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
def __init__(self, config=None):
interrupted_state = load_interrupted_state()
if interrupted_state is not None:
config = interrupted_state["config"]
super().__init__(config)
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 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 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 train(self) -> None:
r"""Main method for DD-PPO SLAM.
Returns:
None
"""
#####################################################################
## init distrib and configuration #####################################################################
self.local_rank, tcp_store = init_distrib_slurm(
self.config.RL.DDPPO.distrib_backend
)
# self.local_rank = 1
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() # server number
self.world_size = distrib.get_world_size()
self.config.defrost()
self.config.TORCH_GPU_ID = self.local_rank # gpu number in one server
self.config.SIMULATOR_GPU_ID = self.local_rank
print("********************* TORCH_GPU_ID: ", self.config.TORCH_GPU_ID)
print("********************* SIMULATOR_GPU_ID: ", self.config.SIMULATOR_GPU_ID)
# 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")
#####################################################################
## build distrib NavSLAMRLEnv environment
#####################################################################
print("#############################################################")
print("## build distrib NavSLAMRLEnv environment")
print("#############################################################")
self.envs = construct_envs(
self.config, get_env_class(self.config.ENV_NAME)
)
observations = self.envs.reset()
print("*************************** observations len:", len(observations))
# semantic process
for i in range(len(observations)):
observations[i]["semantic"] = observations[i]["semantic"].astype(np.int32)
se = list(set(observations[i]["semantic"].ravel()))
print(se)
# print("*************************** observations type:", observations)
# print("*************************** observations type:", observations[0]["map_sum"].shape) # 480*480*23
# print("*************************** observations curr_pose:", observations[0]["curr_pose"]) # []
batch = batch_obs(observations, device=self.device)
print("*************************** batch len:", len(batch))
# print("*************************** batch:", batch)
# print("************************************* current_episodes:", (self.envs.current_episodes()))
#####################################################################
## init actor_critic agent
#####################################################################
print("#############################################################")
print("## init actor_critic agent")
print("#############################################################")
self.map_w = observations[0]["map_sum"].shape[0]
self.map_h = observations[0]["map_sum"].shape[1]
# print("map_: ", observations[0]["curr_pose"].shape)
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(observations, 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
)
)
)
#####################################################################
## init Global Rollout Storage
#####################################################################
print("#############################################################")
print("## init Global Rollout Storage")
print("#############################################################")
self.num_each_global_step = self.config.RL.SLAMDDPPO.num_each_global_step
rollouts = GlobalRolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
self.obs_space,
self.g_action_space,
)
rollouts.to(self.device)
print('rollouts type:', type(rollouts))
print('--------------------------')
# for k in rollouts.keys():
# print("rollouts: {0}".format(rollouts.observations))
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(batch[sensor])
with torch.no_grad():
step_observation = {
k: v[rollouts.step] for k, v in rollouts.observations.items()
}
_, actions, _, = self.actor_critic.act(
step_observation,
rollouts.prev_g_actions[0],
rollouts.masks[0],
)
self.global_goals = [[int(action[0].item() * self.map_w),
int(action[1].item() * self.map_h)]
for action in actions]
# 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)
)
print("*************************** current_episode_reward:", current_episode_reward)
print("*************************** running_episode_stats:", running_episode_stats)
# print("*************************** window_episode_stats:", window_episode_stats)
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("/home/cirlab1/userdir/ybg/projects/habitat-api/data/interrup.pth")
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"]
deif = {}
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
)
# print("************************************* current_episodes:", type(self.envs.count_episodes()))
# print(EXIT.is_set())
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,
),
"/home/cirlab1/userdir/ybg/projects/habitat-api/data/interrup.pth"
)
print("********************EXIT*********************")
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_global_rollout_step(
rollouts, current_episode_reward, running_episode_stats
)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps_delta += delta_steps
# print("************************************* current_episodes:")
for i in range(len(self.envs.current_episodes())):
# print(" ", self.envs.current_episodes()[i].episode_id," ", self.envs.current_episodes()[i].scene_id," ", self.envs.current_episodes()[i].object_category)
if self.envs.current_episodes()[i].scene_id not in deif:
deif[self.envs.current_episodes()[i].scene_id]=[int(self.envs.current_episodes()[i].episode_id)]
else:
deif[self.envs.current_episodes()[i].scene_id].append(int(self.envs.current_episodes()[i].episode_id))
# 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"
),
)
)
# for k in deif:
# deif[k] = list(set(deif[k]))
# deif[k].sort()
# print("deif: k", k, " : ", deif[k])
# checkpoint model
if update % self.config.CHECKPOINT_INTERVAL == 0:
self.save_checkpoint(
f"ckpt.{count_checkpoints}.pth",
dict(step=count_steps),
)
print('=' * 20 + 'Save Model' + '=' * 20)
logger.info(
"Save Model : {}".format(count_checkpoints)
)
count_checkpoints += 1
self.envs.close()
def __init__(self, config: Optional[Config] = None) -> None:
interrupted_state = load_interrupted_state()
if interrupted_state is not None:
config = interrupted_state["config"]
super().__init__(config)
