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>
The headline T_q=S_kv=32K target overflows the 1 MB per-PE scratch
pool: at T_q_local=4K and tile_s=1024 the scores matrix alone is 8 MB.
The prefill kernel's bootstrap section also leaves K_t/V_t/scores/
exp_scores persistent (outside tl.scratch_scope), inflating baseline.
Scale-down to T_q=S_kv=1K (T_q_local=128, fits comfortably) preserves
the C=8 + P=8 architecture demonstration; the true LLaMA 32K headline
awaits a future increment to add Q-axis tiling and tighten bootstrap
scratch discipline.
Verified end-to-end: kernbench run --bench milestone-gqa-headline now
produces sweep.json with all 5 panels. The new panel shows
ipcq_copy=896 (matches (C-1)·n_tiles·2·C·P = 7·1·2·8·8) and
dma_write=64 (one per PE, head-parallel + intra-CUBE PE-SP).
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>
Add 5 of the 6 figure renderers ADR-0057 D3 sub-cycle 4c specifies:
- gqa_op_log_{panel}.png × 4 — per-panel bar chart of the 5 op_log
counts (gemm, ipcq_send, ipcq_recv, dma_read, dma_write).
- gqa_comparison.png — cross-panel grouped bars over the same 5 series.
Sixth figure (gqa_scaling.png) depends on sub-cycle 4b's Q/cube ∈
{1, 2, 4} sweep on multi_user_* panels and is deferred until that
data exists; emit_all_gqa_plots returns just the 5 in-scope paths.
Add MILESTONE_FAST=1 mode to run(): skip the panel sweep, reuse the
committed sweep.json, render figures only. Validation mode unchanged.
The runtime errors clearly when neither env var is set, listing the
two supported modes.
Renderers live in the bench module (the milestone-1h-gemm pattern);
tests/gqa/_gqa_plot_helpers.py re-exports them for figure tests.
Tests: tests/gqa/test_plot_gqa_figures.py — 7 tests, all green:
- 4 parametrized per-panel emit assertions
- 1 comparison emit assertion
- 1 emit_all returns exactly 5 PNG paths
- 1 default out_dir matches the bench _OUTPUT_DIR
Commits the 5 PNG baselines under the bench output dir alongside
sweep.json, mirroring milestone-1h-gemm's committed-figures pattern.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Self-contained eval bench (ADR-0054) that drives the four GQA Llama-70B
panels through run_bench with enable_data=True at validation scale and
emits sweep.json with the v1 schema (ADR-0057 D7).
Panel dispatch table maps each panel to (kernel, SFR install, S_q,
n_ranks, rank_axis):
single_user_prefill mesh_kv_kernel, intracube_pe_ring, S_q=16, n=8, rank_axis=0
multi_user_prefill mesh_kv_kernel, intercube_multisip, S_q=16, n=4, rank_axis=1
single_user_decode mesh_mlo_kernel, intracube_pe_ring, S_q=1, n=8, rank_axis=0
multi_user_decode mesh_mlo_kernel, intercube_multisip, S_q=1, n=4, rank_axis=1
multi_user panels pass _auto_dim_remap=False (avoid d_head=64
colliding with K's global M=64) and rank_axis=1 (cube-level ring,
gates 7 of every 8 PEs to silence).
Each panel runs on a fresh per-config GraphEngine, then op_log is
summarized into gemm/dma/ipcq counts. Both decode panels emit exactly
2*n_ranks GEMMs (one-shot partial attention per rank, ADR-0056 D3).
v1 supports GQA_VALIDATION=1 only; headline mode + figures deferred to
sub-cycles 4b/4c. Sentinel tensor satisfies the run_bench
"at least one request" contract (ADR-0045 D4 / ADR-0054 D2 carve-out).
Tests: tests/attention/test_milestone_gqa_llama70b.py — all 12 pass.
Includes committed sweep.json baseline at the bench's _OUTPUT_DIR so
subsequent test runs reuse it instead of re-simulating.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Per request, the milestone bench output is now tracked in git instead of
gitignored, so the figures/results are viewable on the remote:
- src/kernbench/benches/1H_milestone_output/gemm/ (3 PNGs + gemm_sweep.json)
- src/kernbench/benches/1H_milestone_output/ccl/ (3 per-topology PNGs,
buffer-kind PNG+CSV, FSIM comparison PNG, topology.png, summary.csv)
Drop the .gitignore rule; update ADR-0054 D3 + Negative (EN+KO) to say the
output is committed (regenerable by rerunning the bench). Artifacts produced
by full bench runs (milestone-1h-gemm non-FAST, milestone-1h-ccl).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
mukesh