[https://nvbugs/6476233][fix] Cap max_seq_len on H200 DeepSeek-V3.2 blockscale test

[pranav-nvidia]

Dev Engineer Review

• Computes an architecture-specific max_seq_len for non-SM100/103 DeepSeek-V3.2 FP8 blockscale tests.
• Derives the limit from task input and output budgets and rounds it to an 8 KiB boundary.
• Produces 8192 for MMLU/GSM8K and 40960 for fp8kv.
• Preserves the Blackwell configuration.
• Removes two obsolete H200 waiver entries.
• Reduces KV-cache estimation and CUDA graph warmup OOM risk.
• Keeps LLM API usage consistent.

QA Engineer Review

• Modified test function: test_fp8_blockscale.
• The test is covered by the related test-list entries; this change removes those waiver entries.
• No test-db/ or qa/ files changed.
• Validation passed for all three parameters on 8×H200.
• Accuracy met or exceeded reference scores.
• Verdict: sufficient.

Description

TestDeepSeekV32::test_fp8_blockscale[disable_skip_indexer] and [latency_default] OOM on DGX_H200-8_GPUs-PyTorch-Post-Merge-1, then hang — the OOM leaves the MPI workers unable to shut down cleanly, so the pod runs to the 6h limit and Jenkins loses the agent before results upload.

Root cause

The failure is not in the test body and not in the final KV cache allocation. It is a warmup prefill activation during KV cache size estimation:

py_executor_creator.create_py_executor -> INIT_EXTRA_RESOURCES
  PyExecutor.__init__ -> model_engine.warmup -> _run_autotuner_warmup
    mla.forward_sparse_mla_kvcache_bf16: q_concat = torch.cat([q_nope_out, q_rope], dim=-1)
torch.OutOfMemoryError: Tried to allocate 1.12 GiB

_util._get_token_num_for_estimation sizes the temporary estimation cache from max_seq_len, not max_num_tokens:

max_seq_len_blocks = ceil_div(self._model_engine.max_seq_len + 1, self._tokens_per_block)
cuda_graph_warmup_block = max_seq_len_blocks + max_cuda_graph_bs - 1

The test never set max_seq_len, so it inherited the model's native 163842. At tokens_per_block=64, max_batch_size=24 and the logged 78324 bytes/token that is 2584 blocks → 12.1 GiB, allocated on top of ~100 GiB of weights purely so CUDA graph warmup can run, and released immediately afterwards. That left disable_skip_indexer peaking at 138.89 of 139.80 GiB — 0.91 GiB of headroom (0.65%) even running alone. l0_dgx_h200.yml runs baseline → latency_default → disable_skip_indexer in one session, so two teardowns precede the failing param and it tips over.

This is also why free_gpu_memory_fraction is the wrong knob, and it was verified on hardware: dropping it from 0.7 to 0.5 moved KV usage by 0.01 GiB and failed identically. The fraction scales the final KV cache, which is sized only after the estimation peak has already been survived — consistent with the mapping the codebase itself documents in py_executor_creator.py (INIT_KV_CACHE → "reduce max_num_tokens", not the fraction).

Fix

Cap max_seq_len on the non-SM100/103 branch, sized by the evaluation task the param actually runs. The cap is read off the AccuracyTask classes rather than written as a literal — pad_up(max(task.MAX_INPUT_LEN + task.MAX_OUTPUT_LEN), 8 KiB) over the classes that param evaluates — so it stays in step with the task definitions if their token budgets change. That yields:

• 8192 for the MMLU + GSM8K path — those need 4094 + 2 and 4096 + 256 respectively, so 4352 tokens worst case.
• 40960 when fp8kv, which evaluates GPQA Diamond instead (MAX_INPUT_LEN=4096 + MAX_OUTPUT_LEN=32768 = 36864). A flat 8192 would truncate its generation budget. This is defensive today, since fp8kv skips on pre-Blackwell, but the else branch also covers SM ≥ 100 outside 100/103 where that skip does not apply.

The Blackwell branch is deliberately left untouched — it has its own tuned fraction (NVBug 5955792) and no measurements were taken there.

Also removes the two waives.txt entries this bug added.

Related, not addressed here

Measuring the same log points across an isolated run and an in-sequence run showed roughly 8.9 GiB surviving the previous test's LLM shutdown (~2.4 GiB still live in the torch allocator, ~6.5 GiB outside torch). That carryover is why this param had no margin to begin with. It is a runtime/harness concern rather than a test-config one, and the sibling test_dsa_host_cache_offload already carries @pytest.mark.private_mpi_session for the same reason. This PR does not touch it — the cap is sufficient on its own, as the validation run passed with 5.08 GiB of headroom with the carryover present.

Test Coverage

accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_fp8_blockscale — the two waived params are re-enabled by this PR.

Validated on 8×H200, one pytest session, node ids in l0_dgx_h200.yml order:

3 passed, 2 warnings in 5326.92s (1:28:46)

param	peak / 139.80 GiB	headroom	temp KV during profiling
baseline	126.22 GiB	13.58 GiB	0.71 GiB
latency_default	107.97 GiB	31.83 GiB	0.62 GiB
disable_skip_indexer	134.72 GiB	5.08 GiB	0.72 GiB

The temporary estimation cache falls from 12.26 → 0.62–0.72 GiB, exactly the ceil_div(max_seq_len + 1, tokens_per_block) term — the measured saving is the mechanism, not a fudge factor. disable_skip_indexer goes from a 0.65% margin running alone to 3.6% while sitting behind two teardowns.

The fp8kv arm of the cap is not covered by this run: baseline_fp8kv is not in l0_dgx_h200.yml and skips on pre-Blackwell, so it cannot execute on H200. Its 40960 value is derived from the same sizing formula, which predicted the measured 0.71 GiB at 8192 exactly.

Accuracy is unaffected; every score is at or above its reference:

param	MMLU (ref 88.200)	GSM8K (ref 95.600)
baseline	88.816	95.337
latency_default	88.621	95.944
disable_skip_indexer	88.767	95.603

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[coderabbitai]

Walkthrough

The FP8 block-scale integration test now derives architecture-specific sequence limits and passes them to LLM. Two DGX_H200 DeepSeekV32 FP8 block-scale waiver entries were removed.

Changes

FP8 block-scale test enablement

Layer / File(s)	Summary
Architecture-specific LLM configuration and waiver removal tests/integration/defs/accuracy/test_llm_api_pytorch.py, tests/integration/test_lists/waives.txt	For non-SM100/103 architectures, the test derives max_seq_len from the selected evaluation task, rounds it to an 8K boundary, and passes it to LLM. Two DGX_H200 skip entries were removed.

Estimated code review effort: 2 (Simple) | ~10 minutes

Merge Risk: ⚪ Minimal · up to 66512

This localized change caps max_seq_len for the affected DeepSeek-V3.2 H200 test paths, removes their waivers, and includes successful validation; no actionable merge-blocking risk remains after normal checks.

Suggested reviewers: bowenfu, mzweilz, niukuo

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Title check	✅ Passed	The title clearly identifies the H200 DeepSeek-V3.2 blockscale test fix and uses the required NVBugs and fix format.
Description check	✅ Passed	The description explains the root cause, fix, scope, validation results, test coverage, and checklist status in sufficient detail.

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[brnguyen2]

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

Fix targets the right knob — the estimation cache is sized from max_seq_len, not free_gpu_memory_fraction, so this is the correct lever and the writeup backs it with hardware measurements. Two things worth noting: the cap also applies to baseline (currently passing on H200), which is fine but not mentioned in the description; and per the description ~8.9 GiB carryover across LLM teardowns remains unaddressed, so the sequence stays close to the edge — worth a follow-up bug if there isn't one.

[fredricz-20070104]

Review summary - Approve

Reviewed the full diff; no blocking or major issues found.

Minor, non-blocking notes:

• tests/integration/defs/accuracy/test_llm_api_pytorch.py: fp8kv=40960 arm of the cap is untested on any available runner
• tests/integration/defs/accuracy/test_llm_api_pytorch.py: max_seq_len values are magic numbers

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Inline comments:
In `@tests/integration/defs/accuracy/test_llm_api_pytorch.py`:
- Around line 3408-3415: Add the missing parameterized cases heuristic_topk_mtp1
and cute_dsl_gvr_mtp3 for TestDeepSeekV32.test_fp8_blockscale to both the
l0_dgx_b200 test list and the llm_function_core test list, matching the existing
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