[ROCm] Convert MXFP8 MoE weights to block FP8 on gfx94x

[Oseltamivir]

Purpose

gfx94x does not provide native MX-scaled matrix multiplication. The existing
MXFP8 MoE fallback therefore expands expert weights to BF16 and runs both expert
GEMMs in BF16.

This change converts checkpoint MXFP8 expert weights once at load time into the
existing 128x128 block-FP8 format:

1. Dequantize E4M3FN values with their per-1x32 E8M0 scales to BF16.
2. Requantize each padded 128x128 block with an FP32 amax / 448 scale.
3. Normalize OCP E4M3 values to the gfx94x FNUZ representation and double the
   corresponding scales.
4. Run the regular Triton block-FP8 MoE backend with dynamic 128-element
   activation groups.

Each expert matrix is converted independently to bound temporary load-time
memory. gfx950 retains its existing native MXFP8 path.

Performance Configuration

• The MI300X E128 table uses measured low-token tiles while retaining the
  existing configurations that performed better at larger routed batches.
• A new E16 local-expert table covers TP8+EP8 batches from 64 through 8192
  tokens.
• An independent 100-iteration rerun reduced E16 kernel latency by 13.5% to
  26.4% versus the built-in fallback across all eight measured batch sizes.

Validation

• Changed-file pre-commit hooks: passed, including mypy.
• Targeted MI300X kernel module: 48 passed, 5 skipped.
• Conversion tests verify dtype, shape, bit-exact OCP-to-FNUZ normalization,
  exact FP32 scales, and less than 5% relative reconstruction error.
• Backend-selection tests verify TP and EP resolve to the Triton block-FP8
  experts implementation while preserving the M3 SwiGLU alpha, beta, and clamp.
• Runtime patch applies cleanly to image revision
  4a560dd8db67c270f5e2afb614558271b76f2294.
• Full MI300X TP8 and TP8+EP8 sweep:
  https://github.com/SemiAnalysisAI/InferenceX/actions/runs/27725228435
  • All 18 serving points and both eval jobs passed after retrying three
    node-local Pyxis failures that occurred before model startup.
• Independent accuracy run:
  https://github.com/SemiAnalysisAI/InferenceX/actions/runs/27725256963
  • Across the two full 1,319-example GSM8K runs, strict exact match was
    95.53%-95.98% for TP8 and 95.30%-95.91% for TP8+EP8.
• MI355X matched control/patched guard:
  https://github.com/SemiAnalysisAI/InferenceX/actions/runs/27733137495
  • Both variants retained the native gfx950 MXFP8 backend; the numerical
    smoke test reported 0.037185 relative error.
  • Patched aggregate throughput was within 0.43% at concurrency 4
    (453.13 vs. 455.10 tok/s) and 0.84% at concurrency 64
    (4018.45 vs. 4052.69 tok/s) of the matched control.

End-to-End Interpretation

The unofficial chart overlay is not a before/after comparison. The branch
series uses MI300X with TP8 or TP8+EP8, while the adjacent MI355X series uses
TP4 on a different GPU generation.

Against the previous MI300X result with the same 8K/1K TP/EP and concurrency
shapes
(https://github.com/SemiAnalysisAI/InferenceX/actions/runs/27510667862),
the patched path improves total throughput per GPU:

Parallelism	Concurrency	Previous	Patched	Change
TP8	1	99.88	104.94	+5.1%
TP8	8	463.69	502.55	+8.4%
TP8	32	880.10	981.21	+11.5%
TP8	64	976.30	1236.57	+26.7%
TP8+EP8	128	1110.16	1273.92	+14.8%
TP8+EP8	256	1199.22	1469.05	+22.5%

These are real same-hardware gains, but they do not close the end-to-end gap
to the MI355X TP4 curve in the throughput-oriented region. An earlier MI300X
TP4/DP2 experiment
(https://github.com/SemiAnalysisAI/InferenceX/actions/runs/27664746568)
reached 1393.42 tok/s/GPU at 8K/1K concurrency 256, below the patched
TP8+EP8 result of 1469.05 tok/s/GPU, so changing parallelism alone does not
close that gap.

This PR optimizes MoE weight representation and MoE kernel dispatch. The chart
also includes attention, sparse indexing, KV-cache handling, scheduling,
prefill/decode balance, collectives, and hardware differences. The supported
performance claim is therefore improved MI300X performance relative to its
previous path, not parity with MI355X.

MI300X end-to-end serving results, in total tokens/s/GPU:

Sequence	Parallelism	Concurrency	Total tok/s/GPU
1K/1K	TP8	1	24.37
1K/1K	TP8	2	46.85
1K/1K	TP8	4	83.22
1K/1K	TP8	8	136.60
1K/1K	TP8	16	219.91
1K/1K	TP8	32	346.28
1K/1K	TP8	64	522.52
1K/1K	TP8	128	738.25
1K/1K	TP8+EP8	256	917.31
8K/1K	TP8	1	104.94
8K/1K	TP8	2	197.59
8K/1K	TP8	4	336.79
8K/1K	TP8	8	502.55
8K/1K	TP8	16	715.23
8K/1K	TP8	32	981.21
8K/1K	TP8	64	1236.57
8K/1K	TP8+EP8	128	1273.92
8K/1K	TP8+EP8	256	1469.05

Duplicate searches for open PRs matching MXFP8 gfx94x,
MI300X MXFP8 MoE, and ROCm block FP8 MXFP8 found no competing
implementation.

AI Assistance

OpenAI Codex assisted with implementation, profiling, testing, rebasing, and
PR preparation. The submitting human remains responsible for reviewing and
defending every changed line.

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[mergify]

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

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

[andyluo7]

Coordination note for vLLM #45726

I am preparing a ROCm HY V4 graph-mode follow-up stacked on #54405 and want to
coordinate before opening it because part of the gfx942 path overlaps #45726.

#45726 converts serialized MXFP8 MoE weights to block FP8 on gfx94x and routes
them through the Triton block-FP8 backend. The proposed HY V4 follow-up has a
broader but materially different path:

• it selects conversion from platform capabilities rather than a gfx94x
  product/architecture check;
• it converts both dense and routed-expert MXFP8 weights when native MX
  instructions are unavailable;
• it selects the AITER block-FP8 linear and MoE backends, including two-stage
  MoE with router weighting in stage 2;
• it retains native MXFP8 on capable devices;
• it is validated in graph mode with native MTP on both gfx942 and gfx950,
  including complete 1,319-sample GSM8K runs.

The conversion helpers and ModelOptMxFp8FusedMoE changes overlap directly,
so these should not land as unrelated duplicate implementations. I will share
the exact patch and evidence after human line-by-line review. If #45726 is the
preferred foundation, I can rebase the HY V4 work onto its final form and keep
only the capability-based AITER and model-integration delta.

[mergify]

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

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

[mergify]

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

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