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import argparse
from pathlib import Path
from typing import Any
import numpy as np
import torch
from torch.nn.utils import clip_grad_norm_
from tqdm import trange
from minidreamer.config import ensure_run_dirs, load_config, merge_dicts, save_config
from minidreamer.data.collect_random import collect_bootstrap_dataset
from minidreamer.data.replay_buffer import ReplayBuffer
from minidreamer.evaluation import evaluate_random_policy, evaluate_world_model
from minidreamer.envs.make_env import make_env_from_config
from minidreamer.models.world_model import WorldModel
from minidreamer.planning.cem import DiscreteCEMPlanner
from minidreamer.planning.evaluate_planner import evaluate_planner
from minidreamer.serialization import save_world_model_checkpoint
from minidreamer.utils.common import get_device, seed_everything, write_json, write_jsonl
def train_world_model_updates(
model: WorldModel,
replay: ReplayBuffer,
optimizer: torch.optim.Optimizer,
config: dict[str, Any],
num_updates: int,
device: torch.device,
) -> list[dict[str, float]]:
if num_updates <= 0:
return []
model.train()
logs: list[dict[str, float]] = []
progress = trange(num_updates, desc="world-model-updates", leave=False)
for _ in progress:
batch = ReplayBuffer.batch_to_torch(replay.sample_sequences(split="train"), device=device)
losses = model.compute_losses(batch, config)
optimizer.zero_grad(set_to_none=True)
losses["loss"].backward()
clip_grad_norm_(model.parameters(), float(config["training"].get("grad_clip_norm", 100.0)))
optimizer.step()
log_row = {
"loss": float(losses["loss"].detach().cpu()),
"reward_loss": float(losses["reward_loss"].cpu()),
"done_loss": float(losses["done_loss"].cpu()),
"kl_loss": float(losses["kl_loss"].cpu()),
"recon_loss": float(losses["recon_loss"].cpu()),
}
logs.append(log_row)
progress.set_postfix({key: f"{value:.3f}" for key, value in log_row.items()})
return logs
def optimizer_to_device(optimizer: torch.optim.Optimizer, device: torch.device) -> None:
for state in optimizer.state.values():
for key, value in state.items():
if torch.is_tensor(value):
state[key] = value.to(device)
def load_training_state(
checkpoint_path: str | Path,
config: dict[str, Any],
action_dim: int,
device: torch.device,
) -> tuple[dict[str, Any], WorldModel, torch.optim.Optimizer, dict[str, Any]]:
payload = torch.load(checkpoint_path, map_location=device, weights_only=False)
resolved_config = merge_dicts(payload["config"], config)
model = WorldModel.from_config(resolved_config, action_dim=action_dim).to(device)
model.load_state_dict(payload["model_state"])
optimizer = torch.optim.Adam(model.parameters(), lr=float(resolved_config["training"]["lr"]))
optimizer_state = payload.get("optimizer_state")
if optimizer_state is not None:
optimizer.load_state_dict(optimizer_state)
optimizer_to_device(optimizer, device)
return resolved_config, model, optimizer, payload.get("metadata", {})
def find_existing_run_artifacts(base_dir: str | Path) -> list[Path]:
base = Path(base_dir)
if not base.exists():
return []
artifact_files = [
base / "metrics" / "run_summary.json",
base / "metrics" / "train_metrics.jsonl",
base / "metrics" / "eval_metrics.jsonl",
base / "checkpoints" / "world_model_latest.pt",
base / "replay" / "metadata.json",
]
found = [path for path in artifact_files if path.exists()]
if found:
return found
for subdir_name in ("checkpoints", "metrics", "replay"):
subdir = base / subdir_name
if subdir.exists():
for child in subdir.iterdir():
found.append(child)
break
return found
def collect_planner_steps(
env,
replay: ReplayBuffer,
model: WorldModel,
planner: DiscreteCEMPlanner,
num_steps: int,
random_action_fraction: float,
rng: np.random.Generator,
) -> dict[str, int]:
collected_steps = 0
episodes = 0
success_episodes = 0
model.eval()
while collected_steps < num_steps:
obs, _ = env.reset()
observations = [obs]
actions: list[int] = []
rewards: list[float] = []
terminated_flags: list[float] = []
truncated_flags: list[float] = []
done_flags: list[float] = []
terminated = False
truncated = False
with torch.no_grad():
state = model.posterior_step(model.initial_state(1), None, obs, sample=False)
while not (terminated or truncated):
if rng.random() < random_action_fraction:
action = int(env.action_space.sample())
else:
action = planner.plan(state).action
obs, reward, terminated, truncated, _ = env.step(action)
actions.append(action)
rewards.append(float(reward))
terminated_flags.append(float(terminated))
truncated_flags.append(float(truncated))
done_flags.append(float(terminated or truncated))
observations.append(obs)
collected_steps += 1
if terminated or truncated:
break
state = model.posterior_step(state, action, obs, sample=False)
replay.add_episode(
obs=np.asarray(observations, dtype=np.float32),
actions=np.asarray(actions, dtype=np.int64),
rewards=np.asarray(rewards, dtype=np.float32),
terminated=np.asarray(terminated_flags, dtype=np.float32),
truncated=np.asarray(truncated_flags, dtype=np.float32),
done=np.asarray(done_flags, dtype=np.float32),
)
episodes += 1
success_episodes += int(bool(terminated and np.sum(rewards) > 0.0))
return {
"env_steps": collected_steps,
"episodes": episodes,
"success_episodes": success_episodes,
}
def run_training(
config: dict[str, Any],
output_dir: str | Path,
replay_dir: str | Path | None = None,
resume_checkpoint: str | Path | None = None,
allow_overwrite_existing_output: bool = False,
) -> dict[str, Any]:
seed = config.get("project", {}).get("seed", 0)
seed_everything(seed)
existing_artifacts = find_existing_run_artifacts(output_dir)
if existing_artifacts and resume_checkpoint is None and not allow_overwrite_existing_output:
preview = ", ".join(str(path) for path in existing_artifacts[:3])
raise FileExistsError(
f"Refusing to overwrite existing run directory '{output_dir}'. "
f"Found existing artifacts: {preview}. "
"Choose a new --output-dir, resume with --resume-checkpoint, "
"or pass --allow-overwrite-existing-output to overwrite intentionally."
)
run_dirs = ensure_run_dirs(output_dir)
device = get_device(config.get("training", {}).get("device"))
env = make_env_from_config(config, seed=seed)
action_dim = env.action_space.n
env.close()
if replay_dir is not None and Path(replay_dir).exists():
replay = ReplayBuffer.load(replay_dir)
collection_summary = {"replay_loaded": replay.summary()}
else:
replay, collection_summary = collect_bootstrap_dataset(config, output_dir=run_dirs["replay"], seed=seed)
resume_metadata: dict[str, Any] = {}
if resume_checkpoint is not None:
config, model, optimizer, resume_metadata = load_training_state(
checkpoint_path=resume_checkpoint,
config=config,
action_dim=action_dim,
device=device,
)
else:
model = WorldModel.from_config(config, action_dim=action_dim).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=float(config["training"]["lr"]))
save_config(config, run_dirs["base"] / "resolved_config.yaml")
training_logs: list[dict[str, float]] = []
evaluation_logs: list[dict[str, float]] = []
train_collect_ratio = float(config["collection"].get("train_collect_ratio", 1.0))
total_updates_budget = int(config["training"]["train_steps"])
if resume_checkpoint is not None:
updates_done = int(resume_metadata.get("updates_done", 0))
checkpoint_env_steps = int(resume_metadata.get("env_steps", 0))
if replay.env_steps > checkpoint_env_steps and updates_done < total_updates_budget:
collect_steps_per_iteration = max(1, int(config["collection"].get("collect_steps_per_iteration", 1)))
per_iteration_updates = int(
config["collection"].get(
"gradient_updates_per_iteration",
round(collect_steps_per_iteration * train_collect_ratio),
)
)
missed_iterations = max(0, round((replay.env_steps - checkpoint_env_steps) / collect_steps_per_iteration))
catch_up_updates = min(total_updates_budget - updates_done, per_iteration_updates * missed_iterations)
catch_up_logs = train_world_model_updates(model, replay, optimizer, config, catch_up_updates, device)
training_logs.extend(catch_up_logs)
updates_done += len(catch_up_logs)
else:
initial_updates = min(total_updates_budget, max(1, int(round(replay.env_steps * train_collect_ratio))))
training_logs.extend(train_world_model_updates(model, replay, optimizer, config, initial_updates, device))
updates_done = len(training_logs)
comparison_budgets = config.get("comparison", {}).get("env_steps", [replay.env_steps])
target_env_steps = int(max(comparison_budgets))
rng = np.random.default_rng(seed)
env = make_env_from_config(config, seed=seed)
planner = DiscreteCEMPlanner.from_config(model, env.action_space.n, config)
eval_every_steps = int(config["evaluation"].get("eval_every_env_steps", target_env_steps))
next_eval_step = replay.env_steps
while replay.env_steps < target_env_steps and updates_done < total_updates_budget:
collect_steps = min(
int(config["collection"]["collect_steps_per_iteration"]),
target_env_steps - replay.env_steps,
)
collection_row = collect_planner_steps(
env,
replay,
model,
planner,
num_steps=collect_steps,
random_action_fraction=float(config["collection"].get("random_action_fraction_after_planner", 0.0)),
rng=rng,
)
updates = int(config["collection"].get("gradient_updates_per_iteration", round(collection_row["env_steps"] * train_collect_ratio)))
updates = min(updates, total_updates_budget - updates_done)
training_logs.extend(train_world_model_updates(model, replay, optimizer, config, updates, device))
updates_done = len(training_logs)
replay.save(run_dirs["replay"])
if replay.env_steps >= next_eval_step:
world_model_metrics = evaluate_world_model(config, model, replay, split="val", max_episodes=10)
planner_metrics = evaluate_planner(config, model, episodes=min(10, config["evaluation"]["episodes"]), seed=seed)
random_metrics = evaluate_random_policy(config, episodes=min(10, config["evaluation"]["episodes"]), seed=seed)
eval_row = {
"env_steps": replay.env_steps,
"updates_done": updates_done,
**{f"world_model/{key}": value for key, value in world_model_metrics.items()},
**{f"planner/{key}": value for key, value in planner_metrics.items()},
**{f"random/{key}": value for key, value in random_metrics.items()},
}
evaluation_logs.append(eval_row)
next_eval_step += eval_every_steps
save_world_model_checkpoint(
run_dirs["checkpoints"] / f"world_model_env_steps_{replay.env_steps}.pt",
model,
config,
optimizer=optimizer,
metadata={"env_steps": replay.env_steps, "updates_done": updates_done},
)
env.close()
save_world_model_checkpoint(
run_dirs["checkpoints"] / "world_model_latest.pt",
model,
config,
optimizer=optimizer,
metadata={"env_steps": replay.env_steps, "updates_done": updates_done},
)
write_json(run_dirs["metrics"] / "collection_summary.json", collection_summary)
write_jsonl(run_dirs["metrics"] / "train_metrics.jsonl", training_logs)
write_jsonl(run_dirs["metrics"] / "eval_metrics.jsonl", evaluation_logs)
summary = {
"replay": replay.summary(),
"updates_done": updates_done,
"device": str(device),
}
write_json(run_dirs["metrics"] / "run_summary.json", summary)
return summary
def build_arg_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Train the MiniDreamer world model.")
parser.add_argument("--config", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--replay-dir", type=Path, default=None, help="Optional existing replay directory.")
parser.add_argument("--resume-checkpoint", type=Path, default=None, help="Optional checkpoint to resume from.")
parser.add_argument(
"--allow-overwrite-existing-output",
action="store_true",
help="Allow overwriting an existing run directory when not resuming.",
)
return parser
def main() -> None:
parser = build_arg_parser()
args = parser.parse_args()
config = load_config(args.config)
summary = run_training(
config,
args.output_dir,
replay_dir=args.replay_dir,
resume_checkpoint=args.resume_checkpoint,
allow_overwrite_existing_output=args.allow_overwrite_existing_output,
)
print(summary)
if __name__ == "__main__":
main()
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