From b892e3777ef0e2a7b46f809f447185facdff94d0 Mon Sep 17 00:00:00 2001 From: Robin Meersman Date: Mon, 6 Apr 2026 16:30:38 +0200 Subject: [PATCH] feat: removed backup train code --- experiments/_train_backup.py.back | 435 ------------------------------ 1 file changed, 435 deletions(-) delete mode 100644 experiments/_train_backup.py.back diff --git a/experiments/_train_backup.py.back b/experiments/_train_backup.py.back deleted file mode 100644 index a46e500..0000000 --- a/experiments/_train_backup.py.back +++ /dev/null @@ -1,435 +0,0 @@ -import datetime -import random -import yaml -import subprocess -import sys -import time -from dataclasses import asdict -from functools import partial -from typing import Callable - -import flax -import jax -import jax.numpy as jnp -import matplotlib.pyplot as plt -import numpy as np -import optax -import torch -import tqdm -import tyro -from flax.training.train_state import TrainState -from torch.utils.tensorboard import SummaryWriter - -from brittle_star_project.dataclasses import PPOArgs -from brittle_star_project.dataclasses.EpisodeStatistics import EpisodeStatistics -from brittle_star_project.environment.BrittleStarJaxEnvWrapper import BrittleStarJaxEnvWrapper -from MLPs.mlps import ( - GenericDenseLayersWithActivation, - Actor, - OneDenseLayerMLP, - AgentParams, - Storage, -) -from plots import simple_plot -from ppo import PPO - - -def convert_obs_dict_to_array(obs_dict: dict) -> jnp.ndarray: - return jax.vmap(lambda o: jnp.concatenate([v.flatten() for v in o.values() if v.size > 0]))( - obs_dict - ) - - -def make_env(env_config_path: str | None, num_envs: int) -> Callable: - def thunk(): - if env_config_path is None: - return BrittleStarJaxEnvWrapper.default(num_envs=num_envs) - return BrittleStarJaxEnvWrapper.from_config(env_config_path, num_envs=num_envs) - - return thunk - - -def save_model(model_path: str, agent_state: TrainState, args: PPOArgs): - with open(model_path, "wb") as f: - f.write( - flax.serialization.to_bytes( - [ - vars(args), - [ - agent_state.params["network_params"], - agent_state.params["actor_params"], - agent_state.params["critic_params"], - ], - ] - ) - ) - - -def train(args: PPOArgs): - args.batch_size = args.num_envs * args.num_steps - args.minibatch_size = args.batch_size // args.num_minibatches - args.num_iterations = args.total_timesteps // args.batch_size - - # Try to get git short hash - try: - git_hash = ( - subprocess.check_output(["git", "rev-parse", "--short", "HEAD"]).decode("ascii").strip() - ) - except Exception: - git_hash = "none" - - run_name = f"{args.exp_name}__seed_{args.seed}__{git_hash}__{int(time.time())}" - # args.num_iterations = args.total_timesteps // args.batch_size - args.num_iterations = 5 - print(f"running name: {run_name}") - - if args.run_dir is None: - args.run_dir = f"runs/{run_name}" - - import os - - os.makedirs(args.run_dir, exist_ok=True) - - if args.track: - import wandb - - wandb.init( - project=args.wandb_project_name, - entity=args.wandb_entity, - sync_tensorboard=True, - config=vars(args), - name=run_name, - save_code=True, - ) - - writer = SummaryWriter(args.run_dir) - writer.add_text( - "hyperparameters", - "|param|value|\n|---|---|\n" + "\n".join(f"|{k}|{v}|" for k, v in vars(args).items()), - ) - - random.seed(args.seed) - np.random.seed(args.seed) - key = jax.random.PRNGKey(args.seed) - key, sensor_key, actor_key, critic_key, feature_extractor_key = jax.random.split(key, 5) - - torch.backends.cudnn.deterministic = args.torch_deterministic - device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") - print(f"Running on device: {device}") - - print("Creating the environment...") - env = make_env(env_config_path=args.env_config_path, num_envs=args.num_envs)() - print(f"Environment: {env}") - - episode_stats = EpisodeStatistics( - episode_returns=jnp.zeros(args.num_envs, dtype=jnp.float32), - episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32), - returned_episode_returns=jnp.zeros(args.num_envs, jnp.float32), - returned_episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32), - ) - - def step_env_wrapped(episode_stats: EpisodeStatistics, env_state, action): - next_env_state = env.step(env_state, action) - - # Extract per-environment signals from the state object - reward = next_env_state.reward # (num_envs,) - terminated = next_env_state.terminated # (num_envs,) - truncated = next_env_state.truncated # (num_envs,) - done = terminated | truncated # (num_envs,) - - new_episode_return = episode_stats.episode_returns + reward - new_episode_length = episode_stats.episode_lengths + 1 - - episode_stats = episode_stats.replace( - episode_returns=new_episode_return * (1 - done), - episode_lengths=new_episode_length * (1 - done), - returned_episode_returns=jnp.where( - done, new_episode_return, episode_stats.returned_episode_returns - ), - returned_episode_lengths=jnp.where( - done, new_episode_length, episode_stats.returned_episode_lengths - ), - ) - return ( - episode_stats, - next_env_state, - (convert_obs_dict_to_array(next_env_state.observations), reward, done), - ) - - def linear_schedule(count): - frac = 1.0 - (count // (args.num_minibatches * args.update_epochs)) / args.num_iterations - return args.learning_rate * frac - - print("Initializing the models...") - sensor = GenericDenseLayersWithActivation() - feature_extractor = GenericDenseLayersWithActivation() - actor = Actor(action_dim=env.single_action_space.shape[0]) # continuous actions for MJX - critic = OneDenseLayerMLP() - # messager = OneDenseLayerMLP() - - sample_obs = jnp.concatenate( - [ - v.flatten() - for v in env.single_observation_space.sample(rng=jax.random.PRNGKey(0)).values() - if v.size > 0 - ] - ) - sensor_params = sensor.init(sensor_key, sample_obs) - feature_extractor_params = feature_extractor.init(feature_extractor_key, sample_obs) - actor_params = actor.init(actor_key, sensor.apply(sensor_params, sample_obs)) - critic_params = critic.init( - critic_key, feature_extractor.apply(feature_extractor_params, sample_obs) - ) - - agent_state = TrainState.create( - apply_fn=None, - params=asdict( - AgentParams(sensor_params, actor_params, critic_params, feature_extractor_params) - ), - tx=optax.chain( - optax.clip_by_global_norm(args.max_grad_norm), - optax.inject_hyperparams(optax.adam)( - learning_rate=linear_schedule if args.anneal_lr else args.learning_rate, eps=1e-5 - ), - ), - ) - - sensor.apply = jax.jit(sensor.apply) - feature_extractor.apply = jax.jit(feature_extractor.apply) - actor.apply = jax.jit(actor.apply) - critic.apply = jax.jit(critic.apply) - ppo_instance = PPO(args, sensor, actor, critic, feature_extractor) - - @jax.jit - def get_action_and_value_noise( - agent_state: TrainState, - next_obs: jnp.ndarray, - key: jax.random.PRNGKey, - ): - hidden = sensor.apply(agent_state.params["sensor_params"], next_obs) - hidden_critic = feature_extractor.apply( - agent_state.params["feature_extractor_params"], next_obs - ) - - # Continuous actions: sample from a Gaussian parameterized by the actor - mean, log_std = actor.apply(agent_state.params["actor_params"], hidden) - key, subkey = jax.random.split(key) - noise = jax.random.normal(subkey, shape=mean.shape) - std = jnp.exp(log_std) - action = mean + noise * std - logprob = -0.5 * (((action - mean) / std) ** 2 + 2 * log_std + jnp.log(2 * jnp.pi)).sum(-1) - value = critic.apply(agent_state.params["critic_params"], hidden_critic) - return action, logprob, value.squeeze(-1), key - - # GAE - @jax.jit - def compute_gae_once(carry, inp, gamma, gae_lambda): - advantages = carry - nextdone, nextvalues, curvalues, reward = inp - nextnonterminal = 1.0 - nextdone - delta = reward + gamma * nextvalues * nextnonterminal - curvalues - advantages = delta + gamma * gae_lambda * nextnonterminal * advantages - return advantages, advantages - - @jax.jit - def compute_gae(agent_state, next_obs, next_done, storage): - next_value = critic.apply( - agent_state.params["critic_params"], - sensor.apply(agent_state.params["sensor_params"], next_obs), - ).squeeze(-1) - - advantages = jnp.zeros((args.num_envs,)) - dones = jnp.concatenate([storage.dones, next_done[None, :]], axis=0) - values = jnp.concatenate([storage.values, next_value[None, :]], axis=0) - _, advantages = jax.lax.scan( - partial(compute_gae_once, gamma=args.gamma, gae_lambda=args.gae_lambda), - advantages, - (dones[1:], values[1:], values[:-1], storage.rewards), - reverse=True, - ) - return storage.replace(advantages=advantages, returns=advantages + storage.values) - - # END GAE - - # --- Main training loop --- - global_step = 0 - start_time = time.time() - - # Reset once to get initial state - print("Resetting the environment...") - if not sys.stdout.isatty(): - print(f">>> [HPC] Initial reset started: {time.ctime()}", flush=True) - next_env_state = env.reset(seed=args.seed) - next_obs = convert_obs_dict_to_array(next_env_state.observations) - next_done = jnp.zeros(args.num_envs, dtype=jnp.bool_) - if not sys.stdout.isatty(): - print(f">>> [HPC] Initial reset completed: {time.ctime()}", flush=True) - - def step_once(carry, _, env_step_fn): - agent_state, episode_stats, obs, done, key, env_state = carry - action, logprob, value, key = get_action_and_value_noise(agent_state, obs, key) - - episode_stats, env_state, (next_obs, reward, next_done) = env_step_fn( - episode_stats, env_state, action - ) - - storage = Storage( - obs=obs, - actions=action, - logprobs=logprob, - dones=done, - values=value, - rewards=reward, - returns=jnp.zeros_like(reward), - advantages=jnp.zeros_like(reward), - ) - return (agent_state, episode_stats, next_obs, next_done, key, env_state), storage - - def rollout( - agent_state, episode_stats, next_obs, next_done, key, env_state, step_once_fn, max_steps - ): - (agent_state, episode_stats, next_obs, next_done, key, env_state), storage = jax.lax.scan( - step_once_fn, - (agent_state, episode_stats, next_obs, next_done, key, env_state), - (), - max_steps, - ) - return agent_state, episode_stats, next_obs, next_done, storage, key, env_state - - rollout = partial( - rollout, - step_once_fn=partial(step_once, env_step_fn=step_env_wrapped), - max_steps=args.num_steps, - ) - - print("Starting training...") - iters_bar = tqdm.tqdm( - range(1, args.num_iterations + 1), - disable=not sys.stdout.isatty(), - ) - returns = [] - is_tty = sys.stdout.isatty() - for iteration in iters_bar: - iteration_time_start = time.time() - - if not is_tty and iteration == 1: - print(f">>> [HPC] Starting first rollout (JIT): {time.ctime()}", flush=True) - - agent_state, episode_stats, next_obs, next_done, storage, key, next_env_state = rollout( - agent_state, episode_stats, next_obs, next_done, key, next_env_state - ) - - if not is_tty and iteration == 1: - print(f">>> [HPC] First rollout completed: {time.ctime()}", flush=True) - - global_step += args.num_steps * args.num_envs - storage = compute_gae(agent_state, next_obs, next_done, storage) - - if not is_tty and iteration == 1: - print(f">>> [HPC] Starting first PPO update (JIT): {time.ctime()}", flush=True) - - agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, key = ppo_instance.update_ppo( - agent_state, storage, key - ) - - if not is_tty and iteration == 1: - print(f">>> [HPC] First PPO update completed: {time.ctime()}", flush=True) - - losses.append(jnp.mean(loss)) - - avg_episodic_return = np.mean(jax.device_get(episode_stats.returned_episode_returns)) - iters_bar.set_postfix_str( - f"global_step={global_step}, avg_episodic_return={avg_episodic_return}" - ) - - writer.add_scalar("charts/avg_episodic_return", avg_episodic_return, global_step) - writer.add_scalar( - "charts/avg_episodic_length", - np.mean(jax.device_get(episode_stats.returned_episode_lengths)), - global_step, - ) - writer.add_scalar( - "charts/learning_rate", - agent_state.opt_state[1].hyperparams["learning_rate"].item(), - global_step, - ) - writer.add_scalar("losses/value_loss", v_loss[-1, -1].item(), global_step) - writer.add_scalar("losses/policy_loss", pg_loss[-1, -1].item(), global_step) - writer.add_scalar("losses/entropy", entropy_loss[-1, -1].item(), global_step) - writer.add_scalar("losses/approx_kl", approx_kl[-1, -1].item(), global_step) - writer.add_scalar("losses/loss", loss[-1, -1].item(), global_step) - - # iters_bar.set_postfix_str(f"SPS: {int(global_step / (time.time() - start_time))}") - - writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step) - writer.add_scalar( - "charts/SPS_update", - int(args.num_envs * args.num_steps / (time.time() - iteration_time_start)), - global_step, - ) - - if not is_tty: - sps = int(global_step / (time.time() - start_time)) - remaining_steps = args.total_timesteps - global_step - eta_seconds = int(remaining_steps / sps) if sps > 0 else 0 - eta_str = str(datetime.timedelta(seconds=eta_seconds)) - - print( - f"Iteration {iteration}/{args.num_iterations} | " - f"Step {global_step}/{args.total_timesteps} | " - f"SPS {sps} | " - f"Return {avg_episodic_return:.4f} | " - f"ETA {eta_str}", - flush=True, - ) - - if args.save_model: - model_path = f"{args.run_dir}/{args.exp_name}.cleanrl_model" - with open(model_path, "wb") as f: - f.write( - flax.serialization.to_bytes( - [ - vars(args), - [ - agent_state.params["sensor_params"], - agent_state.params["actor_params"], - agent_state.params["critic_params"], - agent_state.params["feature_extractor_params"], - ], - ] - ) - ) - print(f"model saved to {model_path}") - - env.close() - writer.close() - - print("Saving loss plot...") - plt.plot(losses) - plt.title("PPO Loss, mean over minibatches") - plt.savefig(f"{args.run_dir}/{args.exp_name}_losses.png") - plt.close() - - -def main() -> None: - temp_args = tyro.cli(PPOArgs) - - if temp_args.env_config_path is not None: - with open(temp_args.env_config_path, "r") as f: - config = yaml.safe_load(f) - if config: - # parse PPOArgs with defaults from yaml. - for key, value in config.items(): - if hasattr(temp_args, key): - setattr(temp_args, key, value) - - # Re-parse CLI to ensure they OVERRIDE the yaml - args = tyro.cli(PPOArgs, default=temp_args) - else: - args = temp_args - - train(args) - - -if __name__ == "__main__": - main()