analytical-viz: sidebar memory-min buttons are scope-aware (Attn / FFN / Attn+FFN)

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>

tests/analytical_visualization/autosuggest.py

@@ -61,7 +61,9 @@ def _score_candidate(cp: int, tp: int, pp: int,
                      model: ModelConfig, machine: MachineParams,
                      s_kv: int, mode: str,
                      slack_frac: float = 0.10,
-                     b: int = 1) -> Suggestion:
+                     b: int = 1,
+                     include_attention: bool = True,
+                     include_ffn: bool = True) -> Suggestion:
     # For each triple, try both cp_placement options and keep the one
     # with fewer cubes (breaks the historical "CP always across cubes"
     # default when a smaller pack is possible). The pe placement is only
@@ -82,7 +84,9 @@ def _score_candidate(cp: int, tp: int, pp: int,
             best_placement = _place
     topo = best_topo
     cfg = FullConfig(model=model, topo=topo, machine=machine)
-    mem = compute_memory(cfg)
+    mem = compute_memory(
+        cfg, include_attention=include_attention, include_ffn=include_ffn,
+    )
 
     fits = (not mem.over_budget
             and mem.slack_bytes >= slack_frac * mem.budget_bytes)
@@ -110,7 +114,9 @@ def _score_candidate(cp: int, tp: int, pp: int,
 def auto_suggest(model: ModelConfig, machine: MachineParams,
                  s_kv: int, mode: str = "decode",
                  slack_frac: float = 0.10,
-                 b: int = 1) -> Suggestion:
+                 b: int = 1,
+                 include_attention: bool = True,
+                 include_ffn: bool = True) -> Suggestion:
     """Return the smallest deployment (fewer cubes, then fewer PEs)
     that fits.
 
@@ -127,7 +133,9 @@ def auto_suggest(model: ModelConfig, machine: MachineParams,
     for cp, tp, pp in _iter_candidates(model):
         scored.append(
             _score_candidate(cp, tp, pp, model, machine, s_kv, mode,
-                              slack_frac, b=b)
+                              slack_frac, b=b,
+                              include_attention=include_attention,
+                              include_ffn=include_ffn)
         )
     scored.sort(key=lambda s: (s.cubes_used, s.pes_used, s.pp, s.tp, s.cp))