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0c6c82c 94910ac d2258e5 94910ac 8f91935 b799d1d 8f91935 b799d1d 8f91935 44745f2 9916edb 0c6c82c ce2d64b 0c6c82c 44745f2 ce2d64b 0c6c82c 94910ac d2258e5 94910ac 8f91935 b799d1d 8f91935 9916edb 0c6c82c 44745f2 ce2d64b e5c4ee4 94910ac d2258e5 8f91935 b799d1d ce2d64b 9916edb 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | 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_budget_sweep,
execution_decay_sweep,
execution_horizon_sweep,
execution_planning_study,
execution_prefetch_study,
execution_threshold_sweep,
run_execution_learning,
)
from .optimizer import capacity_search, compare_schedulers, compare_topologies, design_space_search
from .consolidation import repeated_seed_policy_study
from .measurements import calibrate_measurements, import_measurements
from .reports import generate_research_report
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",
"multistep_planning", "forecast_horizon_sweep", "cache_budget_policy_sweep",
],
"consolidation_modes": ["repeated_seed_policy_study", "measurement_import", "heldout_calibration", "markdown_report"],
}
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 == "execution_planning_study":
return execution_planning_study(payload.get("config", payload))
if action == "execution_horizon_sweep":
return execution_horizon_sweep(payload.get("config", payload), payload.get("horizon_values"))
if action == "execution_budget_sweep":
return execution_budget_sweep(payload.get("config", payload), payload.get("budget_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 == "consolidation_study":
return repeated_seed_policy_study(
payload.get("config", payload),
repetitions=int(payload.get("repetitions", 12)),
bootstrap_samples=int(payload.get("bootstrap_samples", 600)),
)
if action == "measurement_import":
return import_measurements(
str(payload.get("content", "")),
source=str(payload.get("source", "auto")),
base_config=payload.get("base_config"),
)
if action == "measurement_calibrate":
return calibrate_measurements(
payload.get("cases", []),
holdout_fraction=float(payload.get("holdout_fraction", 0.33)),
seed=int(payload.get("seed", 7)),
)
if action == "research_report":
return {
"markdown": generate_research_report(payload.get("robust", {}), payload.get("calibration")),
"filename": "inferscale-research-consolidation.md",
}
if action == "metadata":
return metadata()
raise ValueError(f"Unknown action: {action}")
|