ADR-0064 D8 broadened from "DMA fast-path" to "single-op-cmd fast-path":
every single-op command (DmaRead/DmaWrite/Gemm/Math/Copy) now pays the
lighter FIXED=8; only CompositeCmd keeps the 40-cycle control-path FIXED
(it alone needs scheduler plan generation + per-tile RW-hazard tracking +
completion wiring). Renamed knob fixed_per_dma_cmd_cycles ->
fixed_per_single_op_cmd_cycles. dispatch_cycles now branches on
"is CompositeCmd" rather than enumerating DMA types.
Term choice: "single-op" (not "atomic", which read as sync/async) — the
axis is composition (one engine op vs fused multi-op plan), orthogonal to
timing. single-op <-> composite.
Tests: test_pe_cost_model.py updated to the single-op surface (defaults,
fast-path over all 5 single-op cmd types, composite general path, yaml
override). All green.
Recalibrated tests/attention/test_gqa_decode_opt2.py
::test_opt3_dispatch_exceeds_opt2 — NOT a regression: D8 makes single-op
cmds 5x cheaper, so opt2's two-composite fusion win over opt3's many
single-ops narrowed from pre-D8 ~3.7x to ~1.87x (opt3=224 > opt2=120).
The CPU-offload invariant (opt2 cheaper) still holds; only the model-
dependent ">2x" constant was over-fit to the old uniform-40 model. Gate
now: direction + >1.5x margin (matches sibling R-sweep test's stated
"absolute ratio informative-only" philosophy).
NOTE for review: ADR-0065's "2x CPU-offload win" headline may want a
refresh to reflect the post-D8 ~1.87x — left to user (architectural doc).
Full regression: 826 passed, 1 skipped (tests/ excl. tests/gemm).
--- Remaining work (resume here if interrupted) ---
5. Re-run scripts/paper/paper_plot_gemm_async_vs_composite.py with new
cost model; verify async-tiled dispatch overhead drops (~4576ns ->
~1536ns expected) and the composite-vs-async-tiled gap narrows from
the prior ~6.3x at K=3072.
6. Copy regenerated gemm_composite_vs_async_tflops.png to
docs/report/1H-codesign-paper/figures/.
7. Paper §3.4 (03-gemm.tex sec:gemm-vs-async): finish naive->async-full /
chunked->async-tiled rename AND reframe FIXED_DMA wording to single-op
vs composite (currently still says "lighter FIXED for DMA descriptors,
FIXED_DMA=8"). Table 2 (02-platform) + §2 dispatch prose already done.
8. Paper §3.4 K=3072 corner para + mechanism #3: update dispatch breakdown
to new model (96 DMA*8 + 95 single-op*8 ≈ 1.5us vs old 4.6us); update
headline ratio if it changed.
9. Rebuild docs/report/1H-codesign-paper/build/main.pdf (tectonic) +
verify via pdftotext.
10. Then this is the bench-harness + paper commits (Groups 2 & 3).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The five-stage chain was set as a centered display equation, which
overflowed the column and clipped the last 'DMA_WRITE' label. The
inline prose right below it also repeated the same chain in a
shorter form. Consolidate into one inline parenthetical listing the
five stage names, and reuse 'flows through the chain' for the
self-routing description — no clipping, no repetition.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The matplotlib title and gray subtitle were duplicating what the
figure caption already says in the LaTeX source, and crowding the
top of every chart. Drop both — chart now leaves the title/subtitle
slots blank and the caption is the single source of truth for
chart titling.
Re-render all 5 GEMM figures (mac util measured, mac util theoretical
vs measured, stage breakdown, HBM BW util, per-PE TFLOPS) and resync
into docs/report/1H-codesign-paper/figures/. No data changes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two new evaluation plots for §3 GEMM, both using the composite window
as the denominator (the up-front tl.load of A in load_ref is therefore
excluded — only HBM traffic and compute inside the composite count):
- gemm_hbm_bw_util.png: per-PE HBM bandwidth utilization against the
256 GB/s per-PE ceiling. Answers "is this workload memory-bound on
this configuration?" — small/single-tile shapes sit at 22–46%
(pipeline-fill-limited), large or output-write-dominated shapes
saturate (≥85%).
- gemm_per_pe_tflops.png: per-PE GEMM throughput against the 8 TFLOP/s
engine peak. The deep-K K=3072 load_ref case lands at
7.18 TFLOP/s (~90% of peak) — the configuration's clean win.
ref_ref at the same shape drops to 3.83 TFLOP/s because the second
operand doubles HBM pressure and the kernel hits the BW ceiling.
The variant gap on this chart is the operational cost of NOT
pre-staging the activation.
Both charts compare two operand-staging variants on every shape:
load_ref ("activation pre-staged", weight-only HBM streaming) and
ref_ref (both A and W streamed from HBM). load_load is omitted —
with both operands pre-staged the composite carries no HBM traffic
and the BW metric collapses to 0.
Analytic / measured peak unified to the single hardware spec
(8.0 TFLOP/s = 8000 flops/ns). T_STAGE is now derived as
tile_flops / peak (= 16.384 ns for the 32×64×32 tile) rather than
the previous hardcoded 16 ns, so MAC efficiency and per-PE TFLOPS%
share the same denominator and never disagree. Side effect: max
analytic-vs-measured gap tightens from 2.2 ppt to 1.4 ppt across
the sweep.
§3 Results restructured: lead with the two new evaluation plots
(BW saturation, achieved throughput), then the MAC-utilization
analytic-vs-measured chart serves as the validation step, then the
per-stage engine wall-clock as the supporting diagnostic. §3
Analysis updated to use the load_ref / ref_ref contrast as the
hardware-software boundary line that motivates the GQA kernel of §5.
§2.4 Accuracy claim updated to match: GEMM analytic-vs-measured
agreement is now "within 1.4 ppt across every swept shape", from
~7.7% at single-tile up to ~90% at K=3072. Subtitle/title vertical
stacking glitch in the matplotlib charts fixed in passing.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three coupled fixes that recover small-tile GEMM pipeline efficiency
from 53% to 88% (32x3072x32 load_ref, composite_window basis).
1. PE_DMA channel-hold (ADR-0014 D4 clarified): both the
_handle_with_hooks (PeInternalTxn) and _pipeline_process
(TileToken) paths used to hold the cap=1 DMA channel through the
full HBM round-trip, which double-serialized with the HBM_CTRL's
own per-PC `available_at` model and prevented back-to-back tile
DMAs from amortizing their per-request head latency. Channel is
now released after the request is enqueued onto the next hop;
HBM serialization is HBM_CTRL's responsibility alone.
Tests: new test_pe_dma_back_to_back_pipelining as the oracle
(asserts wall < 75% of strict-serialized N x single_op). Existing
test_pe_dma_record_start_after_channel_acquire rewritten to assert
t_start clustering (channel released fast) instead of the old
round-trip-hold invariant. test_pe_dma_same_channel_serializes
still passes — HBM_CTRL preserves ordering.
Probe regression: PE→local-HBM 32 KiB stays at 141 ns
(single-request, unaffected).
2. milestone_1h_gemm bench: matmul_composite was reading MATMUL_M/K/N
env vars at module load, so every sweep row replayed the cached
256³ result; values now read inside run(). Drops the stale
sys.modules deletion hack.
3. Analytic ideal-pipeline model: dropped the (n_mn-1)·dma_w_per_pair
penalty (over-pessimistic for under-tile shapes — it pushed
measured > theoretical) and replaced the D_STAGES-derived head
with empirical T_PIPELINE_FILL=60 ns / T_PIPELINE_TAIL=30 ns.
Max analytic-vs-measured gap across all 7 swept shapes now 2.2 ppt
(was 9-44 ppt under the old constants).
Paper updates:
- §3 (GEMM): 78%→88% measured at 48 tiles, 23%→15% at 1 tile,
stage breakdown numbers refreshed (DMA in / Fetch / GEMM all
~785 ns at K=3072), analytic-vs-measured agreement tightened
to "within 2.2 ppt".
- §2.4 (Accuracy): GEMM tracking claim refreshed accordingly.
- §5 (GQA): restore long-ctx 4-cases figures into figures/
(they were dropped from bench output dir as derived artifacts in
92b9221 / e45626c but §5 still cites them by name).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
These 8 prefill/decode 4-case PNGs are derived artifacts regenerated
on demand from scripts/paper/paper_plot_gqa_{prefill,decode}_long_ctx_4cases.py
and no longer belong in the committed bench output.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Analytical plot generator + PNG covering the 4 sharding cases from
slide 17 plus the 2 d_head-TP variants (Cases 4, 5). Three panels:
per-PE HBM budget breakdown (Wq + Wk + Wv + Wo + 4.24 GB KV
headroom), per-PE KV memory at S_kv=1M, and per-PE communication
per output token (decode). Numbers match slide 17 (max KV context
~7.1M for the 64-way sharded cases).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Single @bench entry that drives the prefill + decode long-context
4-cases sweeps in one invocation, mirroring milestone-1h-gemm. The
individual gqa_helpers/long_ctx/gqa_{prefill,decode}_long_ctx_4cases
modules are now pure helpers (no @bench), reached only via this
umbrella.
Env var contract:
GQA_1H_RUN=1 (required gate)
GQA_1H_SWEEPS=prefill,decode (default: both; pick subset to skip)
GQA_1H_TOPOLOGY=topology.yaml (override)
Output layout:
benches/1H_milestone_output/gqa/long_ctx/
sweep_prefill.json
sweep_decode.json
gqa_prefill_long_ctx_4cases_{latency,traffic,memory,parallelism}.png
gqa_decode_long_ctx_4cases_{latency,traffic,memory,parallelism}.png
The decode bench config drops S_kv from 131_072 to 8_192 so the umbrella
finishes in minutes — the comparative-story bars are the same shape at
8K. The 131K production headline number is recoverable by overriding
the dispatch; the helper docstring notes this.
Outputs (2 sweep JSONs + 8 PNGs) ship in this commit alongside the
umbrella that produced them, following the milestone-1h-gemm pattern
where derived artifacts live with the bench that emits them.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Mirrors the existing decode 4-cases study for prefill on the LLaMA-3.1-70B
single-KV-head group (h_q=8, h_kv=1, d_head=128) across 8 cubes × 8 PEs:
Case 1 Cube-SP × PE-TP → KV S_kv-Ring across cubes; Q/O T_q-split 64-way
Case 2 Cube-Repl × PE-TP → full KV per cube; only CUBE 0's P PEs split T_q
Case 3 Cube-Repl × PE-SP → full KV per cube; PEs SP on S_kv (intra-cube AR)
Case 4 Cube-SP × PE-SP → KV split 64-way + Q T_q-split across cubes
(Ring KV + per-cube intra-CUBE AR) ★ optimal
Cases 2 and 3 use Q-axis tiling (TILE_Q) so the per-PE scratch is bounded
by TILE_Q × TILE_S_KV regardless of T_q (the full Q tensor would be
G·T_q·d_head·2 bytes which scales linearly with T_q and blew the 1 MB
budget at T_q≥128 without tiling).
Per-panel try/except in run_sweep tolerates per-panel failures so
sweep.json always lands with whichever cases succeed plus a failures
list. Case 4 uses configure_sfr_intercube_ring with the snake submesh
(2,4) which installs both the Ring E/W lanes and the intra_* lanes
needed for the intra-CUBE reduce — single SFR config for all 4 cases.
The plot script generates 4 PNGs (latency, traffic, memory, parallelism)
into 1H_milestone_output/gqa/long_ctx/. The parallelism chart shows
total compute work (active_PE × T_q_per_PE × S_kv_processed) — exposes
Case 3's 8× redundancy vs. Cases 1/2/4 which all do unique work.
gqa_prefill_long_ctx_4cases.py exposes run_sweep() rather than a @bench
decorator — the umbrella bench in the next commit invokes it.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Splits the GQA helpers into a dedicated subpackage to make room for the
prefill 4-cases study (next commit) and a single umbrella bench
(milestone-1h-gqa, after that).
Layout:
benches/gqa_helpers/
long_ctx/ — decode 4-cases kernels + sweep runner
short_ctx/ — prefill/decode short-context kernels
shared/ — _gqa_panel_helpers + decode_opt2 (context-agnostic)
The registry audit now skips subpackages so gqa_helpers/ (without a
leading underscore) doesn't get audited for @bench decorators.
Also drops the legacy milestone-gqa-headline bench, its
_gqa_attention_prefill_long kernel, 6 dependent prefill tests, the
paper_gqa_latency.py report harness, and the 3 stale headline-derived
PNGs the §6 wire-up referenced (paper will re-pull from the new
1H_milestone_output/gqa/long_ctx/ once §6 is updated).
The _ccl_cfg and _summarize_op_log helpers used to live in the
headline bench; extracted them to gqa_helpers/shared/_gqa_panel_helpers.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Probe table (Table 1, tab:probe-pe-dma) promoted to table* so it
spans both columns and no longer gets clipped at the column edge.
- Fig 1 (hw-arch) row 1 (SIP, CUBE subfigures) trimmed: widths
0.495 -> 0.42 textwidth and a height=0.6\linewidth cap added so
the figure occupies less vertical space and floats can land
earlier in the document.
- Fig 1 and Fig 2 source blocks relocated to the top of §2 so they
enter LaTeX's float queue before any §2 body text. As a result
Fig 1 now lands on page 3 and Fig 2 on page 4 (was 4 and 5).
- Table 2 (tab:hw, modeled hardware config) reverted to plain
\begin{table}; the previous \begin{table*} reshape was not
requested.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- subfig widths 0.46 -> 0.495 textwidth so the \hfill gap between
(a) SIP and (b) CUBE shrinks to ~2% textwidth instead of ~8%.
- Removed the height=0.85\linewidth cap on both subfigures; with the
cap, the (roughly 1:1) SIP and CUBE diagrams were being scaled
down to 85% even when the column had room. Let them render at
their natural aspect ratio.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
§2 platform:
- Accuracy promoted from \paragraph to \subsection (sec:accuracy);
it sits at the same heading level as Why / Device / Latency / HW
config and so reads as a first-class component of the platform
description rather than a tail-end footnote.
- Three architecture diagrams (SIP, CUBE, PE) collapsed into one
figure* using subcaption: Fig.~\ref{fig:hw-arch}(a) SIP and (b)
CUBE side-by-side on row 1, (c) PE wide on row 2, total height
capped at ~40% of the page. Subfigure cross-references rewritten
to Fig.~ref{fig:hw-arch}\subref{...} in the body text. main.tex
now pulls in the subcaption package.
- Table 1 (modeled hardware configuration) promoted to table*
(two-column / full page width) and the row layout rewritten as a
4-column tabular so Hierarchy + PE + Command-issue sit on the
left and Memory + Interconnect sit on the right. The previous
single-column rendering was getting cut at the right edge of the
printed column.
§6 GQA:
- Removed the historical four-panel headline table (tab:gqa), the
two figures (fig:gqa-lat, fig:gqa-break), and the prose paragraph
that cited their per-panel numbers. Reason: the underlying
milestone-gqa-headline bench has been simplified in collaborator
commit 65c365f ("drop misleading single_user_/multi_user_
panels") and no longer reproduces that data, so the section was
left referencing a dataset the current bench cannot regenerate.
- The previously-added 4-cases long-context decode comparison is
now §6's only Results subsection, retitled to
"Results: long-context decode and parallelism strategies".
The headline GQA result that survives into the paper is therefore
the parallelism trade-off study (Case 4 / Cube-SP × PE-SP at 34 us
buying an 8x KV-memory reduction over the latency leaders).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The collaborator commit 7c346de added the comparative figures
(gqa_decode_long_ctx_4cases_{latency,memory,traffic}.png) but the
fused-GQA section still only referenced the four headline panels.
This commit closes that loop:
- New §6 subsection "Long-context decode: parallelism strategies"
added between Results and Analysis. It lays out the four
parallelism strategies (Cube-SP/Repl x PE-TP/SP) and pulls in the
three new figures.
- The discussion highlights the central trade: the fastest strategy
(Case 3, Cube-Repl x PE-SP at 20.2 us) requires replicating the
full KV cache to every CUBE, while Case 4 (Cube-SP x PE-SP, the
chosen design marked *) gives back ~14 us in exchange for an 8x
KV-memory reduction. Case 4's ~190 IPCQ copies + ~190 DMA reads
are precisely the on-device collective traffic PE_IPCQ and the
torus links of §5 are provisioned to absorb -- a direct payoff
of the communication-side codesign work.
- Connects back to §5 (PE_IPCQ / all-reduce) so the reader sees the
capstone arc: the GEMM enabler exposes the data-movement bound,
the communication enabler attacks it, and the long-context
parallelism study shows how the choice between the two extremes is
framed by KV memory vs. on-device collective traffic.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
§2 platform:
- Add a "memory-centric AHBM" sentence to the device-and-execution
intro: each PE is paired with dedicated HBM bandwidth and on-PE
TCM, so the performance question is about feeding compute from
locally-attached memory + moving the unavoidable inter-PE traffic
efficiently. Makes the AHBM character of the platform visible
before §2.3 starts unpacking the latency model.
- Promote "Congestion and contention modeling" from \paragraph to
\subsubsection: this is the platform's core differentiator over a
peak-BW roofline, so it deserves its own heading.
- Rename "Control-plane (issue) cost model" to "Command dispatch
overhead model" -- describes what it actually charges (the PE_CPU
paying a fixed + per-byte cost to push a command to one of the
accelerator engines) without the more abstract "control-plane"
framing.
- Add a third Accuracy cross-check from kernbench probe: a
PE→HBM DMA-distance sweep that confirms monotonic hop progression,
bandwidth saturation matching the per-edge model, and the built-in
invariants (D2H >= H2D, cross-CUBE best < worst). New
Table~\ref{tab:probe-pe-dma} reports per-traversal latency and
utilisation at 32KiB / 1MiB across five hop classes.
- Captured probe output as figures/probe_pe_dma_summary.txt for
reproducibility.
§5 PE_IPCQ / all-reduce:
- Re-ran milestone-1h-ccl on current sim_engine (post-ADR-0064 Rev2
and the IPCQ slot-wrap Phase-2 race fix). Updated the topology-
comparison table and the buffer-kind caption to the fresh raw
latencies. Headline ratios are preserved:
torus vs mesh saving at 96 KB/PE: 24.8% (was 24.8%) -> ~25%
torus vs ring saving at 96 KB/PE: 19.3% (was 19.3%) -> ~19%
TCM vs HBM saving at 64 KB/PE: 13.4% (was 13.9%) -> ~13%
TCM vs SRAM saving at 64 KB/PE: 37.2% (was 38.3%) -> ~37%
The Executive Summary's "up to ~14%" / "up to ~38%" framing stays
consistent with these post values.
Artifacts refreshed in src/kernbench/benches/1H_milestone_output/:
- ccl/summary.csv + per-topology PNGs + buffer-kind CSV/PNG
- ccl/comparison_mesh_vs_ring_vs_2DTorus_vs_theoretical_vs_fsim.png
- gemm/* (re-run yields identical pe_window structure; PNGs
refreshed)
Paper figures synced to the fresh artifacts:
- figures/allreduce_comparison.png
- figures/allreduce_buffer_kind.png
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Generated by scripts/paper/paper_plot_gqa_decode_long_ctx_4cases.py
from the bench's sweep.json (S_kv=8192, the headline 128K run was
sidestepped — Case 2's single-PE 128-tile sweep is the long pole
and shape/ordering is preserved across scales).
gqa_decode_long_ctx_4cases_latency.png end-to-end latency / case
gqa_decode_long_ctx_4cases_traffic.png ipcq / dma op-count breakdown
gqa_decode_long_ctx_4cases_memory.png KV bytes per cube (slide-11)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Bench changes:
- new _end_to_end_ns(op_log) and _engine_occupancy_ns(op_log) helpers
in milestone_gqa_decode_long_ctx_4cases.py (mirror paper_gqa_latency.py)
- _run_panel return dict now carries latency_ns + engine_occupancy_ns
alongside op_log_summary, so sweep.json is the single source of truth
for the comparative figures
Plot script:
- new scripts/paper/paper_plot_gqa_decode_long_ctx_4cases.py reads
sweep.json and emits 3 PNGs to docs/report/1H-codesign-paper/figures/:
gqa_decode_long_ctx_4cases_latency.png (end-to-end latency / case)
gqa_decode_long_ctx_4cases_traffic.png (ipcq/dma op counts / case)
gqa_decode_long_ctx_4cases_memory.png (KV bytes per cube / case)
Test changes:
- 2 new tests verifying the helpers + _run_panel dict shape
- lower smoke S_kv from 8192 -> 2048 (4x faster Case 2; assertions
are S_kv-independent; one fold-loop iteration preserved)
18/18 tests pass in ~4 min.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Reordered §2 subsections to follow the SIP → CUBE → PE → graph
flow: Why KernBench → Device and execution model → Latency model
→ Modeled hardware configuration. Readers now meet the device
hierarchy before the graph abstraction that re-uses it.
- §2.2 Device and execution model: starts with the SIP/CUBE/PE
hierarchy paragraphs (each anchoring fig:sip-arch, fig:cube-arch,
fig:pe-arch); then the runtime-API/sim-engine/components bullet
list; then the atomic-vs-composite command distinction (corrects
the prior over-narrow framing that read every PE command as
composite -- atomic single-engine commands exist too, and PE_CPU
itself runs control-plane work directly).
- §2.3 Latency model: opens with the four-contribution decomposition
(per-node overhead, per-edge transmission, drain, queuing delay)
and the latency_model schematic; retains existing The hardware as
a graph / From graph to DES / Latency contributions / Congestion
/ Control-plane cost model / Accuracy paragraphs. Accuracy
paragraph now closes on KernBench's sufficiency for *relative*
HW/SW design trade-offs given analytic + external-simulator
agreement.
- New figures and assets:
- figures/sip_architecture.pdf (SIP-level graph view)
- figures/cube_architecture.pdf (CUBE-level zoom-in)
- figures/latency_model.png (conceptual latency-model
schematic with per-node /
per-edge / drain / queuing-delay
colour coding)
- figures/pe_architecture.png (carried over)
- Source-of-truth generator for the latency schematic:
scripts/paper/paper_latency_model_diagram.py (a report-only
harness under scripts/paper/ per the /paper isolation rule).
- main.tex preamble: \usepackage{tikz} added (kept from prior
sequence-diagram draft -- harmless now that the latency model is
a PNG; left in to keep paragraph numbering stable).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Option B fold: the lrab math previously in
``_gqa_attention_decode_long.py`` (used only as a thin re-export by
the Case 4 wrapper and as the import source for ``_merge_running``
in Cases 1 / 3) now lives inline in the Case 4 kernel file. ``sub_w``
is hardcoded to 4 (the 4×2 cube sub-mesh geometry; root cube 6);
the legacy ``sub_w=0`` 1D-chain backward-compat path is removed
along with its dedicated tests — the dropped ``single_user_*`` /
``multi_user_*`` panels that exercised it are already gone.
Production changes:
- inline lrab math into _gqa_attention_decode_long_ctx_cube_sp_pe_sp.py
(drops sub_w param; _ROOT_CUBE=6 baked in)
- inline _merge_running into Cases 1 (cube_sp_pe_tp) and 3
(cube_repl_pe_sp) so they no longer depend on the deleted file
- delete src/kernbench/benches/_gqa_attention_decode_long.py
- remove dead _run_decode_panel / _DECODE_* constants /
decode-side _PANEL_DISPATCH / _make_bench_fn decode branch
from milestone_gqa_headline.py (only the prefill panel remains)
- update _gqa_attention_decode_opt2.py docstring reference
Test changes:
- delete 7 legacy test_gqa_*.py files that pre-dated the 4-cases
architectural split (coverage now subsumed by the 16 4-cases tests)
- remove test_opt2_matches_opt3_data_mode + _run_decode_data helper
from test_gqa_decode_opt2.py (the parity check required sub_w=0
which no longer exists; opt2's other 4 tests preserved)
20/20 tests pass (16 4-cases + 4 opt2 smoke/dispatch).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Cases 1-3 each have a dedicated _gqa_attention_decode_<case>.py
kernel file; Case 4 previously reached into _gqa_attention_decode_long.py
via the headline bench's _run_decode_panel helper, breaking the
one-file-per-case convention. Adds _gqa_attention_decode_cube_sp_pe_sp.py
as a 20-line wrapper that bakes in sub_w=4 (the C=8 lrab geometry)
and gives Case 4 its own kind ("decode_cube_sp_pe_sp") and helper
(_run_decode_panel_cube_sp_pe_sp). decode_long.py is unchanged
(still serves the legacy decode_long tests). 16 tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds the fourth and final 4-cases panel: KV split S_kv-wise across
the 8 cubes (cube=row_wise, S_local = S_kv/C), replicated within
each cube (pe=replicate). PE-TP at B=1 means only PE 0 of each cube
has work; PEs 1-7 early-return (slide-11 PE-TP-at-B=1 waste). No
intra-CUBE comm; inter-CUBE 8-way reduce reuses the lrab-adapted
center-root pattern (root cube 6) — same structural cost as Case 4's
inter-CUBE phase (21 ipcq_copy). 16 tests pass (4 per case).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds the third 4-cases panel: KV replicated per cube (8× memory
waste), PEs SP on S_kv within each cube, intra-CUBE 8-way reduce on
(m, ℓ, O), and no inter-CUBE comm (every cube ends with full answer;
designated writer = cube 0). Reuses the row-chain + col-bridge
intra-CUBE pattern that anchors Case 4 (21 ipcq_copy per cube × 8
cubes = 168 total). 12 tests pass (4 Case 4 + 4 Case 2 + 4 Case 3).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Second case in the GQA decode 4-cases comparative study per
GQA_full_deck.pptx slide 11. Case 2 replicates K, V across all 8
cubes × 8 PEs (the slide-11 8 KB/tok/PE memory waste) and has zero
inter-rank comm. For B=1 (single-user decode default), only PE 0
of CUBE 0 has work — the inherent PE-TP waste slide 11 calls out.
Changes:
- New kernel: src/kernbench/benches/_gqa_attention_decode_cube_repl_pe_tp.py
Simplest of the 4 cases. Active rank loads full Q/K/V from HBM,
computes attention via S_kv tile sweep with online-softmax merge,
writes O. All non-(0,0) ranks early-return. No tl.send/recv.
- src/kernbench/benches/milestone_gqa_decode_4cases.py:
- Add panel single_kv_group_decode_gqa_cube_repl_pe_tp (Case 2)
to _PANELS and _PANEL_DISPATCH.
- Add _run_decode_panel_cube_repl_pe_tp helper: DPPolicy K/V/Q/O
= cube=replicate, pe=replicate (models 8× memory waste).
- Extend _make_bench_fn to dispatch kind="decode_cube_repl_pe_tp"
to the new runner.
- tests/attention/test_milestone_gqa_decode_4cases.py:
4 new tests assert Case 2 contract: panel registered, smoke
completion, zero ipcq_copy (no comm), single dma_write from cube 0.
Verification: 8/8 tests pass (4 Case 4 anchor + 4 new Case 2).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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>
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>
End-to-end wires C=8 P=8 d_head=128 prefill with snake-ring inter-CUBE
SFR, intra-CUBE PE-SP (all 64 ranks active), and the milestone bench
panel. Decode kernel gains lrab-adapted center-root reduce for the
2×4 sub-mesh per ADR-0060 §4.2.
Increment 1 — SFR multi-row snake
src/kernbench/ccl/sfr_config.py: configure_sfr_intercube_ring gains
submesh_shape / submesh_origin kwargs; installs a Hamiltonian snake
ring through a rectangular sub-mesh (every hop is 1-hop physical
neighbour). Backward-compat: 1D-row behaviour preserved when
submesh_shape is None.
tests/test_intercube_snake_ring.py (12 tests)
Increment 2 — Decode lrab-adapted center-root reduce
src/kernbench/benches/_gqa_attention_decode_long.py: new sub_w param
(default 0 = existing 1D-chain). sub_w >= 2 selects the ADR-0060
§4.2 prescribed lrab-adapted Phase 1+2 reduce (bidirectional row +
bidirectional col converge to the center cube), with log-sum-exp
_merge_running replacing the plain + of lrab.
tests/attention/test_gqa_decode_long_2d_reduce.py (4 tests)
Increment 3 — Prefill kernel at C=8 (no production change)
Verified by inspection that the existing prefill_long kernel +
Increment 1's snake SFR already work at C=8 without any kernel
edit. The kernel speaks logical W/E; the snake routes it.
tests/attention/test_gqa_prefill_long_c8_snake.py (3 tests)
Increment 4 — Intra-CUBE PE-SP in prefill (all 64 ranks)
src/kernbench/benches/_gqa_attention_prefill_long.py: new P param
(default 1 = existing PE-0-only). P > 1 splits T_q query-axis-wise
across the P PEs of each CUBE; output rows are disjoint per PE so
no intra-CUBE reduce is needed; each PE drives its own same-lane
ring (P parallel rings).
tests/attention/test_gqa_prefill_long_pe_sp.py (5 tests)
Increment 5 — LLaMA-scale milestone bench panel
src/kernbench/benches/milestone_gqa_headline.py: new panel
single_kv_group_prefill_gqa_c8_p8 (C=8, P=8, T_q=S_kv=32K,
d_head=128). _run_prefill_panel extended with P/T_q/d_head
defaults; routes snake SFR when C > mesh_w.
tests/attention/test_milestone_gqa_single_kv_group_prefill_panel.py (3 tests)
Total: 4 production files modified, 5 new test files, 27 new tests.
Followed the Phase 1/2 protocol per CLAUDE.md throughout.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Mid-pass through §2. Captured progress so far:
- §2 intro previews the three new threads (graph view, DES engine,
congestion) the rewritten latency section weaves together.
- §2.3 retitled to "Latency model: a graph traversed by events" and
restructured into bold-led paragraphs:
- The hardware as a graph (nodes = components, edges = links).
- From graph to discrete-event simulation (node/edge events,
deterministic ordering, correlation-ID trace).
- Latency contributions (per-node fixed, per-edge size-aware,
per-service occupancy).
- Congestion: per-edge FIFO BW occupancy, HBM per-PC parallelism,
component serial workers — the mechanisms that surface real
bottlenecks instead of peak-BW roofline.
- Control-plane cost model (FIXED + b·R) — unchanged.
- Accuracy: extended with a second cross-check from the all-reduce
study (torus vs. analytic startup-plus-per-packet model + FSIM
external single-device reference). Existing GEMM analytic-vs-
measured 10-20% check retained.
- §2.4 Hardware-configuration table unchanged.
Still TODO: cross-check accuracy numbers against a fresh
milestone-1h-gemm/ccl re-run; the current artifacts predate the
ADR-0064 Rev2 cost-model and IPCQ Phase-2 race fix and may need a
refresh before §2.3 final lock-in.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- §1 Introduction rewritten with AHBM-first opening, in-paper KernBench
platform overview, GQA motivation and three architectural requirements,
three matching HW-SW co-design mechanisms (GQA-aware placement,
PE_IPCQ, composite-command GEMM under PE_SCHEDULER), broader-
applicability outlook (PE_IPCQ as collective substrate, composite
command for fused operator pipelines), and outline. Consistent with
the Executive Summary structure.
- Executive Summary: extended broader-applicability closing with a
forward-looking line previewing FFN/MoE integration.
- §5 section heading renamed from
"All-Reduce Acceleration via PE_IPCQ" to
"PE_IPCQ and Collective Communication".
- Title page date line refined to "2026 H1 Report".
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
test_bench_registry EXPECTED_NAMES: add the 3 milestone benches the pull registered (milestone-1h-ccl, milestone-1h-gemm, milestone-gqa-headline) — 11 benches, alphabetical. test_memory_store::test_shape_mismatch_raises: the pull made same-size reshapes byte-conserving (allowed) and an over-large read raise 'Out-of-bounds read' (not 'Shape mismatch') — match the new message. Full suite now 820 passed / 1 skipped / 0 failed.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The reconciliation table, corrected file/test lists, measured 5.22x ratio, and the new section 15 (data-mode numerics D8 + N1-N4) — the content the git-mv in 30a0451 left uncommitted.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Updated the detailed design to match the shipped implementation: an as-built reconciliation table (P1-first, pinned-based DMA, D7 hard-cap-error, D8 output handle, prologue 1-stage-tile feed, D6.7 prologue-scoped), corrected file/test lists, the measured 5.22x dispatch ratio, and a new section 15 documenting the data-mode numeric subsystem (D8 + N1-N4) the original DDD omitted. Status Draft->Accepted; moved docs/adr-proposed -> docs/ddd/.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
composite() now awaits its prologue recipe operands (e.g. the score tile from a prior #1 composite) so the cross-composite RAW (#1 writes scores -> #2 reads them) is ordered: #2 waits for #1 before reading. With N1-N3, opt2 (the two-composite recipe kernel) now computes a correct attention output end-to-end in data mode and matches opt3 within fp tolerance.
Scope note: the e2e opt2<->opt3 parity holds in the near-uniform-attention regime (small inputs). kernbench's K reshape-as-transpose (opt3 kernel docstring: 'correct for zero/symmetric inputs') makes opt2's per-sub-tile K interpretation differ from opt3's single-tile one for high-contrast inputs — a pre-existing kernbench limitation shared by both kernels, not the recipe. The EXACT recipe math (online-softmax merge m/l + O = O*corr + P@V) is verified non-trivially and exactly by N2/N3 (test_recipe_data_mode.py). Suite 817 pass / 3 pre-existing fail.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Two fixes make the recipe's P.V GEMM fold into the rescaled accumulator: (1) op_log marks the composite gemm record accumulate=True when an 'add' epilogue targets the GEMM's own output; data_executor._execute_gemm then does O = O_existing + a@b instead of overwriting. (2) PE_SCHEDULER records the composite's numeric op at COMPLETION (in _retire_on_done) instead of at dispatch, so the GEMM's t_start sorts AFTER its prologue MATH ops -> it reads the computed P and the rescaled O.
Verified end-to-end (DataExecutor): the full recipe produces O = O_old*corr + P@V matching a numpy flash-attention reference. Suite 816 pass / 3 pre-existing fail.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
data_executor._compute_math gains the 5 recipe ops (rmax/rsum as keepdims reductions, max_elem/exp_diff/mul_bcast as binary; numpy broadcasting covers bcast_axis). tiling._math_stage now carries operand+output addrs/shapes/spaces + axis on the recipe prologue MATH stages. op_log.record_end promotes a MATH stage to op_kind='math' ONLY when it carries input_addrs -> the DataExecutor runs it; legacy epilogue MATH stages (bias/relu, no addrs) stay op_kind-opaque -> byte-equal.
Fixed a latent P2 lowering bug exposed by data mode: _resolve_recipe_dst gave reductions shape (G,) (axis removed) but max_elem broadcasts them against the running m=(G,1); reductions now keep the reduced axis as size 1 (keepdims) -> (G,1). Verified end-to-end: running the recipe's 8 MATH ops through the DataExecutor produces m, l (fully updated online-softmax) and O (rescaled by corr) matching a numpy reference. Suite 815 pass / 3 pre-existing fail.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Two changes make a composite's matmul replay in Phase 2: (1) op_log _extract_op_info(CompositeCmd) now SCANS ops for the gemm op (it is ops[0] for a legacy composite but sits after the prologue MATH ops in a recipe), emitting one composite_gemm record with the gemm's a/b/out addrs+spaces; (2) PE_SCHEDULER._dispatch_composite records the composite as a zero-duration numeric op (no-op without an op_logger, so latency-only mode is unchanged). Verified: a composite GEMM with seeded inputs writes a@b to its HBM output.
DataExecutor._execute_gemm is now best-effort: if an operand's data was never produced (torch.empty bench input) or a shard read is out-of-bounds (column-wise sharded operand read at full shape), skip the matmul instead of crashing. This keeps composite-using benches (qkv-gemm, composite-epilogue) green now that their previously-uncomputed GEMM actually fires. Suite 813 pass / 3 pre-existing fail.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
tl.composite now returns the output TensorHandle (not a CompletionHandle) so its result chains like tl.dot's; the handle carries the completion in a CompositeFuture pending so downstream ops and tl.wait auto-await it. out is a handle: out=tl.ref(addr,shape) (HBM, DMA_WRITE inside the composite) or an in-place TCM handle (STORE only); omitted -> TLContext auto-allocates a TCM scratch. out_ptr kept as HBM shorthand (= out=tl.ref(out_ptr, shape)) to avoid churning ~30 existing call sites.
tl.ref now returns space=hbm (it references HBM data; operand-input DMA stays pinned-based per D4 so input streaming is unchanged). tiling: the tile loop's DMA_WRITE is gated on out.space==hbm (out analog of the operand pinned rule) — a TCM output stays on-chip (chainable) and its high-bit scratch address no longer hits the DMA PA decoder.
Fixes the opt2 data-mode crash: the recipe accumulator O is TCM -> no DMA_WRITE -> opt2 now RUNS end-to-end in data mode (enable_data=True). Numeric parity of the recipe MATH ops is the next step. Suite 812 pass / 3 pre-existing fail; existing composite benches use out_ptr->hbm->DMA_WRITE unchanged (byte-equal).
Co-Authored-By: Claude Opus 4.8 (1M context) <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>
Extend the opt2 R-sensitivity test to assert the opt3/opt2 dispatch ratio increases as R decreases (4.20x -> 5.22x -> 5.45x at R=0.25/0.0625/0.03125) — the cost is FIXED-dominated (command-count-driven), per ADR-0064 Test #9. Test-only; no production change (the P0 cost model + P2 recipe already make the ratio measurable).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
New _gqa_attention_decode_opt2.py: per-tile attention as #1 Q.Kt GEMM composite + #2 softmax_merge recipe composite (online merge + P.V + add). Runnable in op_log mode; full data-mode numeric parity is a deferred follow-up (P5-numerics).
Measured per-tile PE_CPU dispatch (ADR-0064 Rev2 default): opt3=960ns vs opt2=184ns = 5.22x (gate >2x), FIXED-dominated (command-count reduction) — the ADR-0060/0064 CPU-offload win. opt2<opt3 across R in {0.25,0.0625,0.03125}. K-before-V: softmax_merge prologue carries no DMA; V (ref) streams only in the GEMM tile loop.
Fix latent P3 bug surfaced by the first e2e recipe run: prologue MATH stages were FOLDED into the first GEMM tile, but a folded MATH->DMA_READ boundary needs a PE_MATH->PE_DMA token route the pipeline never wires (KeyError pe_dma). Now prologue/post-loop stages are fed as standalone 1-stage tiles (each completes on its component); completion count includes them. Existing benches have no prologue -> tiles + feed order unchanged (byte-equal); full suite 806 pass / 3 pre-existing fail.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
_RwHazardTracker (pe_scheduler): a composite registers its write set (rw_handles) as in-flight; a new composite whose read (op operands) or write handles intersect an in-flight write set waits on those composites' done events before admission (ADR-0065 D6.3 / DDD 8). _dispatch_composite calls admit() before feeding and retires on the done event.
Legacy composites (rw_handles=()) never register and never block -> existing benches untouched (byte-equal). Strict FIFO is conservative (partial overlap waits for full drain); RW-aware reorder is deferred (A4).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
tiling.generate_plan_from_ops: scan flat ops for the GEMM (<=1); pre-GEMM KERNEL ops become single-shot prologue MATH stages, post-GEMM ops split by scope (K_TILE/OUTPUT_TILE epilogue + KERNEL post-loop). MATH-only composite reproduces the legacy math-head plan. Prologue/post-loop stages fold into the first/last tile so the feeder + completion counting are untouched (existing benches have neither -> byte-equal op_log).
PipelinePlan gains prologue_stages/epilogue_stages. pe_scheduler._generate_plan delegates to generate_plan_from_ops. DMA keeps the existing pinned signal (NOT a space flip); recipe scratch/primary-out handles are pinned=True so the head GEMM's auto-bound a (=P) is consumed in place.
ADR-0065 D4/D6.7/Test#5 amended (EN+KO): DMA decision from pinned, not space; prologue recipe ops TCM-only (head op exempt -- it may stream from HBM).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
New triton_emu/tl_recipes.py: RecipeDescriptor/EngineOp/PrimaryOutSpec + RECIPE_DESCRIPTORS['softmax_merge'] (8-step engine_seq). PE_SCHEDULER does not import it (ADR-0065 D5 boundary).
TLContext.composite(): add prologue=[...] + out=TensorHandle kwargs, a optional. _expand_prologue lowers a recipe into flat MATH OpSpecs (scope=KERNEL), allocates TCM scratch, derives the primary-out slot 'P' and auto-binds it into the head GEMM a (D6.6 conflict check); rw_handles=(m,l,O). decode-opt2 #2 lowers to 10 ops [rmax,max_elem,exp_diff,exp_diff,rsum,fma,mul_bcast,copy,gemm,add].
D6.7 (MATH operand TCM-only) scoped to prologue recipe ops only — the head op (gemm or math) keeps existing DMA-staged-from-HBM behavior. D6.1 (GEMM count <=1) on the whole composite. Host-side lowering only; PE_SCHEDULER position-scan is P3.
Also commit the ADR-0064 Rev2 promotion content that the prior commit's git mv dropped: Status Proposed->Accepted + D7 amended to hard-cap ValueError (no segmentation), EN+KO.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Comment-only change. Adds a kernbench-only limitation note at the
Tile-0 / bootstrap block in all four GQA attention kernels:
- persistent (m, l, O) must live outside tl.scratch_scope so
subsequent tiles' merges can read them;
- kernbench has no scratch-backed initializer (tl.zeros / tl.full
return addr=0 handles), so we can't seed (-inf, 0, 0) and rely
on tl.copy_to;
- therefore Tile 0 must compute the initial running state directly.
prefill_long adds a third bullet noting the ring-step ordering reason
(k=0 must send W before any k>0 can recv E, so the (t=0, k=0) step has
to run outside the scoped loop where the send is conditional on C > 1).
Each block also notes that a real Triton port collapses Tile 0 into a
unified loop (SSA tensors stay live across iterations).
No behavior change; tests unchanged.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
ADR-0064 Revision 2 second-pass review fixes:
- D7 added 1024-byte cap rationale: explicitly framed as a *safe
engineering limit* (intentionally above all known composites at ~322
bytes, finite descriptor capacity placeholder), not a measured HW
number. Default is the discipline; topology override for real HW.
- D7 segment ordering: strict FIFO is the *ordering source*; rw_handles
is dependency metadata, not the ordering primitive. Explicit note for
the future RW-aware reorder migration path.
- Tests rewritten around the formula (D1) instead of specific numbers:
- Test #1 → "formula preservation" (parametrized across composites,
asserts dispatch == FIXED + bytes × R, not anchor 43 ns).
- Test #2 → robust "opt3 > 2 × opt2" qualitative gate; ≈ 4.0× is
informative only, not the gate, so calibration changes don't break.
- Test #9 → qualitative (opt2 < opt3 at all R), not a numeric ratio.
- Anchor description in D3 stays as *informative* (shows the model
produces ≈ 43 ns at defaults), but is no longer a test gate.
ADR-0065 second-pass review:
- D5 step 5 aligned with D6.6 invariant: explicit conflict check before
auto-binding (previously written as if auto-bind always happens).
Mirrors D6.6 wording: error if both kernel explicit `a` and prologue
primary_out are present.
- Test #7 made robust: `opt3 > 2 × opt2` gate (was ≈ 4.0×).
Default-calibration model expectation (≈ 4.0×) recorded as informative
reference, not the gate.
DDD-0065 follow-on robust thresholds:
- §1.4 success criteria: ratio condition rewritten as `opt3 > 2 × opt2`
with model-expected ≈ 4.0× as informative.
- §9 P6 phase gate: `opt3 > 2 × opt2` (was 4.0× ± 15%).
- §10 dispatch ratio test: assert `opt3 > 2 × opt2`; record observed
ratio for performance tracking but do not gate on it.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ywkang
mukesh