def __init__(self, config=None):
resume_state = load_resume_state(config)
if resume_state is not None:
config = resume_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()
def __init__(self, config=None):
resume_state = load_resume_state(config)
if resume_state is not None:
config = resume_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
# Distributed if the world size would be
# greater than 1
self._is_distributed = get_distrib_size()[2] > 1
self._obs_batching_cache = ObservationBatchingCache()
self.using_velocity_ctrl = (
self.config.TASK_CONFIG.TASK.POSSIBLE_ACTIONS) == [
"VELOCITY_CONTROL"
]
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(),
)
resume_state = load_resume_state(self.config)
if resume_state is not None:
self.agent.load_state_dict(resume_state["state_dict"])
self.agent.optimizer.load_state_dict(resume_state["optim_state"])
lr_scheduler.load_state_dict(resume_state["lr_sched_state"])
requeue_stats = resume_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"]
self.running_episode_stats = requeue_stats["running_episode_stats"]
self.window_episode_stats.update(
requeue_stats["window_episode_stats"])
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 rank0_only() and self._should_save_resume_state():
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,
running_episode_stats=self.running_episode_stats,
window_episode_stats=dict(self.window_episode_stats),
)
save_resume_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,
),
self.config,
)
if EXIT.is_set():
profiling_wrapper.range_pop() # train update
self.envs.close()
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 _init_train(self):
resume_state = load_resume_state(self.config)
if resume_state is not None:
self.config: Config = resume_state["config"]
self.using_velocity_ctrl = (
self.config.TASK_CONFIG.TASK.POSSIBLE_ACTIONS) == [
"VELOCITY_CONTROL"
]
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()
if self.using_velocity_ctrl:
self.policy_action_space = self.envs.action_spaces[0][
"VELOCITY_CONTROL"]
action_shape = (2, )
discrete_actions = False
else:
self.policy_action_space = self.envs.action_spaces[0]
action_shape = None
discrete_actions = True
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.policy_action_space,
ppo_cfg.hidden_size,
num_recurrent_layers=self.actor_critic.net.num_recurrent_layers,
is_double_buffered=ppo_cfg.use_double_buffered_sampler,
action_shape=action_shape,
discrete_actions=discrete_actions,
)
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()
