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()
