analytical-viz: memory-min buttons reset stale placement/mode session keys
auto_suggest only explores (cp, tp, pp, cp_placement); every other TopologyConfig field is left at its dataclass default when the score is computed. The sidebar's memory-min apply used to update only cp/tp/pp/dp/cp_placement, so any stale session_state value from a previous user interaction (most damaging: tp_placement="cube") would carry over into the rerun and produce a topology the memory-min search never scored — visibly, cubes_used could balloon (e.g. Llama 3 70B Attn scope: auto_suggest picks 1 cube; stale tp_placement="cube" made the applied config span 8 cubes). Fix: after picking a Suggestion, reset tp_placement, kv_mode, cp_ring_variant, ep back to their TopologyConfig defaults, and drop the ffn_scope_label key (dynamic string; falls back to the TP+CP default index on re-render). Now the sliders + placement + mode state after apply exactly reproduce the topology auto_suggest scored. test_auto_suggest_cubes_match_default_topology asserts, across four model/skv combinations and all three scopes, that a TopologyConfig built from (cp, tp, pp, cp_placement) plus dataclass defaults has the same cubes_used the Suggestion reports. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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@@ -252,7 +252,19 @@ with st.sidebar.expander("Parallelism", expanded=True):
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st.session_state["tp"] = _snap(_sb_sug.tp, _TP_OPTS)
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st.session_state["pp"] = _snap(_sb_sug.pp, _PP_OPTS)
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st.session_state["dp"] = 1
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st.session_state["ep"] = 1
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st.session_state["cp_placement"] = _sb_sug.cp_placement
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# auto_suggest only explores (cp, tp, pp, cp_placement); reset
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# every other TopologyConfig field to its dataclass default so
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# the applied topology matches what memory-min actually scored
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# (else stale tp_placement="cube" etc. explodes cubes_used).
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st.session_state["tp_placement"] = "pe"
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st.session_state["kv_mode"] = "split"
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st.session_state["cp_ring_variant"] = "kv"
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# ffn_scope_label is a dynamic string (embeds current tp/cp) —
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# dropping the key lets the radio fall back to index=1 which is
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# "TP+CP", the TopologyConfig default.
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st.session_state.pop("ffn_scope_label", None)
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st.session_state["_pl_active_scope"] = _sb_scope
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st.rerun()
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@@ -19,8 +19,9 @@ from tests.analytical_visualization.auto_explore import (
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run_auto_explore,
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score_config,
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)
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from tests.analytical_visualization.autosuggest import auto_suggest
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from tests.analytical_visualization.model_config import (
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FullConfig, MachineParams,
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FullConfig, MachineParams, TopologyConfig,
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)
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from tests.analytical_visualization.model_presets import PRESETS
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@@ -208,6 +209,39 @@ def test_ffn_only_latency_less_than_full_and_different_from_attn():
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)
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def test_auto_suggest_cubes_match_default_topology():
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"""A TopologyConfig built from auto_suggest's returned (cp, tp, pp,
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cp_placement) with all other fields left at their dataclass defaults
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must produce the same cubes_used the Suggestion reports. Guards the
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sidebar apply: any session_state key auto_suggest doesn't explore
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(tp_placement, kv_shard_mode, cp_ring_variant, ep, ffn_shard_scope)
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must be reset to the default before rerun, else the applied topology
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won't match what the memory-min search actually scored.
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"""
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machine = MachineParams()
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matrix = [
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("Llama 3 70B", 8192),
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("Llama 3 70B", 32768),
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("Llama 3 8B", 131072),
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("Qwen 3 32B", 32768),
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]
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for name, skv in matrix:
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m = PRESETS[name].model
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for ia, ff in [(True, False), (False, True), (True, True)]:
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sug = auto_suggest(m, machine, s_kv=skv, mode="decode",
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include_attention=ia, include_ffn=ff)
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topo = TopologyConfig(
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cp=sug.cp, tp=sug.tp, pp=sug.pp,
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s_kv=skv, mode="decode", b=1,
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cp_placement=sug.cp_placement,
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)
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assert topo.cubes_used == sug.cubes_used, (
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f"{name} skv={skv} scope=(ia={ia},ff={ff}): "
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f"topo.cubes_used={topo.cubes_used} != "
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f"sug.cubes_used={sug.cubes_used}"
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)
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def test_parallelism_sensitivity_includes_baseline_value():
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"""Each row's values contain the baseline_value."""
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from tests.analytical_visualization.auto_explore import (
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