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>
First milestone of the decode 4-cases comparative study per
GQA_full_deck.pptx slides 11-17. Case 4 (Cube-SP × PE-SP, the optimal
case per slide 11) is structurally the existing _gqa_attention_decode_long
at sub_w=4 (Increment 2's lrab-adapted center-root reduce). This commit
wires it into a dedicated bench so the remaining cases land alongside.
Changes:
- New bench: src/kernbench/benches/milestone_gqa_decode_4cases.py
Houses the 4 case panels under one milestone-gqa-decode-4cases entry
(gated by GQA_DECODE_4CASES_RUN=1; output to
1H_milestone_output/gqa_decode_4cases/sweep.json). Cases 1-3 are
TBD in subsequent sub-increments (5C.A/B/C).
- New panel: single_kv_group_decode_gqa_cube_sp_pe_sp
C=8, P=8, sub_w=4, T_q=1, S_kv=131_072, d_head=128, h_q=8, h_kv=1.
- src/kernbench/benches/milestone_gqa_headline.py: _run_decode_panel
extended with keyword-only sub_w/T_q/d_head/h_q/h_kv overrides
(defaults preserve existing-panel behaviour).
- tests/attention/test_milestone_gqa_decode_4cases.py: 4 new tests
asserting registration, smoke completion, reduce-to-root at the lrab
center cube (cube 6), and the predicted 189-ipcq Case-4 traffic
pattern (168 intra-CUBE + 21 inter-CUBE lrab Phase 1+2).
- tests/attention/test_milestone_gqa_headline.py: rename
test_sweep_json_has_four_panels -> test_sweep_json_has_expected_panels
and switch hardcoded 4 to len(PANELS) (the panel set grew to 5
with Increment 5's single_kv_group_prefill_gqa_c8_p8).
Deviation noted: slide 13 prescribes AllReduce on (m,ℓ,O); our kernel
does reduce-to-root (only the lrab center cube has the answer) per
ADR-0060 §4. Treated as the kernbench Case-4 baseline.
Verification: all 4 new tests pass; 90 regression tests pass; the
previously-failing test_sweep_json_has_four_panels now passes under
its renamed form.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Land the new GQA fused-attention kernels (ADR-0060) for prefill/decode
across long and short context, the TL discipline primitives they depend
on (ADR-0062 lazy load, ADR-0063 scratch_scope + copy_to), and the
per-op-type CPU issue cost model (ADR-0064). Remove the pre-ADR-0060
mesh-attention baseline now that the unified kernels supersede it.
ADR-0060 (long context)
- _gqa_decode.py: M-fold + 2-level chain reduce-to-root (Level-2
intra-CUBE row-then-col + Level-1 inter-CUBE) — root-only output.
- _gqa_prefill.py: head-parallel + Ring KV rotation around C CUBEs,
online-softmax merge per ring step, per-CUBE distributed output.
- Each merge stage wraps in scratch_scope() and persists running
(m, l, O) via copy_to() to lift the 1 MiB scratch ceiling.
ADR-0060 §B.split.2 (short context, kv_per_cube in {1,2,4,8})
- _gqa_decode_short.py / _gqa_prefill_short.py: no cube-SP; each CUBE
owns whole KV heads; PE-parallel heads with intra-group chain
reduce. Prefill has no Ring KV (each head fully resident).
ADR-0062 (lazy tl.load): future-bearing TensorHandle, auto-wait at
first consuming op (dot/MATH/store/send/copy_to/composite).
ADR-0063 (tl.scratch_scope + tl.copy_to): scoped per-tile arena with
copy_to writeback primitive for persistent running state.
ADR-0064 (CPU issue cost model)
- common/cpu_issue_cost.py: per-op-type table (composite=40 ns,
primitives=5 ns); ratios are load-bearing per D1.
- TLContext: issue_cost_table param; _emit_dispatch_overhead(kind)
consults table with dispatch_cycles fallback (ADR-0046 §D6
back-compat).
- Live PE_CPU paths (greenlet + legacy) construct TLContext with
DEFAULT_CPU_ISSUE_COST so saturation lever (ADR-0060 §1) is
measurable end-to-end.
P7 headline bench: milestone-gqa-headline writes per-panel
op_log_summary to 1H_milestone_output/gqa_headline/sweep.json. No
figure renderers yet (deferred).
Removals (pre-ADR-0060 baseline now superseded):
- benches: _attention_mesh_kv.py, _attention_mesh_mlo.py,
_attention_mesh_mlo_2d.py, milestone_gqa_llama70b.py
- tests: test_attention_*, test_mesh_*, test_milestone_gqa_llama70b
- topology: llama70b_4sip.yaml (only consumer was the deleted diag)
- artifacts: 1H_milestone_output/gqa/ (sweep.json + 5 PNGs)
- tests/gqa/ plot helper + test (broken on Windows Tcl/Tkinter)
- ADR-0060/0061 references to deleted file paths cleaned up
(EN + KO kept in sync).
Tests: 124/124 focused regression green (attention + Phase E + TL
discipline + triton_emu + pe_components). Full regression: 764 pass,
2 pre-existing test_bench_registry failures (stale EXPECTED_NAMES
across multiple benches, not introduced here).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
mukesh