Replace the three memory-optimal Pareto-search buttons in the Parallelism
sidebar with three scope-aware memory-min autosuggest buttons. Each
button sizes for a different per-PE memory footprint:
- Attn -> attention weights + KV cache (no FFN weights)
- FFN/MoE -> FFN weights only (no attention, no KV)
- Attn+FFN -> everything (traditional autosuggest)
memory_layout: per_pe_weight_bytes and compute_memory take
include_attention / include_ffn flags; KV cache is zeroed when attention
scope is excluded.
autosuggest: _score_candidate and auto_suggest forward the flags into
compute_memory, so the smallest-fit search now respects the chosen
scope.
app.py: single "Apply memory-min" caption + 3 column buttons. Each
button runs auto_suggest with its scope filter, snaps the sliders to
the picked (CP, TP, PP), and sets the Physical Layout scope filter to
match. Removes the standalone Apply memory-min button (was duplicating
the Attn+FFN case) and the Pareto-search import.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Two related fixes.
1) Formula strings show B alongside every other symbol. Previously the
numeric FLOPs/mem_bytes/comm_bytes were scaled by B, but the printed
formula strings still read like B=1 amounts. That made the per-stage
table confusing at high B: numbers moved but formulas didn't. Updated
every attention + FFN stage's `formula` / `flops_formula` /
`mem_formula` / `comm_formula` to include B explicitly. Weight-bytes
lines now say "(shared across batch)" / "(weight, B-invariant)" so it's
obvious what does and doesn't scale.
Stages touched: S1..S10 (attention), C1/C2/C3 (attn comm), F1..F5
(FFN), CF1 (FFN AR).
2) Renamed the button/spinner/help copy from "latency-optimal" to
"memory-optimal" to match the smallest-fit pick semantics I put in
place earlier. The buttons already picked smallest-fit; the labels
just still said "latency-optimal" from the previous iteration.
Left the Auto Hardware sensitivity chart's "latency-optimal baseline"
label alone — that panel is a HW co-design view where "how fast can
each HW go?" is the intended question.
Verified: 24 pytest tests still pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Finishes the batch scaffold. All stages that produce per-request work
now scale flops / activation-memory / comm-bytes by B; weight bytes stay
fixed (shared across the batch).
Attention block, previously partial:
- S6 stage_softmax: elems + bytes × B
- S8 stage_merge: flops × B; O/m/l AR bytes M × B
- S9 stage_normalize: flops × B
- S10 stage_wo: flops × B
- C1 comm_cp_ring: decode O/m/l AR M × B; prefill K/V ring + Q+O/m/l
variants both × B
- C2 comm_tp_allreduce (W_O output): bytes × B
- C3 comm_kv_split_allreduce (head-split scores): bytes_per_hop × B
FFN block, previously untouched:
- F1 stage_ffn_rmsnorm: activation × B (weight fixed)
- F2 stage_ffn_gate (via _ffn_gemm): flops × B
- F3 stage_ffn_up: same
- F4 stage_ffn_swiglu: elems + flops × B
- F5 stage_ffn_down: flops × B
- CF1 comm_ffn_allreduce (batched FFN output): bytes × B
Verified with a smoke check on Qwen 3 8B / 128K decode:
B=1 per-layer visible latency: 363 us
B=8 716 us (sub-linear — many stages
stay weight-bound)
B=64 4023 us (approaching linear scaling
as batch dominates)
All 24 pytest tests still pass at default B=1 (backward compat).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Three additions to the tensor sharding view when batch B > 1:
1. Shape label appends "B={B}": e.g. (128, S_kv=131072, B=8).
2. Note under the KV cache: "batch B={B} => {B}x KV bytes per PE".
3. Visual "stack" — up to 5 dashed offset rectangles drawn behind
the KV cache to hint at the batch stacking dimension. Capped at 5
so a big B doesn't overwhelm the diagram.
4. Title also gets B={B} between CP and FFN scope.
Attention/FFN weight tensors are NOT stacked — weights are shared
across the batch (correct: only activations + KV scale with B).
At B=1, all four additions are no-ops so the diagram looks unchanged
from before.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
New sidebar widget in the Workload expander:
Batch B (concurrent requests) — selectbox 1/2/4/…/256, default 1.
Wired end-to-end:
- app.py sidebar → forwards b_batch when constructing the main topo
- auto_suggest(..., b=1) — passes b to _score_candidate, which builds
TopologyConfig with b=b
- run_auto_explore(..., b=1) — sets topo.b = b for every enumerated
candidate before scoring
- joint_explore(..., b=1) — forwards b to _best_parallelism_for_hw
and _best_parallelism_two_stage; both set topo.b = b before scoring
All button handlers (sidebar Apply-scope, Physical Layout scope,
Auto Suggest tab sweep, Auto Hardware tab sweep) now pass b_batch.
Combined with the earlier partial-scaffold commits (memory_layout
scales KV by B; stage_rmsnorm / stage_wq / stage_wkv / stage_kv_append /
_per_hop_qkT_pv scale flops + activation memory by B), changing B in
the sidebar now affects reported latency and per-PE memory footprint
in the visible parts of the pipeline. The remaining FFN + comm-AR
stages still ignore B (they'll be next); their contribution is small
for the memory-bound decode case that matters most, but latency for
FFN-heavy configs at high B will be slightly under-reported until
those are scaled too.
Verified: 24 pytest tests pass at default B=1 (backward compat).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The reload guard didn't include model_config.py. When the batch (B)
field was added to TopologyConfig, Streamlit kept the stale
pre-B dataclass in sys.modules and every downstream module hit
"AttributeError: 'TopologyConfig' object has no attribute 'b'".
Fix: add model_config and model_presets to the reload list, and order
model_config FIRST so downstream modules that reload after it pick up
the new dataclass definition. Also reordered the rest so upstream
dependencies (autosuggest, memory_layout, stage_latencies) reload
before their consumers (auto_explore, auto_hardware).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds a second table beneath the Pareto configurations table, sorted by
(hbm_utilization ↑, pes_used ↑, latency ↑) — top 10 smallest-memory
Pareto configs first.
Columns highlight the memory story:
HBM % | weights (GB) | KV (GB) | PEs | SIPs | lat (ms) | CP TP PP DP | kv | ffn | tp_place | cp_place
Useful when memory pressure is the real binding constraint of a
deployment — e.g., picking a config for a per-PE HBM-limited SIP,
or spotting configs that trade small latency headroom for large
memory savings.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Sidebar Attention/FFN/Attn+FFN buttons and Physical Layout tab buttons
now select the SMALLEST-FIT config from the scope's Pareto set — key
changed from (latency ↑, pes_used ↑, hbm_utilization ↑) to (pes_used ↑,
hbm_utilization ↑, latency ↑). Answers "smallest deployment that's
still Pareto-optimal for this scope" instead of "fastest deployment
achievable".
Left unchanged:
- "Best latency" metric displays in Auto Suggest / Auto Hardware tabs
still show the fastest number (informational; the metric labels say
"Best latency").
- auto_hardware._best_parallelism_for_hw stays latency-min: it's
inside HW co-design, where "how fast can each HW candidate go" is
the primary question.
Verified: 24 pytest tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
WIP toward batch-size support. This first commit is behavior-preserving
at the default B=1 (every new multiplication is by max(1, cfg.topo.b) =
1 today) so all existing tests pass. Follow-up commits will:
- scale the remaining stages (S6/S7/S8/S9/S10 + C1/C2/C3 + FFN + FFN AR)
- add a batch selectbox to the sidebar
- forward b through auto_suggest / auto_explore / auto_hardware
Changes so far:
- TopologyConfig: new b: int = 1 field (batch size).
- memory_layout.per_pe_kv_cache_bytes: * B (each concurrent request
keeps its own KV cache slice).
- stage_rmsnorm / stage_wq / stage_wkv / stage_kv_append /
_per_hop_qkT_pv: FLOPs and activation memory scaled by B; weight
bytes stay fixed (weights shared across the batch).
Verified: 24 pytest tests still pass at default B=1.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Every place that picks a single "best" config from the Pareto set now
uses a compound sort key: (latency_ns ↑, pes_used ↑, hbm_utilization ↑).
Primary = latency; when configs tie on latency (common — cp_ring
variants, some placement variants often produce identical numbers),
prefer smaller-footprint picks.
Places updated:
- app.py: sidebar Apply Attn/FFN/Attn+FFN button
- app.py: Physical Layout tab Attn/FFN/Attn+FFN button
- app.py: Auto Suggest tab "Best latency" metric
- app.py: Auto Hardware tab "Best latency" metric (uses parallelism.pes_used
+ parallelism.hbm_utilization since JointScore wraps ConfigScore)
- auto_hardware.py: _best_parallelism_for_hw iteration key
No behavior change when there's a strict latency winner. When there are
ties, the picked config uses fewer PEs and lower HBM utilization.
Verified: all 24 pytest tests pass (default include_attention=True and
include_ffn=True paths unchanged).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The topology map previously colored a whole cube (border + all PE
fills) by the single cp_rank assigned to that cube in the cube→(pp,cp)
mapping. Under cp_placement=cube this is right (each cube = one CP
rank). Under cp_placement=pe, however, multiple CP ranks are PACKED
into the same cube's PEs, so the whole-cube coloring makes every PE
look identical (defaulting to cp_rank=0's palette entry — bright red).
Fix: in the per-PE loop, if cp_packed = (cp_placement=="pe" and cp>1),
compute each PE's own cp_rank = pe_id // tp and look up its color via
_cp_color(pp_stage, pe_cp_rank, cp_size). Border still uses the
cube-level color (cp_rank=0), so the outer bounding box is unchanged,
but the interior PEs now show all four (or however many) CP-rank
colors visibly.
For cp_placement=cube: unchanged (single pe_fill per cube).
Verified with a headless render at Qwen 3 8B, CP=4, TP=2,
cp_placement=pe: 145 patches → 8 distinct facecolors (4 for the CP
ranks in the packed cube + inactive/border tints), where before it
was fewer distinct colors.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Previously only reloaded auto_explore / auto_hardware / autosuggest /
stage_latencies / memory_layout. When we edit pe_weight_layout,
tensor_sharding, topology_map, pipeline_diagram, or optimization_report,
Streamlit was still holding the pre-edit versions until a full restart.
Add all five to the reload list so any visualization-module edit lands
on the next Streamlit rerun without needing Ctrl+C.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
When cp_placement=pe packs multiple CP ranks intra-cube, the PE-level
layout now:
1. Colors each PE's background by its CP rank (8-color palette wraps
for cp > 8). Cp_placement=cube keeps the historical light-blue
styling (only one CP rank per cube tile).
2. Adds "TP=N | CP=M" to the per-PE header so users can read the
(cp_rank, tp_rank) pair without inferring from position.
3. Fixes a head-assignment bug: _q_heads_for_pe / _kv_heads_for_pe /
_per_pe_bytes used to be called with the raw PE-in-group index,
which treated every PE as a distinct TP rank. Under cp_placement=pe
this gave wrong Q/KV head lists for PEs beyond the first TP group
(PE 2..7 with tp=2, cp=4 would ask for head slots 32..127 in a
32-head model). Now called with tp_rank = pe_id % tp, so all CP
ranks in the cube share the correct head split.
Verified via a headless matplotlib smoke test with CP=4/TP=2 on Qwen 3
8B: 8 PE patches + 1 cube patch → 5 distinct facecolors (cube + 4 CP
ranks), no errors.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
An "Apply memory-min" button was removed when the 3 latency-optimal
buttons were added. Consequence: after clicking any latency-optimal
button, the sidebar sliders drift from the memory-min caption and
there was no way to sync them back without restarting the app or
manually adjusting each slider.
Restore the button, positioned below the caption and above the 3
latency-optimal buttons. Clicking it snaps cp/tp/pp/dp/cp_placement
to what the caption shows and clears _pl_active_scope to "full" so
the Physical Layout tab stops filtering.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Was 512 GB/s in the analytical viz MachineParams default. Bringing the
default down to 128 GB/s to match what the modelled physical link now
represents (parity with inter-cube D2D and topology.yaml).
Files touched (all three needed to keep defaults coherent):
- model_config.py: MachineParams.bw_intra_gbs = 128.0 (was 512.0)
- app.py: sidebar selectbox default index = 0 (128 GB/s) instead of 2 (512)
- auto_hardware.py: _HW_KNOB_DEFAULTS + BALANCED + COARSE bw_intra_gbs
baselines shifted so cost_score at defaults remains 6.0 and the
sensitivity sweep starts from the new baseline.
Verified: 24 pytest tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Two changes to the memory-only auto_suggest so a "fits" that packs into
one cube is preferred over a "fits" that spreads across multiple cubes.
Suggestion dataclass gains cubes_used and cp_placement fields.
_score_candidate: for each (CP,TP,PP) triple, try cp_placement="cube"
(historical default: CP spans cubes) and, when CP·TP fits in one cube's
PE count, also cp_placement="pe" (pack CP into intra-cube PEs). Keep
the placement with fewer cubes; break ties toward "cube".
auto_suggest: switch sort key from (pes_used ↑, pp ↑, tp ↑, cp ↑) to
(cubes_used ↑, pes_used ↑, pp ↑, tp ↑, cp ↑). Fewer cubes wins first
because a cube is the physical die-level unit; PE count is the
tiebreaker.
Sidebar caption now also displays cubes_used + cp_placement so the
user can see the packed layout at a glance. Preset-change auto-reset
also applies the picked cp_placement.
Observed effect (verified):
Qwen 3 8B / 128K decode: CP=4 TP=2, "pe" → 1 cube, 8 PEs
(was 4 cubes with default "cube")
Llama 3.1 70B / 128K: CP=4 TP=16, "cube" → 8 cubes (unchanged;
TP=16 > 8 PEs/cube, can't pack)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
New tab order:
Physical layout / Auto Suggest Parallelism / Memory breakdown /
Per-stage latency / Save & compare / Auto Hardware
Physical layout is what a user typically wants to see first when
loading the app, so it takes the leftmost slot again. Auto Suggest
Parallelism moves to second — still prominent, still usable as a
first step, but doesn't push the layout view behind it.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Replaces the single memory-only "Apply auto-suggest" button in the
sidebar Parallelism expander with three latency-optimal buttons:
"Attention", "FFN/MoE", "Attn+FFN".
Each clicked button runs run_auto_explore for its scope, picks the
latency-minimum Pareto config, snaps the parallelism knobs to the
sidebar's selectbox option sets (CP/TP/PP/DP), and loads the other
knobs directly (tp_placement, cp_placement, cp_ring_variant, kv_mode,
ffn_scope_label). Also sets _pl_active_scope so the Physical Layout
tab's stage-table filter follows the scope automatically.
The caption above the buttons still shows the memory-only autosuggest
values as a reference — separately labeled "(memory-min)" to avoid
confusion with the latency-optimal buttons below.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Clicking one of the three Physical Layout tab buttons now persists the
chosen scope in session_state["_pl_active_scope"] and filters the
per-stage latency tables accordingly:
- attn scope → show only "Attention" stage table
- ffn scope → show only "FFN" stage table
- full scope → show both (default; also matches a fresh sidebar-driven
config with no button click yet)
Added a "Layout scope: {label}" header so the user can tell at a glance
which scope's config is loaded.
The pipeline diagram + topology map + weight/tensor sharding + PE
layout continue to show the full model layout — those diagrams give
context that stays useful even when the user is focusing on attn or
FFN. Deeper filtering (e.g., attention-only pipeline stripe) can come
later if needed.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Streamlit's hot-reload re-executes app.py on every interaction, but
sub-modules imported from app.py stay cached in sys.modules across
reruns. When we edit auto_explore.py or auto_hardware.py while
Streamlit is alive, the app keeps using the pre-edit version until a
full Ctrl+C + restart — leading to confusing "unexpected keyword
argument" errors after a signature change.
Force importlib.reload() on our own modules at the top of app.py so
future signature changes land without a full restart. Only reloads if
the module is already in sys.modules (first run just imports normally).
Applied to:
- tests.analytical_visualization.auto_explore
- tests.analytical_visualization.auto_hardware
- tests.analytical_visualization.autosuggest
- tests.analytical_visualization.stage_latencies
- tests.analytical_visualization.memory_layout
Third-party modules (streamlit, matplotlib, pandas, numpy) NOT reloaded
— unnecessary and slow.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Shortcut: click any of {Attention, FFN/MoE, Attn+FFN/MoE} → runs
run_auto_explore for that scope, picks the latency-minimum Pareto config,
loads it into the sidebar's session_state (cp, tp, pp, dp, tp_placement,
cp_placement, cp_ring_variant, kv_mode, ffn_scope_label), then st.rerun().
Because the Physical Layout tab reads model + machine + cfg from the
sidebar, the sidebar update automatically redraws the pipeline diagram,
per-stage table, and everything below. No preview / parallel-display
state — WYSIWYG.
Ffn_scope_label reconstruction handles the dynamic "(div=…)" suffix the
sidebar shows (same pattern as auto_explore + auto_hardware tabs' "Load
into sidebar" widgets).
Verified: app.py parses; 24 pytest tests still pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Both auto tabs now offer three sweep scopes via three buttons instead
of two:
- Run sweep — Attention → include_attention=T, include_ffn=F
- Run sweep — FFN/MoE → include_attention=F, include_ffn=T
- Run sweep — Attn + FFN/MoE → include_attention=T, include_ffn=T
Each button caches its result under its own session_state key; the most
recently clicked button drives the display. All three caches persist so
users can flip between scopes without re-running.
Core changes:
auto_explore.py + auto_hardware.py:
- New include_attention: bool = True param alongside include_ffn
- _sum_visible_latency, _efficiency, score_config, run_auto_explore,
compute_parallelism_sensitivity, joint_explore, compute_sensitivity,
_best_parallelism_for_hw, _best_parallelism_two_stage all wired.
- Attention and FFN are additive with no overlap: measured 7.35 ms
(attn) + 5.50 ms (ffn) = 12.85 ms (full) for Llama 70B decode 128K.
Bug fix (drive-by): the display block in both tab render functions was
incorrectly nested inside the "cached ctx is stale" warning branch, so
metrics/scatter/table/sensitivity/load only rendered when the cache
was stale. Un-indented and removed the dead trailing else-info block.
Verified:
- 24 pytest tests pass (added 1 new for FFN-only scope invariants)
- Smoke: Llama 70B decode 128K all three scopes produce sensible Pareto:
* Attention only: 7.35 ms (14 pareto configs)
* FFN / MoE only: 5.50 ms (34 pareto configs)
* Attn + FFN/MoE: 12.85 ms (6 pareto configs)
Sums add up exactly, confirming no overlap in stage summation.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Both auto tabs now accept a scope choice at run time:
- "Run sweep — Attention" → include_ffn=False
- "Run sweep — Attn + FFN/MoE" → include_ffn=True
Each button runs an independent sweep and caches its result under its
own session_state key. The most recently clicked button determines the
displayed view; both caches persist so users can flip between the two
scopes without re-running.
Tabs:
- Renamed "Auto Explore" → "Auto Suggest Parallelism"
(accurately reflects that it only varies parallelism knobs; HW is
held at the sidebar values).
- "Auto Hardware" tab unchanged.
- Still 6 top-level tabs; no additional tabs added.
Core changes:
auto_explore.py:
- New include_ffn: bool = True parameter on _sum_visible_latency,
_efficiency, score_config, run_auto_explore, compute_parallelism_
sensitivity. False drops all FFN stages from the summed latency.
auto_hardware.py:
- New include_ffn: bool = True parameter on joint_explore,
compute_sensitivity, _best_parallelism_for_hw, _best_parallelism_
two_stage. Forwards to score_config.
Both defaults keep existing tests byte-identical.
Verified:
- 23 pytest tests pass (added 3 new: attn-only latency lower, attn-only
Pareto non-empty, joint HW attn-only faster than full).
- Smoke: Llama 70B decode 128K:
* Attn+FFN best latency: 12.85 ms (unchanged)
* Attention-only best: 7.35 ms (~57% of full)
* Both sensitivities top-rank bw_hbm_gbs (physics preserved).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds compute_parallelism_sensitivity() + ParallelismSensitivityRow to
auto_explore.py. For a baseline ConfigScore (picked from the Pareto set),
sweeps each parallelism knob (CP, TP, PP, DP, EP) individually while
holding others fixed. Reports latency + memory-fit per value.
Sweep values start at 1 and step by 2 (multiples of 2 rather than only
powers of 2), giving finer granularity for the visual than the enumerator's
sparser set. CP goes up to 256 (per real-deployment scale), TP to 64,
PP to 32.
New UI panel in Auto Explore tab: 5 subplots (log/log), one per knob:
- Solid line = latency where the config fits memory
- Red X markers = infeasible (out of budget)
- Dotted vertical line = baseline value
User picks which Pareto row is the "baseline" via a number input; the
sensitivity chart re-computes around it.
Behaviour caveat noted during verification: for large PP the FFN AR can
cross a SIP boundary (uses stage_latencies.py:730 sips_used tier
selection), producing a step-up in latency. This is the existing model's
choice, honestly reflected in the chart. Whether the FFN AR should span
only one PP stage's ranks (thus not cross SIPs) is a separate discussion
about stage_latencies.py.
Verified:
- 11 pytest tests pass (added 2 new for parallelism sensitivity)
- Smoke: at Llama 70B decode 128K with baseline CP=8/TP=16/PP=1/DP=1:
* CP sweep: 4 fits, 8 = baseline optimum, 16+ overshoots memory
* TP sweep: 8/16 fit, 16 = baseline optimum, 32 slower (spans SIPs)
* PP sweep: 1 = baseline, 2+ slower due to sips_used tier drop for FFN AR
* DP sweep: same shape as PP
* EP sweep: monotone decreasing (bigger EP = smaller per-PE FFN)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Consumes auto_hardware.joint_explore(). UI:
- Sweep-depth radio: two_stage / balanced (default) / coarse
- Run joint sweep button + spinner
- 4 metric cards: HW candidates, feasible joint, Pareto count,
best latency
- Panel 1 (Pareto scatter): latency vs hardware cost proxy, feasible
in grey, Pareto colored by PE count, dashed line connects Pareto in
cost order — this is the "optimal HW config for the model" plot
- Panel 2 (sensitivity bar chart): per-knob relative speedup when
doubled from the baseline. Green bars, sorted biggest first;
annotated with the baseline → doubled values. Answers "where to
invest next?"
- Panel 3 (table): sortable Pareto joint configs with parallelism +
HW spec columns (PE HBM, HBM BW, TFLOPs, PE↔PE, D2D, C2C)
- Load into sidebar: picks a Pareto row and syncs both the HW
selectboxes (Per-PE + Interconnect sub-tabs) AND the parallelism
sliders. Values only get loaded when the target is one of the
selectbox options; otherwise the sidebar keeps its current value.
Session-state cache under _hw_result keyed by (model, s_kv, mode, depth);
if any of these drift, a warning suggests refreshing.
Verified: app.py parses; auto_hardware smoke run on Llama 70B decode
128K in ~20s produces sensible HW co-design signal (HBM BW 33% speedup,
everything else <1.5%).
Next: verification across Qwen 3 8B, Mixtral 8x7B (Commit 6).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
New module extends auto_explore into the hardware co-design space. For a
fixed model + workload, sweeps hardware knobs (pe_hbm_gb, bw_hbm_gbs,
peak_tflops_f16, bw_intra_gbs, bw_inter_gbs, bw_intersip_gbs) and, for
each hardware candidate, searches parallelism for the latency-minimum
that fits memory. Returns:
- all_scores: every (hw, parallelism) pair that fits, sorted by latency
- pareto_scores: 2D Pareto frontier on (latency ↓, cost_score ↓)
- sensitivity: per-knob rel_speedup when doubled from the best-fast HW
baseline. Ranks which HW knob gives the biggest speedup — a co-design
signal.
Three sweep depths trade coverage for time:
- two_stage: 1 HW candidate (defaults) × autosuggest's memory-min
parallelism. Fast (~1s), useful for the sensitivity ranking alone.
- balanced: 64 HW × ~2k reduced-parallelism configs = ~130k joint evals,
~10-20s. Default UI setting.
- coarse: 729 HW × ~2k configs = ~1.4M joint evals, ~2-5 min.
Reduced parallelism sweep for the inner loop: CP × TP × PP × DP ×
kv_shard_mode (1,920 configs), other 4 knobs held at latency-friendly
defaults (ffn_shard_scope='TP+CP', tp_placement='cube', cp_placement='pe',
cp_ring_variant='qoml' for decode, 'kv' for prefill). Full 28,800-config
auto_explore per HW would take 6+ minutes — too slow.
Cost proxy: sum of (knob / knob_default). 6.0 at defaults. Not dollars —
a rough capability score where higher = "more spec'd hardware".
Verified:
- 9 pytest tests pass:
* enumeration counts match expected (1, 64, 729)
* default cost_score = 6.0
* Pareto non-dominated + subset of all_scores
* every knob is monotone-non-worsening when doubled
* for Llama 70B decode, bw_hbm_gbs tops the sensitivity ranking
(physically correct: memory-bound workload)
- Smoke: Llama 70B decode 128K balanced sweep in ~20s produces
5 Pareto configs; best 7.57 ms with 1024 GB/s HBM BW. Doubling
HBM BW gives 33% additional speedup; every other knob < 1.5%.
Next: Streamlit UI tab consuming this in Commit 5.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
New tab consumes auto_explore.run_auto_explore(). UI:
- Header showing current model + workload + per-PE HBM budget
- Run sweep button (spinner while ~28k configs run in ~5-10s)
- 4 metric cards: enumerated, feasible, pareto count, best latency
- 2-panel scatter:
* latency vs PEs (feasible in grey, Pareto coloured by efficiency,
dashed line connects Pareto in PE order to show the trade-off curve)
* HBM utilization vs latency for Pareto, coloured by PE count
- Sortable Pareto table
- "Load into sidebar" widget: pick a row, sets the sidebar
session_state keys (cp, tp, pp, dp, tp_placement, cp_placement,
cp_ring_variant, kv_mode, ffn_scope_label) and st.rerun()s so the
user can flip to another tab and see the full breakdown.
Session-state caches the last sweep result under _auto_explore_result;
if the model/workload/HBM change without re-running, a warning suggests
refreshing.
Ffn_scope_label mapping is dynamic (contains substituted divisors), so
the Load button reconstructs the exact label using the target
cp/tp/dp values before assigning to session_state["ffn_scope_label"].
Verified:
- app.py parses cleanly
- All 9 pytest tests in test_auto_explore.py still pass
- Smoke: matplotlib + pandas + auto_explore imports round-trip
Next: verification pass across Qwen 3 8B and Mixtral 8x7B; polish.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Extends the memory-only autosuggest to search the full 9-dimensional
parallelism space (CP, TP, PP, DP, kv_shard_mode, ffn_shard_scope,
tp_placement, cp_placement, cp_ring_variant) and rank feasible configs
on a 3D Pareto frontier: (latency ↓, pes_used ↓, efficiency ↑).
Throughput is stored on ConfigScore for display but is deliberately NOT
a Pareto axis because for a single-request analysis it collapses to
1 / latency, which would collapse the frontier.
Reuses existing physics (stage_latencies.all_stages + all_ffn_stages,
memory_layout.compute_memory) — no new formulas.
Single-request latency formula fix: PP does NOT reduce single-request
decode/prefill latency because the request has to traverse every layer
sequentially regardless of pipeline depth. The initial version had
latency ~ per_layer × layers_per_stage, which incorrectly rewarded high
PP. Corrected to latency ~ per_layer × model.layers.
Enumerator prunes:
- PP > model.layers
- TP > 4 × h_q
- ffn_shard_scope contains 'DP' when dp=1 (redundant)
- cp_ring_variant='qoml' when cp=1 (no-op)
Full sweep on Llama 3.1 70B: ~28,800 configs enumerated in ~7s, ~7k-10k
feasible (varies with S_kv), 2-7 unique Pareto configs. Faster context
lengths produce richer frontiers; at 1M, memory forces a single
dominant config (128 PEs, HBM 85%).
Verified:
- 9 pytest tests pass (enumerate, score, Pareto, subset invariants)
- Manual: Llama 70B decode at 8K/64K/128K/1M produces physically
sensible Pareto (CP=8/TP=16 wins latency; smaller-PE options
appear at longer context up to memory limits)
Next: Streamlit tab UI in app.py + verification against more presets.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
New tests/analytical_visualization/ module - an interactive dashboard
for exploring memory / latency tradeoffs of transformer inference on
the SIP architecture.
Highlights:
- 30+ model presets (Qwen, Llama 2/3/3.1, Mistral, Gemma 2, Phi 3,
DeepSeek MLA, Mixtral/Qwen 3 MoE, Grok-1, ...)
- Placement toggles: TP and CP each on PE-level vs cube-level
- CP ring variant: K/V ring vs Q+O/m/l ring (prefill); in decode the
O/m/l all-reduce is folded into S8 (no separate C1 row)
- SIP interconnect: ring / mesh2d / torus2d with matching link drawing
- Per-stage latency table with compute + memory + comm formulas,
auto-scaled ns/us/ms, colored by dominant bound
- Ring attention loop indicator on the pipeline diagram (purple arc
over S5-S8 with 'xN hops' badge)
- Tensor sharding view with optional physical PE/cube annotations
- Replication-waste + optimization-hints panel
- Save & compare configurations (config1, config2, ...): summary table
plus side-by-side per-stage attention and FFN latency, best-in-row
highlighting
- Symbol glossary with current values for every symbol used in formulas
Not tied to production sim_engine or runtime API; purely analytical
tooling for design-space exploration.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Concurrency fix (completes the cube_base change): the decode kernel's
output store o_base still used the global cube_id while every load used
the user-local cube_local. For a single user (cube_base=0) they coincide,
so it was masked; a second batched user (cube_base>0) overshot its o shard
into an unmapped VA, mis-decoded to a phantom sip0.cube0.pe8 and raised a
RoutingError. Switch o_base to cube_local (one line); single-user results
are byte-identical (decode smoke/correctness pass).
Batch harness (un-skipped): create all users' tensors first, then submit
all launches deferred and drain together, so deploys don't drive an
already-submitted launch and serialize the batch. Concurrent users on
disjoint CUBE groups now overlap to within 3-5% of the single-user
latency. Swept B in {1,2,capacity} at 8K (high-B dense runs are the
expensive ones; throughput is near-linear in B).
Measured result: aggregate throughput at a full SIP rises from 0.86
(1-kv-per-cube, 2 users) to 1.41 requests/us (8-kv-per-cube, 16 users) —
the dense mapping wins throughput, the spread mapping wins latency.
S5.2 batch paragraph updated projected -> measured; add batch_scaling
figure and plot_batch_scaling.py.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Metric fixes (test harnesses, deploy artifact + wrong constant):
- wall = max(t_end) - min(t_start): exclude the one-time KV-cache deploy
from the measured decode/prefill step (it was 92-99% of wall, mapping-
invariant, and masked the real per-mapping separation).
- PEAK_PE_HBM_BPS 128 -> 256 B/ns (8 channels/PE x 32 GB/s); the old value
was half the modeled per-PE HBM BW, so hbm_bw_util read >1.0 once wall
was corrected. All six short-context sweep CSVs regenerated/repatched.
Result: the four KV mappings now separate along a {64,32,16,8}-active-PE
ladder (decode 8-kv/1-kv = 4.8x at 8K, 7.4x at 64K), not "modest/tied" as
before; decode is bandwidth-bound at 46-76% of the 256 GB/s per-PE ceiling.
Report (S5.2 rewrite):
- Replace the tied-wall / 8x-per-PE-util claims (both deploy artifacts)
with the corrected separation and a density trade-off (per-CUBE KV
footprint, Fig 16 top panel switched to a wall-invariant metric).
- Add a projected latency-vs-batched-throughput analysis (marked
projected, not measured): dense mappings win throughput, 1-kv wins
latency; converges at long context.
- Regenerate Fig 15/16/17/18; fix plot script hardcoded ROOT path.
Batch experiment (Part 2, cube_base):
- Add backward-compatible cube_base=0 scalar to the decode kernel so a
batched user placed at DPPolicy.cube_start addresses 0-based shards.
Default preserves single-user behavior (32 decode tests pass unchanged).
- New batch harness (skipped): concurrent B>=2 launches hit a sim routing
issue (sip0.cube0.pe8); single-user path verified. Concurrency fix next.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Switch primitive hand-tiled Q·Kᵀ / P·V from a deferred-K-sum tl.dot
chain to per-block GemmCmd writes into a shared (M, N) out handle
(implicit MAC-side accumulation). Full-shape coarse Q / K_T / V loads
carry data-mode correctness; per-block DMAs remain for the streaming
architecture dispatch story. The kernel now runs in engine mode
end-to-end, so its 128K latency is measured instead of derived as
primitive + Δdispatch. Drop the coarse-primitive baseline from the
sweep and plots; keep the kernel file for reference.
At S_kv=128K: primitive hand-tiled = 959.5 µs vs composite = 460.7 µs
(2.08× from 5 235 vs 94 PE_CPU commands).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Wires a fourth variant into the Cube-SP × PE-SP long-context decode
composite command-form study to surface the worst-case PE_CPU dispatch
inflation that the coarse composite forms delegate to PE_SCHEDULER
(ADR-0065): per-block DMA of Q/K/V slices, tl.dot per 16³ block,
deferred K-inner sum outside the K loop.
Renamed _tiled.py → _hand_tiled_16x16x16.py; coarse-primitive kernel
retained for the 4-cases bench, paper scripts, and the golden byte-equal
regression guard.
New breakdown bar chart at S_kv=128K shows engine (~460 μs) dominates
all three variants; hand-tiled adds ~68 μs PE_CPU dispatch on top —
composite forms sit essentially on the memory-bound floor.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Complete the composite-command study across both attention regimes and
write up the result, resolving when the composite command helps latency.
Decode (memory-bound, T_q=1, M=8): the command form is latency-neutral —
the kernel is bound by KV streaming and the MAC array is near-idle, so the
composite's only benefit here is host-issue offload (O(n_tiles) -> O(1)
PE_CPU commands). Figure: gqa_decode_long_ctx_composite (latency flat,
command count saturates).
Prefill (compute-bound, large M=G*T_q tile-filling): add three command-form
variants of a single-rank FlashAttention prefill kernel
(_gqa_prefill_compute_bound: primitive / composite / composite_extended).
Here the composite's scheduler-internal per-tile DMA<->compute pipeline
keeps the MAC array fed while the primitive's blocking tl.dot starves it,
so composite wins on MAC utilization (68% flat -> 83%) and wall-clock
(19% faster at ctx=1024), with the margin growing with context (deeper
P.V reduction = more tiles to pipeline) — the compute-bound mirror of the
GEMM result in section 3. Figure: gqa_prefill_compute_bound (latency +
MAC util). Sweep wired as GQA_1H_SWEEPS=prefill_cb; tests cover structure,
e2e, and composite<primitive in the compute-bound regime.
The synthesis: composite has two benefits set by roofline position —
host-issue offload (always) and MAC-array feeding (compute-bound only).
Decode exercises the first, prefill both.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add two command-form variants of the Case-6 (Cube-SP x PE-SP) long-context
decode kernel alongside the primitive baseline:
- composite: per-tile GEMMs issued as one coarse tl.composite over the
full S_local (PE_SCHEDULER tiles/streams K,V) -- O(1) PE_CPU commands
vs the primitive kernel's O(n_tiles).
- composite_extended: Q.K^T composite + softmax_merge recipe composite
(ADR-0065), folding the per-tile online merge + P.V.
Extract the shared two-level (m,l,O) reduce into _gqa_mlo_reduce and
refactor the baseline to use it (byte-equal, guarded by a 64-rank
command-stream digest test).
Engine fix: _make_compute_out omitted pinned=True, so an on-chip TCM
compute result consumed as a composite GEMM operand was scheduled as an
HBM DMA_READ of its bit-61 scratch address -> PhysAddrError in data mode.
Mark compute outputs pinned (resident), matching the composite
auto-output and recipe scratch handles. .pinned is read only by the
tiling DMA gate, so this is byte-equal for existing benches.
Add the composite sweep (emit-level PE_CPU dispatch to 1M + data-mode
latency to 128K), its plot, umbrella wiring (GQA_1H_SWEEPS=composite),
and tests (refactor guard, dispatch saturation, engine-fix, e2e).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds the user-orchestrated async GEMM variants used in paper §3.4 to
contrast with the composite (load_ref) path:
- matmul-async : naive (tl.load full A+B, one tl.dot)
- matmul-async-chunked : depth-inf prefetch (all B-tiles queued)
- matmul-async-chunked-db : depth-2 double-buffer (TCM-bounded)
plus scripts/paper/paper_plot_gemm_async_vs_composite.py (4-way per-PE
TFLOPS sweep + figure) and the regenerated outputs under
1H_milestone_output/gemm/.
Sweep regenerated under the ADR-0064 D8 single-op cost model (commit
2d8271c). At K=3072 the composite-vs-async-tiled gap narrows from the
pre-D8 ~6.3x to ~2.8x (composite 7.18 / async-tiled 2.54 TFLOPS): D8
makes the async-tiled kernel's ~191 single-op dispatches 5x cheaper, so
its dispatch overhead drops to ~1.5us. Qualitative order persists:
composite > async-full (3.91) > async-tiled (2.54).
test_bench_registry.py: register matmul-async / -chunked / -chunked-db
(also picks up the earlier milestone-gqa-headline -> milestone-1h-gqa
rename already present in the tree).
--- Remaining work (resume here if interrupted) ---
- Paper §3.4 (03-gemm.tex sec:gemm-vs-async): finish naive->async-full /
chunked->async-tiled rename AND reframe "FIXED_DMA=8 for DMA descriptors"
to single-op(8) vs composite(40). Figure already copied to
docs/report/1H-codesign-paper/figures/gemm_composite_vs_async_tflops.png.
- Paper §3.4 K=3072 para + mechanism #3: update dispatch breakdown to new
model (191 single-op * 8c ~= 1.5us, was 4.6us) and headline gap
~6.3x -> ~2.8x.
- Rebuild build/main.pdf (tectonic) + verify via pdftotext.
- Then commit Group 3 (paper + diagrams + PDF).
- Table 2 / §2 prose / ADR-0064 D8 already done in 2d8271c.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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>
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>
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>
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>
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>
mukesh
ywkang
eusonice