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The Rock8 v1 — all-AMD release (RDNA2/3/3.5/4)

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@The-Monk The-Monk released this 16 Aug 16:25
· 0 commits to master since this release
rock8-v1
7469ae6

The Rock8 — all-AMD release line

Banked kernel improvements from the roc10 development line, compiled and
guarded for all consumer RDNA generations in one fatbin. The frozen
hackathon submission (roc8 branch) is untouched; this branch is the
downloadable release line.

Build (all-AMD fatbin)

cmake -B build -DGGML_HIP=ON \
  -DCMAKE_HIP_COMPILER=$ROCM/lib/llvm/bin/clang++ \
  -DCMAKE_HIP_ARCHITECTURES="gfx1030;gfx1100;gfx1151;gfx1201" \
  -DAMDGPU_TARGETS="gfx1030;gfx1100;gfx1151;gfx1201" \
  -DCMAKE_BUILD_TYPE=Release
cmake --build build -j

Add/remove gfx targets freely; per-arch behavior is compile-guarded from
the ISA grid (tools/bench-card/isa-grid.{md,json} — 17 arches x 32
datapath instructions, llvm-mc-probed).

What engages where

capability RDNA4 (gfx120x) RDNA3/3.5 (gfx110x/115x) RDNA2 (gfx103x) CDNA
Q1_0/Q2_0 SWAR tile unpacks (+12.6%/+8.9% pp measured on RDNA4) Y Y Y Y (generic int SWAR)
Q1_0/Q2_0 fold-identity dp4a decode Y Y Y (i8 dot4) Y (i8 dot4)
gated-delta-net DPP wave reduction (+2% pp) Y Y Y butterfly fallback
MXFP6 native MMQ tile (+40% kernel-local) Y compiled out compiled out compiled out
F8E4M3 T77 dot2 decode Y compiled out compiled out compiled out
2:4 sparse SWMMAC prefill Y compiled out compiled out compiled out
RDNA4 launch-geometry tables (rpb/nwarps/fusion) Y upstream defaults upstream defaults upstream defaults
experimental routes (iu4 W4A4, fp8 SR, dot2f16, per-channel) env-gated, RDNA4 runtime-checked compiled out compiled out compiled out

RDNA3 testers — expected results

Correctness gates (bit-exact or noted; wikitext-2, --chunks 4):

  • Bonsai-27B Q1_0 prefill-path PPL: 11.6466 (bit-exact vs perm/select
    reference paths — any other value is a bug, please report)
  • Q2_0 g128 prefill-path PPL: 10.1465
  • gdn DPP path: PPL parity within ±0.02 of the above (reassociation only)

Performance: expect the SWAR prefill gains (+6–30% depending on baseline)
and the dp4a decode paths to transfer; RDNA4-only rows above will report
compiled out/fallback and that is correct behavior. Please capture
llama-bench tg128/pp2048 plus junction temps — bench-card tooling in
tools/bench-card/ automates full provenance capture if you want it.

Strix Halo (Ryzen AI Max, gfx1151) testers

Covered by the default build line as-is (gfx1151 is in the target list;
RDNA 3.5 resolves to the RDNA3-tier guards). APU-specific notes:

  • Memory: unified LPDDR5X (~256 GB/s class). Ternary decode is
    memory-bound, so expect proportionally lower tg than dGPUs — the
    interesting Halo result is that a 27B 1-bit model (3.5 GB) fits and
    decodes usefully at all, and the SWAR VALU savings are worth more
    per byte here than on discrete cards. Rough ceiling math: Q1_0 27B
    ≈ 256/3.4 → ~75 t/s theoretical; report whatever fraction you achieve
    together with the model of your unit.
  • Stack: needs a ROCm build with gfx1151 enabled (ROCm 7.x /
    TheRock wheels both carry it). Use -ngl 99; unified memory means no
    VRAM-size gymnastics, but check the BIOS carveout if allocation fails.
  • Gates are identical: the bit-exact PPL values above are
    architecture-independent — same numbers or it is a bug.
  • If you capture results, junction/skin temps and the power profile
    (balanced vs performance) matter on APUs — note them alongside
    tg128/pp2048.

Speculative decoding (MTP) on ternary targets

Bonsai-27B is a Qwen3.6-27B ternary train (identical 248,320-token vocab).
Any Qwen3.6-27B gguf that carries the MTP/nextn layer can donate its head
to a Bonsai target via tools/graft_mtp.py <bonsai.gguf> <out.gguf> (15
blk.64.* tensors + block_count 65 + nextn_predict_layers=1). Then:

llama-speculative-simple -m out.gguf --spec-type draft-mtp \
    --spec-draft-n-max 2 -n 512 --temp 0 -p "..."

Measured on gfx1201 (temp 0 — exact-match verify, output bit-identical
to plain greedy):

target accept effective tg vs plain
Q2_0 g128 49.2% 62.9 +20.9%
Q1_0 32.7% 61.4 -8.5% (skip)

n-max=2 is optimal; deeper drafts lower accept and throughput. Q1_0 needs
a Bonsai-distilled head (tracked with PrismML). The script only grafts
tensors you already have — no weights are distributed with this repo.

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