analytical-viz: Auto Suggest tab — add Top-10 by memory footprint table
Adds a second table beneath the Pareto configurations table, sorted by (hbm_utilization ↑, pes_used ↑, latency ↑) — top 10 smallest-memory Pareto configs first. Columns highlight the memory story: HBM % | weights (GB) | KV (GB) | PEs | SIPs | lat (ms) | CP TP PP DP | kv | ffn | tp_place | cp_place Useful when memory pressure is the real binding constraint of a deployment — e.g., picking a config for a per-PE HBM-limited SIP, or spotting configs that trade small latency headroom for large memory savings. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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@@ -1685,6 +1685,35 @@ def _render_auto_explore_tab():
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_df = pd.DataFrame(_rows)
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st.dataframe(_df, width='stretch', hide_index=True)
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# ── Top-N by memory (smallest → largest) ─────────────────────
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st.markdown(
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"**Top 10 configurations by memory footprint** "
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"(smallest HBM% first). Useful when memory pressure is the "
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"real binding constraint of your deployment."
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)
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_pareto_by_mem = sorted(
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_res.pareto_scores,
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key=lambda s: (s.hbm_utilization, s.pes_used, s.total_latency_ns),
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)[:10]
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_mem_rows = []
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for i, s in enumerate(_pareto_by_mem):
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_mem_rows.append({
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"#": i,
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"HBM %": round(s.hbm_utilization * 100, 1),
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"weights (GB)": round(s.weights_gb, 2),
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"KV (GB)": round(s.kv_gb, 2),
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"PEs": s.pes_used,
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"SIPs": s.sips_used,
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"lat (ms)": round(s.latency_ms, 3),
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"CP": s.cp, "TP": s.tp, "PP": s.pp, "DP": s.dp,
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"kv": s.kv_shard_mode,
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"ffn": s.ffn_shard_scope,
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"tp_place": s.tp_placement,
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"cp_place": s.cp_placement,
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})
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_df_mem = pd.DataFrame(_mem_rows)
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st.dataframe(_df_mem, width='stretch', hide_index=True)
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# ── Parallelism sensitivity subplots ──────────────────────
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st.markdown(
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"**Parallelism sensitivity** — for the *baseline* config picked "
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