[Bugfix][ROCm] Fix MiniMax-M3 FP8 KV cache dtype

[cquil11]

Purpose

Fixes #45562.

MiniMax-M3's sparse-attention backend reinterprets the byte-backed FP8 KV
cache as torch.float8_e4m3fn for every E4M3 configuration. That is incorrect
on ROCm gfx942, where current_platform.fp8_dtype() is
torch.float8_e4m3fnuz.

FN and FNUZ use different encodings. Reinterpreting FNUZ cache bytes as FN
changes the K/V values before the sparse-attention kernels consume them. The
prefill and decode wrappers also omitted float8_e4m3fnuz from their FP8 dtype
checks, so those kernels did not consistently take the FP8 conversion path.

This change:

• uses current_platform.fp8_dtype() for E4M3 KV-cache views;
• retains explicit torch.float8_e5m2 handling;
• recognizes FN, FNUZ, and E5M2 in both sparse-attention wrappers;
• adds regression coverage for platform dtype selection and FP8 classification.

This PR is stacked on #45381 because MiniMax-M3 support is still on that
PR's m3_release branch and is not yet present on main.

Test Plan

1. Run the focused dtype regression tests on MI300X using
   vllm/vllm-openai-rocm:minimax-m3.
2. Hot-patch the two Python modules into that same image.
3. Start MiniMaxAI/MiniMax-M3-MXFP8 with TP=8, expert parallelism,
   TRITON_ATTN, FP8 KV cache, eager mode, and prefix caching disabled.
4. Run the complete 1,319-sample GSM8K 5-shot evaluation at temperature 0 and
   concurrency 256.
5. Compare against the unpatched FP8-KV run and the BF16-KV reference.

Test Result

Focused MI300X regression tests:

6 passed, 36 deselected in 10.50s

Full GSM8K:

KV-cache configuration	Strict match	Flexible extract
FP8 KV, unpatched	0.0099	0.0296
FP8 KV, patched	0.9575	0.9568
BF16 KV reference	0.9666	0.9659

The patched run completed all 1,319 requests with HTTP 200 and no server
errors. The unpatched and BF16 reference runs are:

• Unpatched FP8 KV: https://github.com/SemiAnalysisAI/InferenceX/actions/runs/27482907707
• BF16 KV reference: https://github.com/SemiAnalysisAI/InferenceX/actions/runs/27485772194

A direct MI300X kernel comparison also showed the effect of the dtype
reinterpretation:

KV interpretation	Relative L2 error	Cosine similarity	Output norm ratio
Correct FNUZ	0.0387	0.9993	0.999x
FN hardcode	3.227	0.8748	4.066x

InferenceX workflow validation:

• Eval-only run: https://github.com/SemiAnalysisAI/InferenceX/actions/runs/27489726328
• MI300X eval job: https://github.com/SemiAnalysisAI/InferenceX/actions/runs/27489726328/job/81252925896
• Complete GSM8K (1,319 samples): strict 0.960576, flexible 0.959818

The run also verifies the physical KV-cache representation at runtime on all
eight TP ranks. For a live cache write, the verifier asserts one-byte backing
storage and compares 128 K plus 128 V output bytes from the compiled ROCm
reshape_and_cache_flash kernel against
input.to(torch.float8_e4m3fnuz).view(torch.uint8) with zero tolerance.
Each rank reported:

MINIMAX_M3_FP8_KV_RUNTIME_VERIFIED config=fp8 storage_dtype=torch.uint8 view_dtype=torch.float8_e4m3fnuz element_size=1 logical_bytes=634355712 bf16_equivalent_bytes=1268711424 checked_kv_values=256

This confirms that the cache is physically stored as one-byte FP8 FNUZ and
that the compiled write kernel produces FNUZ bytes; it is not only accepting
the --kv-cache-dtype fp8 flag.

Static checks:

ruff 0.14.0 check: passed
ruff 0.14.0 format --check: passed
git diff --check: passed

PR written with AI support but reviewed by human.

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[cquil11]

@hongxiayang

[functionstackx]

+viz @chunfangamd @andyluo7

[hongxiayang]

Thanks for the PR. added comments

[cquil11]

@hongxiayang comments addressed

[tjtanaa]

Will come back to this PR once #45381 model support PR is merged.

[cquil11]

Rebased the fix commits onto main after #45381 merged. GitHub would not reopen this PR because its m3_release base no longer exists, so the replacement is #45720.