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 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>
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>
mukesh