 FP8 config from the deployment guide. Same branch without the KDA decode change vs this PR, 500 GSM8K questions (5-shot), decode ms per step (median of 3):
GSM8K
bs 1
bs 16
bs 64
padding fix only
91.0%
10.40
18.78
29.82
this PR
90.4%
7.94
14.52
23.02
The accuracy difference is 15 vs 12 discordant questions (McNemar p = 0.70).
KDA decode layer with GLM-5.3-Flash shapes on 1x H100, CUDA graph replay, us per step (fused / FLA):
KDA heads per rank
bs 1
bs 16
bs 64
bs 256
64 (TP1)
120 / 188
177 / 401
328 / 897
933 / 3096
16 (TP4)
49 / 114
64 / 184
105 / 287
257 / 801
After 64 decode steps from the same state, the layer output differs from the FLA path by 0.41-0.67% relative L2 (cosine >= 0.99998) over 8-64 heads and bs 1-256, and the recurrent state by 0.22-0.24%.
End to end on 1x H100 with a dummy-weight proxy: all 45 layers at their real shapes (KDA, NoPE MLA with the kpool indexer, mHC, FP8 block experts), routed experts cut to 8 with top-8 so the MoE cost does not depend on routing, text only, no MTP. load_format="dummy" skips load_weights(), so the proxy runs the KDA finalize_weights() from an initialize_dummy_state hook. Decode ms per step with only the padding fix / with both changes. Each cell is the mean of two interleaved runs, each run the median of 3:
KDA and MLA heads
bs 1
bs 8
bs 32
bs 64
64 (TP1 shapes)
15.4 / 13.0
18.4 / 14.0
29.3 / 18.9
44.4 / 24.0
16 (TP4 per-rank shapes)
12.6 / 10.1
14.0 / 10.5
18.6 / 13.2
26.5 / 16.3
At bs 64 the two runs differ by up to 1.5 ms; the other batch sizes are within 0.15 ms.
With the real KDA weights of layers 0 and 2 (official FP8 checkpoint, these projections are BF16 there), 8 GSM8K prompts decoded token by token for 128 tokens from a zero state: the layer output of the fused path has cosine 0.99999 and 0.36-0.41% relative L2 to the FLA path at steps 1, 8, 32, 64 and 128, and 0.38-0.40% over all 128 steps. The final recurrent state differs by 0.2%, and the conv window is identical.
Test Coverage
tests/unittest/_torch/attention/sparse/glm_kpool/test_kernels.py: test_fp8_sparse_core_matches_dequantized_cache_and_replays_graph now also runs at width 2051. On 1x H100 the two new cases fail on main with the assertion above and pass with this PR. The file is already in l0_h100.yml through unittest/_torch/attention.
tests/unittest/_torch/modules/kimi_kda/test_kda_decode_op.py now runs on SM90: 22 passed on 1x H100 (the whole file skipped there before). Added to l0_h100.yml.
tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_bf16_state_pool.py: the decode case passes on 1x H100; the prefill cases still skip there.
tests/unittest/_torch/kernels/parallel/test_kda_decode.py: passes on 1x H100.
The KDA and kpool test files above also pass on 1x B200 with this PR (kpool 174 passed, KDA 97 passed).
tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_decode_state_parity.py (fused decode vs the fallback over 6 steps, FP32 and BF16 state) now runs on SM90 too: 4 passed on 1x H100. Added to l0_h100.yml.
New tests/unittest/_torch/modules/kimi_kda/test_kda_kernel_dispatch.py (cpu_only) checks the per-arch decode, prefill and verify paths. 7 passed with no visible GPU. Added to l0_cpu.yml.
The numbers above are from the nvcr.io/nvidia/tensorrt-llm/release:1.4.0rc2.dev202610210802 container (main at b44310c96) with this branch's glm_kpool/backend.py and _kda_kernels.py copied over the installed package (only glm_kpool/backend.py for the padding-fix-only runs).
After merging main ([None][perf] Integrate FlashInfer KDA prefill with fused convolution #20653 reworked the KDA prefill and mixer), the GPU tests above were rerun on the 1.4.0rc2.dev202610260007 container (main at b96ba434e) on 1x H100: kpool 168 passed, 6 skipped (native SM100 cases); KDA 99 passed, 2 skipped (indexed prefill); the CPU dispatch test 7 passed.
Merged main again on 11/03. The GPU tests were rerun on the 1.4.0rc3.dev202611030007 container (main at ff1aa0f7d3) on 1x H100: kpool 282 passed, 11 skipped; KDA 99 passed, 2 skipped; the CPU dispatch test 7 passed. The 16-head proxy also runs on it (decode 9.55 ms per step at bs 1, 13.76 at bs 64, this PR only).
Same container, the 16-head proxy again (padding fix only / both changes, decode ms per step, mean of two interleaved runs, runs within 0.8 ms): bs 1 12.24 / 9.76, bs 8 13.47 / 10.18, bs 32 17.49 / 12.91, bs 64 22.21 / 15.38.
Not run: MTP on Hopper with the real checkpoint (the dummy-weight proxy runs with MTP 3 on 1x H100 after the merge), and Blackwell end to end.
PR Checklist
Please review the following before submitting your PR:
PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
If PR introduces API changes, an appropriate PR label is added - either api-compatible or api-breaking. For api-breaking, include BREAKING in the PR title.
Any new dependencies have been scanned for license and vulnerabilities
FlashMLA's SM90 sparse kernel asserts top-k % 128 == 0, but glm_kpool
padded the selected rows to a multiple of 64. GLM-5.3-Flash selects
2051 rows, padded to 2112, so the first prefill on Hopper failed the
assertion. Pad to 128 on SM90; other archs keep 64.
Cover the model's selection width in the sparse-core test.
Signed-off-by: Chen Cheng <85005591+ischencheng@users.noreply.github.com>
trtllm::kda_decode supports SM90 (the dispatcher picks the legacy
kernels there), but KDAKernelDispatch gated decode together with the
Blackwell-only CuTe DSL prefill and verify kernels, so Hopper decode
fell back to the unfused FLA path. Gate decode on its own predicate
that also accepts SM90; prefill and verify keep their gate.
Run the decode op tests on H100 and add a CPU test for the per-arch
kernel selection.
Signed-off-by: Chen Cheng <85005591+ischencheng@users.noreply.github.com>
GLM kpool top-k alignment changes to 128, and its FP8 sparse-core test covers widths 67 and 2051. Kimi KDA optimized decode support adds SM90. Optimized prefill and verify remain limited to SM100, SM103, and SM107.
Changes
GLM Kpool Sparse Attention
Layer / File(s)
Summary
Top-k alignment and width coverage tensorrt_llm/_torch/attention/backends/sparse/glm_kpool/backend.py, tests/unittest/_torch/attention/sparse/glm_kpool/test_kernels.py
The backend sets top-k alignment to 128 and updates the alignment comments. The FP8 sparse-core test covers widths 67 and 2051.
A new decode-support predicate includes SM90. Optimized prefill and verify remain limited to SM100, SM103, and SM107. Dispatch tests cover six SM versions and the SM90 FLA decode path when optimized decode is disabled.
Decode test eligibility tests/unittest/_torch/modules/kimi_kda/test_kda_decode_op.py, tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_bf16_state_pool.py, tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_decode_state_parity.py, tests/integration/test_lists/test-db/l0_cpu.yml, tests/integration/test_lists/test-db/l0_h100.yml
Decode and state-pool tests use the decode-support predicate. The CPU and H100 integration test lists add Kimi KDA dispatch and decode tests.
NVIDIA/TensorRT-LLM#20509: The change updates KDA decode support gating and uses the decode-specific predicate in related tests. It does not address the issue’s other reported skips or the indexed-pool parity failure.
Suggested reviewers: litaotju
Merge Risk: Low · up to 3c993
The production change lacks a focused regression test for the SM100 128-head kernel path, but no concrete runtime failure is established.
Pre-merge checks: 4
Check name
Status
Explanation
Title check
The title "[None][fix] Run GLM-5.3-Flash on Hopper" follows the repository template (ticket None, type fix). It names the main goal, running GLM-5.3-Flash on SM90. The changeset covers that goal throu…
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Description check
The description follows the repository template. It has the Description, Test Coverage, and PR Checklist sections. It explains the SM90 assertion for 2051 selected rows and the padding change from 64 …
Full details: Title check
Explanation
The title "[None][fix] Run GLM-5.3-Flash on Hopper" follows the repository template (ticket None, type fix). It names the main goal, running GLM-5.3-Flash on SM90. The changeset covers that goal through a sparse-attention padding change and a separate KDA decode predicate that accepts SM90, plus tests. The title does not name either technical change, but it is not misleading and it is not vague.
Full details: Description check
Explanation
The description follows the repository template. It has the Description, Test Coverage, and PR Checklist sections. It explains the SM90 assertion for 2051 selected rows and the padding change from 64 to 128. It also explains the new is_kda_decode_supported() predicate that lets Hopper use optimized KDA decode, while prefill and verify keep their gate. The Test Coverage section lists specific test files, hardware, container versions, and results, plus what was not run (MTP on Hopper with the real checkpoint, and Blackwell end to end). The PR title template is not part of the description body, so it is not assessed here.
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5 points — Review requires checking architecture-specific alignment, invalid-index semantics, FP8 recursive dispatch and graph replay. The additional Hopper KDA decode enablement described in the body warrants checking kernel eligibility and recurrent numerical behavior; its omitted implementation makes this five-point estimate provisional.
Importance
Important — The visible change corrects a hardware-sensitive sparse-attention contract: SM90 indices must be aligned to 128 before the FlashMLA call. The supplied test identifies this core as shared by prefill, decode and verify.
Affinity (0–1)
0.815 component/tag match; 0.611 after PIC preference
Outstanding points at selection
5 → 5 (+0)
Points already reserved on this PR
5
Effective weight
0.5
Completed review points (preceding 168 hours)
30
Combined points after assignment
35
Demand-derived target
29.50 points (soft share)
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0.00 points
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Whole PR / qualification or coverage constraint exceeds a soft share; assignment is allowed.
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Approving — the comments below are optional touch-ups, not blockers.
The padding fix matches the assertion, and the 2051-width parametrization covers it on H100 through the existing unittest/_torch/attention entry in l0_h100.
The KDA change does more than swap a kernel. With decode_kernel_path == "optimized", Hopper now also runs the mixer's projection fusion (kimi_kda_mixer.py:336) and the direct-pool decode (kimi_kda_mixer.py:815). Before this PR those paths ran only on Blackwell. test_kda_decode_op.py exercises them on a synthetic layer, but per the description no real GLM-5.3-Flash checkpoint has run on H100. Since the PR title is "Run GLM-5.3-Flash on Hopper", please run one end-to-end H100 smoke/accuracy check on the real checkpoint before merge, or retitle the PR to say what was actually verified (the sparse assert fix plus fused decode parity).
The description's latency table is cut off after the header row ("| KDA heads"). Please restore it so the perf claim for Hopper decode is on record.
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Optional: CSA2 pads to 128 on every arch (csa2/backend.py:680), and the description says DeepSeek-V4 does the same. Padding to 128 unconditionally here would remove the arch branch and make the index-row shape identical across GPUs. On Blackwell it costs at most one extra 64-row tile of masked work. If you keep the branch, note that Hopper-specific alignment now lives in three places. A shared helper next to the FlashMLA import would keep the next caller from repeating the 64-vs-128 mistake.
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switched to 128 on every arch in 77030d6, which also covers the SM100 128-head kernel. CSA2 and DSv4 already pad their flash_mla_sparse_fwd calls to 128, so I kept it local here. Reran the kpool and KDA tests on 1x B200 and 1x H100, all pass.
128 satisfies every flash_mla_sparse_fwd instance (SM90 and the SM100
128-head kernel need it, the SM100 64-head kernel needs 64), so the
arch check is not needed.
Signed-off-by: Chen Cheng <85005591+ischencheng@users.noreply.github.com>
@brnguyen2 thanks for the review! Ran the official FP8 checkpoint on 4x H200 (TP4/EP4, BF16 KV, otherwise the guide's B200 FP8 config). It compares this PR with the same branch minus the KDA decode change, so the fused projections and the direct-pool decode run on real weights:
GSM8K (500, 5-shot)
decode ms/step bs 1 / 16 / 64
padding fix only
91.0%
10.40 / 18.78 / 29.82
this PR
90.4%
7.94 / 14.52 / 23.02
15 vs 12 discordant questions, McNemar p = 0.70. It's in the description now.
The KDA table shows up in the description for me, pasting it here too (us per step on 1x H100, fused / FLA):
The fused decode now runs on SM90, so check its projections and direct
pool updates against the fallback there too.
Signed-off-by: Chen Cheng <85005591+ischencheng@users.noreply.github.com>
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🪄 Fix comments: Fix CodeRabbit comments on this PR
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at
@tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_decode_state_parity.py:
- Line 44: Update test_decode_state_matches_fallback_over_many_steps to verify
that SM90 uses fused decode by asserting _qkvg_proj_weight and _bfa_proj_weight
are initialized or observing the optimized decode call; preserve the existing
skip when is_kda_decode_supported() is false.
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forward_decode falls back when finalized optimized weights are unavailable. Assert that _qkvg_proj_weight and _bfa_proj_weight are initialized, or observe the optimized decode call. Otherwise, the parity test can compare fallback behavior on both sides without covering fused decode.
Coverage: test_decode_state_matches_fallback_over_many_steps covers four batch/state-dtype combinations over six steps, but does not verify fast-path selection.
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Review comment at
@tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_decode_state_parity.py at
line 44:
Update test_decode_state_matches_fallback_over_many_steps to verify that SM90
uses fused decode by asserting _qkvg_proj_weight and _bfa_proj_weight are
initialized or observing the optimized decode call; preserve the existing skip
when is_kda_decode_supported() is false.
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Merged main to fix the conflict with #20653, and turned on its new decode state parity test for H100 too (passes there). Reran the tests and the proxy on the 10/26 nightly, numbers are in the description.
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Minor · Functional Correctness · Quick win
Add coverage for the 128-head kernel.
_KERNEL_TOPK_ALIGN now applies the 128-entry requirement to the SM100 128-head kernel. The test only uses heads=[16, 64]. Both values select the 64-head kernel, so the 128-head path is not exercised. Add 128 to the test parameterization or add a focused SM100 case.
As per path instructions, production-code changes require meaningful test coverage.
Prompt for AI Agents
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Review comment at
@tensorrt_llm/_torch/attention/backends/sparse/glm_kpool/backend.py at line 134:
Add test coverage for the SM100 128-head kernel by including 128 in the existing
head-count parameterization or adding a focused SM100 case. Verify the test
exercises the path affected by _KERNEL_TOPK_ALIGN; retain coverage for the
existing 16- and 64-head cases.
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Dev Engineer Review
is_kda_decode_supported(), which includes SM90. Prefill and verify retain their existing architecture gate.QA Engineer Review
The change modifies six test files and two CI test-list files. Coverage includes sparse-core behavior at widths 67 and 2051, KDA decode parity and dispatch, and BF16 state-pool decode eligibility. The changed KDA tests appear in the CPU, H100, B200, and GB300 CI lists as applicable; no list entry was found for the sparse-core test. Coverage verdict: needs follow-up, because Hopper MTP and Blackwell end-to-end validation were not run.
Per-File QA Perspective
tensorrt_llm/_torch/attention/backends/sparse/glm_kpool/backend.py— Verify that 128-row padding meets the SM90 sparse-kernel requirement and preserves behavior for SM100 kernels.tensorrt_llm/_torch/modules/kimi_kda/_kda_kernels.py— Verify thatis_kda_decode_supported()enables optimized decode on SM90 while prefill and verify keep their existing architecture gate.tests/unittest/_torch/attention/sparse/glm_kpool/test_kernels.py— Extends sparse-core coverage to widths 67 and 2051. No CI or manual-QA test-list entry was found.tests/unittest/_torch/modules/kimi_kda/test_kda_decode_op.py— Uses the decode-specific support check for GPU eligibility. Listed in the H100, B200, and GB300 CI lists.tests/unittest/_torch/modules/kimi_kda/test_kda_kernel_dispatch.py— Covers architecture-based dispatch and the disabled optimized-decode option. Listed in the CPU CI list.tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_bf16_state_pool.py— Uses the decode support check for CUDA-device eligibility. Listed in the B200 and GB300 multi-GPU CI lists.tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_decode_state_parity.py— Uses the decode support check to gate Hopper and Blackwell state-parity coverage. Listed in the H100, B200, and GB300 CI lists.tests/integration/test_lists/test-db/l0_cpu.yml— Adds the KDA dispatch test to the CPU CI list.tests/integration/test_lists/test-db/l0_h100.yml— Adds KDA decode and decode-state-parity tests to the H100 CI list.Description
GLM-5.3-Flash fails on Hopper in the first prefill (attention warmup):
glm_kpoolpads the selected rows to a multiple of 64 beforeflash_mla_sparse_fwd. The SM90 kernel needs a multiple of 128, which is what the DeepSeek-V4 and CSA2 calls to the same function already pad to. GLM-5.3-Flash selects 2051 rows (512 pools of 4 plus a 3-token tail), so they were padded to 2112. This pads to 128 on every arch, which also covers the SM100 128-head kernel.With that fixed, the 34 KDA layers still decode through the unfused FLA path on Hopper.
trtllm::kda_decoderuns on SM90 (its dispatcher picks the legacy kernels there, andtests/unittest/_torch/kernels/parallel/test_kda_decode.pyalready runs it on H100), butKDAKernelDispatchgated decode on the same predicate as the Blackwell-only CuTe DSL prefill and verify kernels. Decode now has its own predicate,is_kda_decode_supported(), which also accepts SM90. Prefill and verify keep their gate, and SM100/103/107 select the same KDA paths as before.Official FP8 checkpoint (
zai-org/GLM-5.3-Flash) on 4x H200, TP4 / EP4, BF16 KV, otherwise the B200 FP8 config from the deployment guide. Same branch without the KDA decode change vs this PR, 500 GSM8K questions (5-shot), decode ms per step (median of 3):The accuracy difference is 15 vs 12 discordant questions (McNemar p = 0.70).
KDA decode layer with GLM-5.3-Flash shapes on 1x H100, CUDA graph replay, us per step (fused / FLA):
After 64 decode steps from the same state, the layer output differs from the FLA path by 0.41-0.67% relative L2 (cosine >= 0.99998) over 8-64 heads and bs 1-256, and the recurrent state by 0.22-0.24%.
End to end on 1x H100 with a dummy-weight proxy: all 45 layers at their real shapes (KDA, NoPE MLA with the kpool indexer, mHC, FP8 block experts), routed experts cut to 8 with top-8 so the MoE cost does not depend on routing, text only, no MTP.
load_format="dummy"skipsload_weights(), so the proxy runs the KDAfinalize_weights()from aninitialize_dummy_statehook. Decode ms per step with only the padding fix / with both changes. Each cell is the mean of two interleaved runs, each run the median of 3:At bs 64 the two runs differ by up to 1.5 ms; the other batch sizes are within 0.15 ms.
With the real KDA weights of layers 0 and 2 (official FP8 checkpoint, these projections are BF16 there), 8 GSM8K prompts decoded token by token for 128 tokens from a zero state: the layer output of the fused path has cosine 0.99999 and 0.36-0.41% relative L2 to the FLA path at steps 1, 8, 32, 64 and 128, and 0.38-0.40% over all 128 steps. The final recurrent state differs by 0.2%, and the conv window is identical.
Test Coverage
tests/unittest/_torch/attention/sparse/glm_kpool/test_kernels.py:test_fp8_sparse_core_matches_dequantized_cache_and_replays_graphnow also runs at width 2051. On 1x H100 the two new cases fail on main with the assertion above and pass with this PR. The file is already inl0_h100.ymlthroughunittest/_torch/attention.tests/unittest/_torch/modules/kimi_kda/test_kda_decode_op.pynow runs on SM90: 22 passed on 1x H100 (the whole file skipped there before). Added tol0_h100.yml.tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_bf16_state_pool.py: the decode case passes on 1x H100; the prefill cases still skip there.tests/unittest/_torch/kernels/parallel/test_kda_decode.py: passes on 1x H100.tests/unittest/_torch/modules/kimi_kda/test_kimi_kda_decode_state_parity.py(fused decode vs the fallback over 6 steps, FP32 and BF16 state) now runs on SM90 too: 4 passed on 1x H100. Added tol0_h100.yml.tests/unittest/_torch/modules/kimi_kda/test_kda_kernel_dispatch.py(cpu_only) checks the per-arch decode, prefill and verify paths. 7 passed with no visible GPU. Added tol0_cpu.yml.nvcr.io/nvidia/tensorrt-llm/release:1.4.0rc2.dev202610210802container (main atb44310c96) with this branch'sglm_kpool/backend.pyand_kda_kernels.pycopied over the installed package (onlyglm_kpool/backend.pyfor the padding-fix-only runs).1.4.0rc2.dev202610260007container (main atb96ba434e) on 1x H100: kpool 168 passed, 6 skipped (native SM100 cases); KDA 99 passed, 2 skipped (indexed prefill); the CPU dispatch test 7 passed.1.4.0rc3.dev202611030007container (main atff1aa0f7d3) on 1x H100: kpool 282 passed, 11 skipped; KDA 99 passed, 2 skipped; the CPU dispatch test 7 passed. The 16-head proxy also runs on it (decode 9.55 ms per step at bs 1, 13.76 at bs 64, this PR only).PR Checklist
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