analytical-viz: attention-only vs attn+FFN via two sweep buttons

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>

tests/analytical_visualization/test_auto_explore.py

@@ -161,6 +161,33 @@ def test_parallelism_sensitivity_has_all_five_knobs():
     assert knobs == {"cp", "tp", "pp", "dp", "ep"}
 
 
+def test_attention_only_latency_is_lower_than_full():
+    """include_ffn=False must produce lower latency than include_ffn=True
+    for the same model+workload (FFN cost is dropped)."""
+    model = PRESETS["Llama 3.1 70B"].model
+    machine = MachineParams()
+    r_full = run_auto_explore(model, machine, s_kv=8192, mode="decode",
+                                include_ffn=True)
+    r_attn = run_auto_explore(model, machine, s_kv=8192, mode="decode",
+                                include_ffn=False)
+    best_full = min(r_full.pareto_scores, key=lambda s: s.total_latency_ns)
+    best_attn = min(r_attn.pareto_scores, key=lambda s: s.total_latency_ns)
+    assert best_attn.total_latency_ns < best_full.total_latency_ns, (
+        f"attn-only ({best_attn.latency_us:.2f} us) should be less than "
+        f"full ({best_full.latency_us:.2f} us)"
+    )
+
+
+def test_attention_only_pareto_non_empty():
+    """The attention-only sweep must still produce a non-empty Pareto set."""
+    model = PRESETS["Llama 3.1 70B"].model
+    machine = MachineParams()
+    res = run_auto_explore(model, machine, s_kv=8192, mode="decode",
+                             include_ffn=False)
+    assert res.total_feasible > 0
+    assert len(res.pareto_scores) > 0
+
+
 def test_parallelism_sensitivity_includes_baseline_value():
     """Each row's values contain the baseline_value."""
     from tests.analytical_visualization.auto_explore import (