[lucifer1004]

Description

Fixes #20373.

For a non-causal, unwindowed DFlash draft layer on the TRTLLM backend, dflash_forward called trtllm_batch_context_with_kv_cache. At small batch that kernel has very little parallelism over a long context. Without a mask, every block query attends the same seq_lens_after keys, so this PR runs the attention as B * block_size single-token trtllm_batch_decode_with_kv_cache requests over the request's pages (q_len_per_req=1, page table and sequence lengths repeated per block query). That gives multi-CTA split-KV over the context.

• _NONCAUSAL_DECODE_ROWS_MAX_BATCH_SIZE = 32: the decode-row form is used only when the draft batch is at most this size. Each row reads the whole KV, so the form reads block_size times more KV; at small batch the context kernel launches few CTAs, so the rows still win. Above the limit the context-FMHA call is unchanged. The value is not tuned per architecture: every SM100-family GPU takes the same gate, including SM103 (B300 / GB300), where it has not been measured or tested (see the sweep below).
• _prepare_dflash_trtllm_gen_buffers sizes the workspace and the multi-CTA KV counter buffer for max(max_batch_size, min(max_batch_size, limit) * block_size) requests, so the limit also bounds those buffers (at most 32 * block_size requests for the row form).
• Causal layers and sliding-window layers are unchanged. validate_block_attention_windows still rejects non-causal windows on TRTLLM.
• The batch size is static inside a captured CUDA graph, so the branch is fixed per graph. The repeated page table / sequence lengths are built once per forward and shared by all layers.

Kimi K3 + GQA DSpark drafter (block size 7), 8 GPUs, SM107, batch 1: decode step -0.15 ms at 2K context, -1.05 ms at 32K, -2.21 ms at 64K, -3.12 ms at 96K and -4.21 ms at 128K. Acceptance and our accuracy checks were unchanged (details in #20373). Batch > 1 is measured at kernel level only.

Kernel-level batch sweep behind the limit

flashinfer trtllm-gen, block size 7, page 32, HND, bf16, head_dim 64, call arguments as in modeling_dflash.py; median of CUDA-graph replays. Each number is decode-row time / context-FMHA time for the same block attention (below 1 means the rows are faster). 8 / 2 and 12 / 2 are 64 / 16- and 96 / 16-head drafters split over TP8.

SM107:

q / kv heads	KV	B=1	B=2	B=4	B=8	B=16	B=32
8 / 2	32K	0.07	0.08	0.13	0.29	0.55	0.42
8 / 2	128K	0.03	0.05	0.10	0.28	0.56	0.42
64 / 16	32K	0.29	0.57	0.43	0.47	0.43	0.46
64 / 16	128K	0.28	0.56	0.42	0.47	0.44	0.54

SM100 (GB200):

q / kv heads	KV	B=1	B=4	B=16	B=32	B=64	B=128	B=256
8 / 2	32K	0.04	0.12	0.44	0.33	0.32	0.35	0.35
8 / 2	128K	0.03	0.11	0.43	0.32	0.32	0.35	0.35
12 / 2	32K	0.04	0.12	0.22	0.22	0.22	0.23	–
12 / 2	128K	0.03	0.11	0.22	0.21	0.21	0.23	–
64 / 16	32K	0.22	0.33	0.37	0.37	0.37	0.37	0.38
64 / 16	128K	0.21	0.32	0.37	0.38	0.47	0.50	–

• The rows are faster at every point measured: 1.8-2.4x at B=32 on SM107, 2.7-4.7x on SM100, and no crossover up to B=256 on SM100. 32 is the largest batch measured on both, so the limit sits there; the data points toward relaxing it rather than lowering it per SM count.
• Rows vs context max abs difference 2.4e-4 over 66 cases, no NaNs. The flashinfer workspace stays at about 32 MB whatever the limit; the multi-CTA counters grow from a few KB to a few hundred KB at B=256.
• Not measured: SM103, B > 32 on SM107, end-to-end serving at B > 1, and mixed context lengths within a batch (all requests in a sweep batch had the same length).

Test Coverage

• tests/unittest/_torch/speculative/hw_agnostic/test_kimi_k3_dspark_semantics.py::test_plain_dflash_trtllm_block_decode_matches_full_attention_reference (new, added to l0_b200.yml): a plain non-causal DFlash drafter on the TRTLLM backend with a paged cache (two pages, disjoint page ranges per request, unequal context lengths). It checks against the existing eager full-attention reference with decode rows at batch 1, 2 and 32 (the ceiling), and with the context FMHA at batch 33 and at batch 2 (ceiling lowered to 1 in the test). It also counts the kernel calls, so a silent fallback to the other form fails. Parity is checked as relative L2 error < 1e-2 and max abs difference <= 8 bf16 ulp at the largest expected magnitude. Skipped without flashinfer or without an SM100-family GPU.
• tests/unittest/_torch/modeling/test_modeling_speculative.py::test_dflash_trtllm_gen_buffers_cover_noncausal_decode_rows (new, CPU): checks the request count passed to the workspace and counter sizing below, at and above the ceiling.
• test_dflash_trtllm_gen_buffers_reject_capture_time_allocation (existing): its second call now grows the batch to 16 instead of 4. With block size 4 the first version of this PR sized batch 2 and batch 4 both for 4 requests, so the workspace did not grow and the expected capture-time error never fired (the index-buffer check raised instead).
• Results at 13cbe46 (branch diff applied on a build from an earlier main, single node each), on SM107 and on SM100 (GB200): GPU -k trtllm_block_decode 3/3 passed, CPU -k dflash_trtllm_gen_buffers 7/7 passed. SM103 not run. The batch 32 / 33 cases added in 0375e7f failed in CI #78262 on the original 0.02 max-abs bound (0.039 for both attention forms, i.e. bf16 rounding). At 3d14121, on SM107 and SM100: GPU 5/5 and CPU 7/7 passed; max abs diff is 1 bf16 ulp (batch 1 / 2) to 3 ulp (batch 32 / 33) at the largest expected magnitude, relative L2 4.2-4.9e-3, identical on both architectures and for both attention forms. 6945f26 only changes the max-abs bound to the ulp form above.
• Kernel-level batch sweeps on SM107 (B = 1..32) and SM100 (B = 1..256): see the section above. Not measured end to end at B > 1, with mixed context lengths within a batch, or on SM103.
• 38bb679 only rewrites the comment on _NONCAUSAL_DECODE_ROWS_MAX_BATCH_SIZE (no code change).
• pre-commit on the changed files: clean.

PR Checklist

• PR description clearly explains what and why.
• PR follows the TRT-LLM coding guidelines to the best of my knowledge.
• Test cases are provided for new code paths.
• No API changes, no new dependencies, no CODEOWNERS changes.
• Please check this after reviewing the above items as appropriate for this PR.

Dev Engineer Review

For non-causal, unwindowed TRTLLM block attention, batches up to 32 use B * block_size single-query decode rows. Workspace and multi-CTA counter sizing support the expanded request count while retaining capacity for at least max_batch_size requests.

Batches above 32 and other attention modes retain context attention. Causal and sliding-window behavior is unchanged, and TRTLLM continues to reject non-causal sliding windows. Each decode row reads the full KV sequence, trading additional KV reads for decode-kernel parallelism. End-to-end serving above batch 1 and mixed context lengths within a batch were not measured.

QA Engineer Review

Tests cover decode-row dispatch and parity, buffer sizing at batch sizes 1, 2, 32, 33, and 200, and CUDA graph capture allocation errors. The B100/B200 pre-merge list includes the trtllm_block_decode selector. Earlier GPU and buffer-test passes were reported, but no results were reported for the final maximum-error bound of 8 * ulp_bf16(max |expected|). SM103, end-to-end serving above batch 1, and mixed context lengths within a batch remain untested. Coverage verdict: needs follow-up.

Per-File QA Perspective

• tensorrt_llm/_torch/models/modeling_dflash.py — Verify decode-row dispatch for non-causal, unwindowed batches up to 32, and context-attention dispatch above 32 or for other modes. Verify workspace and counter sizing for the expanded request count.
• tests/unittest/_torch/modeling/test_modeling_speculative.py — test_dflash_trtllm_gen_buffers_cover_noncausal_decode_rows checks buffer sizing for batches 1, 2, 32, 33, and 200. The file also tests capture-time allocation rejection; these unit tests are not listed in the cited integration test-list entry.
• tests/unittest/_torch/speculative/hw_agnostic/test_kimi_k3_dspark_semantics.py — test_plain_dflash_trtllm_block_decode_matches_full_attention_reference checks dispatch and parity for decode rows at batches 1, 2, and 32, and context attention at batch 33 or batch 2 with the ceiling lowered to 1. Its trtllm_block_decode cases are listed in tests/integration/test_lists/test-db/l0_b200.yml; results for the final parity bound were not reported.
• tests/integration/test_lists/test-db/l0_b200.yml — Adds the trtllm_block_decode selector to the B100/B200 pre-merge list. The entry notes that this TRTLLM block-attention test requires SM100-only trtllm-gen.

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Walkthrough

For non-causal, unwindowed DFlash TRTLLM attention, batches of at most 32 now use one decode row per block token. Workspace and counter sizing account for the expanded request count. Other cases retain the context-attention path.

Changes

DFlash TRTLLM attention

Layer / File(s)	Summary
Decode request capacity tensorrt_llm/_torch/models/modeling_dflash.py, tests/unittest/_torch/modeling/test_modeling_speculative.py	A threshold of 32 controls expanded workspace and counter sizing. Parameterized tests check request counts for batch sizes 1, 2, 32, 33, and 200. The capture-time allocation failure case changes from batch size 4 to 16.
Decode-row dispatch tensorrt_llm/_torch/models/modeling_dflash.py	For non-causal, unwindowed attention at batch sizes of at most 32, the forward pass repeats page tables and sequence lengths for each block token and calls paged KV decode. Other cases do not enter this branch.
Dispatch and parity validation tests/unittest/_torch/speculative/hw_agnostic/test_kimi_k3_dspark_semantics.py, tests/integration/test_lists/test-db/l0_b200.yml	CUDA- and backend-gated tests check kernel selection and compare outputs with the eager reference. The B100/B200 pre-merge test list includes the TRTLLM block-decode cases.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~20 minutes

Change: Other · Severity of issue fixed: Medium

Sequence Diagram(s)

sequenceDiagram
  participant DFlashForCausalLM
  participant TRTLLM attention backend
  participant Paged KV cache
  DFlashForCausalLM->>TRTLLM attention backend: Send repeated page tables and sequence lengths for eligible batches
  TRTLLM attention backend->>Paged KV cache: Read each request's paged KV data
  TRTLLM attention backend-->>DFlashForCausalLM: Return block attention outputs

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Merge Risk: ⚪ Minimal · up to 6945f

The change includes coverage for the batch-32 decode path and the fallback boundary. No concrete merge-blocking risk is established; ordinary CI should confirm the final test results.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 41.67% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 12 functions across 3 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Linked Issues check		Issue #20373 requires faster non-causal, unwindowed DFlash block attention on TRTLLM. The PR adds the decode-row path with repeated page tables and sequence lengths, and sizes the workspace and multi-…
Out of Scope Changes check		The batch-32 limit extends the optimization proposed by issue #20373. The added GPU test-list entry and unit tests support the new path, fallback, parity, and buffer-sizing behavior. No unrelated prod…
Title check		The title clearly identifies the issue, change type, and primary performance change: enabling non-causal unwindowed DFlash block attention to use decode rows up to batch 32.
Description check		The description is complete. It explains the problem and solution, documents scope and limitations, provides benchmark data, lists relevant test coverage and results, and includes the PR checklist.

Full details: Linked Issues check

Explanation

Issue #20373 requires faster non-causal, unwindowed DFlash block attention on TRTLLM. The PR adds the decode-row path with repeated page tables and sequence lengths, and sizes the workspace and multi-CTA counter buffer for the expanded request count. Batches above the limit retain context FMHA. Causal and sliding-window paths remain unchanged. Tests cover path selection, output parity, and buffer sizing.

Full details: Out of Scope Changes check

Explanation

The batch-32 limit extends the optimization proposed by issue #20373. The added GPU test-list entry and unit tests support the new path, fallback, parity, and buffer-sizing behavior. No unrelated production, API, dependency, or ownership changes are identified in the supplied change summary.

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[lucifer1004]

Batch sweep behind raising _NONCAUSAL_DECODE_ROWS_MAX_BATCH_SIZE from 1 to 32 (13cbe46).

Kernel level, SM107 only: flashinfer trtllm-gen, block size 7, page 32, HND, bf16, head_dim 64, call arguments as in modeling_dflash.py. Each number is the decode-row time divided by the context-FMHA time for the same block attention (median of CUDA-graph replays; lower is better for the rows):

q / kv heads	KV length	B=1	B=2	B=4	B=8	B=16	B=32
8 / 2	32K	0.07	0.08	0.13	0.29	0.55	0.42
8 / 2	128K	0.03	0.05	0.10	0.28	0.56	0.42
64 / 16	32K	0.29	0.57	0.43	0.47	0.43	0.46
64 / 16	128K	0.28	0.56	0.42	0.47	0.44	0.54

8 / 2 is a 64 / 16-head drafter split over TP8. The context kernel's time barely changes with B up to 16 (it launches few CTAs at these shapes), while the rows grow roughly linearly with B, but the rows are still 1.8-2.4x faster at B=32. Numerically, rows vs context max abs difference 2.4e-4, and both are within 1.5e-4 of an fp32 SDPA reference (checked for B <= 2); no NaNs.

32 is the largest batch measured, so I set the ceiling there rather than removing it; it also bounds the workspace / counter sizing at 32 * block_size requests. Not measured: B200 / SM100 (fewer SMs, so the crossover may differ), B > 32, and end-to-end serving at B > 1.

[coderabbitai]

🧹 Nitpick comments: 1

tests/unittest/_torch/speculative/hw_agnostic/test_kimi_k3_dspark_semantics.py: 1

892-916: 🎯 Functional Correctness | 🔵 Trivial | ⚡ Quick win

Cover the batch-32 decode-row path with a real batch-32 input.

The TRTLLM path selects batch_decode_with_kv_cache for non-causal, unwindowed attention when B <= 32. The parity test covers only batches 1 and 2. The sizing test does not call attention or check output.

Add a batch-32 parity case and provide 32 requests. Adding only the parameter is insufficient because GQA_CTX_LENS[:batch_size] currently creates at most two requests.

Suggested fix

-GQA_CTX_LENS = (40, 20)
+GQA_CTX_LENS = (40, 20) * 16
...
-    [(1, None, "decode_rows"), (2, None, "decode_rows"), (2, 1, "context")],
-    ids=["decode_rows_b1", "decode_rows_b2", "context"],
+    [
+        (1, None, "decode_rows"),
+        (2, None, "decode_rows"),
+        (32, None, "decode_rows"),
+        (2, 1, "context"),
+    ],
+    ids=["decode_rows_b1", "decode_rows_b2", "decode_rows_b32", "context"],

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Review comment at
@tests/unittest/_torch/speculative/hw_agnostic/test_kimi_k3_dspark_semantics.py
around lines 892 - 916:
Extend test_plain_dflash_trtllm_block_decode_matches_full_attention_reference
with a batch-size-32 decode_rows case and provide at least 32 context lengths
for its requests; update GQA_CTX_LENS as needed so the test exercises the real
batch-32 attention path and checks parity.

---

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Nitpick comments:
Review comments at
@tests/unittest/_torch/speculative/hw_agnostic/test_kimi_k3_dspark_semantics.py:
- Around line 892-916: Extend
test_plain_dflash_trtllm_block_decode_matches_full_attention_reference with a
batch-size-32 decode_rows case and provide at least 32 context lengths for its
requests; update GQA_CTX_LENS as needed so the test exercises the real batch-32
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• tensorrt_llm/_torch/models/modeling_dflash.py
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[lucifer1004]

Test report for 13cbe46 (branch diff applied on a build from an earlier main; single node each):

• SM107 and SM100 (GB200): GPU tests/unittest/_torch/speculative/hw_agnostic/test_kimi_k3_dspark_semantics.py -k trtllm_block_decode 3/3 passed (decode_rows_b1, decode_rows_b2, context); CPU tests/unittest/_torch/modeling/test_modeling_speculative.py -k dflash_trtllm_gen_buffers 7/7 passed. The earlier failure of test_dflash_trtllm_gen_buffers_reject_capture_time_allocation is fixed.
• Against the unpatched build the new cases fail as expected (GPU 3 failed; CPU 4 failed / 3 passed).

Kernel-level batch sweep on SM100 (GB200), same method as the SM107 sweep above (block size 7, page 32, HND, bf16, head_dim 64, call arguments as in modeling_dflash.py, median of CUDA-graph replays). Decode-row time / context-FMHA time:

q / kv heads	KV	B=1	B=4	B=16	B=32	B=64	B=128	B=256
8 / 2	32K	0.04	0.12	0.44	0.33	0.32	0.35	0.35
8 / 2	128K	0.03	0.11	0.43	0.32	0.32	0.35	0.35
12 / 2	32K	0.04	0.12	0.22	0.22	0.22	0.23	–
12 / 2	128K	0.03	0.11	0.22	0.21	0.21	0.23	–
64 / 16	32K	0.22	0.33	0.37	0.37	0.37	0.37	0.38
64 / 16	128K	0.21	0.32	0.37	0.38	0.47	0.50	–

(8 / 2 and 12 / 2 are 64 / 16- and 96 / 16-head models split over TP8; B = 2, 8, 48, 96, 192 were also measured and fit the same pattern.)

• No crossover anywhere on SM100: ratios 0.03-0.44 for 8 / 2 up to B=256, 0.03-0.27 for 12 / 2 up to B=128, 0.21-0.50 for 64 / 16. At B=32 the rows are about 2.7-4.7x faster (1.8-2.4x on SM107). The context kernel's time grows in steps with B and then roughly linearly, with about the same slope as the rows, so the ratio levels off; only unsplit 64 / 16 at 128K drifts up (0.38 at B=32 to 0.50 at B=128) as the rows become bandwidth-bound.
• Numerics: rows vs context max abs difference 2.4e-4 over 66 cases, no NaNs.
• Memory cost of the ceiling is small: the flashinfer workspace stays at about 32 MB independent of the ceiling (rows need only a few KB more at B=32); the multi-CTA counters grow from a few KB to a few hundred KB at B=256.

So 32 fits both SM100 and SM107; if it is changed later, the data points toward relaxing it rather than lowering it per SM count. I'm keeping 32, the value measured on both. Not covered: SM103, end-to-end serving at B > 1, and mixed context lengths within a batch (all requests in a batch had the same length).

[mikeiovine]

LGTM

[lucifer1004]

@coderabbitai Re the nitpick on covering batch 32 with real input: added in 0375e7f. GQA_CTX_LENS now provides 34 requests, and the parity test runs decode rows at batch 32 (the ceiling) and the context FMHA at batch 33 (just above it), in addition to batch 1 / 2 rows and batch 2 with the ceiling lowered to 1.

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---

@lucifer1004 Thanks for the update. Confirmed that GQA_CTX_LENS supplies 34 requests. The parity test now covers decode rows at batch 32 and context FMHA at batch 33, with kernel-call assertions to detect an unintended fallback. This addresses the boundary-coverage nitpick. I did not run the GPU tests.

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PR_Github #78262 [ run ] completed with state FAILURE. Commit: 0375e7f
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[lucifer1004]

CI #78262 (0375e7f): the two new batch-32 / batch-33 cases of test_plain_dflash_trtllm_block_decode_matches_full_attention_reference failed on B200. Both failed the same way: max abs diff 0.0390625 > 0.02 against the eager reference, for decode_rows_b32 and for context_b33 alike. The batch 1 / 2 cases passed. The context-FMHA path at batch 33 is the code this PR does not change, and both forms land on exactly the same error, so I read this as the 0.02 max-abs bound (sized for 2 requests) being too tight for 32-33 requests in bf16, not as a kernel problem.

In 3f97121 the parity check bounds the relative L2 error (< 1e-2) and allows max abs < 0.0625 (a few bf16 ulps). A wrong page, mask or request index would still produce O(1) errors. The failure message now also reports max |expected|. I also merged current main into the branch (3d14121); the other failures in that run (test_phase_selective_forward.py, the DSA indexer tests) are outside this PR, and the first is fixed on main by #20362. I'll re-run the GPU test before triggering CI again.

[tensorrt-cicd]

PR_Github #78617 [ run ] completed with state SUCCESS. Commit: da6edb3
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[lucifer1004]

CI #78617 (pipeline 64769, main merged in to pick up the schema waiver from #20478): the failures are unrelated to this PR.

• H100_PCIe-PyTorch-Perf-1: the Llama 3.1 8B perf test failed at executor start-up with CUDA-capable device(s) is/are busy or unavailable, on the first run and on the retry. That is the GPU node, not this change. Fail-fast then aborted the remaining stages, so this PR's DFlash tests did not run this time.
• CPU-Generic-arm-1: test_pytorch_model_engine.py::test_decoder_forward_routes_compile_bypass[True-*] fails with KeyType.__new__() got an unexpected keyword argument 'is_first_draft'. This is on current main: [None][chore] Remove is_first_draft dead code #19867 removed KeyType.is_first_draft, but this test (added in [TRTLLM-16012][feat] Restrict torch.compile to piecewise graph capture only #18366) still passes it. Other PRs based on main after [None][chore] Remove is_first_draft dead code #19867 fail the same way, and [None][chore] Remove two-model speculative decoding leftovers #20464 contains the one-line test fix.

I'll rerun once that test is fixed on main.

[lucifer1004]

Merged current main (0372799). It now waives test_pytorch_model_engine.py::test_decoder_forward_routes_compile_bypass[True-*] (#20505), the is_first_draft failure in the last run. Merging changed none of this PR's files. Rerunning CI.

[lucifer1004]

/bot run

[tensorrt-cicd]

PR_Github #78859 [ run ] triggered by Bot. Commit: 0c9f583 Link to invocation

[tensorrt-cicd]

PR_Github #78859 [ run ] completed with state SUCCESS. Commit: 0c9f583
/LLM/main/L0_MergeRequest_PR pipeline #64971 completed with status: 'FAILURE'

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[lucifer1004]

CI #78859 failed outside this PR:

• CPU-Generic-arm-1: five unittest/llmapi/test_mpi_session.py::test_remote_mpi_session[*] cases timed out (exit status 124). The same cases passed on CPU-Generic-x86-1 in this run, and they have failed the same way on arm in other recent PR pipelines; this PR does not touch the MPI session code.
• This PR's tests passed: the test_plain_dflash_trtllm_block_decode_matches_full_attention_reference cases on DGX_B200-PyTorch-7 (including decode_rows_b32 and context_b33) and test_dflash_trtllm_gen_buffers_cover_noncausal_decode_rows on A30-PyTorch-1.

Rerunning.

[lucifer1004]

/bot run

[tensorrt-cicd]

PR_Github #78917 [ run ] triggered by Bot. Commit: 0c9f583 Link to invocation

[brnguyen2]

Approving — the comments below are optional touch-ups, not blockers.

The decode-row form is exact for this case: with no mask, every block query attends the same seq_lens_after keys. The buffer sizing covers every batch the gate admits (B ≤ page-table rows), and the kernel-call counting in the parity test closes the silent-fallback gap. Two small things:

• Batch > 1 is unmeasured end to end. The description says so, but the default gate still turns on the row form up to B=32 in production, and that form reads block_size× the KV. Please post at least one e2e decode-step number at a mid batch (e.g. B=8 or 16, long context) so the threshold has support outside the kernel microbenchmarks.
• SM103 (B300/GB300) also gets this path (is_sm_100f admits it), and neither the threshold nor the new test has been run there. Please either add a B300 data point or note in the description that 32 has not been validated on SM103.

[github-actions]

Automatically added "ci: full pre-merge approved" because this PR has satisfied the required GitHub review approvals. Unresolved review conversations and other required checks remain independent merge requirements.

[lucifer1004]

@brnguyen2 thanks for the review. On the two points in the summary:

• SM103: noted in the description. 32 has not been measured or tested on SM103 (B300 / GB300); it takes the same gate, and the new GPU test is in l0_b200.yml only.
• End to end at B > 1: not measured yet; the only end-to-end numbers are at batch 1. I will measure a mid batch (B=8 or 16) at long context and post the decode-step numbers here.

[lucifer1004]

/bot run

[tensorrt-cicd]

PR_Github #78927 [ run ] triggered by Bot. Commit: 38bb679 Link to invocation

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PR_Github #78917 [ run ] completed with state ABORTED. Commit: 0c9f583

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[lucifer1004]

@brnguyen2 here is the B > 1 data I promised. Please read it as kernel / layer-level numbers, not end-to-end: it times only the drafter's block attention, not the QKV / o_proj projections, norms, RoPE or MLP, and not a full decode step. Setup: branch diff applied on a build from an earlier main, SM107, single GPU, no weights.

Shape: a 64/16-head GQA drafter split over TP8 (8 q / 2 kv heads per rank), head_dim 64, 5 layers, all full attention, BF16 paged KV (page 32, HND), block 7.

What was compared: the two forms the non-causal, unwindowed block attention can take, each called exactly as modeling_dflash.py calls it:

• rows (this PR): B×7 single-query decode requests, with the block table and seq lens repeat_interleave'd once per step.
• context: the single context-FMHA call that you get with _NONCAUSAL_DECODE_ROWS_MAX_BATCH_SIZE = 1, i.e. the pre-PR path.

How it was timed: CUDA graph replay, median, µs.

• Attention op is the attention call alone.
• Layer adds the K/V append and each form's metadata. For context that is cum_seq_lens_q / _kv, rebuilt every layer; for rows it is nothing, because the repeat_interleave happens once per step (about 3 µs) and is counted in the step instead.
• Per step (5 layers) is the 5 layers on separate KV pools, plus that once-per-step repeat_interleave for rows.

context	B	attention op, context / rows (ratio)	layer, context / rows	per step (5 layers), context / rows	saving per step
32K	8	234.7 / 65.7 (0.28)	237.8 / 73.9	1201 / 375	826 µs
32K	16	239.8 / 131.3 (0.55)	242.5 / 136.1	1212 / 685	528 µs
128K	8	884.7 / 252.4 (0.29)	887.3 / 255.8	4437 / 1284	3153 µs
128K	16	890.1 / 492.9 (0.55)	892.9 / 497.4	4465 / 2500	1964 µs

• The measured per-step saving is within 2% of 5 × (op difference), so almost all of it comes from the attention kernel itself; the metadata on either side is a few µs.
• Up to B=16 the context kernel barely changes with B (it leaves most of the GPU idle at 8/2 heads), while rows grows linearly with B. So the advantage shrinks as B grows, but at B=16 rows still takes about 0.55× the time of context.
• For reference, at TP4 (16 q / 4 kv heads per rank) the per-step saving is 513 / 1316 µs at 32K and 1942 / 5075 µs at 128K, for B = 8 / 16.

Numerics:

• In all 8 cases (both head splits), both forms were compared, for every request in the batch, against an fp32 SDPA reference. Both stay within 1 bf16 ulp at max |ref|, with relative L2 about 2.4e-3. That is well inside the PR test's bound of ≤ 8 ulp and rel L2 < 1e-2. No NaNs.
• Rows vs context directly, max abs diff over the 5 layers: 2.4e-4 at 32K and 1.2e-4 at 128K.
• On the same build, test_kimi_k3_dspark_semantics.py -k trtllm_block_decode passes (5/5).
• A one-off check reused the PR test's tiny drafter at B = 8 and 16 and ran it twice, once with ceiling 32 (rows) and once with ceiling 1 (context). Both runs match the eager reference (1.5 / 2.0 ulp), and their outputs are bit-identical.

Not covered yet:

• End-to-end two-node TP8 decode numbers; I'll add them when that setup is available.
• SM103.

[tensorrt-cicd]

PR_Github #78927 [ run ] completed with state SUCCESS. Commit: 38bb679
/LLM/main/L0_MergeRequest_PR pipeline #65026 completed with status: 'FAILURE'

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[lucifer1004]

CI #78927 failed outside this PR (38bb679 only changes a comment):

• CPU-Generic-arm-1: unittest/llmapi/test_mpi_session.py::test_remote_mpi_session[flashinfer_workspace] and [flashinfer_temporary_cleanup] timed out (exit status 124), the same arm-only MPI session timeout seen on other recent pipelines; this is the hard failure.
• B300-PyTorch-1 / B300-PyTorch-2: test_dsa_indexer.py::test_indexer_decode_with_paged_kv_cache_fp4[deepgemm-4-4] and test_minimax_m3_sparse_attn_decode.py::test_msa_prefill_selector_strides_cuda_graph[column-slice-2-16-bf16] were reported as "Test terminated unexpectedly" while the stages could not reach the Slurm login host (SSH connection refused during result upload); both cases passed on retry in the same run.
• The remaining non-green stages were aborted by fail-fast, or had no failures that were not waived.

This PR's test passed: DGX_B200-PyTorch-7 ran test_kimi_k3_dspark_semantics.py -k "trtllm_block_decode" and it PASSED. Re-running.

[lucifer1004]

/bot run

[tensorrt-cicd]

PR_Github #78998 [ run ] triggered by Bot. Commit: 38bb679 Link to invocation

[tensorrt-cicd]

PR_Github #78998 [ run ] completed with state SUCCESS. Commit: 38bb679
/LLM/main/L0_MergeRequest_PR pipeline #65078 completed with status: 'FAILURE'

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[lucifer1004]

CI #78998 failed outside this PR. It reused the passing stages from #78927 and only re-ran CPU-Generic-arm-1 and the B300 stages:

• CPU-Generic-arm-1: unittest/disaggregated/test_cluster_storage.py::TestEtcdClusterStorage::test_expire hung and was reported as "Test terminated unexpectedly", also on rerun. It is an etcd cluster-storage test, unrelated to the DFlash attention change here, and it passed in the other 29 recent runs (x86 and arm). unittest/tools/test_periodic_junit.py::test_hang_dumps_traceback failed once in the same shard and passed on rerun.
• B300-PyTorch-1 / -2 were cancelled by fail-fast.

Re-running.

[lucifer1004]

/bot run

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PR_Github #79035 [ run ] triggered by Bot. Commit: 38bb679 Link to invocation

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PR_Github #79035 [ run ] completed with state SUCCESS. Commit: 38bb679
/LLM/main/L0_MergeRequest_PR pipeline #65107 completed with status: 'SUCCESS'

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