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ad6e529 99f2a33 ce305e4 ad6e529 b54dd97 ad6e529 ce305e4 99f2a33 b26ff77 ad6e529 99f2a33 ad6e529 99f2a33 b26ff77 ad6e529 b54dd97 ad6e529 b54dd97 ad6e529 b54dd97 ad6e529 b54dd97 ad6e529 2b146bf 5aadfb7 2b146bf ad6e529 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 | """Environment logic for Eco-LLM inference routing."""
from __future__ import annotations
from typing import Optional
from uuid import uuid4
from openenv.core.env_server.interfaces import Environment
from openenv.core.env_server.types import EnvironmentMetadata
from server.models import ModelChoice, RLAction, RLObservation, RLReward, RLState, Strategy
from graders import GRADERS
from tasks import TASKS, TASK_REGISTRY
from server.tasks import (
CARBON_SCHEDULE,
ENERGY_COSTS,
KB_ENERGY_COST,
KB_LATENCY_COST,
LATENCY_COSTS,
LATENCY_MU,
MODEL_ORDER,
MODEL_RANK,
MAX_STEPS_PER_EPISODE,
MAX_CARBON_PENALTY,
QuerySpec,
TaskSpec,
get_task,
)
class EcoLLMInferenceRoutingEnvironment(Environment[RLAction, RLObservation, RLState]):
"""Deterministic hierarchical RL environment for LLM routing."""
SUPPORTS_CONCURRENT_SESSIONS: bool = True
def __init__(self) -> None:
super().__init__()
self.tasks = TASKS
self.task_registry = TASK_REGISTRY
self.graders = GRADERS
self.current_task: TaskSpec = get_task("task_1")
self._state = RLState(
episode_id=str(uuid4()),
step_count=0,
task_id=self.current_task.task_id,
step_index=0,
query_index=0,
total_energy=0.0,
total_latency=0.0,
cache_contents=[],
carbon_history_index=0,
)
def reset(
self,
seed: Optional[int] = None,
episode_id: Optional[str] = None,
task_id: Optional[str] = None,
**kwargs: object,
) -> RLObservation:
del seed, kwargs
if task_id is None:
task_id = "task_1"
self.current_task = get_task(task_id)
self._state = RLState(
episode_id=episode_id or str(uuid4()),
step_count=0,
task_id=self.current_task.task_id,
step_index=0,
query_index=0,
total_energy=0.0,
total_latency=0.0,
cache_contents=[],
carbon_history_index=0,
)
return self._build_observation(
reward=self._empty_reward(["reset"]),
done=False,
)
def step(
self,
action: RLAction,
timeout_s: Optional[float] = None,
**kwargs: object,
) -> RLObservation:
del timeout_s, kwargs
if self._episode_complete:
raise RuntimeError(
f"Episode {self._state.episode_id!r} is already complete. "
"Call reset() to start a new episode."
)
if self._state.step_count >= MAX_STEPS_PER_EPISODE:
return self._build_observation(
reward=self._empty_reward(["step_limit_exceeded"]),
done=True,
)
self._state.step_count += 1
trace = [f"strategy={action.strategy.value}", f"model={action.model_choice.value}"]
query = self.current_query
current_carbon = self.current_carbon_intensity
if action.strategy == Strategy.WAIT:
self._advance_carbon()
self._state.step_index += 1
reward = RLReward(
score=0.0,
energy_cost=0.0,
latency_cost=0.0,
carbon_penalty=0.0,
latency_penalty=0.0,
bonuses=0.0,
total_reward=0.0,
correct=False,
strategy_trace=trace + ["wait"],
)
return self._build_observation(reward=reward, done=False)
correct = False
energy_cost = 0.0
latency_cost = 0.0
bonuses = 0.0
if action.strategy == Strategy.USE_CACHE and query.text in self.cache_contents:
correct = True
bonuses += 0.5
trace.append("cache_hit")
elif action.strategy == Strategy.CALL_KB:
energy_cost += KB_ENERGY_COST
latency_cost += KB_LATENCY_COST
correct = query.kb_available
trace.append("kb_lookup")
elif action.strategy == Strategy.DO_CASCADE:
cascade_models = self._cascade_models(action.model_choice)
for cascade_model in cascade_models:
energy_cost += ENERGY_COSTS[cascade_model]
latency_cost += LATENCY_COSTS[cascade_model]
trace.append(f"cascade:{cascade_model.value}")
model_correct = self._model_answers_correctly(query, cascade_model)
if model_correct:
correct = True
if action.exit_flag:
trace.append("cascade_exit")
break
else:
chosen_model = action.model_choice
energy_cost += ENERGY_COSTS[chosen_model]
latency_cost += LATENCY_COSTS[chosen_model]
correct = self._model_answers_correctly(query, chosen_model)
trace.append(f"infer:{chosen_model.value}")
if action.strategy == Strategy.EARLY_EXIT and correct:
trace.append("early_exit_success")
if action.strategy == Strategy.EARLY_EXIT and correct:
bonuses += 0.1
if action.model_choice == ModelChoice.LARGE:
bonuses += self.current_task.large_model_penalty
if self.current_task.large_model_penalty:
trace.append("task_large_penalty")
score = 1.0 if correct else 0.0
carbon_penalty = min(current_carbon * energy_cost, MAX_CARBON_PENALTY)
latency_penalty = LATENCY_MU * latency_cost
total_reward = score - carbon_penalty - latency_penalty + bonuses
self._state.total_energy += energy_cost
self._state.total_latency += latency_cost
self._state.step_index += 1
if correct and query.text not in self.cache_contents:
self._state.cache_contents.append(query.text)
if action.strategy != Strategy.WAIT:
self._advance_query()
self._advance_carbon()
done = self._episode_complete
reward = RLReward(
score=score,
energy_cost=round(energy_cost, 4),
latency_cost=round(latency_cost, 4),
carbon_penalty=round(carbon_penalty, 4),
latency_penalty=round(latency_penalty, 4),
bonuses=round(bonuses, 4),
total_reward=round(total_reward, 4),
correct=correct,
strategy_trace=trace,
)
return self._build_observation(reward=reward, done=done)
@property
def state(self) -> RLState:
return self._state
@property
def cache_contents(self) -> list[str]:
return list(self._state.cache_contents)
@property
def current_query(self) -> QuerySpec:
idx = self._state.query_index
queries = self.current_task.queries
# Clamp to last query only for building the terminal observation
return queries[min(idx, len(queries) - 1)]
@property
def current_carbon_intensity(self) -> float:
return CARBON_SCHEDULE[self._state.carbon_history_index % len(CARBON_SCHEDULE)]
@property
def _episode_complete(self) -> bool:
return self._state.query_index >= len(self.current_task.queries)
def get_metadata(self) -> EnvironmentMetadata:
return EnvironmentMetadata(
name="Eco-LLM Inference Routing Environment",
description=(
"Hierarchical RL environment for carbon-aware LLM query routing "
"across accuracy, latency, energy, and caching objectives."
),
version="1.0.0",
)
def _build_observation(self, reward: RLReward, done: bool) -> RLObservation:
query = self.current_task.queries[-1] if done else self.current_query
return RLObservation(
query=query.text,
cache_contents=self.cache_contents,
carbon_intensity=self.current_carbon_intensity,
done=done,
reward=reward.total_reward,
reward_details=reward,
kb_available=query.kb_available,
score=0.0,
metadata={
"task_id": self.current_task.task_id,
"difficulty": self.current_task.difficulty,
"state": self._state.public_payload(),
},
)
def _empty_reward(self, trace: list[str]) -> RLReward:
return RLReward(
score=0.0,
energy_cost=0.0,
latency_cost=0.0,
carbon_penalty=0.0,
latency_penalty=0.0,
bonuses=0.0,
total_reward=0.0,
correct=False,
strategy_trace=trace,
)
def _advance_query(self) -> None:
if self._state.query_index < len(self.current_task.queries):
self._state.query_index += 1
def _advance_carbon(self) -> None:
self._state.carbon_history_index = (self._state.carbon_history_index + 1) % len(CARBON_SCHEDULE)
def _cascade_models(self, upper_bound: ModelChoice) -> list[ModelChoice]:
max_index = MODEL_RANK[upper_bound]
return MODEL_ORDER[: max_index + 1]
def _model_answers_correctly(self, query: QuerySpec, model: ModelChoice) -> bool:
return MODEL_RANK[model] >= MODEL_RANK[query.minimum_model]
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