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>
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>
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