The legacy panel names suggested batched serving semantics they never
had — all four modeled a single user with KV sharded differently
(C=1 single-cube; C=4 multi-cube Cube-SP), at toy dims (T_q=4, S_kv≤128).
The single-KV-group C=8 panel + the new milestone-gqa-decode-4cases
bench cover the meaningful comparisons; pytest regression already
covers C=1/C=4 configurations end-to-end at richer scale.
Changes:
- milestone_gqa_headline.py: drop the 4 legacy panels; _PANELS now
contains only single_kv_group_prefill_gqa_c8_p8. Update docstring.
- tests/attention/test_milestone_gqa_headline.py: drop the 3 legacy-
panel architectural tests (Ring-KV traffic, root-only decode write,
per-CUBE distributed output) and test_decode_panels_use_real_gqa
(no decode panels in this bench anymore). Equivalent properties
are asserted in test_milestone_gqa_single_kv_group_prefill_panel.py
(64 dma_writes, 896 ipcq_copy) and test_milestone_gqa_decode_4cases.py
(1 dma_write at cube 6, 189 ipcq_copy).
- tests/attention/test_milestone_gqa_single_kv_group_prefill_panel.py:
drop test_existing_prefill_panel_runner_backward_compat (it exercised
multi_user_prefill_gqa which no longer exists).
- scripts/paper/paper_plot_gqa.py: replace the 4 legacy _LABELS entries
with the single single_kv_group_prefill_gqa_c8_p8 label.
- Regenerate 1H_milestone_output/gqa_headline/sweep.json from the new
panel set.
Verification: 9/9 tests pass across the 3 affected test files.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
New `/paper` slash-command skill that synthesizes ADR/SPEC content and live
KernBench benchmark results into a sectioned LaTeX technical paper compiled
to PDF with Tectonic (auto-installed). The skill negotiates a TOC, grounds
every number in committed artifacts or fresh bench runs, and keeps
report-only benches isolated.
This commit also includes the first generated report:
- docs/report/1H-codesign-paper/ — main.tex + per-section .tex, figures,
toc.md contract, and the built 8-page main.pdf. Covers the platform
(source-level kernels, latency model + accuracy, HW config from
topology.yaml), GEMM via composite command, All-Reduce via PE_IPCQ, and
fused GQA combining both, plus discussion/conclusion/2H future work.
- scripts/paper/ — isolated report harnesses (not registered benches):
paper_gqa_latency.py harvests per-panel GQA end-to-end latency + engine
occupancy (the milestone only emitted op-counts); paper_plot_gqa.py
renders the GQA figures.
GEMM/All-Reduce reuse committed milestone figures/CSVs; GQA results are
generated fresh. Honest flags retained: PE_CPU dispatch cost is 0 in this
config, and the proposed two-composite softmax_merge decode is marked
designed-not-measured.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
mukesh
ywkang