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trainer.py
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executable file
·305 lines (234 loc) · 15.3 KB
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from collections import defaultdict
from functools import partial
from pathlib import Path
import shutil
import sys
import time
from typing import Any, Dict, Optional, Tuple
import torch
import torch.nn as nn
from tqdm import tqdm
import wandb
from agent import Agent
from collector import Collector
from envs import SingleProcessEnv, MultiProcessEnv
from episode import Episode
from make_reconstructions import make_reconstructions_from_batch
from models.actor_critic import ActorCritic
from models.world_model import WorldModel, TransformerConfig
from utils import configure_optimizer, EpisodeDirManager, set_seed
from config import *
from envs import make_unity_gym
from dataset import EpisodesDatasetRamMonitoring, EpisodesDataset
from models.tokenizer import Tokenizer, Encoder, Decoder, EncoderDecoderConfig
import datetime
def create_foldername():
now = datetime.datetime.now()
year = str(now.year)
month = str(now.month).zfill(2)
day = str(now.day).zfill(2)
hour = str(now.hour).zfill(2)
minute = str(now.minute).zfill(2)
second = str(now.second).zfill(2)
foldername = f"{year}-{month}-{day}_{hour}-{minute}-{second}"
return foldername
class Trainer:
def __init__(self):
wandb.init(
config=dict(train_cfg),
reinit=True,
resume=True,
**train_cfg.wandb
)
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False
if train_cfg.common.seed is not None:
set_seed(train_cfg.common.seed)
self.start_epoch = 1
self.device = torch.device(train_cfg.common.device)
self.cfg = train_cfg
self.base_output = Path('output') / create_foldername() if not train_cfg.common.resume else Path(train_cfg.common.resume_path)
self.ckpt_dir = self.base_output / 'checkpoints'
self.media_dir = self.base_output / 'media'
self.episode_dir = self.media_dir / 'episodes'
self.reconstructions_dir = self.media_dir / 'reconstructions'
if not train_cfg.common.resume:
self.base_output.mkdir(exist_ok=True, parents=True)
self.ckpt_dir.mkdir(exist_ok=True, parents=False)
self.media_dir.mkdir(exist_ok=True, parents=False)
self.episode_dir.mkdir(exist_ok=True, parents=False)
self.reconstructions_dir.mkdir(exist_ok=True, parents=False)
episode_manager_train = EpisodeDirManager(self.episode_dir / 'train', max_num_episodes=train_cfg.collector_train.num_episodes_to_save)
episode_manager_test = EpisodeDirManager(self.episode_dir / 'test', max_num_episodes=train_cfg.collector_test.num_episodes_to_save)
self.episode_manager_imagination = EpisodeDirManager(self.episode_dir / 'imagination', max_num_episodes=train_cfg.evaluation_settings.actor_critic.num_episodes_to_save)
def create_env(num_envs):
env_fn = partial(make_unity_gym, size=env_cfg.size)
return MultiProcessEnv(env_fn, num_envs, should_wait_num_envs_ratio=1.0) if num_envs > 1 else SingleProcessEnv(env_fn)
if self.cfg.training_settings.should:
train_env = create_env(train_cfg.collector_train.num_env)
self.train_dataset = EpisodesDatasetRamMonitoring(**col_cfg.train)
self.train_collector = Collector(train_env, self.train_dataset, episode_manager_train)
if self.cfg.evaluation_settings.should:
test_env = create_env(train_cfg.collector_test.num_env)
self.test_dataset = EpisodesDataset(**col_cfg.test)
self.test_collector = Collector(test_env, self.test_dataset, episode_manager_test)
assert self.cfg.training_settings.should or self.cfg.evaluation_settings.should
env = train_env if self.cfg.training_settings.should else test_env
tokenizer = Tokenizer(
vocab_size=tok_cfg.tokenizer.vocab_size,
embed_dim=tok_cfg.tokenizer.embed_dim,
encoder=Encoder(EncoderDecoderConfig(**tok_cfg.tokenizer.encoder)),
decoder=Decoder(EncoderDecoderConfig(**tok_cfg.tokenizer.decoder))
)
world_model = WorldModel(obs_vocab_size=tokenizer.vocab_size, act_vocab_size=env.num_actions, config=TransformerConfig(**worldmodel_cfg))
actor_critic = ActorCritic(**ac_cfg, act_vocab_size=env.num_actions)
self.agent = Agent(tokenizer, world_model, actor_critic).to(self.device)
print(f'{sum(p.numel() for p in self.agent.tokenizer.parameters())} parameters in agent.tokenizer')
print(f'{sum(p.numel() for p in self.agent.world_model.parameters())} parameters in agent.world_model')
print(f'{sum(p.numel() for p in self.agent.actor_critic.parameters())} parameters in agent.actor_critic')
self.optimizer_tokenizer = torch.optim.Adam(self.agent.tokenizer.parameters(), lr=train_cfg.training_settings.learning_rate)
self.optimizer_world_model = configure_optimizer(self.agent.world_model, train_cfg.training_settings.learning_rate, train_cfg.training_settings.world_model.weight_decay)
self.optimizer_actor_critic = torch.optim.Adam(self.agent.actor_critic.parameters(), lr=train_cfg.training_settings.learning_rate)
if train_cfg.common.resume:
self.load_checkpoint()
def run(self) -> None:
for epoch in range(self.start_epoch, 1 + self.cfg.common.epochs):
print(f"\nEpoch {epoch} / {self.cfg.common.epochs}\n")
start_time = time.time()
to_log = []
if self.cfg.training_settings.should:
if epoch <= self.cfg.collector_train.stop_after_epochs:
to_log += self.train_collector.collect(self.agent, epoch, **self.cfg.collector_train.config)
to_log += self.train_agent(epoch)
if self.cfg.evaluation_settings.should and (epoch % self.cfg.evaluation_settings.every == 0):
self.test_dataset.clear()
to_log += self.test_collector.collect(self.agent, epoch, **self.cfg.collector_test.config)
to_log += self.eval_agent(epoch)
if self.cfg.training_settings.should:
self.save_checkpoint(epoch, save_agent_only=not self.cfg.common.do_checkpoint)
to_log.append({'duration': (time.time() - start_time) / 3600})
for metrics in to_log:
wandb.log({'epoch': epoch, **metrics})
self.finish()
print("end!")
def train_agent(self, epoch: int) -> None:
self.agent.train()
self.agent.zero_grad()
metrics_tokenizer, metrics_world_model, metrics_actor_critic = {}, {}, {}
cfg_tokenizer = self.cfg.training_settings.tokenizer
cfg_world_model = self.cfg.training_settings.world_model
cfg_actor_critic = self.cfg.training_settings.actor_critic
w = self.cfg.training_settings.sampling_weights
if epoch > cfg_tokenizer.start_after_epochs:
metrics_tokenizer = self.train_component(self.agent.tokenizer, self.optimizer_tokenizer, sequence_length=1, sample_from_start=True, sampling_weights=w, **cfg_tokenizer)
self.agent.tokenizer.eval()
if epoch > cfg_world_model.start_after_epochs:
metrics_world_model = self.train_component(self.agent.world_model, self.optimizer_world_model, sequence_length=self.cfg.common.sequence_length, sample_from_start=True, sampling_weights=w, tokenizer=self.agent.tokenizer, **cfg_world_model)
self.agent.world_model.eval()
if epoch > cfg_actor_critic.start_after_epochs:
metrics_actor_critic = self.train_component(self.agent.actor_critic, self.optimizer_actor_critic, sequence_length=1 + self.cfg.training_settings.actor_critic.burn_in, sample_from_start=False, sampling_weights=w, tokenizer=self.agent.tokenizer, world_model=self.agent.world_model, **cfg_actor_critic)
self.agent.actor_critic.eval()
return [{'epoch': epoch, **metrics_tokenizer, **metrics_world_model, **metrics_actor_critic}]
def train_component(self, component: nn.Module, optimizer: torch.optim.Optimizer, steps_per_epoch: int, batch_num_samples: int, grad_acc_steps: int, max_grad_norm: Optional[float], sequence_length: int, sampling_weights: Optional[Tuple[float]], sample_from_start: bool, **kwargs_loss: Any) -> Dict[str, float]:
loss_total_epoch = 0.0
intermediate_losses = defaultdict(float)
for _ in tqdm(range(steps_per_epoch), desc=f"Training {str(component)}", file=sys.stdout):
optimizer.zero_grad()
for _ in range(grad_acc_steps):
batch = self.train_dataset.sample_batch(batch_num_samples, sequence_length, sampling_weights, sample_from_start)
batch = self._to_device(batch)
losses = component.compute_loss(batch, **kwargs_loss) / grad_acc_steps
loss_total_step = losses.loss_total
loss_total_step.backward()
loss_total_epoch += loss_total_step.item() / steps_per_epoch
for loss_name, loss_value in losses.intermediate_losses.items():
intermediate_losses[f"{str(component)}/train/{loss_name}"] += loss_value / steps_per_epoch
if max_grad_norm is not None:
torch.nn.utils.clip_grad_norm_(component.parameters(), max_grad_norm)
optimizer.step()
metrics = {f'{str(component)}/train/total_loss': loss_total_epoch, **intermediate_losses}
return metrics
@torch.no_grad()
def eval_agent(self, epoch: int) -> None:
self.agent.eval()
metrics_tokenizer, metrics_world_model = {}, {}
cfg_tokenizer = self.cfg.evaluation_settings.tokenizer
cfg_world_model = self.cfg.evaluation_settings.world_model
cfg_actor_critic = self.cfg.evaluation_settings.actor_critic
if epoch > cfg_tokenizer.start_after_epochs:
metrics_tokenizer = self.eval_component(self.agent.tokenizer, cfg_tokenizer.batch_num_samples, sequence_length=1)
if epoch > cfg_world_model.start_after_epochs:
metrics_world_model = self.eval_component(self.agent.world_model, cfg_world_model.batch_num_samples, sequence_length=self.cfg.common.sequence_length, tokenizer=self.agent.tokenizer)
if epoch > cfg_actor_critic.start_after_epochs:
self.inspect_imagination(epoch)
if cfg_tokenizer.save_reconstructions:
batch = self._to_device(self.test_dataset.sample_batch(batch_num_samples=3, sequence_length=self.cfg.common.sequence_length))
make_reconstructions_from_batch(batch, save_dir=self.reconstructions_dir, epoch=epoch, tokenizer=self.agent.tokenizer)
return [metrics_tokenizer, metrics_world_model]
@torch.no_grad()
def eval_component(self, component: nn.Module, batch_num_samples: int, sequence_length: int, **kwargs_loss: Any) -> Dict[str, float]:
loss_total_epoch = 0.0
intermediate_losses = defaultdict(float)
steps = 0
pbar = tqdm(desc=f"Evaluating {str(component)}", file=sys.stdout)
for batch in self.test_dataset.traverse(batch_num_samples, sequence_length):
batch = self._to_device(batch)
losses = component.compute_loss(batch, **kwargs_loss)
loss_total_epoch += losses.loss_total.item()
for loss_name, loss_value in losses.intermediate_losses.items():
intermediate_losses[f"{str(component)}/eval/{loss_name}"] += loss_value
steps += 1
pbar.update(1)
intermediate_losses = {k: v / steps for k, v in intermediate_losses.items()}
metrics = {f'{str(component)}/eval/total_loss': loss_total_epoch / steps, **intermediate_losses}
return metrics
@torch.no_grad()
def inspect_imagination(self, epoch: int) -> None:
mode_str = 'imagination'
batch = self.test_dataset.sample_batch(batch_num_samples=self.episode_manager_imagination.max_num_episodes, sequence_length=1 + self.cfg.training_settings.actor_critic.burn_in, sample_from_start=False)
outputs = self.agent.actor_critic.imagine(self._to_device(batch), self.agent.tokenizer, self.agent.world_model, horizon=self.cfg.evaluation_settings.actor_critic.horizon, show_pbar=True)
to_log = []
for i, (o, a, r, d) in enumerate(zip(outputs.observations.cpu(), outputs.actions.cpu(), outputs.rewards.cpu(), outputs.ends.long().cpu())): # Make everything (N, T, ...) instead of (T, N, ...)
episode = Episode(o, a, r, d, torch.ones_like(d))
episode_id = (epoch - 1 - self.cfg.training_settings.actor_critic.start_after_epochs) * outputs.observations.size(0) + i
self.episode_manager_imagination.save(episode, episode_id, epoch)
metrics_episode = {k: v for k, v in episode.compute_metrics().__dict__.items()}
metrics_episode['episode_num'] = episode_id
metrics_episode['action_histogram'] = wandb.Histogram(episode.actions.numpy(), num_bins=self.agent.world_model.act_vocab_size)
to_log.append({f'{mode_str}/{k}': v for k, v in metrics_episode.items()})
return to_log
def _save_checkpoint(self, epoch: int, save_agent_only: bool) -> None:
torch.save(self.agent.state_dict(), self.ckpt_dir / 'last.pt')
if not save_agent_only:
torch.save(epoch, self.ckpt_dir / 'epoch.pt')
torch.save({
"optimizer_tokenizer": self.optimizer_tokenizer.state_dict(),
"optimizer_world_model": self.optimizer_world_model.state_dict(),
"optimizer_actor_critic": self.optimizer_actor_critic.state_dict(),
}, self.ckpt_dir / 'optimizer.pt')
ckpt_dataset_dir = self.ckpt_dir / 'dataset'
ckpt_dataset_dir.mkdir(exist_ok=True, parents=False)
self.train_dataset.update_disk_checkpoint(ckpt_dataset_dir)
if self.cfg.evaluation_settings.should:
torch.save(self.test_dataset.num_seen_episodes, self.ckpt_dir / 'num_seen_episodes_test_dataset.pt')
def save_checkpoint(self, epoch: int, save_agent_only: bool) -> None:
tmp_checkpoint_dir = Path('checkpoints_tmp')
shutil.copytree(src=self.ckpt_dir, dst=tmp_checkpoint_dir, ignore=shutil.ignore_patterns('dataset'))
self._save_checkpoint(epoch, save_agent_only)
shutil.rmtree(tmp_checkpoint_dir)
def load_checkpoint(self) -> None:
assert self.ckpt_dir.is_dir()
self.start_epoch = torch.load(self.ckpt_dir / 'epoch.pt') + 1
self.agent.load(self.ckpt_dir / 'last.pt', device=self.device)
ckpt_opt = torch.load(self.ckpt_dir / 'optimizer.pt', map_location=self.device)
self.optimizer_tokenizer.load_state_dict(ckpt_opt['optimizer_tokenizer'])
self.optimizer_world_model.load_state_dict(ckpt_opt['optimizer_world_model'])
self.optimizer_actor_critic.load_state_dict(ckpt_opt['optimizer_actor_critic'])
self.train_dataset.load_disk_checkpoint(self.ckpt_dir / 'dataset')
if self.cfg.evaluation_settings.should:
self.test_dataset.num_seen_episodes = torch.load(self.ckpt_dir / 'num_seen_episodes_test_dataset.pt')
print(f'Successfully loaded model, optimizer and {len(self.train_dataset)} episodes from {self.ckpt_dir.absolute()}.')
def _to_device(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
return {k: batch[k].to(self.device) for k in batch}
def finish(self) -> None:
wandb.finish()