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4fbc241 1d6826f 4fbc241 | 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 | from __future__ import annotations
import uuid
from typing import Any
from openenv.core import Environment
from llmserve_env.models import EpisodeLog, ServeAction, ServeObservation, ServeState
from llmserve_env.task_catalog import get_task_config
from server.reward_calculator import RewardCalculator
from server.serving_backend import ServingBackend, create_serving_backend
from server.slo_monitor import SLOMonitor
from server.workload_generator import WorkloadGenerator
class LLMServeEnvironment(Environment[ServeAction, ServeObservation, ServeState]):
SUPPORTS_CONCURRENT_SESSIONS = False
def __init__(self, seed: int = 42, mode: str | None = None, backend: ServingBackend | None = None) -> None:
super().__init__()
self.seed = seed
try:
self.backend = backend or create_serving_backend(mode=mode, seed=seed)
except Exception as e:
raise RuntimeError(f"Failed to create serving backend: {e}") from e
try:
self.reward_calculator = RewardCalculator()
except Exception as e:
raise RuntimeError(f"Failed to create reward calculator: {e}") from e
self.task_config: dict[str, Any] | None = None
self.workload_generator: WorkloadGenerator | None = None
self.slo_monitor: SLOMonitor | None = None
self.actions: list[ServeAction] = []
self.observations: list[ServeObservation] = []
self.rewards: list[float] = []
self._state = ServeState(
episode_id=str(uuid.uuid4()),
step_count=0,
task_id="uninitialized",
total_requests_served=0,
total_slo_violations=0,
cumulative_reward=0.0,
elapsed_simulated_time_s=0.0,
workload_phase="warmup",
done=False,
)
def reset(
self,
seed: int | None = None,
episode_id: str | None = None,
task_id: str = "static_workload",
**_: Any,
) -> ServeObservation:
if seed is not None:
self.seed = seed
self.task_config = get_task_config(task_id)
self.workload_generator = WorkloadGenerator(self.task_config, seed=self.seed)
self.backend.reset(seed=self.seed)
self.slo_monitor = SLOMonitor()
self.actions = []
self.observations = []
self.rewards = []
self._state = ServeState(
episode_id=episode_id or str(uuid.uuid4()),
step_count=0,
task_id=task_id,
total_requests_served=0,
total_slo_violations=0,
cumulative_reward=0.0,
elapsed_simulated_time_s=0.0,
workload_phase="warmup",
done=False,
)
workload = self.workload_generator.next_snapshot(step_index=0)
observation = self._build_initial_observation(workload)
self.observations.append(observation)
return observation
def step(
self,
action: ServeAction,
timeout_s: float | None = None,
**_: Any,
) -> ServeObservation:
del timeout_s
if self.task_config is None or self.workload_generator is None or self.slo_monitor is None:
raise RuntimeError("reset() must be called before step().")
if self._state.done:
return self._build_terminal_observation("Episode already completed.")
next_step_index = self._state.step_count + 1
workload = self.workload_generator.next_snapshot(step_index=next_step_index)
metrics = self.backend.run_step(self._state.task_id, action, workload)
compliance, violations = self.slo_monitor.evaluate(
p99_ttft_ms=metrics.p99_ttft_ms,
target_ms=float(self.task_config["slo_p99_ttft_ms"]),
active_requests=max(1, metrics.requests_served),
)
metrics.slo_violations += violations
memory_cap = float(self.task_config.get("memory_cap_gb", 40.0))
kv_cache_occupancy = min(1.0, metrics.gpu_memory_used_gb / memory_cap)
reward = self.reward_calculator.calculate(
task_id=self._state.task_id,
metrics=metrics,
slo_compliance_rate=compliance,
quantization_tier=action.quantization_tier,
priority_fraction=workload.priority_fraction,
)
done = next_step_index >= int(self.task_config["max_steps"])
observation = ServeObservation(
queue_depth=workload.queue_depth,
active_requests=metrics.requests_served,
kv_cache_occupancy=kv_cache_occupancy,
mean_prompt_length=workload.mean_prompt_length,
p50_ttft_ms=metrics.p50_ttft_ms,
p99_ttft_ms=metrics.p99_ttft_ms,
p50_itl_ms=metrics.p50_itl_ms,
throughput_tps=metrics.throughput_tps,
slo_compliance_rate=compliance,
gpu_memory_used_gb=metrics.gpu_memory_used_gb,
estimated_cost_per_1k=metrics.estimated_cost_per_1k,
request_arrival_rate=workload.arrival_rate,
spec_acceptance_rate=metrics.spec_acceptance_rate,
eviction_events=metrics.eviction_events,
step_index=next_step_index,
task_id=self._state.task_id,
reward=reward,
done=done,
metadata={
"phase": workload.phase,
"priority_fraction": workload.priority_fraction,
"task_name": self.task_config["name"],
"is_throttled": metrics.is_throttled,
"preemption_events": metrics.preemption_events,
**self.backend.describe(),
},
)
self.actions.append(action)
self.observations.append(observation)
self.rewards.append(reward)
self._state.step_count = next_step_index
self._state.total_requests_served += metrics.requests_served
self._state.total_slo_violations += metrics.slo_violations
self._state.cumulative_reward += reward
self._state.elapsed_simulated_time_s += float(self.task_config["step_window_s"])
self._state.workload_phase = workload.phase
self._state.done = done
return observation
@property
def state(self) -> ServeState:
return self._state
def export_episode_log(self) -> EpisodeLog:
return EpisodeLog(
task_id=self._state.task_id,
actions=self.actions,
observations=self.observations,
rewards=self.rewards,
final_state=self._state,
)
def _build_initial_observation(self, workload: Any) -> ServeObservation:
return ServeObservation(
queue_depth=workload.queue_depth,
active_requests=0,
kv_cache_occupancy=0.0,
mean_prompt_length=workload.mean_prompt_length,
p50_ttft_ms=0.0,
p99_ttft_ms=0.0,
p50_itl_ms=0.0,
throughput_tps=0.0,
slo_compliance_rate=1.0,
gpu_memory_used_gb=0.0,
estimated_cost_per_1k=0.0,
request_arrival_rate=workload.arrival_rate,
spec_acceptance_rate=0.0,
eviction_events=0,
step_index=0,
task_id=self._state.task_id,
reward=0.0,
done=False,
metadata={
"phase": workload.phase,
"task_name": self.task_config["name"] if self.task_config else "",
**self.backend.describe(),
},
)
def _build_terminal_observation(self, message: str) -> ServeObservation:
last = self.observations[-1]
return last.model_copy(update={"done": True, "reward": 0.0, "metadata": {**last.metadata, "message": message}})
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