TASKS.md: loop_216's SME2 baseline passes after its tolerance fix

TASKS.md

@@ -1,6 +1,6 @@
 # Tasks and baselines
 
-Every definition in this dataset, with the expert baseline it is scored against on each hardware tier: whether it runs there, and whether it is specialized for that tier. Generated 2026-10-02 from the dataset, the recorded runs and a check of each baseline's code; the SME2 column was updated on 2026-10-20 from a check of every SIMD Loops baseline on M4.
+Every definition in this dataset, with the expert baseline it is scored against on each hardware tier: whether it runs there, and whether it is specialized for that tier. Generated 2026-10-02 from the dataset, the recorded runs and a check of each baseline's code; the SME2 column was updated on 2026-10-20 from a check of every SIMD Loops baseline on M4, and `loop_216` on 2026-10-29 after a tolerance fix in the code repository (PRs #111 and #115).
 
 | Mark | Meaning |
 |---|---|
@@ -22,10 +22,10 @@ How each source's baseline is chosen and marked is explained under *Notes on the
 |---|---|---|---|---|
 | ncnn | 23 | 0 / 23 / 0 / 0 / 0 | 0 / 23 / 0 / 0 / 0 | 0 / 23 / 0 / 0 / 0 |
 | llama.cpp | 26 | 0 / 26 / 0 / 0 / 0 | 0 / 26 / 0 / 0 / 0 | 0 / 26 / 0 / 0 / 0 |
-| SIMD Loops | 47 | 27 / 0 / 0 / 3 / 17 | 11 / 18 / 0 / 7 / 11 | 6 / 16 / 0 / 10 / 15 |
+| SIMD Loops | 47 | 27 / 0 / 0 / 3 / 17 | 11 / 18 / 0 / 7 / 11 | 7 / 16 / 0 / 9 / 15 |
 | KleidiAI | 10 | 3 / 0 / 0 / 0 / 7 | 0 / 2 / 0 / 0 / 8 | 7 / 0 / 0 / 0 / 3 |
 | e2e (llama.cpp GEMMs) | 18 | 0 / 18 / 0 / 0 / 0 | 0 / 18 / 0 / 0 / 0 | 0 / 18 / 0 / 0 / 0 |
-| **All** | **124** | 30 / 67 / 0 / 3 / 24 | 11 / 87 / 0 / 7 / 19 | 13 / 83 / 0 / 10 / 18 |
+| **All** | **124** | 30 / 67 / 0 / 3 / 24 | 11 / 87 / 0 / 7 / 19 | 14 / 83 / 0 / 9 / 18 |
 
 ## ncnn (23)
 
@@ -133,7 +133,7 @@ Loops without the Eval set mark are in the dataset but not in the main results.
 | `loop_128` |  | 10 |  |  | — | — | ✗ |
 | `loop_130` |  | 10 |  |  | — | ✗ | — |
 | `loop_135` |  | 10 |  |  | — | ✗ | — |
-| `loop_216` |  | 10 |  |  | — | — | ✗ |
+| `loop_216` |  | 10 |  |  | — | — | ★ |
 | `loop_217` |  | 10 |  |  | — | — | ★ |
 | `loop_218` |  | 10 |  |  | — | — | ★ |
 | `loop_220` |  | 10 |  |  | — | — | ★ |
@@ -203,13 +203,12 @@ These definitions load only with the e2e branch for now (code repo issue #94).
 - `loop_114` (`baseline-sme2` on M4): about 3.3x slower than the scalar starting point auto-vectorized for M4, so it fails the rule that a baseline be at most 2x slower than that.
 - `loop_127` (`baseline-sme2` on M4): about 5.3x slower than the scalar starting point auto-vectorized for M4, so it fails the rule that a baseline be at most 2x slower than that (the first evaluations of the earlier M4 agent runs gave 4.6x).
 - `loop_128` (`baseline-sme2` on M4): 2.01x slower than the scalar starting point auto-vectorized for M4, so it fails the rule that a baseline be at most 2x slower than that, just over the limit.
-- `loop_216` (`baseline-sme2` on M4): wrong results on 2 of 10 workloads at the default tolerance (1 of 256 outputs, up to 1.4e-3 relative). It is an FP32 GEMV with reductions of up to 522k terms; the scalar starting point fails the same outputs with the same values, and both pass under the tolerance used for `gemm` definitions, so the fix is a tolerance override for this definition.
 
 ## Notes on the baselines
 
 - **ncnn** → `baseline-ncnn-arm`, the upstream ncnn kernel, the same on every tier. ncnn is built with its SVE options off, so this is NEON code everywhere: ✓, never ★.
 - **llama.cpp and e2e** → `baseline-llamacpp-arm`, the ggml CPU kernel built natively on each machine. Which kernels reach ggml's SVE code has not been checked, so these are ✓; ggml's KleidiAI path is off. ggml picks its kernel by op, data type and CPU features, not by shape, so a cell with no recorded run is ✓ when the same op and data type ran on that machine (8 llama.cpp definitions on Graviton4). e2e has recorded runs only on Graviton4 and is ✓ on Graviton3 and M4 for the same reason (q4_K ran on both in the llama.cpp set; q5_K and q6_K have not).
-- **SIMD Loops** → one hand-written Arm baseline per tier. `baseline-sve2` uses SVE2-only instructions in some loops (★) and is SVE code in the rest (✓). Of the 32 `baseline-sme2`, 25 are SVE code run in streaming mode (`__arm_locally_streaming`, tag `sme2-kernel:streaming-sve`) and 7 use ZA (tag `sme2-kernel:za`: the matrix loops 216–221 and 223). Streaming SVE counts as ✓, not ★, because it uses nothing SME adds (ZA, outer products, SME2 multi-vector instructions); the ZA ones are ★. All 32 were run on M4 on 2026-10-20 (3 passes on every workload, code at main bc8c53d): 30 pass every workload, `loop_032` cannot run there and `loop_216` fails 2 workloads; the 2x check against the auto-vectorized starting point flags 8 more. All of these are ✗, see the notes below.
+- **SIMD Loops** → one hand-written Arm baseline per tier. `baseline-sve2` uses SVE2-only instructions in some loops (★) and is SVE code in the rest (✓). Of the 32 `baseline-sme2`, 25 are SVE code run in streaming mode (`__arm_locally_streaming`, tag `sme2-kernel:streaming-sve`) and 7 use ZA (tag `sme2-kernel:za`: the matrix loops 216–221 and 223). Streaming SVE counts as ✓, not ★, because it uses nothing SME adds (ZA, outer products, SME2 multi-vector instructions); the ZA ones are ★. All 32 were run on M4 on 2026-10-20 (3 passes on every workload, code at main bc8c53d): `loop_032` cannot run there, `loop_216` failed 2 workloads at the default tolerance and passes since its tolerance fix (code repository PRs #111 and #115), and the other 30 pass every workload; the 2x check against the auto-vectorized starting point flags 8 of them. Those 9 are ✗, see the notes below.
 - **KleidiAI** → `baseline-kleidiai-arm`: each kernel uses either an SVE micro-kernel (Graviton tiers) or an SME2 one (M4), not both.
 
 The SVE2 column of SIMD Loops (including the notes on ✗) and of the KleidiAI SVE kernels comes from running each baseline directly on a Graviton4 (c8g) on 2026-10-02. On 2026-10-03 the SIMD Loops and KleidiAI baselines were also checked on Graviton3 and Graviton4 against the inclusion rule that a baseline be at most 2x slower than the scalar starting point auto-vectorized for that machine; every failure is in the notes on ✗.