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))
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)
obs_space = self.envs.observation_spaces[0]
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 = list(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 _worker_fn(world_rank: int, world_size: int, port: int,
unused_params: bool):
device = (torch.device("cuda")
if torch.cuda.is_available() else torch.device("cpu"))
tcp_store = distrib.TCPStore( # type: ignore
"127.0.0.1", port, world_size, world_rank == 0)
distrib.init_process_group("gloo",
store=tcp_store,
rank=world_rank,
world_size=world_size)
config = get_config("habitat_baselines/config/test/ppo_pointnav_test.yaml")
obs_space = gym.spaces.Dict({
IntegratedPointGoalGPSAndCompassSensor.cls_uuid:
gym.spaces.Box(
low=np.finfo(np.float32).min,
high=np.finfo(np.float32).max,
shape=(2, ),
dtype=np.float32,
)
})
action_space = ActionSpace({"move": EmptySpace()})
actor_critic = PointNavBaselinePolicy.from_config(config, obs_space,
action_space)
# This use adds some arbitrary parameters that aren't part of the computation
# graph, so they will mess up DDP if they aren't correctly ignored by it
if unused_params:
actor_critic.unused = nn.Linear(64, 64)
actor_critic.to(device=device)
ppo_cfg = config.RL.PPO
agent = DDPPO(
actor_critic=actor_critic,
clip_param=ppo_cfg.clip_param,
ppo_epoch=ppo_cfg.ppo_epoch,
num_mini_batch=ppo_cfg.num_mini_batch,
value_loss_coef=ppo_cfg.value_loss_coef,
entropy_coef=ppo_cfg.entropy_coef,
lr=ppo_cfg.lr,
eps=ppo_cfg.eps,
max_grad_norm=ppo_cfg.max_grad_norm,
use_normalized_advantage=ppo_cfg.use_normalized_advantage,
)
agent.init_distributed()
rollouts = RolloutStorage(
ppo_cfg.num_steps,
2,
obs_space,
action_space,
ppo_cfg.hidden_size,
num_recurrent_layers=actor_critic.net.num_recurrent_layers,
is_double_buffered=False,
)
rollouts.to(device)
for k, v in rollouts.buffers["observations"].items():
rollouts.buffers["observations"][k] = torch.randn_like(v)
# Add two steps so batching works
rollouts.advance_rollout()
rollouts.advance_rollout()
# Get a single batch
batch = next(rollouts.recurrent_generator(rollouts.buffers["returns"], 1))
# Call eval actions through the internal wrapper that is used in
# agent.update
value, action_log_probs, dist_entropy, _ = agent._evaluate_actions(
batch["observations"],
batch["recurrent_hidden_states"],
batch["prev_actions"],
batch["masks"],
batch["actions"],
)
# Backprop on things
(value.mean() + action_log_probs.mean() + dist_entropy.mean()).backward()
# Make sure all ranks have very similar parameters
for param in actor_critic.parameters():
if param.grad is not None:
grads = [param.grad.detach().clone() for _ in range(world_size)]
distrib.all_gather(grads, grads[world_rank])
for i in range(world_size):
assert torch.isclose(grads[i], grads[world_rank]).all()
class PPOTrainer(BaseRLTrainer):
r"""Trainer class for PPO algorithm
Paper: https://arxiv.org/abs/1707.06347.
"""
supported_tasks = ["Nav-v0"]
SHORT_ROLLOUT_THRESHOLD: float = 0.25
_is_distributed: bool
_obs_batching_cache: ObservationBatchingCache
envs: VectorEnv
agent: PPO
actor_critic: Policy
def __init__(self, config=None):
interrupted_state = load_interrupted_state()
if interrupted_state is not None:
config = interrupted_state["config"]
super().__init__(config)
self.actor_critic = None
self.agent = None
self.envs = None
self.obs_transforms = []
self._static_encoder = False
self._encoder = None
self._obs_space = None
# Distirbuted if the world size would be
# greater than 1
self._is_distributed = get_distrib_size()[2] > 1
self._obs_batching_cache = ObservationBatchingCache()
@property
def obs_space(self):
if self._obs_space is None and self.envs is not None:
self._obs_space = self.envs.observation_spaces[0]
return self._obs_space
@obs_space.setter
def obs_space(self, new_obs_space):
self._obs_space = new_obs_space
def _all_reduce(self, t: torch.Tensor) -> torch.Tensor:
r"""All reduce helper method that moves things to the correct
device and only runs if distributed
"""
if not self._is_distributed:
return t
orig_device = t.device
t = t.to(device=self.device)
torch.distributed.all_reduce(t)
return t.to(device=orig_device)
def _setup_actor_critic_agent(self, ppo_cfg: Config) -> None:
r"""Sets up actor critic and agent for PPO.
Args:
ppo_cfg: config node with relevant params
Returns:
None
"""
logger.add_filehandler(self.config.LOG_FILE)
policy = baseline_registry.get_policy(self.config.RL.POLICY.name)
observation_space = self.obs_space
self.obs_transforms = get_active_obs_transforms(self.config)
observation_space = apply_obs_transforms_obs_space(
observation_space, self.obs_transforms
)
self.actor_critic = policy.from_config(
self.config, observation_space, self.envs.action_spaces[0]
)
self.obs_space = observation_space
self.actor_critic.to(self.device)
if (
self.config.RL.DDPPO.pretrained_encoder
or self.config.RL.DDPPO.pretrained
):
pretrained_state = torch.load(
self.config.RL.DDPPO.pretrained_weights, map_location="cpu"
)
if self.config.RL.DDPPO.pretrained:
self.actor_critic.load_state_dict(
{
k[len("actor_critic.") :]: v
for k, v in pretrained_state["state_dict"].items()
}
)
elif self.config.RL.DDPPO.pretrained_encoder:
prefix = "actor_critic.net.visual_encoder."
self.actor_critic.net.visual_encoder.load_state_dict(
{
k[len(prefix) :]: v
for k, v in pretrained_state["state_dict"].items()
if k.startswith(prefix)
}
)
if not self.config.RL.DDPPO.train_encoder:
self._static_encoder = True
for param in self.actor_critic.net.visual_encoder.parameters():
param.requires_grad_(False)
if self.config.RL.DDPPO.reset_critic:
nn.init.orthogonal_(self.actor_critic.critic.fc.weight)
nn.init.constant_(self.actor_critic.critic.fc.bias, 0)
self.agent = (DDPPO if self._is_distributed else PPO)(
actor_critic=self.actor_critic,
clip_param=ppo_cfg.clip_param,
ppo_epoch=ppo_cfg.ppo_epoch,
num_mini_batch=ppo_cfg.num_mini_batch,
value_loss_coef=ppo_cfg.value_loss_coef,
entropy_coef=ppo_cfg.entropy_coef,
lr=ppo_cfg.lr,
eps=ppo_cfg.eps,
max_grad_norm=ppo_cfg.max_grad_norm,
use_normalized_advantage=ppo_cfg.use_normalized_advantage,
)
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 _init_train(self):
if self.config.RL.DDPPO.force_distributed:
self._is_distributed = True
if is_slurm_batch_job():
add_signal_handlers()
if self._is_distributed:
local_rank, tcp_store = init_distrib_slurm(
self.config.RL.DDPPO.distrib_backend
)
if rank0_only():
logger.info(
"Initialized DD-PPO with {} workers".format(
torch.distributed.get_world_size()
)
)
self.config.defrost()
self.config.TORCH_GPU_ID = local_rank
self.config.SIMULATOR_GPU_ID = local_rank
# Multiply by the number of simulators to make sure they also get unique seeds
self.config.TASK_CONFIG.SEED += (
torch.distributed.get_rank() * self.config.NUM_ENVIRONMENTS
)
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)
self.num_rollouts_done_store = torch.distributed.PrefixStore(
"rollout_tracker", tcp_store
)
self.num_rollouts_done_store.set("num_done", "0")
if rank0_only() and self.config.VERBOSE:
logger.info(f"config: {self.config}")
profiling_wrapper.configure(
capture_start_step=self.config.PROFILING.CAPTURE_START_STEP,
num_steps_to_capture=self.config.PROFILING.NUM_STEPS_TO_CAPTURE,
)
self._init_envs()
ppo_cfg = self.config.RL.PPO
if torch.cuda.is_available():
self.device = torch.device("cuda", self.config.TORCH_GPU_ID)
torch.cuda.set_device(self.device)
else:
self.device = torch.device("cpu")
if rank0_only() and not os.path.isdir(self.config.CHECKPOINT_FOLDER):
os.makedirs(self.config.CHECKPOINT_FOLDER)
self._setup_actor_critic_agent(ppo_cfg)
if self._is_distributed:
self.agent.init_distributed(find_unused_params=True)
logger.info(
"agent number of parameters: {}".format(
sum(param.numel() for param in self.agent.parameters())
)
)
obs_space = self.obs_space
if self._static_encoder:
self._encoder = self.actor_critic.net.visual_encoder
obs_space = spaces.Dict(
{
"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,
}
)
self._nbuffers = 2 if ppo_cfg.use_double_buffered_sampler else 1
self.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,
is_double_buffered=ppo_cfg.use_double_buffered_sampler,
)
self.rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs(
observations, device=self.device, cache=self._obs_batching_cache
)
batch = apply_obs_transforms_batch(batch, self.obs_transforms)
if self._static_encoder:
with torch.no_grad():
batch["visual_features"] = self._encoder(batch)
self.rollouts.buffers["observations"][0] = batch
self.current_episode_reward = torch.zeros(self.envs.num_envs, 1)
self.running_episode_stats = dict(
count=torch.zeros(self.envs.num_envs, 1),
reward=torch.zeros(self.envs.num_envs, 1),
)
self.window_episode_stats = defaultdict(
lambda: deque(maxlen=ppo_cfg.reward_window_size)
)
self.env_time = 0.0
self.pth_time = 0.0
self.t_start = time.time()
@rank0_only
@profiling_wrapper.RangeContext("save_checkpoint")
def save_checkpoint(
self, file_name: str, extra_state: Optional[Dict] = None
) -> None:
r"""Save checkpoint with specified name.
Args:
file_name: file name for checkpoint
Returns:
None
"""
checkpoint = {
"state_dict": self.agent.state_dict(),
"config": self.config,
}
if extra_state is not None:
checkpoint["extra_state"] = extra_state
torch.save(
checkpoint, os.path.join(self.config.CHECKPOINT_FOLDER, file_name)
)
def load_checkpoint(self, checkpoint_path: str, *args, **kwargs) -> Dict:
r"""Load checkpoint of specified path as a dict.
Args:
checkpoint_path: path of target checkpoint
*args: additional positional args
**kwargs: additional keyword args
Returns:
dict containing checkpoint info
"""
return torch.load(checkpoint_path, *args, **kwargs)
METRICS_BLACKLIST = {"top_down_map", "collisions.is_collision"}
@classmethod
def _extract_scalars_from_info(
cls, info: Dict[str, Any]
) -> Dict[str, float]:
result = {}
for k, v in info.items():
if k in cls.METRICS_BLACKLIST:
continue
if isinstance(v, dict):
result.update(
{
k + "." + subk: subv
for subk, subv in cls._extract_scalars_from_info(
v
).items()
if (k + "." + subk) not in cls.METRICS_BLACKLIST
}
)
# Things that are scalar-like will have an np.size of 1.
# Strings also have an np.size of 1, so explicitly ban those
elif np.size(v) == 1 and not isinstance(v, str):
result[k] = float(v)
return result
@classmethod
def _extract_scalars_from_infos(
cls, infos: List[Dict[str, Any]]
) -> Dict[str, List[float]]:
results = defaultdict(list)
for i in range(len(infos)):
for k, v in cls._extract_scalars_from_info(infos[i]).items():
results[k].append(v)
return results
def _compute_actions_and_step_envs(self, buffer_index: int = 0):
num_envs = self.envs.num_envs
env_slice = slice(
int(buffer_index * num_envs / self._nbuffers),
int((buffer_index + 1) * num_envs / self._nbuffers),
)
t_sample_action = time.time()
# sample actions
with torch.no_grad():
step_batch = self.rollouts.buffers[
self.rollouts.current_rollout_step_idxs[buffer_index],
env_slice,
]
profiling_wrapper.range_push("compute actions")
(
values,
actions,
actions_log_probs,
recurrent_hidden_states,
) = self.actor_critic.act(
step_batch["observations"],
step_batch["recurrent_hidden_states"],
step_batch["prev_actions"],
step_batch["masks"],
)
# 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
actions = actions.to(device="cpu")
self.pth_time += time.time() - t_sample_action
profiling_wrapper.range_pop() # compute actions
t_step_env = time.time()
for index_env, act in zip(
range(env_slice.start, env_slice.stop), actions.unbind(0)
):
self.envs.async_step_at(index_env, act.item())
self.env_time += time.time() - t_step_env
self.rollouts.insert(
next_recurrent_hidden_states=recurrent_hidden_states,
actions=actions,
action_log_probs=actions_log_probs,
value_preds=values,
buffer_index=buffer_index,
)
def _collect_environment_result(self, buffer_index: int = 0):
num_envs = self.envs.num_envs
env_slice = slice(
int(buffer_index * num_envs / self._nbuffers),
int((buffer_index + 1) * num_envs / self._nbuffers),
)
t_step_env = time.time()
outputs = [
self.envs.wait_step_at(index_env)
for index_env in range(env_slice.start, env_slice.stop)
]
observations, rewards_l, dones, infos = [
list(x) for x in zip(*outputs)
]
self.env_time += time.time() - t_step_env
t_update_stats = time.time()
batch = batch_obs(
observations, device=self.device, cache=self._obs_batching_cache
)
batch = apply_obs_transforms_batch(batch, self.obs_transforms)
rewards = torch.tensor(
rewards_l,
dtype=torch.float,
device=self.current_episode_reward.device,
)
rewards = rewards.unsqueeze(1)
not_done_masks = torch.tensor(
[[not done] for done in dones],
dtype=torch.bool,
device=self.current_episode_reward.device,
)
done_masks = torch.logical_not(not_done_masks)
self.current_episode_reward[env_slice] += rewards
current_ep_reward = self.current_episode_reward[env_slice]
self.running_episode_stats["reward"][env_slice] += current_ep_reward.where(done_masks, current_ep_reward.new_zeros(())) # type: ignore
self.running_episode_stats["count"][env_slice] += done_masks.float() # type: ignore
for k, v_k in self._extract_scalars_from_infos(infos).items():
v = torch.tensor(
v_k,
dtype=torch.float,
device=self.current_episode_reward.device,
).unsqueeze(1)
if k not in self.running_episode_stats:
self.running_episode_stats[k] = torch.zeros_like(
self.running_episode_stats["count"]
)
self.running_episode_stats[k][env_slice] += v.where(done_masks, v.new_zeros(())) # type: ignore
self.current_episode_reward[env_slice].masked_fill_(done_masks, 0.0)
if self._static_encoder:
with torch.no_grad():
batch["visual_features"] = self._encoder(batch)
self.rollouts.insert(
next_observations=batch,
rewards=rewards,
next_masks=not_done_masks,
buffer_index=buffer_index,
)
self.rollouts.advance_rollout(buffer_index)
self.pth_time += time.time() - t_update_stats
return env_slice.stop - env_slice.start
@profiling_wrapper.RangeContext("_collect_rollout_step")
def _collect_rollout_step(self):
self._compute_actions_and_step_envs()
return self._collect_environment_result()
@profiling_wrapper.RangeContext("_update_agent")
def _update_agent(self):
ppo_cfg = self.config.RL.PPO
t_update_model = time.time()
with torch.no_grad():
step_batch = self.rollouts.buffers[
self.rollouts.current_rollout_step_idx
]
next_value = self.actor_critic.get_value(
step_batch["observations"],
step_batch["recurrent_hidden_states"],
step_batch["prev_actions"],
step_batch["masks"],
)
self.rollouts.compute_returns(
next_value, ppo_cfg.use_gae, ppo_cfg.gamma, ppo_cfg.tau
)
self.agent.train()
value_loss, action_loss, dist_entropy = self.agent.update(
self.rollouts
)
self.rollouts.after_update()
self.pth_time += time.time() - t_update_model
return (
value_loss,
action_loss,
dist_entropy,
)
def _coalesce_post_step(
self, losses: Dict[str, float], count_steps_delta: int
) -> Dict[str, float]:
stats_ordering = sorted(self.running_episode_stats.keys())
stats = torch.stack(
[self.running_episode_stats[k] for k in stats_ordering], 0
)
stats = self._all_reduce(stats)
for i, k in enumerate(stats_ordering):
self.window_episode_stats[k].append(stats[i])
if self._is_distributed:
loss_name_ordering = sorted(losses.keys())
stats = torch.tensor(
[losses[k] for k in loss_name_ordering] + [count_steps_delta],
device="cpu",
dtype=torch.float32,
)
stats = self._all_reduce(stats)
count_steps_delta = int(stats[-1].item())
stats /= torch.distributed.get_world_size()
losses = {
k: stats[i].item() for i, k in enumerate(loss_name_ordering)
}
if self._is_distributed and rank0_only():
self.num_rollouts_done_store.set("num_done", "0")
self.num_steps_done += count_steps_delta
return losses
@rank0_only
def _training_log(
self, writer, losses: Dict[str, float], prev_time: int = 0
):
deltas = {
k: (
(v[-1] - v[0]).sum().item()
if len(v) > 1
else v[0].sum().item()
)
for k, v in self.window_episode_stats.items()
}
deltas["count"] = max(deltas["count"], 1.0)
writer.add_scalar(
"reward",
deltas["reward"] / deltas["count"],
self.num_steps_done,
)
# 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, self.num_steps_done)
writer.add_scalars(
"losses",
losses,
self.num_steps_done,
)
# log stats
if self.num_updates_done % self.config.LOG_INTERVAL == 0:
logger.info(
"update: {}\tfps: {:.3f}\t".format(
self.num_updates_done,
self.num_steps_done
/ ((time.time() - self.t_start) + prev_time),
)
)
logger.info(
"update: {}\tenv-time: {:.3f}s\tpth-time: {:.3f}s\t"
"frames: {}".format(
self.num_updates_done,
self.env_time,
self.pth_time,
self.num_steps_done,
)
)
logger.info(
"Average window size: {} {}".format(
len(self.window_episode_stats["count"]),
" ".join(
"{}: {:.3f}".format(k, v / deltas["count"])
for k, v in deltas.items()
if k != "count"
),
)
)
def should_end_early(self, rollout_step) -> bool:
if not self._is_distributed:
return False
# This is where the preemption of workers happens. If a
# worker detects it will be a straggler, it preempts itself!
return (
rollout_step
>= self.config.RL.PPO.num_steps * self.SHORT_ROLLOUT_THRESHOLD
) and int(self.num_rollouts_done_store.get("num_done")) >= (
self.config.RL.DDPPO.sync_frac * torch.distributed.get_world_size()
)
@profiling_wrapper.RangeContext("train")
def train(self) -> None:
r"""Main method for training DD/PPO.
Returns:
None
"""
self._init_train()
count_checkpoints = 0
prev_time = 0
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: 1 - self.percent_done(),
)
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"]
self.env_time = requeue_stats["env_time"]
self.pth_time = requeue_stats["pth_time"]
self.num_steps_done = requeue_stats["num_steps_done"]
self.num_updates_done = requeue_stats["num_updates_done"]
self._last_checkpoint_percent = requeue_stats[
"_last_checkpoint_percent"
]
count_checkpoints = requeue_stats["count_checkpoints"]
prev_time = requeue_stats["prev_time"]
self._last_checkpoint_percent = requeue_stats[
"_last_checkpoint_percent"
]
ppo_cfg = self.config.RL.PPO
with (
TensorboardWriter(
self.config.TENSORBOARD_DIR, flush_secs=self.flush_secs
)
if rank0_only()
else contextlib.suppress()
) as writer:
while not self.is_done():
profiling_wrapper.on_start_step()
profiling_wrapper.range_push("train update")
if ppo_cfg.use_linear_clip_decay:
self.agent.clip_param = ppo_cfg.clip_param * (
1 - self.percent_done()
)
if EXIT.is_set():
profiling_wrapper.range_pop() # train update
self.envs.close()
if REQUEUE.is_set() and rank0_only():
requeue_stats = dict(
env_time=self.env_time,
pth_time=self.pth_time,
count_checkpoints=count_checkpoints,
num_steps_done=self.num_steps_done,
num_updates_done=self.num_updates_done,
_last_checkpoint_percent=self._last_checkpoint_percent,
prev_time=(time.time() - self.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
self.agent.eval()
count_steps_delta = 0
profiling_wrapper.range_push("rollouts loop")
profiling_wrapper.range_push("_collect_rollout_step")
for buffer_index in range(self._nbuffers):
self._compute_actions_and_step_envs(buffer_index)
for step in range(ppo_cfg.num_steps):
is_last_step = (
self.should_end_early(step + 1)
or (step + 1) == ppo_cfg.num_steps
)
for buffer_index in range(self._nbuffers):
count_steps_delta += self._collect_environment_result(
buffer_index
)
if (buffer_index + 1) == self._nbuffers:
profiling_wrapper.range_pop() # _collect_rollout_step
if not is_last_step:
if (buffer_index + 1) == self._nbuffers:
profiling_wrapper.range_push(
"_collect_rollout_step"
)
self._compute_actions_and_step_envs(buffer_index)
if is_last_step:
break
profiling_wrapper.range_pop() # rollouts loop
if self._is_distributed:
self.num_rollouts_done_store.add("num_done", 1)
(
value_loss,
action_loss,
dist_entropy,
) = self._update_agent()
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step() # type: ignore
self.num_updates_done += 1
losses = self._coalesce_post_step(
dict(value_loss=value_loss, action_loss=action_loss),
count_steps_delta,
)
self._training_log(writer, losses, prev_time)
# checkpoint model
if rank0_only() and self.should_checkpoint():
self.save_checkpoint(
f"ckpt.{count_checkpoints}.pth",
dict(
step=self.num_steps_done,
wall_time=(time.time() - self.t_start) + prev_time,
),
)
count_checkpoints += 1
profiling_wrapper.range_pop() # train update
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
"""
if self._is_distributed:
raise RuntimeError("Evaluation does not support distributed mode")
# 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()
if config.VERBOSE:
logger.info(f"env config: {config}")
self._init_envs(config)
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, cache=self._obs_batching_cache
)
batch = apply_obs_transforms_batch(batch, self.obs_transforms)
current_episode_reward = torch.zeros(
self.envs.num_envs, 1, device="cpu"
)
test_recurrent_hidden_states = torch.zeros(
self.config.NUM_ENVIRONMENTS,
self.actor_critic.net.num_recurrent_layers,
ppo_cfg.hidden_size,
device=self.device,
)
prev_actions = torch.zeros(
self.config.NUM_ENVIRONMENTS,
1,
device=self.device,
dtype=torch.long,
)
not_done_masks = torch.zeros(
self.config.NUM_ENVIRONMENTS,
1,
device=self.device,
dtype=torch.bool,
)
stats_episodes: Dict[
Any, Any
] = {} # dict of dicts that stores stats per episode
rgb_frames = [
[] for _ in range(self.config.NUM_ENVIRONMENTS)
] # 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,
cache=self._obs_batching_cache,
)
batch = apply_obs_transforms_batch(batch, self.obs_transforms)
not_done_masks = torch.tensor(
[[not done] for done in dones],
dtype=torch.bool,
device="cpu",
)
rewards = torch.tensor(
rewards_l, dtype=torch.float, device="cpu"
).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 not_done_masks[i].item():
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)
not_done_masks = not_done_masks.to(device=self.device)
(
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
"""
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 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())))
observations = self.envs.reset()
batch = batch_obs(observations)
rollouts = RolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
)
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(batch[sensor])
rollouts.to(self.device)
episode_rewards = torch.zeros(self.envs.num_envs, 1)
episode_counts = torch.zeros(self.envs.num_envs, 1)
current_episode_reward = torch.zeros(self.envs.num_envs, 1)
window_episode_reward = deque(maxlen=ppo_cfg.reward_window_size)
window_episode_counts = 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),
)
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
for update in range(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)
for step in range(ppo_cfg.num_steps):
delta_pth_time, delta_env_time, delta_steps = self._collect_rollout_step(
rollouts,
current_episode_reward,
episode_rewards,
episode_counts,
)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps += delta_steps
delta_pth_time, value_loss, action_loss, dist_entropy = self._update_agent(
ppo_cfg, rollouts)
pth_time += delta_pth_time
window_episode_reward.append(episode_rewards.clone())
window_episode_counts.append(episode_counts.clone())
losses = [value_loss, action_loss]
stats = zip(
["count", "reward"],
[window_episode_counts, window_episode_reward],
)
deltas = {
k:
((v[-1] -
v[0]).sum().item() if len(v) > 1 else v[0].sum().item())
for k, v in stats
}
deltas["count"] = max(deltas["count"], 1.0)
writer.add_scalar("reward", deltas["reward"] / deltas["count"],
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)))
logger.info(
"update: {}\tenv-time: {:.3f}s\tpth-time: {:.3f}s\t"
"frames: {}".format(update, env_time, pth_time,
count_steps))
window_rewards = (window_episode_reward[-1] -
window_episode_reward[0]).sum()
window_counts = (window_episode_counts[-1] -
window_episode_counts[0]).sum()
if window_counts > 0:
logger.info(
"Average window size {} reward: {:3f}".format(
len(window_episode_reward),
(window_rewards / window_counts).item(),
))
else:
logger.info("No episodes finish in current window")
# checkpoint model
if update % self.config.CHECKPOINT_INTERVAL == 0:
self.save_checkpoint(f"ckpt.{count_checkpoints}.pth")
count_checkpoints += 1
self.envs.close()
def train(self) -> None:
r"""Main method for training PPO.
Returns:
None
"""
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.envs.observation_spaces[0],
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs(observations, device=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)
running_episode_stats = dict(
count=torch.zeros(self.envs.num_envs, 1),
reward=torch.zeros(self.envs.num_envs, 1),
)
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
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
for update in range(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)
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
(
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
self.envs.close()
def train(self, ckpt_path="", ckpt=-1, start_updates=0) -> None:
r"""Main method for training PPO.
Returns:
None
"""
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
ppo_cfg = self.config.RL.PPO
task_cfg = self.config.TASK_CONFIG.TASK
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
# Initialize auxiliary tasks
observation_space = self.envs.observation_spaces[0]
aux_cfg = self.config.RL.AUX_TASKS
init_aux_tasks, num_recurrent_memories, aux_task_strings = \
self._setup_auxiliary_tasks(aux_cfg, ppo_cfg, task_cfg, observation_space)
rollouts = RolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
observation_space,
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
num_recurrent_memories=num_recurrent_memories)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs(observations, device=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
self._setup_actor_critic_agent(ppo_cfg, task_cfg, aux_cfg,
init_aux_tasks)
logger.info("agent number of parameters: {}".format(
sum(param.numel() for param in self.agent.parameters())))
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(
lambda: deque(maxlen=ppo_cfg.reward_window_size))
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0
count_checkpoints = 0
if ckpt != -1:
logger.info(
f"Resuming runs at checkpoint {ckpt}. Timing statistics are not tracked properly."
)
assert ppo_cfg.use_linear_lr_decay is False and ppo_cfg.use_linear_clip_decay is False, "Resuming with decay not supported"
# This is the checkpoint we start saving at
count_checkpoints = ckpt + 1
count_steps = start_updates * ppo_cfg.num_steps * self.config.NUM_PROCESSES
ckpt_dict = self.load_checkpoint(ckpt_path, map_location="cpu")
self.agent.load_state_dict(ckpt_dict["state_dict"])
if "optim_state" in ckpt_dict:
self.agent.optimizer.load_state_dict(ckpt_dict["optim_state"])
else:
logger.warn("No optimizer state loaded, results may be funky")
if "extra_state" in ckpt_dict and "step" in ckpt_dict[
"extra_state"]:
count_steps = ckpt_dict["extra_state"]["step"]
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
for update in range(start_updates, 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)
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
delta_pth_time, value_loss, action_loss, dist_entropy, aux_task_losses, aux_dist_entropy, aux_weights = 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(
"entropy",
dist_entropy,
count_steps,
)
writer.add_scalar("aux_entropy", aux_dist_entropy, count_steps)
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] + aux_task_losses
writer.add_scalars(
"losses",
{
k: l
for l, k in zip(losses, ["value", "policy"] +
aux_task_strings)
},
count_steps,
)
writer.add_scalars(
"aux_weights",
{k: l
for l, k in zip(aux_weights, aux_task_strings)},
count_steps,
)
writer.add_scalar(
"success",
deltas["success"] / deltas["count"],
count_steps,
)
# Log stats
if update > 0 and update % self.config.LOG_INTERVAL == 0:
logger.info(
"update: {}\tvalue_loss: {}\t action_loss: {}\taux_task_loss: {} \t aux_entropy {}"
.format(update, value_loss, action_loss,
aux_task_losses, aux_dist_entropy))
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"{self.checkpoint_prefix}.{count_checkpoints}.pth",
dict(step=count_steps))
count_checkpoints += 1
self.envs.close()
def train(self):
# Get environments for training
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
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)
#logger.info(
# "agent number of parameters: {}".format(
# sum(param.numel() for param in self.agent.parameters())
# )
#)
# Change for the actual value
cfg = self.config.RL.PPO
rollouts = RolloutStorage(
cfg.num_steps,
self.envs.num_envs,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
cfg.hidden_size,
)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs(observations)
for sensor in rollouts.observations:
print(batch[sensor].shape)
# Copy the information to the wrapper
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
episode_rewards = torch.zeros(self.envs.num_envs, 1)
episode_counts = torch.zeros(self.envs.num_envs, 1)
#current_episode_reward = torch.zeros(self.envs.num_envs, 1)
#window_episode_reward = deque(maxlen=ppo_cfg.reward_window_size)
#window_episode_counts = 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),
)
'''
def train(self) -> None:
r"""
Main method for training PPO
Returns:
None
"""
assert (
self.config is not None
), "trainer is not properly initialized, need to specify config file"
self.envs = construct_envs(self.config, NavRLEnv)
ppo_cfg = self.config.TRAINER.RL.PPO
self.device = torch.device("cuda", ppo_cfg.pth_gpu_id)
if not os.path.isdir(ppo_cfg.checkpoint_folder):
os.makedirs(ppo_cfg.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())))
observations = self.envs.reset()
batch = batch_obs(observations)
rollouts = RolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
)
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(batch[sensor])
rollouts.to(self.device)
episode_rewards = torch.zeros(self.envs.num_envs, 1)
episode_counts = torch.zeros(self.envs.num_envs, 1)
current_episode_reward = torch.zeros(self.envs.num_envs, 1)
window_episode_reward = deque(maxlen=ppo_cfg.reward_window_size)
window_episode_counts = deque(maxlen=ppo_cfg.reward_window_size)
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0
count_checkpoints = 0
with (get_tensorboard_writer(
log_dir=ppo_cfg.tensorboard_dir,
purge_step=count_steps,
flush_secs=30,
)) as writer:
for update in range(ppo_cfg.num_updates):
if ppo_cfg.use_linear_lr_decay:
update_linear_schedule(
self.agent.optimizer,
update,
ppo_cfg.num_updates,
ppo_cfg.lr,
)
if ppo_cfg.use_linear_clip_decay:
self.agent.clip_param = ppo_cfg.clip_param * (
1 - update / ppo_cfg.num_updates)
for step in range(ppo_cfg.num_steps):
t_sample_action = time.time()
# sample actions
with torch.no_grad():
step_observation = {
k: v[step]
for k, v in rollouts.observations.items()
}
(
values,
actions,
actions_log_probs,
recurrent_hidden_states,
) = self.actor_critic.act(
step_observation,
rollouts.recurrent_hidden_states[step],
rollouts.masks[step],
)
pth_time += time.time() - t_sample_action
t_step_env = time.time()
outputs = self.envs.step([a[0].item() for a in actions])
observations, rewards, dones, infos = [
list(x) for x in zip(*outputs)
]
env_time += time.time() - t_step_env
t_update_stats = time.time()
batch = batch_obs(observations)
rewards = torch.tensor(rewards, dtype=torch.float)
rewards = rewards.unsqueeze(1)
masks = torch.tensor(
[[0.0] if done else [1.0] for done in dones],
dtype=torch.float,
)
current_episode_reward += rewards
episode_rewards += (1 - masks) * current_episode_reward
episode_counts += 1 - masks
current_episode_reward *= masks
rollouts.insert(
batch,
recurrent_hidden_states,
actions,
actions_log_probs,
values,
rewards,
masks,
)
count_steps += self.envs.num_envs
pth_time += time.time() - t_update_stats
window_episode_reward.append(episode_rewards.clone())
window_episode_counts.append(episode_counts.clone())
t_update_model = time.time()
with torch.no_grad():
last_observation = {
k: v[-1]
for k, v in rollouts.observations.items()
}
next_value = self.actor_critic.get_value(
last_observation,
rollouts.recurrent_hidden_states[-1],
rollouts.masks[-1],
).detach()
rollouts.compute_returns(next_value, ppo_cfg.use_gae,
ppo_cfg.gamma, ppo_cfg.tau)
value_loss, action_loss, dist_entropy = self.agent.update(
rollouts)
rollouts.after_update()
pth_time += time.time() - t_update_model
losses = [value_loss, action_loss]
stats = zip(
["count", "reward"],
[window_episode_counts, window_episode_reward],
)
deltas = {
k:
((v[-1] -
v[0]).sum().item() if len(v) > 1 else v[0].sum().item())
for k, v in stats
}
deltas["count"] = max(deltas["count"], 1.0)
writer.add_scalar("reward", deltas["reward"] / deltas["count"],
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 % ppo_cfg.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))
window_rewards = (window_episode_reward[-1] -
window_episode_reward[0]).sum()
window_counts = (window_episode_counts[-1] -
window_episode_counts[0]).sum()
if window_counts > 0:
logger.info(
"Average window size {} reward: {:3f}".format(
len(window_episode_reward),
(window_rewards / window_counts).item(),
))
else:
logger.info("No episodes finish in current window")
# checkpoint model
if update % ppo_cfg.checkpoint_interval == 0:
self.save_checkpoint(f"ckpt.{count_checkpoints}.pth")
count_checkpoints += 1
def train(self, ckpt_path="", ckpt=-1, start_updates=0) -> None:
r"""Main method for training 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()
random.seed(self.config.TASK_CONFIG.SEED + self.world_rank)
np.random.seed(self.config.TASK_CONFIG.SEED + self.world_rank)
self.config.defrost()
self.config.TORCH_GPU_ID = self.local_rank
self.config.SIMULATOR_GPU_ID = self.local_rank
self.config.freeze()
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))
ppo_cfg = self.config.RL.PPO
task_cfg = self.config.TASK_CONFIG.TASK
observation_space = self.envs.observation_spaces[0]
aux_cfg = self.config.RL.AUX_TASKS
init_aux_tasks, num_recurrent_memories, aux_task_strings = self._setup_auxiliary_tasks(
aux_cfg, ppo_cfg, task_cfg, observation_space)
rollouts = RolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
observation_space,
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
num_recurrent_memories=num_recurrent_memories)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs(observations, device=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
self._setup_actor_critic_agent(ppo_cfg, task_cfg, aux_cfg,
init_aux_tasks)
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)))
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), # including bonus
)
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
prev_time = 0
if ckpt != -1:
logger.info(
f"Resuming runs at checkpoint {ckpt}. Timing statistics are not tracked properly."
)
assert ppo_cfg.use_linear_lr_decay is False and ppo_cfg.use_linear_clip_decay is False, "Resuming with decay not supported"
# This is the checkpoint we start saving at
count_checkpoints = ckpt + 1
count_steps = start_updates * ppo_cfg.num_steps * self.config.NUM_PROCESSES
ckpt_dict = self.load_checkpoint(ckpt_path, map_location="cpu")
self.agent.load_state_dict(ckpt_dict["state_dict"])
if "optim_state" in ckpt_dict:
self.agent.optimizer.load_state_dict(ckpt_dict["optim_state"])
else:
logger.warn("No optimizer state loaded, results may be funky")
if "extra_state" in ckpt_dict and "step" in ckpt_dict[
"extra_state"]:
count_steps = ckpt_dict["extra_state"]["step"]
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_updates = 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_updates, 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_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()
(
delta_pth_time,
value_loss,
action_loss,
dist_entropy,
aux_task_losses,
aux_dist_entropy,
aux_weights,
) = self._update_agent(ppo_cfg, rollouts)
pth_time += delta_pth_time
stats_ordering = list(sorted(running_episode_stats.keys()))
stats = torch.stack(
[running_episode_stats[k] for k in stats_ordering],
0).to(self.device)
distrib.all_reduce(stats)
for i, k in enumerate(stats_ordering):
window_episode_stats[k].append(stats[i].clone())
stats = torch.tensor(
[
dist_entropy,
aux_dist_entropy,
] + [value_loss, action_loss] + aux_task_losses +
[count_steps_delta],
device=self.device,
)
distrib.all_reduce(stats)
if aux_weights is not None and len(aux_weights) > 0:
distrib.all_reduce(
torch.tensor(aux_weights, device=self.device))
count_steps += stats[-1].item()
if self.world_rank == 0:
num_rollouts_done_store.set("num_done", "0")
avg_stats = [
stats[i].item() / self.world_size
for i in range(len(stats) - 1)
]
losses = avg_stats[2:]
dist_entropy, aux_dist_entropy = avg_stats[:2]
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,
)
writer.add_scalar(
"entropy",
dist_entropy,
count_steps,
)
writer.add_scalar("aux_entropy", aux_dist_entropy,
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"] +
aux_task_strings)
},
count_steps,
)
writer.add_scalars(
"aux_weights",
{k: l
for l, k in zip(aux_weights, aux_task_strings)},
count_steps,
)
# Log stats
formatted_aux_losses = [
"{:.3g}".format(l) for l in aux_task_losses
]
if update > 0 and update % self.config.LOG_INTERVAL == 0:
logger.info(
"update: {}\tvalue_loss: {:.3g}\t action_loss: {:.3g}\taux_task_loss: {} \t aux_entropy {:.3g}\t"
.format(
update,
value_loss,
action_loss,
formatted_aux_losses,
aux_dist_entropy,
))
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"{self.checkpoint_prefix}.{count_checkpoints}.pth",
dict(step=count_steps))
count_checkpoints += 1
self.envs.close()
def benchmark(self) -> None:
if TIME_DEBUG: s = time.time()
#self.config.defrost()
#self.config.TASK_CONFIG.ENVIRONMENT.ITERATOR_OPTIONS.MAX_SCENE_REPEAT_EPISODES = 10
#self.config.freeze()
if torch.cuda.device_count() <= 1:
self.config.defrost()
self.config.TORCH_GPU_ID = 0
self.config.SIMULATOR_GPU_ID = 0
self.config.freeze()
self.envs = construct_envs(self.config, eval(self.config.ENV_NAME))
if ADD_IL:
self.il_envs = construct_envs(self.config,
eval(self.config.ENV_NAME),
no_val=True)
self.collect_mode = 'RL'
if TIME_DEBUG: s = log_time(s, 'construct envs')
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)
# if 'SMT' in self.config.POLICY:
# sd = torch.load('visual_embedding18.pth')
# self.actor_critic.net.visual_encoder.load_state_dict(sd['visual_encoder'])
# self.actor_critic.net.prev_action_embedding.load_state_dict(sd['prev_action_embedding'])
# self.actor_critic.net.visual_encoder.cuda()
# self.actor_critic.net.prev_action_embedding.cuda()
# self.envs.setup_embedding_network(self.actor_critic.net.visual_encoder, self.actor_critic.net.prev_action_embedding)
logger.info("agent number of parameters: {}".format(
sum(param.numel() for param in self.agent.parameters())))
num_train_processes, num_val_processes = self.config.NUM_PROCESSES, self.config.NUM_VAL_PROCESSES
total_processes = num_train_processes + num_val_processes
OBS_LIST = self.config.OBS_TO_SAVE
self.num_processes = num_train_processes
rollouts = RolloutStorage(ppo_cfg.num_steps,
num_train_processes,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
self.actor_critic.net.num_recurrent_layers,
OBS_LIST=OBS_LIST)
rollouts.to(self.device)
batch = self.envs.reset()
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(
batch[sensor][:num_train_processes])
self.last_observations = batch
self.last_recurrent_hidden_states = torch.zeros(
self.actor_critic.net.num_recurrent_layers, total_processes,
ppo_cfg.hidden_size).to(self.device)
self.last_prev_actions = torch.zeros(
total_processes, rollouts.prev_actions.shape[-1]).to(self.device)
self.last_masks = torch.zeros(total_processes, 1).to(self.device)
# 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
if ADD_IL:
rollouts2 = RolloutStorage(
ppo_cfg.num_steps,
num_train_processes,
self.il_envs.observation_spaces[0],
self.il_envs.action_spaces[0],
ppo_cfg.hidden_size,
self.actor_critic.net.num_recurrent_layers,
OBS_LIST=OBS_LIST)
rollouts2.to(self.device)
batch2 = self.il_envs.reset()
for sensor in rollouts2.observations:
rollouts2.observations[sensor][0].copy_(
batch2[sensor][:num_train_processes])
self.saved_last_obs = batch2
self.saved_last_recurrent_hidden_states = torch.zeros(
self.actor_critic.net.num_recurrent_layers, total_processes,
ppo_cfg.hidden_size).to(self.device)
self.saved_last_prev_actions = torch.zeros(
total_processes,
rollouts2.prev_actions.shape[-1]).to(self.device)
self.saved_last_masks = torch.zeros(total_processes,
1).to(self.device)
batch2 = None
else:
rollouts2 = 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(
lambda: deque(maxlen=ppo_cfg.reward_window_size))
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0 if not hasattr(self,
'resume_steps') else self.resume_steps
count_checkpoints = 0
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
if TIME_DEBUG: s = log_time(s, 'setup all')
for update in range(100):
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 TIME_DEBUG: s = log_time(s, 'collect rollout start')
modes = ['RL']
if ADD_IL:
modes += ['IL']
for collect_mode in modes:
self.collect_mode = collect_mode
use_rollouts = rollouts if self.collect_mode == 'RL' else rollouts2
if ADD_IL: self.exchange_lasts()
for step in range(ppo_cfg.num_steps):
(
delta_pth_time,
delta_env_time,
delta_steps,
) = self._collect_rollout_step(use_rollouts,
current_episode_reward,
running_episode_stats)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps += delta_steps
#print(delta_env_time, delta_pth_time)
if TIME_DEBUG: s = log_time(s, 'collect rollout done')
(delta_pth_time, value_loss, action_loss, dist_entropy,
il_loss) = self._update_agent(ppo_cfg, rollouts, rollouts2)
#print(delta_pth_time)
pth_time += delta_pth_time
use_rollouts.after_update()
if TIME_DEBUG: s = log_time(s, 'update agent')
for k, v in running_episode_stats.items():
window_episode_stats[k].append(v.clone())
deltas = {
k: ((v[-1][:self.num_processes] -
v[0][:self.num_processes]).sum().item()
if len(v) > 1 else v[0][:self.num_processes].sum().item())
for k, v in window_episode_stats.items()
}
deltas["count"] = max(deltas["count"], 1.0)
#self.write_tb('train', writer, deltas, count_steps, losses)
eval_deltas = {
k: ((v[-1][self.num_processes:] -
v[0][self.num_processes:]).sum().item()
if len(v) > 1 else v[0][self.num_processes:].sum().item())
for k, v in window_episode_stats.items()
}
eval_deltas["count"] = max(eval_deltas["count"], 1.0)
#self.write_tb('val', writer, eval_deltas, 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"),
))
logger.info("validation metrics: {}".format(
" ".join("{}: {:.3f}".format(k, v / eval_deltas["count"])
for k, v in eval_deltas.items()
if k != "count"), ))
self.envs.close()
def train(self) -> None:
r"""Main method for training PPO.
Returns:
None
"""
self.add_new_based_on_cfg()
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, train=True)
if self.config.PRETRAINED_CHECKPOINT_PATH:
ckpt_dict = self.load_checkpoint(
self.config.PRETRAINED_CHECKPOINT_PATH, map_location="cpu")
self.agent.load_state_dict(ckpt_dict["state_dict"], strict=False)
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.envs.observation_spaces[0],
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
num_recurrent_layers=self.actor_critic.net.num_recurrent_layers)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs_augment_aux(observations, self.envs.get_shared_mem())
for sensor in rollouts.observations:
if sensor in batch:
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
info_data_keys = ["discovered", "collisions_wall", "collisions_prox"]
log_data_keys = [
"episode_rewards", "episode_go_rewards", "episode_counts",
"current_episode_reward", "current_episode_go_reward"
] + info_data_keys
log_data = dict(
{k: torch.zeros(self.envs.num_envs, 1)
for k in log_data_keys})
info_data = dict({k: log_data[k] for k in info_data_keys})
win_keys = log_data_keys
win_keys.pop(win_keys.index("current_episode_reward"))
win_keys.pop(win_keys.index("current_episode_go_reward"))
windows = dict({
k: deque(maxlen=ppo_cfg.reward_window_size)
for k in log_data.keys()
})
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),
)
train_steps = min(self.config.NUM_UPDATES,
self.config.HARD_NUM_UPDATES)
log_interval = self.config.LOG_INTERVAL
num_updates = self.config.NUM_UPDATES
agent = self.agent
ckpt_interval = self.config.CHECKPOINT_INTERVAL
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
for update in range(train_steps):
if ppo_cfg.use_linear_clip_decay:
agent.clip_param = ppo_cfg.clip_param * linear_decay(
update, num_updates)
for step in range(ppo_cfg.num_steps):
delta_pth_time, delta_env_time, delta_steps = self._collect_rollout_step(
rollouts, log_data["current_episode_reward"],
log_data["current_episode_go_reward"],
log_data["episode_rewards"],
log_data["episode_go_rewards"],
log_data["episode_counts"], info_data)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps += delta_steps
delta_pth_time, value_loss, action_loss, dist_entropy,\
aux_loss = self._update_agent(ppo_cfg, rollouts)
# TODO check if LR is init
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step()
pth_time += delta_pth_time
# ==================================================================================
# -- Log data for window averaging
for k, v in windows.items():
windows[k].append(log_data[k].clone())
value_names = ["value", "policy", "entropy"] + list(
aux_loss.keys())
losses = [value_loss, action_loss, dist_entropy] + list(
aux_loss.values())
stats = zip(list(windows.keys()), list(windows.values()))
deltas = {
k:
((v[-1] -
v[0]).sum().item() if len(v) > 1 else v[0].sum().item())
for k, v in stats
}
act_ep = deltas["episode_counts"]
counts = max(act_ep, 1.0)
deltas["episode_counts"] *= counts
for k, v in deltas.items():
deltas[k] = v / counts
writer.add_scalar(k, deltas[k], count_steps)
writer.add_scalars("losses",
{k: l
for l, k in zip(losses, value_names)},
count_steps)
# log stats
if update > 0 and update % 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))
if act_ep > 0:
log_txt = f"Average window size {len(windows['episode_counts'])}"
for k, v in deltas.items():
log_txt += f" | {k}: {v:.3f}"
logger.info(log_txt)
logger.info(
f"Aux losses: {list(zip(value_names, losses))}")
else:
logger.info("No episodes finish in current window")
# ==================================================================================
# checkpoint model
if update % ckpt_interval == 0:
self.save_checkpoint(f"ckpt.{count_checkpoints}.pth")
count_checkpoints += 1
self.envs.close()
def train(self) -> None:
r"""Main method for training PPO.
Returns:
None
"""
if TIME_DEBUG: s = time.time()
self.envs = construct_envs(
self.config, eval(self.config.ENV_NAME)
)
if TIME_DEBUG: s = log_time(s, 'construct envs')
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)
if 'SMT' in self.config.POLICY:
sd = torch.load('visual_embedding18_explore.pth') if 'Explore' in self.config.POLICY else torch.load('visual_embedding18.pth')
self.actor_critic.net.visual_encoder.load_state_dict(sd['visual_encoder'])
self.actor_critic.net.prev_action_embedding.load_state_dict(sd['prev_action_embedding'])
self.actor_critic.net.visual_encoder.cuda()
self.actor_critic.net.prev_action_embedding.cuda()
total_num = self.config.NUM_PROCESSES + self.config.NUM_VAL_PROCESSES
args_list = {'visual_encoder': self.actor_critic.net.visual_encoder,
'prev_action_embedding': self.actor_critic.net.prev_action_embedding}
self.envs.call(['setup_embedding_network']*total_num, [args_list]*total_num)
logger.info(
"agent number of parameters: {}".format(
sum(param.numel() for param in self.agent.parameters())
)
)
num_train_processes, num_val_processes = self.config.NUM_PROCESSES, self.config.NUM_VAL_PROCESSES
total_processes = num_train_processes + num_val_processes
OBS_LIST = self.config.OBS_TO_SAVE
self.num_processes = num_train_processes
rollouts = RolloutStorage(
ppo_cfg.num_steps,
num_train_processes,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
self.actor_critic.net.num_recurrent_layers,
OBS_LIST = OBS_LIST
)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs(observations, device=self.device)
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(batch[sensor][:num_train_processes])
self.last_observations = batch
self.last_recurrent_hidden_states = torch.zeros(self.actor_critic.net.num_recurrent_layers, total_processes, ppo_cfg.hidden_size).to(self.device)
self.last_prev_actions = torch.zeros(total_processes, rollouts.prev_actions.shape[-1]).to(self.device)
self.last_masks = torch.zeros(total_processes,1).to(self.device)
# 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(
lambda: deque(maxlen=ppo_cfg.reward_window_size)
)
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0 if not hasattr(self, 'resume_steps') else self.resume_steps
start_steps = 0 if not hasattr(self, 'resume_steps') else self.resume_steps
count_checkpoints = 0
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
if TIME_DEBUG: s = log_time(s, 'setup all')
with TensorboardWriter(
self.config.TENSORBOARD_DIR, flush_secs=self.flush_secs
) as writer:
for update in range(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 TIME_DEBUG: s = log_time(s, 'collect rollout start')
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
if TIME_DEBUG: s = log_time(s, 'collect rollout done')
(
delta_pth_time,
value_loss,
action_loss,
dist_entropy,
il_loss
) = self._update_agent(ppo_cfg, rollouts)
pth_time += delta_pth_time
rollouts.after_update()
if TIME_DEBUG: s = log_time(s, 'update agent')
for k, v in running_episode_stats.items():
window_episode_stats[k].append(v.clone())
deltas = {
k: (
(v[-1][:self.num_processes] - v[0][:self.num_processes]).sum().item()
if len(v) > 1
else v[0][:self.num_processes].sum().item()
)
for k, v in window_episode_stats.items()
}
deltas["count"] = max(deltas["count"], 1.0)
losses = [value_loss, action_loss, dist_entropy, il_loss]
self.write_tb('train', writer, deltas, count_steps, losses)
eval_deltas = {
k: (
(v[-1][self.num_processes:] - v[0][self.num_processes:]).sum().item()
if len(v) > 1
else v[0][self.num_processes:].sum().item()
)
for k, v in window_episode_stats.items()
}
eval_deltas["count"] = max(eval_deltas["count"], 1.0)
self.write_tb('val', writer, eval_deltas, count_steps)
# log stats
if update > 0 and update % self.config.LOG_INTERVAL == 0:
logger.info(
"update: {}\tfps: {:.3f}\t".format(
update, (count_steps - start_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"
),
)
)
logger.info(
"validation metrics: {}".format(
" ".join(
"{}: {:.3f}".format(k, v / eval_deltas["count"])
for k, v in eval_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 train(self) -> None:
r"""Main method for training PPO.
Returns:
None
"""
self.add_new_based_on_cfg()
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, train=True)
if self.config.PRETRAINED_CHECKPOINT_PATH:
ckpt_dict = self.load_checkpoint(
self.config.PRETRAINED_CHECKPOINT_PATH, map_location="cpu")
self.agent.load_state_dict(ckpt_dict["state_dict"], strict=False)
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.envs.observation_spaces[0],
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
num_recurrent_layers=self.actor_critic.net.num_recurrent_layers)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs_augment_aux(observations)
for sensor in rollouts.observations:
if sensor in batch:
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
episode_rewards = torch.zeros(self.envs.num_envs, 1)
episode_go_rewards = torch.zeros(self.envs.num_envs,
1) # Grid oracle rewars
episode_counts = torch.zeros(self.envs.num_envs, 1)
current_episode_reward = torch.zeros(self.envs.num_envs, 1)
current_episode_go_reward = torch.zeros(self.envs.num_envs,
1) # Grid oracle rewars
window_episode_reward = deque(maxlen=ppo_cfg.reward_window_size)
window_episode_go_reward = deque(maxlen=ppo_cfg.reward_window_size)
window_episode_counts = 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),
)
train_steps = min(self.config.NUM_UPDATES,
self.config.HARD_NUM_UPDATES)
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
for update in range(train_steps):
if ppo_cfg.use_linear_clip_decay:
self.agent.clip_param = ppo_cfg.clip_param * linear_decay(
update, self.config.NUM_UPDATES)
for step in range(ppo_cfg.num_steps):
delta_pth_time, delta_env_time, delta_steps = self._collect_rollout_step(
rollouts,
current_episode_reward,
current_episode_go_reward,
episode_rewards,
episode_go_rewards,
episode_counts,
)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps += delta_steps
delta_pth_time, value_loss, action_loss, dist_entropy,\
aux_loss = self._update_agent(ppo_cfg, rollouts)
# TODO check if LR is init
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step()
pth_time += delta_pth_time
window_episode_reward.append(episode_rewards.clone())
window_episode_go_reward.append(episode_go_rewards.clone())
window_episode_counts.append(episode_counts.clone())
value_names = ["value", "policy", "entropy"] + list(
aux_loss.keys())
losses = [value_loss, action_loss, dist_entropy] + list(
aux_loss.values())
stats = zip(
["count", "reward", "reward_go"],
[
window_episode_counts, window_episode_reward,
window_episode_go_reward
],
)
deltas = {
k:
((v[-1] -
v[0]).sum().item() if len(v) > 1 else v[0].sum().item())
for k, v in stats
}
deltas["count"] = max(deltas["count"], 1.0)
writer.add_scalar("reward", deltas["reward"] / deltas["count"],
count_steps)
writer.add_scalar("reward_go",
deltas["reward_go"] / deltas["count"],
count_steps)
writer.add_scalars(
"losses",
{k: l
for l, k in zip(losses, value_names)},
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))
window_rewards = (window_episode_reward[-1] -
window_episode_reward[0]).sum()
window_go_rewards = (window_episode_go_reward[-1] -
window_episode_go_reward[0]).sum()
window_counts = (window_episode_counts[-1] -
window_episode_counts[0]).sum()
if window_counts > 0:
logger.info(
"Average window size {} reward: {:3f} reward_go: {:3f}"
.format(
len(window_episode_reward),
(window_rewards / window_counts).item(),
(window_go_rewards / window_counts).item(),
))
logger.info(
f"Aux losses: {list(zip(value_names, losses))}")
else:
logger.info("No episodes finish in current window")
# checkpoint model
if update % self.config.CHECKPOINT_INTERVAL == 0:
self.save_checkpoint(f"ckpt.{count_checkpoints}.pth")
count_checkpoints += 1
self.envs.close()
class PPOTrainer_(BaseRLTrainer):
supported_tasks = ["Nav-v0"]
def __init__(self, config=None):
super().__init__(config)
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
self._setup_actor_critic_agent(self.config)
self.rollout = RolloutStorage(self.config.RL.PPO.num_steps,
self.envs.num_envs,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
self.config.RL.PPO.hidden_size)
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
self.rollout.to(self.device)
def _setup_actor_critic_agent(self, ppo_cfg):
print(self.config)
# len(observation_spaces)==NUM_PROCESSES==4?
print(self.envs.observation_spaces[0])
print(self.envs.action_spaces[0])
self.actor_critic = PointNavBaselinePolicy_(
cnn_parameter={
"observation_spaces": self.envs.observation_spaces[0],
"feature_dim": self.config.RL.PPO.cnn_output_size
},
depth_decoder_parameter={},
rnn_parameter={
"input_dim": 0,
"hidden_dim": self.config.RL.PPO.hidden_size,
"n_layer": 1
},
actor_parameter={
"action_spaces": 4,
},
critic_parameter={},
use_splitnet_auxiliary=self.config.RL.PPO.use_splitnet_auxiliary,
)
self.agent = PPO_(
self.actor_critic,
self.config,
)
def _collect_rollout_step(self, current_episode_reward,
running_episode_stats):
'''
obss : "rgb"
"depth"
"pointgoal_with_gps_compass"
'''
### PASS DATA
with torch.no_grad():
obs = {
k: v[self.rollout.step]
for k, v in self.rollout.observations.items()
}
hidden_states = self.rollout.recurrent_hidden_states[
self.rollout.step]
inverse_dones = self.rollout.masks[self.rollout.step]
(actions_log_probs, actions, value, distributions,
rnn_hidden_state) = (self.actor_critic.act(
obs, hidden_states, inverse_dones))
### PASS ENV
res = self.envs.step([action.item() for action in actions])
### Process
observations, rewards, dones, infos = [list(x) for x in zip(*res)]
observations = batch_obs(observations, self.device)
rewards = torch.tensor(rewards, dtype=torch.float,
device=self.device).unsqueeze(-1)
dones = torch.tensor(dones, dtype=torch.float, device=self.device)
inverse_dones = torch.tensor([[0] if done else [1] for done in dones],
dtype=torch.float,
device=self.device)
self.rollout.insert(
observations,
rnn_hidden_state,
actions,
actions_log_probs,
value,
rewards,
inverse_dones,
)
# print("rewards:", rewards)
# print("dones:", dones)
current_episode_reward += rewards
running_episode_stats["reward"] += (
1 - inverse_dones) * current_episode_reward
running_episode_stats["count"] += 1 - inverse_dones
for k, v in self._extract_scalars_from_infos(infos).items():
v = torch.tensor(v,
dtype=torch.float,
device=current_episode_reward.device).unsqueeze(1)
if k not in running_episode_stats:
running_episode_stats[k] = torch.zeros_like(
running_episode_stats["count"])
running_episode_stats[k] += (1 - inverse_dones) * v
current_episode_reward *= inverse_dones
return
def _save_checkpoint(self, checkpoint_name):
checkpoint_dict = {
"state_dict": self.agent.state_dict(),
"config": self.config,
}
torch.save(
checkpoint_dict,
os.path.join(self.config.CHECKPOINT_FOLDER, checkpoint_name),
)
def load_checkpoint(self, checkpoint_path, *args, **kwargs):
return torch.load(checkpoint_path, *args, **kwargs)
def _update_agent(self):
with torch.no_grad():
last_obs = {
k: v[self.rollout.step]
for k, v in self.rollout.observations.items()
}
hidden_states = self.rollout.recurrent_hidden_states[
self.rollout.step]
inverse_dones = self.rollout.masks[self.rollout.step]
next_value = self.actor_critic.get_value(last_obs, hidden_states,
inverse_dones).detach()
self.rollout.compute_returns(next_value, self.config.RL.PPO.use_gae,
self.config.RL.PPO.gamma,
self.config.RL.PPO.tau)
loss, loss_auxiliary = self.agent.update(self.rollout)
self.rollout.after_update()
return loss, loss_auxiliary
def train(self) -> None:
#### init
obs = self.envs.reset()
#### 先暫存一個 obs
batch = batch_obs(obs, device=self.device)
for sensor in self.rollout.observations:
self.rollout.observations[sensor][0].copy_(batch[sensor])
#### Para
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
#### PPO LOG PARA
count_steps = 0
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(
lambda: deque(maxlen=self.config.RL.PPO.reward_window_size))
#### 開始訓練
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
for epoch in range(self.config.NUM_UPDATES):
#### decay
if self.config.RL.PPO.use_linear_lr_decay:
lr_scheduler.step()
if (epoch + 1) % self.config.CHECKPOINT_INTERVAL == 0:
self.agent.entropy_coef = self.agent.entropy_coef * 0.9
print(self.agent.entropy_coef)
#### 蒐集rollout
print("=== collect rollout ===")
for step in range(self.config.RL.PPO.num_steps):
self._collect_rollout_step(current_episode_reward,
running_episode_stats)
count_steps += self.envs.num_envs
#### 更新
loss, loss_auxiliary = self._update_agent()
#### LOGGER
writer.add_scalars("loss", loss, epoch * self.envs.num_envs)
writer.add_scalars("loss_auxiliary", loss_auxiliary,
epoch * self.envs.num_envs)
#### PPO LOG PARA
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)
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)
#### PPO LOG PARA
if epoch % (1) == 0:
print("count_steps:", count_steps)
print("deltas:", deltas)
print("metrics:", metrics)
if epoch % self.config.CHECKPOINT_INTERVAL == 0:
self._save_checkpoint(f"checkpoint.{epoch}.pth")
self.envs.close()
# 每個checkpoint call一次
# TEST_EPISODE_COUNT 跑幾次EPISODE
# 還沒結束就將observations存到rgb_frames中，並且做後處裡，top_down_map...
# Dones之後，generate_video，存到disk跟tensorboard
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)
current_episode_reward = torch.zeros(self.envs.num_envs,
1,
device=self.device)
test_recurrent_hidden_states = torch.zeros(
1,
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() # 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)
pbar = tqdm.tqdm(total=self.config.TEST_EPISODE_COUNT)
self.actor_critic.eval()
while (len(stats_episodes) < self.config.TEST_EPISODE_COUNT
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, not_done_masks))
prev_actions.copy_(actions)
outputs = self.envs.step([a[0].item() for a in actions])
observations, rewards, dones, infos = [
list(x) for x in zip(*outputs)
]
batch = batch_obs(observations, device=self.device)
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,
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 = dict()
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:
frame = observations_to_image(observations[i], 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 = dict()
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()
METRICS_BLACKLIST = {"top_down_map", "collisions.is_collision"}
@classmethod
def _extract_scalars_from_info(cls, info: Dict[str,
Any]) -> Dict[str, float]:
result = {}
for k, v in info.items():
if k in cls.METRICS_BLACKLIST:
continue
if isinstance(v, dict):
result.update({
k + "." + subk: subv
for subk, subv in cls._extract_scalars_from_info(
v).items()
if (k + "." + subk) not in cls.METRICS_BLACKLIST
})
# Things that are scalar-like will have an np.size of 1.
# Strings also have an np.size of 1, so explicitly ban those
elif np.size(v) == 1 and not isinstance(v, str):
result[k] = float(v)
return result
@classmethod
def _extract_scalars_from_infos(
cls, infos: List[Dict[str, Any]]) -> Dict[str, List[float]]:
results = defaultdict(list)
for i in range(len(infos)):
for k, v in cls._extract_scalars_from_info(infos[i]).items():
results[k].append(v)
return results
