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>
Wire per-flit loop in engine._wire (ADR-0033 Phase 2c) iterates
nbytes/flit_bytes times per hop, firing ~2 SimPy events per flit.
At long-context decode, K/V tile loads are hundreds of KB per shard,
producing thousands of flit-iterations per hop. The default 256-byte
flit size made the wire loop the dominant wall-clock cost.
Raising flit_bytes to 4096 (still a multiple of hbm_ctrl.burst_bytes=256
per ADR-0033 D1) cuts flit iterations 16x with no meaningful change to
modeled latency. Wormhole pipelining and the available_at BW-share
chain in _wire behave identically at any flit size.
Measured impact on Case 4 (Cube-SP x PE-SP) decode, C=8, P=8, T_q=1,
LLaMA-3.1-70B single-KV-group, enable_data=False:
S_kv flit=256 wall flit=4096 wall speedup latency drift
128K 233.71 s 18.16 s 12.9x +0.05% (0.25us)
256K 485.75 s 25.31 s 19.2x +0.01% (0.09us)
512K 1045.64 s 48.45 s 21.6x +0.01% (0.17us)
1M ~2250 s (proj) 104.75 s ~21x within extrap
Total sweep at flit=4096: ~3.3 min. Baseline would have been ~65 min.
1M decode iteration is now practical.
op_log records are byte-identical (same counts, same components); the
tiny latency drift (order of a few ns) comes from the last flit's
env.timeout landing on a slightly different granularity when flits are
larger. Well within noise for any test that asserts on structural
invariants (dma_write cubes, ipcq_copy counts).
Verified:
- tests/attention/test_milestone_gqa_decode_long_ctx_4cases.py: 18/18 pass
(also 7x faster: 174s -> 24.83s per full run)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per-instance cache keyed by (id(adj), start, goal). All 4 adj dicts
(_adj, _adj_all, _adj_local, _adj_mcpu_dma) are built in __init__ and
never mutated (topology is static per ADR-0006 / SPEC §0.1), so id(adj)
is stable for the router's lifetime. Cache is populated on the
successful-return paths; RoutingError paths intentionally re-run each
call (rare, keeps error semantics unchanged).
Motivation: cProfile of Case 4 decode at S_kv=8K showed 1,142
_run_dijkstra_with_dist calls consuming ~1.95s tottime. Paths depend
only on (adj, src, dst) so ~99% of those calls are recomputing the same
result.
Verified:
- tests/attention/test_milestone_gqa_decode_long_ctx_4cases.py: 18/18 pass
- 128K decode wall: 237.28s -> 233.71s (-1.5%)
- Modeled kernel latency: 461.13us (byte-identical before/after)
- op_log_len: 3057 (unchanged)
Small win but no risk: memoization returns byte-identical results and
paths cannot change during a sim run. Larger event-count reductions
require touching the per-hop hot path (zero-latency chain collapse
etc.).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Three coupled fixes so enable_data=False now reports byte-identical
kernel-body sim latency (max(t_end) - min(t_start) over op_log) as
enable_data=True, and skips setup-write sim events that were pure
wall-clock overhead.
Before this change, at Case 4 (Cube-SP x PE-SP) decode, S_kv=8K:
enable_data=False -> latency_ns = 0 (op_log empty)
enable_data=True -> latency_ns = 30.646 us (correct)
After:
enable_data=False -> latency_ns = 30.646 us (byte-equal)
enable_data=True -> latency_ns = 30.646 us (unchanged)
engine.py: always create OpLogger. Previously OpLogger was gated on
enable_data=True together with MemoryStore, so op_log stayed empty when
data mode was off. Decouple: MemoryStore is gated (Phase 2 DataExecutor
still needs it) but OpLogger runs unconditionally, receiving
memory_store=None when data mode is off. OpLogger already guards its
arr.copy() snapshot paths on `if self._memory_store is not None`.
pe_cpu.py: always use the ADR-0020 greenlet execution path. The
if store is not None: _execute_greenlet(); else: _execute_legacy branch
gated the *execution model* on data-mode presence. The legacy
command-list path predates ADR-0020 and doesn't route IpcqSendCmd
through PE_IPCQ (it goes to PE_SCHEDULER instead), so all 189 Case-4
ipcq_copy fabric transfers were silently no-op'd in that mode. Forcing
greenlet gives identical sim behavior in both modes. KernelRunner
already guards its store reads on `self._store is not None`.
context.py: gate setup MemoryWriteMsg on memory_store presence. Under
enable_data=False there is no MemoryStore to populate and no Phase 2
replay, so the per-shard sim events for Q/K/V deploy are pure wall-clock
overhead with no effect on reported kernel latency (Yangwook's max-min
formula excludes pre-kernel ops). Handle count for Case 4 at S_kv=8K
drops 229 -> 37 under enable_data=False.
Verified:
- tests/attention/test_milestone_gqa_decode_long_ctx_4cases.py: 18/18 pass
- Parity probe: enable_data=False/True both report 30.646 us kernel sim
time, 1649 op_log records, 189 ipcq_copy events
Known follow-up: pe_cpu._execute_legacy is now dead code but left in
place; separate cleanup once we confirm no downstream caller.
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>
Both 1-kv (2 users x 64 PE) and 8-kv (16 users x 8 PE) fill all 128 PEs of
the SIP; the throughput gap is per-PE HBM efficiency. Splitting a head over
8 PEs leaves each a single tile, so pipeline-fill and the 8-PE softmax
reduce dominate -> 46% HBM util; one head per PE streams contiguously with
no reduce -> 76%. Throughput ratio (1.6x) tracks the utilization ratio
(1.7x): decode is bandwidth-bound, so throughput follows total HBM
efficiency, not PE count. 1-kv spends hardware on latency (8 PE/head for a
4.8x speedup = 60% strong-scaling efficiency).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Beyond a mapping's SIP capacity (16/C) the throughput plateaus: extra
users run in waves at the saturated rate, they are not un-runnable. Draw
a dashed horizontal extension at the ceiling so 1-kv-per-cube (cap 2)
reads as saturating, not stopping, at B=2. Caption updated.
Co-Authored-By: Claude Opus 4.8 (1M context) <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>
- Soften short-context claim: 1-kv-/8-kv-per-cube are tied at 8K and
separate as context grows (1-kv faster at the large end).
- Add a bridge noting the six-case placement taxonomy (§5.1) is the
long-context lens; short context turns on head-to-CUBE distribution.
- Remove caption-duplicating numbers from the long-context conclusion.
- Reduce the closing subsection to a GQA-scoped Summary; defer the
cross-cutting hardware-investment thesis to the Discussion.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Make reduce_mlo derive its submesh dimensions (sub_w, sub_h, root_col,
root_row, root_cube) from C at call time, with peer-existence guards on
every inter-cube send/recv so any C ≥ 1 completes cleanly (previously
hardcoded to C=8's 4×2 mesh; non-rectangular C like 7 or 12 hit
IpcqDeadlock at the root cube's east receive). Byte-equal at C=8 —
existing composite digest tests still pass 8/8.
Callsites in the four Case-6 kernels (primitive / primitive-tiled /
composite / composite_extended) updated to pass C into reduce_mlo and
use root_cube_for(C) for the final tl.store gate.
Add the multi-model attention bench: sweeps the composite kernel at
S_kv=128K across six GQA models (Gemma-2 27B, LLaMA-3 8B, Qwen 2.5 7B,
LLaMA-3 70B, Qwen 2.5 72B, Command R+), with per-model topology
C = h_q (cubes per KV group = query heads per KV group). Captures
per-op-kind occupancy (matmul GEMM+MATH, comm DMA) alongside latency
and PE_CPU dispatch. Three-panel plot writes to bench-output and
paper-figures dir.
Headline result: same-h_q-different-family models land within 0.2 µs
(model-agnostic given fixed h_kv=1 per KV group + d_head=128); latency
climbs 263 → 492 µs as C climbs 2 → 12, driven by reduce depth
(comm/matmul ratio 0.4 → 0.6 across the sweep).
Co-Authored-By: Claude Opus 4.7 (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>
Research artifact (NOT wired into the production sweep) for the decode composite-vs-primitive investigation. Models a primitive decode kernel that hand-blocks each tl.dot into 16x16x16 GemmCmds, charging per-MAC-block PE_CPU dispatch -- testing whether faithfully charging the dispatch that the composite form offloads to PE_SCHEDULER flips the 'composite gives no decode latency benefit' result.
Finding: it does NOT. Even with up to 512 serialized blocking GemmCmds per matmul (vs 2 coarse tl.dot), end-to-end decode latency is unchanged (8192: 28.9us, 32768: 114.9us) -- the inter-cube (m,l,O) reduce DMA tail dominates and the local-attention GEMM/dispatch sits in critical-path slack. Confirms the two-regime conclusion (24c7054): composite helps compute-bound prefill, not memory/reduce-bound decode.
Caveats: measured at S_kv 8K/32K (reduce-dominated); large-S_kv streaming regime extrapolated only. The -5.5% tiled<untiled is reduce-tail ordering noise, not fully nailed down.
Co-Authored-By: Claude Opus 4.8 (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>
- §6 GQA: rewrite long-context decode from 4-case to 6-case. Data
Placement Policy now presents the six placement options and their
intrinsic per-PE memory / per-token comm costs (no winner predicted);
the long-context subsection selects the best placement for that regime
(Case 6 ★, both-axes S_kv shard) from the measured 6-case sweep. Add
KV-sharding diagram + analytical budget/summary figures.
- Regenerate the 6-row decode sweep (milestone-1h-gqa) and the
sweep-dependent decode panels (latency/traffic/parallelism/memory) so
figures, prose, and sweep_decode.json are mutually consistent.
- §5 All-Reduce: add IPCQ design alternatives (architecture + decision
matrix) as design-rationale schematics (illustrative step-counts, not
measured) and the bench-generated topology diagram.
- §4 GEMM: rename "user-orchestrated/user-level" -> "kernel-orchestrated/
kernel-level" (orchestration runs in the kernel program vs the
scheduler-orchestrated composite); minor accuracy fixes (7.18 TFLOP/s
~10% below peak; ~781 ns DMA).
- Recompile build/main.pdf.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Replaces the unsourced 32 KB/layer placeholder in the analytical chart
with T*(N-1)/N where T = h_q * S_q * (d_head*2 + 8) ~ 2.1 KB
(per-PE (m, l, O) payload) and N is the participant count of the
reduce-only chain per stage. Cases 2/3/6 analytical bars now match
the simulator-measured numbers within ~10% (was 6-30x over).
Also:
- Bumps the measurement S_kv from 8 K to 64 K to verify Cases 1, 2, 3, 6
are genuinely S_kv-independent and Cases 4, 5 scale linearly.
- In-bar descriptor on the paired chart is now wrapped to fit the bar
width, black, no background box, and centered between the analytical
and measured bars so the label applies to both.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Drops the per-case panel outline from linewidth 2.2 to 0.9 with 85% alpha.
The case-color frame still anchors each panel but no longer competes
with the swimlanes, flit packets, and annotations inside.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds two new IPCQ architecture views in the latency_model.png aesthetic:
a 2x2 flow figure (one per case) and a 4x1 stacked figure for taller
horizontal panels. Refactors per-case panel drawing into shared helpers
and disambiguates the flit labels (HMQ desc -> Hd so it does not
collide with data D). Two-row legend keys.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Drops the control-data overlay from the paper figures folder and
adds the per-send latency cycle-stack PNG instead.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Removes the (measured) comm column, widens the Notes column with
shorter wrapped text, scales the table figure down, and wraps the
heading to two lines so it sits flush with the table.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
6 generated figures comparing IPCQ (chosen) against the 3 HW alternatives
(Doorbell+Polling, HMQ, RDMA-CQ): architecture blocks, latency stack,
op counts, sequence flow, decision matrix, and control/data plane overlay.
Three figures also copied into the paper's figures/ folder for inclusion.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
mukesh
ywkang
eusonice