Add MiniMax-M3 (MXFP4/AttnFP8) model support

[thpereir]

The in-tree MiniMax-M3 model already covers the BF16 checkpoint. This
adds the small pieces the quantized amd/MiniMax-M3-MXFP4-AttnFP8 build
needs, without disturbing the BF16 path.

• config.py: register the minimax_m3_vl multimodal wrapper and parse its
  text sub-config (which declares no model_type) with the base
  PretrainedConfig so every field is retained and no deepseek/MLA
  defaults leak in; stamp model_type=minimax_m3 from the top-level type.
  The quark quantization_config (already propagated from the root) and
  the original architectures are preserved, so loading resolves to the
  existing MiniMaxM3Sparse model. The BF16 checkpoint keeps its direct
  minimax_m3 model_type and is unaffected.
• linear.py: pad the MXFP4 Linear contraction dim to 256. The a4w4 asm
  GEMM reads K in 256-wide tiles, so an unaligned K (M3's shared-expert
  down_proj at TP=8, K=384) faults on GPU. LinearBase._pad_mxfp4_input_dim()
  zero-pads the fp4x2 weight, its e8m0 scale, and the activation up to
  256-alignment; no-op when already aligned.

Validated: GSM8K 93.9% at TP=8 (full 1319, 5-shot), matching the TP=1
baseline; lossless vs the aligned baseline (cosine 0.9934).

Motivation

Properly run MiniMax M3 MXFP4 on ATOM with TP=8

Test Result

Tested with ATOM with TP=8:

python -m atom.entrypoints.openai_server \
  --model amd/MiniMax-M3-MXFP4-AttnFP8 \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --block-size 128 \
  --server-port 8000

lm-eval

lm_eval \
  --model local-chat-completions \
  --model_args "model=amd/MiniMax-M3-MXFP4-AttnFP8,base_url=http://127.0.0.1:8000/v1/chat/completions,num_concurrent=32,max_gen_toks=16384" \
  --tasks gsm8k \
  --num_fewshot 5 \
  --batch_size 1 \
  --apply_chat_template \
  --fewshot_as_multiturn

Results, for reference with TP=1 gsm8k gives ~0.9424:

Build	flexible-extract	strict-match
Baseline — origin/main (no fix)	0.7665 ± 0.0117	0.7657 ± 0.0117
Fixed — this branch	0.9378 ± 0.0065	0.9386 ± 0.0065

Submission Checklist

• Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.