Avoid AITER FP8 BMM for MLA K projection

[rohash123]

Purpose

This changes the ROCm AITER FP8 MLA decode path so the K-side up-projection stays dense after the post-load transform, while the V-side up-projection continues to use the AITER FP8 BMM.

The K-side MLA projection is a skinny batched matmul. Requantizing the transformed K weight and routing it through AITER FP8 BMM adds overhead for this shape and adds another quantization step. Keeping K dense reuses the existing torch.bmm path, keeps the K-side math tied directly to the dequantized model weight, and leaves the V-side AITER path unchanged.

What changed:

• remove the AITER FP8 runtime branch for the K-side MLA projection
• keep W_UK_T dense in process_weights_after_loading for the AITER FP8 path
• keep quantizing/precompiling W_V for the V-side AITER FP8 BMM
• add a unit test that checks both the post-load storage contract and the K-side projection math

This PR was developed with AI assistance. I reviewed the changed code paths and ran the checks below.

Duplicate-work check

No linked issue exists for this change. I checked for open duplicate work with:

• gh pr list --repo vllm-project/vllm --state open --search "AITER MLA FP8 BMM warmup"
• gh pr list --repo vllm-project/vllm --state open --search "rocm aiter mla fp8 bmm"
• gh pr list --repo vllm-project/vllm --state open --search "triton_fp8_bmm mla warmup"
• gh issue list --repo vllm-project/vllm --state open --search "AITER MLA FP8 BMM warmup"

The closest related open PR is #52096, which is also mine. It is related but not a duplicate: #52096 changes startup precompile bounds for the AITER FP8 BMM path, while this PR changes the runtime K-side MLA projection path. If one lands first, the other may need a small rebase in the same process_weights_after_loading area.

Test Plan

Local focused correctness:

PYTHONPATH=. uv run --no-sync --python 3.12 \
  --with-requirements requirements/common.txt \
  --with pytest --with torch --with tblib \
  pytest -q --confcutdir=tests/v1/attention \
  tests/v1/attention/test_mla_backends.py::test_mla_kv_cache_spec_uses_layer_cache_dtype \
  tests/v1/attention/test_mla_backends.py::test_mla_post_load_preserves_runtime_weight_addresses \
  tests/v1/attention/test_mla_backends.py::test_mla_aiter_fp8_post_load_keeps_k_side_dense

Local lint:

pre-commit run --files \
  vllm/model_executor/layers/attention/mla_attention.py \
  tests/v1/attention/test_mla_backends.py

MI300X focused correctness and microbench remain from the earlier draft run. I have not rerun full serve/generation on this branch after rebasing to current origin/main.

Test Result

Local focused correctness: 4 passed, 14 warnings.

Local lint: pre-commit run --files ... passed.

Earlier MI300X focused correctness: 4 passed, 14 warnings.

Earlier MI300X isolated K-side projection results:

tokens	old AITER FP8 K BMM median us	dense K torch.bmm median us	median speedup
1	35.205	14.536	+142.2%
2	34.709	14.630	+137.2%
4	34.777	14.795	+135.1%
8	34.813	14.665	+137.4%
16	34.778	14.692	+136.7%
32	34.852	14.800	+135.5%
64	34.728	13.852	+150.7%
128	34.272	13.934	+146.0%
256	34.517	13.887	+148.6%

Caveat: I could not complete a full editable vLLM ROCm build on the current RunPod image because it ships PyTorch 2.10.0+rocm7.1.1, while current vLLM's ROCm _C_stable_libtorch extension expects a newer PyTorch stable C++ API. The build failed before this patch was involved, at _C_stable_libtorch with missing torch::stable::Tensor::layout. A full serve/generation check should be run in the official vLLM ROCm dev image or another environment with the current supported ROCm/PyTorch stack.

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @rohash123.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork