def _init_envs(self, config=None): if config is None: config = self.config self.envs = construct_envs( config, get_env_class(config.ENV_NAME), workers_ignore_signals=is_slurm_batch_job(), )
def vector_example(): """ Initialises a `habitat.VectorEnv` environment, then takes random steps. """ baseline_config = get_config("configs/tasks/pointnav.yaml") config = habitat.get_config("configs/tasks/pointnav.yaml") config.defrost() config.TASK_CONFIG = baseline_config.TASK_CONFIG config.NUM_ENVIRONMENTS = 14 config.SENSORS = baseline_config.SENSORS config.SIMULATOR_GPU_ID = 0 config.freeze() env_class = habitat.Env envs = construct_envs(config, env_class) _ = envs.reset() n_steps = 500 n_envs = envs.num_envs n_episodes = envs.number_of_episodes[0] for episode in range(n_episodes): count_steps = 0 print( f"Starting episode {episode}/{n_episodes} in parallel processes across {n_envs} environments." ) scene_names = [ ep.scene_id.split("/")[-1].strip(".glb") for ep in envs.current_episodes() ] for i, scene in enumerate(scene_names): print(f"Stepping around in Environment {i+1}: {scene}..") for step in range(n_steps): random_actions = [ action_space.sample() for action_space in envs.action_spaces ] _ = envs.step(random_actions) count_steps += 1 print("Episodes finished after {} steps.".format(count_steps))
def train(self) -> None: r"""Main method for DD-PPO. Returns: None """ self.local_rank, tcp_store = init_distrib_slurm( self.config.RL.DDPPO.distrib_backend ) add_signal_handlers() # Stores the number of workers that have finished their rollout num_rollouts_done_store = distrib.PrefixStore( "rollout_tracker", tcp_store ) num_rollouts_done_store.set("num_done", "0") self.world_rank = distrib.get_rank() self.world_size = distrib.get_world_size() self.config.defrost() self.config.TORCH_GPU_ID = self.local_rank self.config.SIMULATOR_GPU_ID = self.local_rank # Multiply by the number of simulators to make sure they also get unique seeds self.config.TASK_CONFIG.SEED += ( self.world_rank * self.config.NUM_PROCESSES ) self.config.freeze() random.seed(self.config.TASK_CONFIG.SEED) np.random.seed(self.config.TASK_CONFIG.SEED) torch.manual_seed(self.config.TASK_CONFIG.SEED) if torch.cuda.is_available(): self.device = torch.device("cuda", self.local_rank) torch.cuda.set_device(self.device) else: self.device = torch.device("cpu") self.envs = construct_envs( self.config, get_env_class(self.config.ENV_NAME), workers_ignore_signals=True, ) ppo_cfg = self.config.RL.PPO if ( not os.path.isdir(self.config.CHECKPOINT_FOLDER) and self.world_rank == 0 ): os.makedirs(self.config.CHECKPOINT_FOLDER) self._setup_actor_critic_agent(ppo_cfg) self.agent.init_distributed(find_unused_params=True) if self.world_rank == 0: logger.info( "agent number of trainable parameters: {}".format( sum( param.numel() for param in self.agent.parameters() if param.requires_grad ) ) ) observations = self.envs.reset() batch = batch_obs(observations, device=self.device) batch = apply_obs_transforms_batch(batch, self.obs_transforms) obs_space = self.obs_space if self._static_encoder: self._encoder = self.actor_critic.net.visual_encoder obs_space = SpaceDict( { "visual_features": spaces.Box( low=np.finfo(np.float32).min, high=np.finfo(np.float32).max, shape=self._encoder.output_shape, dtype=np.float32, ), **obs_space.spaces, } ) with torch.no_grad(): batch["visual_features"] = self._encoder(batch) rollouts = RolloutStorage( ppo_cfg.num_steps, self.envs.num_envs, obs_space, self.envs.action_spaces[0], ppo_cfg.hidden_size, num_recurrent_layers=self.actor_critic.net.num_recurrent_layers, ) rollouts.to(self.device) for sensor in rollouts.observations: rollouts.observations[sensor][0].copy_(batch[sensor]) # batch and observations may contain shared PyTorch CUDA # tensors. We must explicitly clear them here otherwise # they will be kept in memory for the entire duration of training! batch = None observations = None current_episode_reward = torch.zeros( self.envs.num_envs, 1, device=self.device ) running_episode_stats = dict( count=torch.zeros(self.envs.num_envs, 1, device=self.device), reward=torch.zeros(self.envs.num_envs, 1, device=self.device), ) window_episode_stats = defaultdict( lambda: deque(maxlen=ppo_cfg.reward_window_size) ) t_start = time.time() env_time = 0 pth_time = 0 count_steps = 0 count_checkpoints = 0 start_update = 0 prev_time = 0 lr_scheduler = LambdaLR( optimizer=self.agent.optimizer, lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES), ) interrupted_state = load_interrupted_state() if interrupted_state is not None: self.agent.load_state_dict(interrupted_state["state_dict"]) self.agent.optimizer.load_state_dict( interrupted_state["optim_state"] ) lr_scheduler.load_state_dict(interrupted_state["lr_sched_state"]) requeue_stats = interrupted_state["requeue_stats"] env_time = requeue_stats["env_time"] pth_time = requeue_stats["pth_time"] count_steps = requeue_stats["count_steps"] count_checkpoints = requeue_stats["count_checkpoints"] start_update = requeue_stats["start_update"] prev_time = requeue_stats["prev_time"] with ( TensorboardWriter( self.config.TENSORBOARD_DIR, flush_secs=self.flush_secs ) if self.world_rank == 0 else contextlib.suppress() ) as writer: for update in range(start_update, self.config.NUM_UPDATES): if ppo_cfg.use_linear_lr_decay: lr_scheduler.step() if ppo_cfg.use_linear_clip_decay: self.agent.clip_param = ppo_cfg.clip_param * linear_decay( update, self.config.NUM_UPDATES ) if EXIT.is_set(): self.envs.close() if REQUEUE.is_set() and self.world_rank == 0: requeue_stats = dict( env_time=env_time, pth_time=pth_time, count_steps=count_steps, count_checkpoints=count_checkpoints, start_update=update, prev_time=(time.time() - t_start) + prev_time, ) save_interrupted_state( dict( state_dict=self.agent.state_dict(), optim_state=self.agent.optimizer.state_dict(), lr_sched_state=lr_scheduler.state_dict(), config=self.config, requeue_stats=requeue_stats, ) ) requeue_job() return count_steps_delta = 0 self.agent.eval() for step in range(ppo_cfg.num_steps): ( delta_pth_time, delta_env_time, delta_steps, ) = self._collect_rollout_step( rollouts, current_episode_reward, running_episode_stats ) pth_time += delta_pth_time env_time += delta_env_time count_steps_delta += delta_steps # This is where the preemption of workers happens. If a # worker detects it will be a straggler, it preempts itself! if ( step >= ppo_cfg.num_steps * self.SHORT_ROLLOUT_THRESHOLD ) and int(num_rollouts_done_store.get("num_done")) > ( self.config.RL.DDPPO.sync_frac * self.world_size ): break num_rollouts_done_store.add("num_done", 1) self.agent.train() if self._static_encoder: self._encoder.eval() ( delta_pth_time, value_loss, action_loss, dist_entropy, ) = self._update_agent(ppo_cfg, rollouts) pth_time += delta_pth_time stats_ordering = sorted(running_episode_stats.keys()) stats = torch.stack( [running_episode_stats[k] for k in stats_ordering], 0 ) distrib.all_reduce(stats) for i, k in enumerate(stats_ordering): window_episode_stats[k].append(stats[i].clone()) stats = torch.tensor( [value_loss, action_loss, count_steps_delta], device=self.device, ) distrib.all_reduce(stats) count_steps += stats[2].item() if self.world_rank == 0: num_rollouts_done_store.set("num_done", "0") losses = [ stats[0].item() / self.world_size, stats[1].item() / self.world_size, ] deltas = { k: ( (v[-1] - v[0]).sum().item() if len(v) > 1 else v[0].sum().item() ) for k, v in window_episode_stats.items() } deltas["count"] = max(deltas["count"], 1.0) writer.add_scalar( "reward", deltas["reward"] / deltas["count"], count_steps, ) # Check to see if there are any metrics # that haven't been logged yet metrics = { k: v / deltas["count"] for k, v in deltas.items() if k not in {"reward", "count"} } if len(metrics) > 0: writer.add_scalars("metrics", metrics, count_steps) writer.add_scalars( "losses", {k: l for l, k in zip(losses, ["value", "policy"])}, count_steps, ) # log stats if update > 0 and update % self.config.LOG_INTERVAL == 0: logger.info( "update: {}\tfps: {:.3f}\t".format( update, count_steps / ((time.time() - t_start) + prev_time), ) ) logger.info( "update: {}\tenv-time: {:.3f}s\tpth-time: {:.3f}s\t" "frames: {}".format( update, env_time, pth_time, count_steps ) ) logger.info( "Average window size: {} {}".format( len(window_episode_stats["count"]), " ".join( "{}: {:.3f}".format(k, v / deltas["count"]) for k, v in deltas.items() if k != "count" ), ) ) # checkpoint model if update % self.config.CHECKPOINT_INTERVAL == 0: self.save_checkpoint( f"ckpt.{count_checkpoints}.pth", dict(step=count_steps), ) count_checkpoints += 1 self.envs.close()
def _eval_checkpoint( self, checkpoint_path: str, writer: TensorboardWriter, checkpoint_index: int = 0, ) -> None: r"""Evaluates a single checkpoint. Args: checkpoint_path: path of checkpoint writer: tensorboard writer object for logging to tensorboard checkpoint_index: index of cur checkpoint for logging Returns: None """ # Map location CPU is almost always better than mapping to a CUDA device. ckpt_dict = self.load_checkpoint(checkpoint_path, map_location="cpu") if self.config.EVAL.USE_CKPT_CONFIG: config = self._setup_eval_config(ckpt_dict["config"]) else: config = self.config.clone() ppo_cfg = config.RL.PPO config.defrost() config.TASK_CONFIG.DATASET.SPLIT = config.EVAL.SPLIT config.freeze() if len(self.config.VIDEO_OPTION) > 0: config.defrost() config.TASK_CONFIG.TASK.MEASUREMENTS.append("TOP_DOWN_MAP") config.TASK_CONFIG.TASK.MEASUREMENTS.append("COLLISIONS") config.freeze() logger.info(f"env config: {config}") self.envs = construct_envs(config, get_env_class(config.ENV_NAME)) self._setup_actor_critic_agent(ppo_cfg) self.agent.load_state_dict(ckpt_dict["state_dict"]) self.actor_critic = self.agent.actor_critic observations = self.envs.reset() batch = batch_obs(observations, device=self.device) batch = apply_obs_transforms_batch(batch, self.obs_transforms) current_episode_reward = torch.zeros(self.envs.num_envs, 1, device=self.device) test_recurrent_hidden_states = torch.zeros( self.actor_critic.net.num_recurrent_layers, self.config.NUM_PROCESSES, ppo_cfg.hidden_size, device=self.device, ) prev_actions = torch.zeros(self.config.NUM_PROCESSES, 1, device=self.device, dtype=torch.long) not_done_masks = torch.zeros(self.config.NUM_PROCESSES, 1, device=self.device) stats_episodes: Dict[Any, Any] = { } # dict of dicts that stores stats per episode rgb_frames = [[] for _ in range(self.config.NUM_PROCESSES) ] # type: List[List[np.ndarray]] if len(self.config.VIDEO_OPTION) > 0: os.makedirs(self.config.VIDEO_DIR, exist_ok=True) number_of_eval_episodes = self.config.TEST_EPISODE_COUNT if number_of_eval_episodes == -1: number_of_eval_episodes = sum(self.envs.number_of_episodes) else: total_num_eps = sum(self.envs.number_of_episodes) if total_num_eps < number_of_eval_episodes: logger.warn( f"Config specified {number_of_eval_episodes} eval episodes" ", dataset only has {total_num_eps}.") logger.warn(f"Evaluating with {total_num_eps} instead.") number_of_eval_episodes = total_num_eps pbar = tqdm.tqdm(total=number_of_eval_episodes) self.actor_critic.eval() while (len(stats_episodes) < number_of_eval_episodes and self.envs.num_envs > 0): current_episodes = self.envs.current_episodes() with torch.no_grad(): ( _, actions, _, test_recurrent_hidden_states, ) = self.actor_critic.act( batch, test_recurrent_hidden_states, prev_actions, not_done_masks, deterministic=False, ) prev_actions.copy_(actions) # type: ignore # NB: Move actions to CPU. If CUDA tensors are # sent in to env.step(), that will create CUDA contexts # in the subprocesses. # For backwards compatibility, we also call .item() to convert to # an int step_data = [a.item() for a in actions.to(device="cpu")] outputs = self.envs.step(step_data) observations, rewards_l, dones, infos = [ list(x) for x in zip(*outputs) ] batch = batch_obs(observations, device=self.device) batch = apply_obs_transforms_batch(batch, self.obs_transforms) not_done_masks = torch.tensor( [[0.0] if done else [1.0] for done in dones], dtype=torch.float, device=self.device, ) rewards = torch.tensor(rewards_l, dtype=torch.float, device=self.device).unsqueeze(1) current_episode_reward += rewards next_episodes = self.envs.current_episodes() envs_to_pause = [] n_envs = self.envs.num_envs for i in range(n_envs): if ( next_episodes[i].scene_id, next_episodes[i].episode_id, ) in stats_episodes: envs_to_pause.append(i) # episode ended if not_done_masks[i].item() == 0: pbar.update() episode_stats = {} episode_stats["reward"] = current_episode_reward[i].item() episode_stats.update( self._extract_scalars_from_info(infos[i])) current_episode_reward[i] = 0 # use scene_id + episode_id as unique id for storing stats stats_episodes[( current_episodes[i].scene_id, current_episodes[i].episode_id, )] = episode_stats if len(self.config.VIDEO_OPTION) > 0: generate_video( video_option=self.config.VIDEO_OPTION, video_dir=self.config.VIDEO_DIR, images=rgb_frames[i], episode_id=current_episodes[i].episode_id, checkpoint_idx=checkpoint_index, metrics=self._extract_scalars_from_info(infos[i]), tb_writer=writer, ) rgb_frames[i] = [] # episode continues elif len(self.config.VIDEO_OPTION) > 0: # TODO move normalization / channel changing out of the policy and undo it here frame = observations_to_image( {k: v[i] for k, v in batch.items()}, infos[i]) rgb_frames[i].append(frame) ( self.envs, test_recurrent_hidden_states, not_done_masks, current_episode_reward, prev_actions, batch, rgb_frames, ) = self._pause_envs( envs_to_pause, self.envs, test_recurrent_hidden_states, not_done_masks, current_episode_reward, prev_actions, batch, rgb_frames, ) num_episodes = len(stats_episodes) aggregated_stats = {} for stat_key in next(iter(stats_episodes.values())).keys(): aggregated_stats[stat_key] = ( sum(v[stat_key] for v in stats_episodes.values()) / num_episodes) for k, v in aggregated_stats.items(): logger.info(f"Average episode {k}: {v:.4f}") step_id = checkpoint_index if "extra_state" in ckpt_dict and "step" in ckpt_dict["extra_state"]: step_id = ckpt_dict["extra_state"]["step"] writer.add_scalars( "eval_reward", {"average reward": aggregated_stats["reward"]}, step_id, ) metrics = {k: v for k, v in aggregated_stats.items() if k != "reward"} if len(metrics) > 0: writer.add_scalars("eval_metrics", metrics, step_id) self.envs.close()
def train(self) -> None: r"""Main method for training PPO. Returns: None """ profiling_wrapper.configure( capture_start_step=self.config.PROFILING.CAPTURE_START_STEP, num_steps_to_capture=self.config.PROFILING.NUM_STEPS_TO_CAPTURE, ) self.envs = construct_envs(self.config, get_env_class(self.config.ENV_NAME)) ppo_cfg = self.config.RL.PPO self.device = (torch.device("cuda", self.config.TORCH_GPU_ID) if torch.cuda.is_available() else torch.device("cpu")) if not os.path.isdir(self.config.CHECKPOINT_FOLDER): os.makedirs(self.config.CHECKPOINT_FOLDER) self._setup_actor_critic_agent(ppo_cfg) logger.info("agent number of parameters: {}".format( sum(param.numel() for param in self.agent.parameters()))) rollouts = RolloutStorage( ppo_cfg.num_steps, self.envs.num_envs, self.obs_space, self.envs.action_spaces[0], ppo_cfg.hidden_size, ) rollouts.to(self.device) observations = self.envs.reset() batch = batch_obs(observations, device=self.device) batch = apply_obs_transforms_batch(batch, self.obs_transforms) for sensor in rollouts.observations: rollouts.observations[sensor][0].copy_(batch[sensor]) # batch and observations may contain shared PyTorch CUDA # tensors. We must explicitly clear them here otherwise # they will be kept in memory for the entire duration of training! batch = None observations = None current_episode_reward = torch.zeros(self.envs.num_envs, 1) running_episode_stats = dict( count=torch.zeros(self.envs.num_envs, 1), reward=torch.zeros(self.envs.num_envs, 1), ) window_episode_stats: DefaultDict[str, deque] = defaultdict( lambda: deque(maxlen=ppo_cfg.reward_window_size)) t_start = time.time() env_time = 0 pth_time = 0 count_steps = 0 count_checkpoints = 0 lr_scheduler = LambdaLR( optimizer=self.agent.optimizer, lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES ), # type: ignore ) with TensorboardWriter(self.config.TENSORBOARD_DIR, flush_secs=self.flush_secs) as writer: for update in range(self.config.NUM_UPDATES): profiling_wrapper.on_start_step() profiling_wrapper.range_push("train update") if ppo_cfg.use_linear_lr_decay: lr_scheduler.step() # type: ignore if ppo_cfg.use_linear_clip_decay: self.agent.clip_param = ppo_cfg.clip_param * linear_decay( update, self.config.NUM_UPDATES) profiling_wrapper.range_push("rollouts loop") for _step in range(ppo_cfg.num_steps): ( delta_pth_time, delta_env_time, delta_steps, ) = self._collect_rollout_step(rollouts, current_episode_reward, running_episode_stats) pth_time += delta_pth_time env_time += delta_env_time count_steps += delta_steps profiling_wrapper.range_pop() # rollouts loop ( delta_pth_time, value_loss, action_loss, dist_entropy, ) = self._update_agent(ppo_cfg, rollouts) pth_time += delta_pth_time for k, v in running_episode_stats.items(): window_episode_stats[k].append(v.clone()) deltas = { k: ((v[-1] - v[0]).sum().item() if len(v) > 1 else v[0].sum().item()) for k, v in window_episode_stats.items() } deltas["count"] = max(deltas["count"], 1.0) writer.add_scalar("reward", deltas["reward"] / deltas["count"], count_steps) # Check to see if there are any metrics # that haven't been logged yet metrics = { k: v / deltas["count"] for k, v in deltas.items() if k not in {"reward", "count"} } if len(metrics) > 0: writer.add_scalars("metrics", metrics, count_steps) losses = [value_loss, action_loss] writer.add_scalars( "losses", {k: l for l, k in zip(losses, ["value", "policy"])}, count_steps, ) # log stats if update > 0 and update % self.config.LOG_INTERVAL == 0: logger.info("update: {}\tfps: {:.3f}\t".format( update, count_steps / (time.time() - t_start))) logger.info( "update: {}\tenv-time: {:.3f}s\tpth-time: {:.3f}s\t" "frames: {}".format(update, env_time, pth_time, count_steps)) logger.info("Average window size: {} {}".format( len(window_episode_stats["count"]), " ".join("{}: {:.3f}".format(k, v / deltas["count"]) for k, v in deltas.items() if k != "count"), )) # checkpoint model if update % self.config.CHECKPOINT_INTERVAL == 0: self.save_checkpoint(f"ckpt.{count_checkpoints}.pth", dict(step=count_steps)) count_checkpoints += 1 profiling_wrapper.range_pop() # train update self.envs.close()