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0c6c82c 94910ac d2258e5 94910ac 8f91935 44745f2 0c6c82c ce2d64b 0c6c82c 44745f2 ce2d64b 0c6c82c 94910ac d2258e5 94910ac 8f91935 0c6c82c 44745f2 ce2d64b e5c4ee4 94910ac d2258e5 8f91935 ce2d64b 0c6c82c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | from __future__ import annotations
from .agentic import (
adaptive_alpha_sweep,
adaptive_tiering_study,
agent_affinity_sweep,
agent_memory_budget_sweep,
compare_agent_memory_policies,
compare_agent_policies,
run_agent_session_simulation,
ttl_retention_sweep,
)
from .execution import (
execution_decay_sweep,
execution_prefetch_study,
execution_threshold_sweep,
run_execution_learning,
)
from .optimizer import capacity_search, compare_schedulers, compare_topologies, design_space_search
from .profiles import ACCELERATORS, MODELS
from .research import STUDIES, paired_study, robustness_study
from .simulator import SCHEDULERS, run_simulation
def metadata() -> dict:
return {
"models": list(MODELS.keys()),
"accelerators": list(ACCELERATORS.keys()),
"schedulers": sorted(SCHEDULERS),
"topologies": ["colocated", "disaggregated_pd"],
"research_studies": STUDIES,
"profile_type": "analytical-reference",
"agentic_modes": [
"session_simulation", "policy_compare", "ttl_sweep",
"memory_policy_compare", "memory_budget_sweep", "affinity_sweep",
"predictive_tiering", "adaptive_alpha_sweep",
],
"execution_learning_modes": [
"single_run", "prefetch_policy_compare", "confidence_threshold_sweep", "transition_decay_sweep",
],
}
def execute(action: str, payload: dict) -> dict:
if action == "simulate":
return run_simulation(payload)
if action == "capacity":
config = payload.get("config", payload)
return capacity_search(
config,
min_rate=float(payload.get("min_rate", 0.25)),
max_rate=float(payload.get("max_rate", 32.0)),
iterations=int(payload.get("iterations", 8)),
repetitions=int(payload.get("repetitions", 2)),
headroom=float(payload.get("headroom", 0.20)),
)
if action == "compare":
config = payload.get("config", payload)
return compare_schedulers(config, payload.get("schedulers"))
if action == "topology_compare":
config = payload.get("config", payload)
return compare_topologies(config)
if action == "design_space":
config = payload.get("config", payload)
return design_space_search(config, bool(payload.get("include_disaggregated", True)))
if action == "paired_study":
config = payload.get("config", payload)
return paired_study(
config,
study=str(payload.get("study", "prefix_cache")),
repetitions=int(payload.get("repetitions", 12)),
bootstrap_samples=int(payload.get("bootstrap_samples", 500)),
)
if action == "agent_simulate":
return run_agent_session_simulation(payload.get("config", payload))
if action == "agent_compare":
return compare_agent_policies(payload.get("config", payload))
if action == "agent_ttl_sweep":
return ttl_retention_sweep(payload.get("config", payload), payload.get("ttl_values"))
if action == "agent_memory_compare":
return compare_agent_memory_policies(payload.get("config", payload))
if action == "agent_memory_sweep":
return agent_memory_budget_sweep(payload.get("config", payload), payload.get("budget_multipliers"))
if action == "agent_affinity_sweep":
return agent_affinity_sweep(payload.get("config", payload), payload.get("slack_values_ms"))
if action == "agent_predictive_tiering":
return adaptive_tiering_study(
payload.get("config", payload),
horizon_s=float(payload.get("horizon_s", 120.0)),
shift_fraction=float(payload.get("shift_fraction", 0.55)),
shift_multiplier=float(payload.get("shift_multiplier", 2.5)),
alpha=float(payload.get("alpha", 0.30)),
)
if action == "agent_adaptive_alpha_sweep":
return adaptive_alpha_sweep(
payload.get("config", payload),
payload.get("alpha_values"),
horizon_s=float(payload.get("horizon_s", 120.0)),
shift_fraction=float(payload.get("shift_fraction", 0.55)),
shift_multiplier=float(payload.get("shift_multiplier", 2.5)),
)
if action == "execution_learning_run":
return run_execution_learning(payload.get("config", payload))
if action == "execution_prefetch_study":
return execution_prefetch_study(payload.get("config", payload))
if action == "execution_threshold_sweep":
return execution_threshold_sweep(payload.get("config", payload), payload.get("thresholds"))
if action == "execution_decay_sweep":
return execution_decay_sweep(payload.get("config", payload), payload.get("decay_values"))
if action == "robustness_study":
config = payload.get("config", payload)
return robustness_study(
config,
study=str(payload.get("study", "pd_vs_colocated")),
samples=int(payload.get("samples", 32)),
uncertainty=float(payload.get("uncertainty", 0.20)),
)
if action == "metadata":
return metadata()
raise ValueError(f"Unknown action: {action}")
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