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2e4d1df | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 | """PPO-trained batch-size controller for voltage regulation.
Loads a trained stable-baselines3 PPO model and uses it for deterministic
inference within the openg2g Controller interface.
"""
from __future__ import annotations
from fractions import Fraction
from pathlib import Path
import numpy as np
from env import ObservationConfig, build_observation, compute_zone_mask, decode_action
from openg2g.clock import SimulationClock
from openg2g.controller.base import Controller
from openg2g.datacenter.base import LLMBatchSizeControlledDatacenter, LLMDatacenterState
from openg2g.datacenter.command import DatacenterCommand, SetBatchSize
from openg2g.datacenter.config import InferenceModelSpec
from openg2g.events import EventEmitter
from openg2g.grid.command import GridCommand
from openg2g.grid.opendss import OpenDSSGrid
def load_sb3_policy(model_path: str | Path, *, algorithm: str = "PPO"):
"""Load a stable-baselines3 policy by algorithm name.
`algorithm` is the SB3 class name (`"PPO"`, `"SAC"`, `"DQN"`,
`"A2C"`, `"TD3"`, `"DDPG"`). Resolved by attribute lookup on the
`stable_baselines3` package; mismatches raise an explicit error.
Trailing `.zip` on `model_path` is stripped because SB3's `load`
appends the extension itself.
"""
import stable_baselines3 as sb3
try:
algo_cls = getattr(sb3, algorithm)
except AttributeError as exc:
raise ValueError(
f"Unknown SB3 algorithm {algorithm!r}; expected one of PPO, SAC, DQN, A2C, TD3, DDPG."
) from exc
resolved = Path(model_path).resolve()
load_path = str(resolved.with_suffix("")) if resolved.suffix == ".zip" else str(resolved)
return algo_cls.load(load_path)
def _load_vecnormalize(vecnormalize_path: str | Path, observation_space, action_space):
"""Load a saved VecNormalize wrapper, restoring its running obs/reward statistics.
Returns a VecNormalize whose `normalize_obs` reproduces what the policy
saw during training. We attach a 1-env DummyVecEnv whose obs/action spaces
match the trained model so SB3's loader is happy.
"""
import gymnasium as gym
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
class _DummyEnv(gym.Env):
metadata = {"render_modes": []} # noqa: RUF012
def __init__(self, obs_space, act_space):
super().__init__()
self.observation_space = obs_space
self.action_space = act_space
self.render_mode = None
def reset(self, *, seed=None, options=None):
return self.observation_space.sample(), {}
def step(self, action):
return self.observation_space.sample(), 0.0, False, False, {}
def close(self):
pass
venv = DummyVecEnv([lambda: _DummyEnv(observation_space, action_space)])
vn = VecNormalize.load(str(vecnormalize_path), venv)
vn.training = False
vn.norm_reward = False
return vn
def _detect_action_mode(action_space) -> str:
"""Infer the action mode from a saved model's action space shape."""
from gymnasium import spaces as gspaces
if isinstance(action_space, gspaces.Discrete):
return "coupled"
if isinstance(action_space, gspaces.MultiDiscrete):
nvec = tuple(int(d) for d in action_space.nvec)
if all(d == 3 for d in nvec):
return "delta"
raise ValueError(
f"Unrecognised action space {action_space!r}: expected Discrete (coupled) or MultiDiscrete([3]*N) (delta)"
)
class PPOBatchSizeController(
Controller[LLMBatchSizeControlledDatacenter[LLMDatacenterState], OpenDSSGrid],
):
"""Batch-size controller using a trained PPO policy (single site).
Args:
inference_models: Model specifications served in the datacenter.
model_path: Path to saved SB3 PPO model (.zip).
obs_config: Observation space configuration.
dt_s: Control interval (seconds).
site_id: Site identifier for multi-datacenter setups.
vecnormalize_path: Optional path to a saved VecNormalize stats pickle
(`*_vecnormalize.pkl`). If provided, observations are normalized
with the saved running mean/var before being passed to the policy;
this MUST match the wrapper used during training, otherwise the
policy sees out-of-distribution input.
"""
def __init__(
self,
inference_models: tuple[InferenceModelSpec, ...],
*,
datacenter: LLMBatchSizeControlledDatacenter[LLMDatacenterState],
grid: OpenDSSGrid,
model_path: str | Path,
obs_config: ObservationConfig,
dt_s: Fraction = Fraction(1),
algorithm: str = "PPO",
vecnormalize_path: str | Path | None = None,
) -> None:
self._models = inference_models
self._datacenter = datacenter
self._grid = grid
self._sb3_model = load_sb3_policy(model_path, algorithm=algorithm)
self._vecnormalize = (
_load_vecnormalize(vecnormalize_path, self._sb3_model.observation_space, self._sb3_model.action_space)
if vecnormalize_path is not None
else None
)
self._obs_config = obs_config
self._dt_s = dt_s
self._feasible = {s.model_label: tuple(s.feasible_batch_sizes) for s in inference_models}
self._prev_batch: dict[str, int] = {}
self._zone_mask: np.ndarray | None = None
self._zone_masks: dict[str, np.ndarray] | None = None
self._zone_masks_computed = False
# Detect action mode from model's action space
self._action_mode = _detect_action_mode(self._sb3_model.action_space)
n_feasible = min(len(f) for f in self._feasible.values())
self._coupled_max_shift = n_feasible - 1
self._init_prev_batch()
def _init_prev_batch(self) -> None:
self._prev_batch = {s.model_label: self._obs_config.get_initial_batch(s.model_label) for s in self._models}
@property
def dt_s(self) -> Fraction:
return self._dt_s
def reset(self) -> None:
self._init_prev_batch()
self._zone_masks_computed = False
def step(
self,
clock: SimulationClock,
events: EventEmitter,
) -> list[DatacenterCommand | GridCommand]:
datacenter = self._datacenter
grid = self._grid
# Grid must be started before v_index is valid, so zone masks are
# computed lazily on the first step.
if not self._zone_masks_computed:
if self._obs_config.zone_buses is not None:
self._zone_mask = compute_zone_mask(grid.v_index, self._obs_config.zone_buses)
if self._obs_config.zone_summary:
self._zone_masks = {
zname: compute_zone_mask(grid.v_index, tuple(zbuses))
for zname, zbuses in self._obs_config.zone_summary.items()
}
self._zone_masks_computed = True
obs = build_observation(grid, datacenter, self._obs_config, self._prev_batch, self._zone_mask, self._zone_masks)
if self._vecnormalize is not None:
obs = self._vecnormalize.normalize_obs(obs)
action, _ = self._sb3_model.predict(obs, deterministic=True)
batch_sizes = decode_action(
action,
self._action_mode,
self._obs_config.model_labels,
self._feasible,
self._prev_batch,
self._coupled_max_shift,
)
self._prev_batch = batch_sizes
events.emit("controller.ppo.step", {"batch_size_by_model": batch_sizes})
return [SetBatchSize(batch_size_by_model=batch_sizes, target=datacenter)]
class SharedPPOBatchSizeController(
Controller[LLMBatchSizeControlledDatacenter[LLMDatacenterState], OpenDSSGrid],
):
"""Shared PPO controller that outputs batch sizes for ALL sites jointly.
Requires the coordinator to have all datacenter sites registered.
Outputs one `SetBatchSize` command per site.
Args:
model_path: Path to saved SB3 PPO model (.zip).
obs_config: Combined observation config (all models from all sites).
site_model_mapping: Maps site_id → list of model labels at that site.
dt_s: Control interval (seconds).
"""
def __init__(
self,
*,
datacenter: LLMBatchSizeControlledDatacenter[LLMDatacenterState],
grid: OpenDSSGrid,
model_path: str | Path,
obs_config: ObservationConfig,
site_model_mapping: dict[str, list[str]],
dt_s: Fraction = Fraction(1),
algorithm: str = "PPO",
vecnormalize_path: str | Path | None = None,
) -> None:
self._datacenter = datacenter
self._grid = grid
self._sb3_model = load_sb3_policy(model_path, algorithm=algorithm)
self._vecnormalize = (
_load_vecnormalize(vecnormalize_path, self._sb3_model.observation_space, self._sb3_model.action_space)
if vecnormalize_path is not None
else None
)
self._obs_config = obs_config
self._site_model_mapping = site_model_mapping
self._dt_s = dt_s
self._feasible = dict(obs_config.feasible_batch_sizes)
self._prev_batch: dict[str, int] = {}
self._zone_mask: np.ndarray | None = None
self._zone_masks: dict[str, np.ndarray] | None = None
self._zone_masks_computed = False
self._action_mode = _detect_action_mode(self._sb3_model.action_space)
n_feasible = min(len(f) for f in self._feasible.values())
self._coupled_max_shift = n_feasible - 1
# Per-site datacenter routing (set by attach_datacenters() before
# coord.run()). The shared policy needs every DC's
# batch/itl/replicas/power state to build the joint observation; the
# site-id → DC mapping additionally lets step() route each per-site
# SetBatchSize command back to its specific DC.
self._dcs_by_sid: dict[str, LLMBatchSizeControlledDatacenter[LLMDatacenterState]] = {}
self._all_datacenters: list = []
self._init_prev_batch()
def attach_datacenters(
self,
datacenters: dict[str, LLMBatchSizeControlledDatacenter[LLMDatacenterState]],
) -> None:
"""Register the site-id → DC mapping so the shared policy can both
observe the joint multi-site state and route per-site
`SetBatchSize` commands to the correct DC. Call once after the
Coordinator is constructed and before `coord.run()`.
"""
self._dcs_by_sid = dict(datacenters)
self._all_datacenters = list(datacenters.values())
def _init_prev_batch(self) -> None:
self._prev_batch = {label: self._obs_config.get_initial_batch(label) for label in self._obs_config.model_labels}
@property
def dt_s(self) -> Fraction:
return self._dt_s
def reset(self) -> None:
self._init_prev_batch()
self._zone_masks_computed = False
def step(
self,
clock: SimulationClock,
events: EventEmitter,
) -> list[DatacenterCommand | GridCommand]:
grid = self._grid
if not self._zone_masks_computed:
if self._obs_config.zone_buses is not None:
self._zone_mask = compute_zone_mask(grid.v_index, self._obs_config.zone_buses)
if self._obs_config.zone_summary:
self._zone_masks = {
zname: compute_zone_mask(grid.v_index, tuple(zbuses))
for zname, zbuses in self._obs_config.zone_summary.items()
}
self._zone_masks_computed = True
# Shared policies need joint state from every site, but the Coordinator
# hands each controller only its own DC. `attach_datacenters` must be
# called before run().
if not self._all_datacenters:
raise RuntimeError(
"SharedPPOBatchSizeController.step() called before attach_datacenters(); "
"a shared policy requires the full per-site DC mapping to build its joint observation."
)
obs = build_observation(
grid, self._all_datacenters, self._obs_config, self._prev_batch, self._zone_mask, self._zone_masks
)
if self._vecnormalize is not None:
obs = self._vecnormalize.normalize_obs(obs)
action, _ = self._sb3_model.predict(obs, deterministic=True)
all_batch = decode_action(
action,
self._action_mode,
self._obs_config.model_labels,
self._feasible,
self._prev_batch,
self._coupled_max_shift,
)
self._prev_batch = all_batch
events.emit("controller.ppo.step", {"batch_size_by_model": all_batch})
commands: list[DatacenterCommand | GridCommand] = []
for sid, labels in self._site_model_mapping.items():
site_batch = {label: all_batch[label] for label in labels if label in all_batch}
if not site_batch:
continue
commands.append(SetBatchSize(batch_size_by_model=site_batch, target=self._dcs_by_sid[sid]))
return commands
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