Date registered: 2026-07-23 America/Toronto
Status at registration: the default-off source candidate was under static review. No candidate binary had been built, no component GPU gate had run, and no endpoint service had been launched.
The retained profile attributes 0.272194 ms per 47-layer target cycle to
materializing BF16 router logits as FP32 and 0.560374 ms to TopKGating.
The candidate removes only the materialized BF16-to-FP32 tensor. It must
preserve the incumbent operation:
BF16 [8,256] logits
-> exact widening to FP32
-> FP32 sigmoid
-> add FP32 [256] correction bias for selection only
-> lower-expert-ID tie break
-> top 10
-> renormalize the unbiased FP32 sigmoid weights
The router-local scaling factor is exactly 1.0; Laguna’s configured 2.5
scale is applied later by the MoE runner because
apply_routed_scale_to_output=True. Router softcapping is zero. The 47
checkpoint correction-bias tensors are FP32 [256] tensors and are all
exactly zero, but the component gate also uses changing nonzero FP32 biases.
The candidate is a separate _moe_C entry point. It does not change the
generic BF16 top-k behavior, whose sigmoid is rounded through BF16 and is not
an exact substitute for the incumbent FP32 path.
To preserve the incumbent expert-to-lane mapping, the specialization is fixed to four warps per workgroup and an eight-byte BF16 load. Each of 32 lanes owns the same eight experts as the FP32 kernel:
lane l: 4*l .. 4*l+3 and 128+4*l .. 131+4*l
The XOR reduction order remains 16, 8, 4, 2, 1, with two four-warp
workgroups for eight rows.
The default-off selector is:
VLLM_XPU_LAGUNA_M8_BF16_ROUTER_TOPK=1
It may skip router_logits.float() only for Laguna target verifier execution
at exactly M=8 under the existing exact batched-M1 path. It must require 256
experts, top 10, sigmoid routing, normalization, an FP32 correction bias,
router-local scale 1.0, no softcap, no EPLB/redundant/hash routing, and the
specialized extension symbol.
A matching flagged M=8 call must raise on contract or binary drift. M=1, M=2..7 verifier tails, prefill, DFlash draft layers, non-Laguna models, and all other shapes keep the incumbent FP32-materialization path.
Source frozen before build:
689ee3643f320e4a10c621ddd829620bc2f5b3b3;af6811818ef797aa86aef51bda15ae9c49040f7b;6 passed;py_compile, git diff --check, and oneAPI 2025.3 clang-format
checks passed; andFocused build and binary freeze, completed before the first component GPU process:
2025.3.3,
/opt/intel/oneapi/compiler/2025.3/bin/icpx;ninja -C build/temp _moe_C.abi3.so;/media/steve/CorsairExternal/llm-optimization-artifacts/laguna-s-2.1/binaries/m8-bf16-router-topk-af68118-20260723/build-_moe_C.log;32945e426019a2ecd6e1aa1e82f7a2655a5268f3f7b65e05c7c6645c0438078b;_moe_C SHA256:
f222d3e2d2a8a331e3c85f12e0d02a17aa7a89147bbbcc8ac2c2a816629a405f;_moe_C SHA256:
0057b266d567731a9f9f592cefd9103bbf027ebb83c876d26c17ffb09994a3a0;
andBoth binaries and the symbol/compiler checks are preserved under the build-log directory above.
Static gate freeze:
experiments/laguna-s-2.1-xpu-b70/tools/gate_laguna_m8_bf16_router_topk.py;87b1bbe450dac954ddb86620912999f3a22e877d79fc65300bdd393de73132de;13b3c7ff9d1d6c304523ba706e9ad2758f53126770370ea5b717e52f277ee7e3;454893a5083128a37aeb4918aa6f4641d8a8a49ee1456b17844ada0a53f936b5;
andRun the tracked component harness once on each physical B70 with exactly one visible device. Every card receives the same 333 changing epochs:
[8,256] tensors with changing FP32 biases; and3/4, 127/128, 131/132, and
255/0, adjacent BF16 values around the cutoff, adjacent FP32 bias values,
signed zero, sigmoid saturation, both four-value load groups per lane, and
rotations across every expert and top-k slot.The production fixtures must be formed with the incumbent batched-M1 BF16 gate
projection from the retained [8,3072] mlp-input traces and the exact
checkpoint gate weight for that layer. Their manifest and hashes are frozen as
gate evidence. A missing or differently hashed production fixture is a
failure, not permission to run a synthetic-only gate.
For each epoch, compare:
logits.float() followed by incumbent FP32 topk_sigmoid; andRequire raw-byte equality and torch.equal for FP32 weights, int32 expert IDs,
and int32 token/expert source indices. Also require candidate repeat
determinism, unchanged logits and bias, ten distinct in-range IDs per row,
the exact slot * 8 + row source mapping, and the intended lower-ID winners
in designed ties. The specialized dispatch must be proven rather than inferred.
After correctness, time only cast plus top-k for A and direct top-k for B. Exclude gate projection, fixture generation, hashing, and CPU work. Reuse output buffers and synchronize only at arm boundaries:
Every physical card must independently satisfy all of:
0.20 ms per 47-layer cycle;20%; andOne mismatch, nondeterministic repeat, failed card, or missed timing threshold stops this lane before an endpoint. Results are not averaged across cards to mask a failure.
The frozen component gate passed on all four physical B70s. Each card passed
all 333 pre-timing changing epochs, 1,998 torch.equal checks, 2,664 raw-byte
checks, candidate-repeat determinism, unchanged-input checks, explicit
lower-expert-ID ties, and the 111-epoch post-timing replay. Synthetic,
production-source, projected production-fixture, and aggregate output hashes
were identical across cards.
All cards won 31/31 paired A-B-B-A blocks:
| Rank | A cast+top-k ms/cycle | B direct BF16 ms/cycle | Saved ms | Gain |
|---|---|---|---|---|
| 0 | 0.938074 | 0.456029 | 0.482089 | 51.3965% |
| 1 | 0.911805 | 0.455101 | 0.456650 | 50.0925% |
| 2 | 0.922209 | 0.454956 | 0.467250 | 50.6665% |
| 3 | 0.906338 | 0.454877 | 0.451434 | 49.8086% |
This clears every preregistered component threshold and authorizes the cold endpoint A-B-B-A protocol. Raw evidence:
/media/steve/CorsairExternal/llm-optimization-artifacts/laguna-s-2.1/logs/m8-bf16-router-topk-component-689ee36-af68118-20260723T043723Z/
Compact tracked result:
data/laguna-s-2.1-m8-bf16-router-topk-component-20260723.json
Only if all four component gates pass, use the approved eager depth-7 record stack with route-parallel W2 and expert N-tile interleave enabled and QKNorm, attention-MM, graph, and unrelated candidates disabled. Change only the new router selector.
Use the fixed 13-prompt suite with SHA256
9fdaacfdc4de59407a73cbe0d8130fa0f6abe91fed782e399a58adbc035ea638,
the canonical q=1 teacher, seed 1, BF16 KV, enable_thinking=false,
max_tokens=512, token IDs returned, one active request, and a new cold
service for every leg. There is no generation warm-up, repeated prompt,
prefix/history/ngram reuse, concurrent request, or fifth rescue run. Keep all
four devices free for 60 seconds between legs.
The order is sequential A-B-B-A:
Stop after B1 unless:
0.15 ms per DFlash target cycle in aggregate request
decode time; andEndpoint tooling was frozen before A1:
experiments/laguna-s-2.1-xpu-b70/tools/run_router_topk_crossover_leg.sh;b92c5a41de86f1112d2782c6a4fbe79afe518e0d7592c6c7a04a699c8e6e4a9b;experiments/laguna-s-2.1-xpu-b70/tools/analyze_router_topk_crossover.py;ae9e7b420f2eb95237ba21ec9dd366846aed7e268e7ad7d444e26c3b384ae923;The runner rejects ambient benchmark-sensitive vLLM, Laguna, Level Zero, SYCL,
Unified Runtime, oneCCL/libfabric, CPU-threading, compiler, and loader
variables. It then pins the approved route-interleaved stack, model/draft
revisions, tokenizer, chat template, model index/code, runtime packages,
driver tooling, source commits, and native binaries. Every leg byte-hashes all
LFS weights against the frozen Hugging Face revision manifests before starting
the service. An independent pre-run audit matched all 16 LFS files:
15 target shards plus the DFlash weight, 74,137,878,672 bytes total, with
zero mismatches in 39.863 s.
The runner also refuses path reuse/traversal, validates exactly four idle B70 devices before startup, and performs bounded process-group shutdown plus port/process/four-device idle proof on both successful and failed legs.
The first A1 invocation with runner
c1a58a0bec4869190183a32061b1f2c24f96e79a6ee69900bf2310a19b6087d2
stopped inside the XPU idle preflight because the local mawk rejected a
multiline parenthesized assignment. It exited before the service, model, or any
endpoint generation started. The failed preflight is preserved at:
/media/steve/CorsairExternal/llm-optimization-artifacts/laguna-s-2.1/runs/m8-bf16-router-topk-abba-689ee36-af68118-20260723T051304Z/01-A1-control/
Only that header-expression syntax was made mawk-portable. The frozen
preflight capture then passed the exact validator, the analyzer’s runner hash
was updated, and a new run directory is required for the real A1.
Every executed leg must have 13/13 full token arrays bitwise equal to the
canonical q=1 teacher, 13/13 cached_tokens=0, one request per unique cold
prompt, 512-token long-then-next exact 2/2, rollover exact 1/1, and exact
cross-leg token equality. Record draft cycles, drafted/accepted totals,
accepted-position histograms, per-row timings, and target-cycle-normalized
decode time.
If the full block runs, call it a reproducible endpoint win only if B1 beats
A1 and B2 beats A2 in headline throughput, each candidate wins at least 9/13
rows with positive median paired change, each saves at least 0.15 ms per
target cycle, each pair’s acceptance rate differs by no more than 0.10
percentage point, and the lower candidate exceeds the lower control.
A LocalMaxxing record additionally requires the lower candidate to exceed the
approved 33.438926675602126 tok/s record. Only that lower candidate may be
submitted after a complete payload and evidence audit. Otherwise preserve the
candidate as an exact win, loss, or inconclusive result with no submission.
The real A1/B1 phase ran under the frozen runner and analyzer at:
/media/steve/CorsairExternal/llm-optimization-artifacts/laguna-s-2.1/runs/m8-bf16-router-topk-abba-689ee36-af68118-20260723T051657Z/
Both legs passed every quality, honesty, benchmark-identity, stack-identity, freshness, and cleanup check. Each was 13/13 bitwise exact against the canonical q=1 teacher with all 13 cached-token counts zero. The 512-token long-then-next check passed 2/2 and the rollover check passed 1/1 in each leg. Because both legs exactly matched the same teacher, their output token arrays also matched one another. There was no generation warm-up, prompt reuse, history acceleration, concurrent request, or retained endpoint.
The measured phase-1 comparison was:
| Metric | A1 control | B1 candidate | B1 - A1 |
|---|---|---|---|
| Headline median tok/s | 32.9690119264 | 32.3101222212 | -0.6588897052 (-1.9985%) |
| Mean tok/s | 38.3387134160 | 38.6575563099 | +0.3188428939 |
| p10 tok/s | 25.6497789812 | 26.4776872145 | +0.8279082334 |
| Aggregate decode seconds | 159.2497690071 | 157.7562722090 | -1.4934967981 |
| Target-cycle-normalized time | 92.7488462476 ms | 91.7187629122 ms | -1.0300833354 ms (-1.1106%) |
| DFlash cycles | 1,717 | 1,720 | +3 |
| Draft tokens | 12,019 | 12,040 | +21 |
| Accepted tokens | 4,645 | 4,642 | -3 |
| Acceptance rate | 38.6471420251% | 38.5548172757% | -0.0923247494 percentage point |
The candidate won 10/13 paired prompt rows. Its median paired improvement was
+0.2127728662 tok/s (+0.7138466435%), and it passed the row-win,
paired-median, cycle-saving, and acceptance-delta gates. The three losses were
the median-defining shell-safety-review row (-1.9985%),
structured-extraction (-2.3034%), and prose-decision-memo (-1.7132%).
Consequently the preregistered B1 headline > A1 headline gate failed.
The frozen analyzer classified the experiment phase1_failed_stop. Per the
preregistered early-stop rule, B2 and A2 were not run. This is evidence that
the specialization removes real work, but it is not a promotable endpoint win
under the frozen official metric. No LocalMaxxing payload was staged or
submitted.
Tracked summary:
data/laguna-s-2.1-m8-bf16-router-topk-endpoint-phase1-20260723.json
Artifact hashes:
phase1-analysis.json:
30f0627b089fc9251823c57f394be6ce13b5e8ad26babbbf247308191c1de8dc;phase1-analysis.md:
869513c4f45f832b68d8d115b02e2298a9d46b8d1c9bd84077710f9dc942e5ed;bench.json:
75a237a8221236b98b422c4d78290a3666220050d02c31a402937c1f45ec0807;exactness-vs-q1.json:
878a21c14226db6214aa26957d79280fe2544a9c2faba76d10c8a94b38ba5194;bench.json:
a3ea84617082a7d76bc49d16b51695279d6d916990d04b2b7bdd63cc18f4fe05;exactness-vs-q1.json:
d760056da1ec53e9e404707156a3f3474b40ec40fa11260896a0f5fa71037c2c.