Frank
a34322aeb0
The managed-skill library was regenerated: 94 narrow per-incident skills
replaced by 25 broader ones (radiance-*, llamacpp-*, homepage-*, fleet/
endpoint ops). Git renders this as mass delete + mass add because whole
directories changed name; old content stays in history at 12c5e88.
Surface check: the 25 on disk are exactly the skills the harness advertises
this session.
2.7 KiB
2.7 KiB
name, description
| name | description |
|---|---|
| radiance-precision-selection | Choose tensor precisions for a radiance (StillDeadcode) rad-convert recipe on RDNA4: what is native, what is supported, what the hardware rewards. |
Radiance precision selection
Use when writing/choosing a rad-convert recipe for radiance on RDNA4 (gfx1201), or when arguing why a model was converted a particular way.
Project map (which repo is authoritative)
codeberg.org/StillDeadcode/radiance— standalone engine (C++/HIP). Recipes:data/recipes/*.recipe; docs:docs/{GUIDE,MOE-W4,KERNELS,PLUGIN,TOOLS,OPS,ARCHITECTURES,DOCKER}.md. Prefer the RAW form of Codeberg URLs when reading:/raw/branch/main/<path>.codeberg.org/StillDeadcode/libr4d— kernel library; entry points encode exact geometry and reject mismatch (attn_decode_h256_gqa6_fp8kv,gdn_chunk_scan_k128_v128_c64_bf16,ar_oneshot_2rank_exact). Kernel names are the ground truth for which dtypes have a fast path.codeberg.org/StillDeadcode/vllm-radiance— deprecated vLLM wrapper; README still carries the best prose on the FP8 dispatcher and kernel list.
Precision ladder
| Precision | Role |
|---|---|
| block-FP8 (E4M3 + bf16 block scales) | native tuned GEMM/attention/KV — "preshuffled block-FP8 dispatcher" |
| MXFP4 (OCP group-32) / W4A8 | supported weight packing; activations dynamically quantized to FP8 E4M3, compute is FP8 WMMA |
| bf16 | KV (auto), skinny GEMMs, Gated-DeltaNet kernels |
| int8 / int4 | draft head and integer GEMM paths |
Decision rules
- Prefer block-FP8 for anything the engine qualifies: generic upstream AITER linear in place of the FP8 dispatcher measured 9–11 % slower; all upstream-qualified models are FP8 (Qwen3.8-27B-FP8, Qwen3.6-27B-FP8, Qwen3.6-35B-A3B-FP8, Gemma-4-31B-it-FP8).
- Weight packing is a storage choice, activation dtype is the compute choice: MXFP4 buys bytes, not an FP4 compute path.
- The matrix core is integer-biased: f16 WMMA is half the int8 rate and a quarter of the int4 rate ⇒ int4 > int8 > f16. FP8/INT8 kernels must fuse double-K WMMA to saturate the 128-bit memory interface (GPUOpen WMMA guide part 2) — a "slow" quantized kernel usually means a missing K-fused variant, not an inherent dtype cost.
- Keep low-bit tricks on the draft head (int4/int8) where verification tolerates them; keep lm_head and hard-selection tensors (QSA indexer top-k) at high precision — the shipped recipes quantify the cost (int8 lm_head beats E4M3 by ~0.6 top-1 points).
Verification
- Read the recipe's own header comments: radiance recipes state measured KL / top-1 / prefill-decode deltas for their choice, and name the
--reuseinputs. - Confirm the kernel exists before promising a dtype: grep libr4d entry points for the dtype suffix (
fp8kv,bf16,exact).