[https://nvbugspro.nvidia.com/bug/6886263] Fix FlashInfer sparse attention packed KV cache support

[yingguo-trt]

What does this PR do?

Type of change: Bug fix

Fixes FlashInfer skip-softmax calibration and serving with packed KV caches, including the layout used by vLLM 0.30.0 and 0.31.0. The adapter assumed a five-dimensional [blocks, 2, page, heads, dim] cache, but these versions provide [blocks, heads, page, 2 * dim]. As a result, calibration fails during engine warmup with:

ValueError: FlashInfer KV cache must have logical shape [blocks, 2, page, heads, dim]

A shared helper now extracts logical [blocks, page, heads, dim] K/V views for both calibration and serving. Packed caches use transpose(1, 2).split(head_size, dim=-1) without copying the cache; legacy five-dimensional caches retain their existing indexing.

Usage

Use the existing FlashInfer calibration command. <CKPT_COPY> should be a private writable checkpoint copy/view because --update_checkpoint_config updates its config.json; <RULER_DATA_DIR> is the prepared calibration data directory.

python examples/vllm_serve/calibrate_sparse_attn.py "<CKPT_COPY>" \
  --calib_data_dir "<RULER_DATA_DIR>" \
  --calib_samples 64 --calib_max_seqlen 16384 \
  --target_sparse_ratio 0.5 --decode_tokens 32 \
  --max_model_len 32768 --dtype bfloat16 \
  --attention_backend FLASHINFER --tensor_parallel_size 4 \
  --update_checkpoint_config

Testing

Environment: BF16 Llama-3.1-8B-Instruct, 4 H200 GPUs, TP4, FLASHINFER, with 32 decode steps.

Before the fix: Jenkins #4044 reproduced the cache-shape error during engine warmup with vLLM 0.30.0.

With the fix: the FlashInfer calibration-and-serving E2E passed on both versions:

vLLM	CI run	Result
0.30.0	#4045	1 passed, 0 failed, 0 skipped
0.31.0	#4047	1 passed, 0 failed, 0 skipped

Each run completed prefill/decode calibration, exported and merged the sparse configuration, then loaded the checkpoint in a fresh HTTP server and generated a response. The 0.31.0 artifacts also confirm ModelOptSparseFlashInferImpl during calibration and serving on all 32 attention layers.

Both passing runs used commit 698154ff0d0266270235f98d57246f616e20eeff. The rebased production fix in 92c48f240781dd70af4581f1a059089d4d288c2d has the identical fix patch and an identical plugins/vllm.py file. Commit b052e671b9ec303c4306b9187997656282a9fd6d only extends regression tests; the current head has not been E2E rerun.

Regression coverage: extended two existing FlashInfer adapter tests under tests/gpu_vllm/torch/sparsity/attention_sparsity/, adding one packed-cache variant each to calibration and serving while retaining the legacy variants. Small CPU tensors check K/V values, logical shape, strides, and shared storage at the downstream kernel boundary; the actual adapter/view conversion is used and the downstream kernels remain mocked. No model load or additional E2E matrix is introduced.

Pre-commit and git diff --check passed for the test changes. A limited CPU check of the real cache-view helper with the five test input layouts passed. Full pytest could not collect locally because nvidia-modelopt package metadata is absent; vLLM/Triton are also unavailable. GPU/vLLM CI is pending fork-runner vetting for this head.

The E2E validates the calibration/serving flow; it does not measure actual serving tile skips, accuracy, or performance.

Before your PR is "Ready for review"

Make sure you read and follow Contributor guidelines and your commits are signed (git commit -s -S).

Make sure you read and follow the Security Best Practices.

• Is this change backward compatible?: ✅ — preserves the legacy five-dimensional cache layout.
• If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A — no copied code or new dependency.
• Did you write any new necessary tests?: ✅ — extended two existing calibration/serving adapter tests with packed-cache regression coverage.
• Did you update Changelog?: ❌ — no changelog entry included.
• Did you get Claude approval on this PR?: ❌ — not yet obtained.

Additional Information

• Related feature: [1/n] Adds skip-softmax calibration through the vLLM serving path #1992.
• Tracking: NVBug 6886263.
• The legacy cache writer remains appropriate for older vLLM versions. In vLLM 0.30.0 and 0.31.0, forward_includes_kv_cache_update=False, so native vLLM cache updates run before the adapter and the legacy writer is bypassed.

Summary by CodeRabbit

• Bug Fixes
  • Improved compatibility with FlashInfer key/value caches in supported layouts, including packed layouts, during calibration and regular operation. Cache views now preserve the expected values, strides, and shared storage, helping prevent errors when processing these configurations. Unsupported cache shapes continue to raise an error.

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📝 Walkthrough

Walkthrough

FlashInfer KV cache handling supports two cache layouts. Calibration and the regular ModelOpt path use a shared helper to obtain logical key and value views. Tests check cache contents, strides, and storage sharing for the supported layouts.

Changes

FlashInfer cache layout support

Layer / File(s)	Summary
Normalize and validate cache views modelopt/torch/sparsity/attention_sparsity/plugins/vllm.py, tests/gpu_vllm/torch/sparsity/attention_sparsity/test_sparse_attn_worker.py, tests/gpu_vllm/torch/sparsity/attention_sparsity/test_vllm_calibration.py	A shared helper returns logical key and value views for two supported cache layouts and raises ValueError for unsupported shapes. Calibration and the regular ModelOpt path use the helper. Tests check values, strides, and storage sharing for the supported layouts.

Priority: ⬇️ Low

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

Change: Bug fix

Suggested reviewers: cjluo-nv, kaix-nv

Merge Risk: ⚪ Minimal · up to b052e

FlashInfer sparse attention reads packed KV caches correctly on vLLM 0.30 and 0.31. vLLM's own cache update runs before the adapter on those versions, so the older write path is not used.

🚥 Pre-merge checks | ✅ 5 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 75.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 8 functions across 3 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

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Security Anti-Patterns	✅ Passed	PASS. The pull request changes only the FlashInfer adapter and GPU regression tests. The added Python code introduces no torch.load(..., weights_only=False), numpy.load(..., allow_pickle=True), hardco…
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly identifies the FlashInfer sparse attention fix and specifically names packed KV cache support, which matches the main changes.

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Inline comments:
Review comments at @modelopt/torch/sparsity/attention_sparsity/plugins/vllm.py:
- Around line 851-852: Update _flashinfer_cache_write to obtain key and value
cache tensors through _flashinfer_kv_cache_views using impl.head_size, then pass
those normalized views to the native cache-write operation instead of indexing
kv_cache directly.

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Codecov Report

❌ Patch coverage is 0% with 7 lines in your changes missing coverage. Please review.
✅ Project coverage is 71.53%. Comparing base (90ba9fb) to head (b052e67).
⚠️ Report is 10 commits behind head on main.

Files with missing lines	Patch %	Lines
.../torch/sparsity/attention_sparsity/plugins/vllm.py	0.00%	7 Missing ⚠️

Additional details and impacted files

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##             main    #2697      +/-   ##
==========================================
- Coverage   71.54%   71.53%   -0.01%     
==========================================
  Files         640      640              
  Lines       71316    71319       +3     
==========================================
- Hits        51020    51016       -4     
- Misses      20296    20303       +7     

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[cjluo-nv]

Bot review (gpt-6.1-sol) — DM the bot to share feedback.

Nudge: the layout conversion looks correct, but the packed-cache fix lacks checked-in regression coverage despite the reported passing E2E runs.

Needs action:

• Add parameterized FlashInfer regression coverage under tests/gpu_vllm/torch/sparsity/attention_sparsity/ for packed and legacy caches through calibration and serving; verify K/V values, logical dimensions, and shared storage.

No action needed:

• The packed transpose/split preserves cache storage and matches the existing FlashAttention conversion; legacy indexing is unchanged.
• The PR documents successful calibration-and-serving E2E runs on vLLM 0.30.0 and 0.31.0, including why the legacy cache writer is bypassed.

[yingguo-trt]

@cjluo-nv Addressed the regression-coverage review in b052e67. Extended the existing calibration and sparse-prefill adapter tests in test_vllm_calibration.py and test_sparse_attn_worker.py, adding one packed-cache variant each and retaining legacy coverage. Both check K/V values, logical shape, strides, and shared storage with small tensors; no model load or new E2E matrix.

Pre-commit and the limited CPU cache-view check passed. Full adapter pytest remains unrun locally due to missing dependencies; GPU/vLLM CI awaits vetting for the new head.

[shengliangxu]

LGTM