fix(b12x): prewarm full-CKV prefill kernels before KV sizing

[malaiwah]

Problem

The B12X sparse-MLA constructor prewarms the ordinary extend plan, but TP4/DCP4 full-CKV gather uses a distinct local-head UnifiedPrefillMGKernel specialization. Its CuTe module and two DCP-specific Triton helpers can therefore first compile under a real long prompt, after automatic KV sizing has consumed the spare VRAM.

On GLM-5.2 EXL3 TP4/DCP4/MTP3, the first 16K prompt produced late-JIT warnings for UnifiedPrefillMGKernel, _build_prefill_chunk_metadata_kernel, and _map_global_topk_to_gathered_ckv_kernel; physical free memory fell from about 2.4 GiB/rank at idle to 245-255 MiB/rank. This did not OOM in that run, but it defeats the startup memory-safety contract.

Change

• prewarm the full-CKV local-head extend plan in addition to the ordinary gathered-head plan;
• prewarm the global-top-k remap/mask with the active DCP/interleave shape;
• route B12X through the sparse-prefill metadata warmup with the runtime DCP rank/size/interleave;
• add a CPU routing regression test.

The serving hot path is unchanged.

Validation so far

• focused CPU routing test: passed;
• Ruff/pre-commit source checks: passed (the branch base has four pre-existing mypy findings in b12x_mla_sparse.py);
• exact GG v20 r31 + vLLM [Model Runner V2] Account for prompt-logprobs memory #258/fix(exl3): share compatible native prefill buffers #270 + B12X [GG] fix DCP packed A2A buffer lifetime during graph prewarm #130 GPU qualification is in progress on 4x RTX PRO 6000 Blackwell. Before/after startup capacity, first-long-prompt JIT, physical high-water, PP/TG/MAL, and correctness evidence will be posted before marking ready.

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📥 Commits

Reviewing files that changed from the base of the PR and between e2666d9 and 163422d.

📒 Files selected for processing (3)

• tests/model_executor/test_sparse_mla_triton_warmup.py
• vllm/model_executor/warmup/sparse_mla_triton_warmup.py
• vllm/v1/attention/backends/mla/b12x_mla_sparse.py

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[malaiwah]

Test Results (automated)

Host: macOS M4 Max, CPU-only (no CUDA)
Command: .venv/bin/python -m pytest tests/model_executor/test_sparse_mla_triton_warmup.py --noconftest -x -v

Tests could not be collected due to an import error. The test module imports vllm.model_executor.warmup.sparse_mla_triton_warmup, which triggers a transitive import chain that fails on a missing dependency (zmq) in this CPU-only environment.

==================================== ERRORS ====================================
____ ERROR collecting tests/model_executor/test_sparse_mla_triton_warmup.py ____
ImportError while importing test module '/private/tmp/test-pr-271/tests/model_executor/test_sparse_mla_triton_warmup.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/Users/mbelleau/.local/share/uv/python/cpython-3.12.13-macos-aarch64-none/lib/python3.12/importlib/__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
tests/model_executor/test_sparse_mla_triton_warmup.py:8: in <module>
    from vllm.model_executor.warmup import sparse_mla_triton_warmup
vllm/model_executor/__init__.py:4: in <module>
    from vllm.model_executor.parameter import BasevLLMParameter, PackedvLLMParameter
vllm/model_executor/parameter.py:12: in <module>
    from vllm.distributed import (
vllm/distributed/__init__.py:4: in <module>
    from .communication_op import *
vllm/distributed/communication_op.py:9: in <module>
    from .parallel_state import get_tp_group
vllm/distributed/parallel_state.py:49: in <module>
    from vllm.distributed.utils import (
vllm/distributed/utils.py:33: in <module>
    from vllm.utils.network_utils import get_tcp_uri
vllm/utils/network_utils.py:17: in <module>
    import zmq
E   ModuleNotFoundError: No module named 'zmq'
------------------------------- Captured stdout --------------------------------
WARNING 08-12 15:48:55 [__init__.py:27] The vLLM package was not found, so its version could not be inspected. This may cause platform detection to fail.
=============================== warnings summary ===============================
vllm/__init__.py:7
  /private/tmp/test-pr-271/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash:
  No module named 'vllm._version'
    from .version import __version__, __version_tuple__  # isort:skip

-- Docs: https://docs.pytest.org/en/stable/how-to/capture/warnings.html
=========================== short test summary info ============================
ERROR tests/model_executor/test_sparse_mla_triton_warmup.py
!!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!
========================= 1 warning, 1 error in 1.28s ==========================

---

Automated test run by @malaiwah's agent. Results are from a CPU-only environment; GPU-dependent tests may behave differently on CUDA hardware.

[malaiwah]

Test Results (automated — re-run with fixed dependencies)

Host: macOS M4 Max, CPU-only (no CUDA)
Command: .venv/bin/python -m pytest tests/model_executor/test_sparse_mla_triton_warmup.py --noconftest -x -v

============================= test session starts ==============================
platform darwin -- Python 3.12.13, pytest-9.1.1, pluggy-1.6.0 -- /Users/mbelleau/Projects/vllm-voipmonitor/.venv/bin/python
cachedir: .pytest_cache
rootdir: /private/tmp/retest-271
configfile: pyproject.toml
plugins: anyio-4.14.2
collecting ... collected 1 item

tests/model_executor/test_sparse_mla_triton_warmup.py::test_b12x_prefill_metadata_warmup_uses_runtime_dcp_shape PASSED [100%]

=============================== warnings summary ===============================
vllm/__init__.py:7
  /private/tmp/retest-271/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash:
  No module named "vllm._version"
    from .version import __version__, __version_tuple__  # isort:skip

-- Docs: https://docs.pytest.org/stable/en/stable/how-to/capture-warnings.html
========================= 1 passed, 1 warning in 2.34s =========================

---

Automated test run by @malaiwah's agent. Results are from a CPU-only environment; GPU-dependent tests may behave differently on CUDA hardware.

[malaiwah]

B3 fix: warmup read V2-only runner attributes, silently compiling the wrong Triton specialization

The bug

sparse_mla_triton_warmup read the DCP size and interleave via:

dcp_world_size = int(getattr(runner, "dcp_size", 1))
cp_kv_cache_interleave_size = int(getattr(runner, "cp_interleave", 1))

These attributes exist only on the V2 runner (vllm/v1/worker/gpu/model_runner.py, lines 247/250). The V1 production runner (vllm/v1/worker/gpu_model_runner.py) defines dcp_world_size (= parallel_config.decode_context_parallel_size, line 514) and reads the interleave from parallel_config.cp_kv_cache_interleave_size everywhere — there is no dcp_size and no cp_interleave.

On V1 both getattr calls returned their default of 1. Since DCP_SIZE and DCP_INTERLEAVE are tl.constexpr in _map_global_topk_to_gathered_ckv_kernel, the warmup compiled the dcp=1/interleave=1 variant. The real specialization still JIT'd under the first long prefill after KV sizing — exactly the failure this PR exists to prevent, with no error or warning. The test hardcoded SimpleNamespace(dcp_size=4, cp_interleave=64) (the V2 names), so it structurally could not catch this.

The fix

Added _dcp_params(runner) which reads from the single source both runners derive from:

• runner.vllm_config.parallel_config.decode_context_parallel_size
• runner.vllm_config.parallel_config.cp_kv_cache_interleave_size

…with no silent default — if the config attribute is genuinely absent, that raises AttributeError (a bug worth surfacing) instead of silently degrading to DCP1. dcp_rank is read directly from runner.dcp_rank (both runners define it). Applied to both call sites (sparse_mla_triton_warmup and the B12X branch of sparse_mla_triton_warmup_if_needed).

Tests

1. Rebuilt the fixture to mirror the real runner interface (vllm_config.parallel_config with decode_context_parallel_size / cp_kv_cache_interleave_size), not invented attribute names. Parametrized over DCP1/interleave-1 and DCP4/interleave-64. The runner fixture deliberately omits dcp_size/cp_interleave, so the DCP4 case fails if someone reintroduces getattr(runner, "dcp_size", 1) — verified by temporarily reverting and observing the failure (dcp_world_size degrades 4→1, interleave 64→1).
2. Added coverage for the 96-line _prewarm_extend_kernels_once path (previously zero coverage): asserts both the full-CKV local-head extend plan (_ckv_extend_plan) and the global top-k → gathered-CKV remap (_map_global_topk_to_gathered_ckv) are prewarmed with the runtime DCP specialization (dcp_size=4, cp_kv_cache_interleave_size=64).

Verification

python -m pytest tests/model_executor/test_sparse_mla_triton_warmup.py --noconftest -v

All 3 tests pass (2 parametrized DCP-shape cases + 1 prewarm-coverage case). Run on CPU-only macOS; the prewarm test redirects CUDA allocations to CPU via monkeypatched tensor factories (the real kernels/plans are mocked), so it exercises the method's control flow without a GPU.

Not fixed (out of scope)

None — all findings assigned to this PR (B3 + the prewarm coverage gap) are addressed.

[malaiwah]

This makes memory accounting more accurate and prevent surprise runtime allocations. It is important for those (like me) that runs with very little room on 4x RTX6000 Pro and GLM-5.2.