[None][feat] Upgrade Blackwell cuteDSL MLA kernel for packed q heads and compact input

[pengbowang-nv]

Port from flashinfer-ai/flashinfer@4890932

Update integrated FP8 kernel to a new compute pattern.
Update FP8 and FP16 kernel to support packed Q head to improve perf of H=96 (kimi k3) when seq_len_q >= 4

Dev Engineer Review

• Ports the Blackwell CuteDSL MLA updates for packed FP8 and FP16 query heads.
• Adds compact variable-length query support with masked M128 tiles and request-local offsets.
• Updates split-KV scheduling, reducer limits, workspace sizing, and 32-bit overflow checks.
• Removes fold_sq and changes MLA public method signatures. Callers must adopt the new parameters.
• Review findings and test results are unavailable.

QA Engineer Review

No test changes.

Per-File QA Perspective

• tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py: Verify packed-query occupancy, tail masking, split-KV selection, workspace sizing, and fixed-length caller compatibility.
• tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_decode_fp16.py: Verify compact queries, cumulative-length handling, causal masking, softmax statistics, split-KV reduction, workspace limits, and API compatibility.
• tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_helpers.py: Verify query-tile validation and scheduler behavior for invalid request-local rows.

Description

Test Coverage

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Walkthrough

Changes

The MLA FP16 decode kernel now supports compact variable-length queries. It packs flattened query/head rows into M128 tiles, handles masked tails, threads request-local offsets through split-KV execution, and supports configurable reducer capacity and optional softmax statistics. Workspace sizing and split-KV heuristics now use packed query-tile counts.

MLA variable-query decode

Layer / File(s)	Summary
Query tile contracts tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_helpers.py, tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_decode_fp16.py	The kernel defines packed M128 query layouts, request-local query metadata, variable-query validation, and reducer configuration.
Query input wiring tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_decode_fp16.py	Execution paths accept cumulative query lengths, convert compact tensors, compute query-tile grids, and pass query metadata to split and reduction kernels.
Kernel query processing tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_decode_fp16.py	Kernel paths load request-local rows, apply flattened causal masking, and store compact or fixed-query outputs, LSE values, and softmax statistics.
Workspace and split-KV integration tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py, tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_decode_fp16.py	Workspace layouts, occupancy calculations, split-KV heuristics, and implementability checks use padded M128 extents and reducer limits. The old fold_sq path is removed.
Validation and CLI support tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_decode_fp16.py	Reference validation checks compact outputs and optional softmax statistics. Benchmark generation and CLI execution accept compact-query options and cumulative query lengths.

Priority: ➖ Normal

Estimated code review effort: 5 (Critical) | ~120 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant CLI
  participant BlackwellMultiHeadLatentAttentionForwardFP16
  participant split_kv_kernel
  participant reduction_kernel
  CLI->>BlackwellMultiHeadLatentAttentionForwardFP16: run compact query inputs
  BlackwellMultiHeadLatentAttentionForwardFP16->>split_kv_kernel: query tensors and cumulative query lengths
  split_kv_kernel->>reduction_kernel: split-KV results and query tile metadata
  reduction_kernel-->>BlackwellMultiHeadLatentAttentionForwardFP16: output, LSE, and optional statistics

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Merge Risk: 🔵 Low · up to 6f45a

Noncanonical compact inputs can produce incorrect decode results, while the new compact execution path lacks targeted regression coverage. These bounded issues should be addressed before broad use.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 58.33% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 36 functions across 2 files. (1 skipped: …	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
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 Blackwell cuteDSL MLA kernel upgrade, packed query-head support, and compact input support. It follows the required [None][feat] format.
Description check	✅ Passed	The description explains the purpose and main changes and includes the repository checklist. The Test Coverage section is empty, but the description is otherwise relevant and sufficiently complete.

Full details: Docstring Coverage

Explanation

Docstring coverage is 58.33% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 36 functions across 2 files. (1 skipped: 1 too large.)

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Actionable comments posted: 2

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Inline comments:
In
`@tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_decode_fp16.py`:
- Around line 507-524: Validate compact variable-Q tensor strides before
flattening: in _flatten_compact_q_rows require token_stride to equal num_heads *
head_stride, and reject noncanonical layouts rather than constructing an
incorrect view. Apply equivalent validation to the compact LSE and
softmax-statistics flattening paths, ensuring all compact row layouts are
checked before their flattened tensors are created.
- Around line 146-150: Add a focused GPU regression test under
tests/unittest/_torch/ for compact variable-query execution, invoking the
relevant MLA decode path with is_var_q=True and mixed per-request query lengths
including zero and seq_len_q, while keeping total queries nonzero. Validate each
request’s output and LSE against a PyTorch reference, and cover cumulative query
offsets, tail rows, compact LSE and softmax statistics with
emit_softmax_stats=True plus one alternate reducer_d_tiles or split_kv
configuration; also verify all-zero query batches are rejected by run().

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📥 Commits

Reviewing files that changed from the base of the PR and between 73c7063 and 6f45a76.

📒 Files selected for processing (4)

• tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py
• tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_decode_fp16.py
• tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_decode_fp8.py
• tensorrt_llm/_torch/cute_dsl_kernels/blackwell/attention/mla/mla_helpers.py

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