analytical-viz: add sidebar Batch B selectbox + wire through all suggesters

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>

tests/analytical_visualization/auto_explore.py

@@ -419,6 +419,7 @@ def run_auto_explore(
     mode: str = "decode",
     include_attention: bool = True,
     include_ffn: bool = True,
+    b: int = 1,
 ) -> AutoExploreResult:
     """Enumerate all configs, score each, extract Pareto frontier.
 
@@ -433,6 +434,8 @@ def run_auto_explore(
     all_scores: list[ConfigScore] = []
     total_enumerated = 0
     for topo in enumerate_configs(model, s_kv, mode):
+        # Inject the caller's batch B into every candidate.
+        topo.b = b
         total_enumerated += 1
         cfg = FullConfig(model=model, topo=topo, machine=machine)
         all_scores.append(score_config(