class UnsupervisedLearning(BaseRunner):
def __init__(
self,
algo,
n_updates,
seed=None,
affinity=None,
log_interval_updates=1e3,
snapshot_gap_intervals=None, # units: log_intervals
):
n_updates = int(n_updates)
affinity = dict() if affinity is None else affinity
save__init__args(locals())
def startup(self):
p = psutil.Process()
try:
if self.affinity.get("master_cpus",
None) is not None and self.affinity.get(
"set_affinity", True):
p.cpu_affinity(self.affinity["master_cpus"])
cpu_affin = p.cpu_affinity()
except AttributeError:
cpu_affin = "UNAVAILABLE MacOS"
logger.log(f"Runner {getattr(self, 'rank', '')} master CPU affinity: "
f"{cpu_affin}.")
if self.affinity.get("master_torch_threads", None) is not None:
torch.set_num_threads(self.affinity["master_torch_threads"])
logger.log(f"Runner {getattr(self, 'rank', '')} master Torch threads: "
f"{torch.get_num_threads()}.")
if self.seed is None:
self.seed = make_seed()
set_seed(self.seed)
# self.rank = rank = getattr(self, "rank", 0)
# self.world_size = world_size = getattr(self, "world_size", 1)
self.algo.initialize(
n_updates=self.n_updates,
cuda_idx=self.affinity.get("cuda_idx", None),
)
self.initialize_logging()
def initialize_logging(self):
self._opt_infos = {k: list() for k in self.algo.opt_info_fields}
self._start_time = self._last_time = time.time()
self._cum_time = 0.0
if self.snapshot_gap_intervals is not None:
logger.set_snapshot_gap(self.snapshot_gap_intervals *
self.log_interval_updates)
self.pbar = ProgBarCounter(self.log_interval_updates)
def shutdown(self):
logger.log("Pretraining complete.")
self.pbar.stop()
def get_itr_snapshot(self, itr):
return dict(
itr=itr,
algo_state_dict=self.algo.state_dict(),
)
def save_itr_snapshot(self, itr):
"""
Calls the logger to save training checkpoint/snapshot (logger itself
may or may not save, depending on mode selected).
"""
logger.log("saving snapshot...")
params = self.get_itr_snapshot(itr)
logger.save_itr_params(itr, params)
logger.log("saved")
def store_diagnostics(self, itr, opt_info):
for k, v in self._opt_infos.items():
new_v = getattr(opt_info, k, [])
v.extend(new_v if isinstance(new_v, list) else [new_v])
self.pbar.update((itr + 1) % self.log_interval_updates)
def log_diagnostics(self, itr, val_info, *args, **kwargs):
self.save_itr_snapshot(itr)
new_time = time.time()
self._cum_time = new_time - self._start_time
epochs = (itr * self.algo.batch_size /
(self.algo.replay_buffer.size *
(1 - self.algo.validation_split)))
logger.record_tabular("Iteration", itr)
logger.record_tabular("Epochs", epochs)
logger.record_tabular("CumTime (s)", self._cum_time)
logger.record_tabular("UpdatesPerSecond", itr / self._cum_time)
if self._opt_infos:
for k, v in self._opt_infos.items():
logger.record_tabular_misc_stat(k, v)
for k, v in zip(val_info._fields, val_info):
logger.record_tabular_misc_stat("val_" + k, v)
self._opt_infos = {k: list() for k in self._opt_infos} # (reset)
logger.dump_tabular(with_prefix=False)
if itr < self.n_updates - 1:
logger.log(
f"Optimizing over {self.log_interval_updates} iterations.")
self.pbar = ProgBarCounter(self.log_interval_updates)
def train(self):
self.startup()
self.algo.train()
for itr in range(self.n_updates):
logger.set_iteration(itr)
with logger.prefix(f"itr #{itr} "):
opt_info = self.algo.optimize(itr) # perform one update
self.store_diagnostics(itr, opt_info)
if (itr + 1) % self.log_interval_updates == 0:
self.algo.eval()
val_info = self.algo.validation(itr)
self.log_diagnostics(itr, val_info)
self.algo.train()
self.shutdown()
def do_training_mt(loader, model, opt, dev, aug_model, min_bc_module,
n_batches):
# @torch.jit.script
def do_loss_forward_back(obs_batch_obs, obs_batch_task, obs_batch_var,
obs_batch_source, acts_batch):
# we don't use the value output
logits_flat, _ = model(obs_batch_obs, task_ids=obs_batch_task)
losses = F.cross_entropy(logits_flat,
acts_batch.long(),
reduction='none')
if min_bc_module is not None:
# weight using a model-dependent strategy
mbc_weights = min_bc_module(obs_batch_task, obs_batch_var,
obs_batch_source)
assert mbc_weights.shape == losses.shape, (mbc_weights.shape,
losses.shape)
loss = (losses * mbc_weights).sum()
else:
# no weighting
loss = losses.mean()
loss.backward()
return losses.detach().cpu().numpy()
# make sure we're in train mode
model.train()
# for logging
loss_ewma = None
losses = []
per_task_losses = collections.defaultdict(lambda: [])
progress = ProgBarCounter(n_batches)
inf_batch_iter = repeat_dataset(loader)
ctr_batch_iter = zip(range(1, n_batches), inf_batch_iter)
for batches_done, loader_batch in ctr_batch_iter:
# (task_ids_batch, obs_batch, acts_batch)
# copy to GPU
obs_batch = loader_batch['obs']
acts_batch = loader_batch['acts']
# reminder: attributes are .observation, .task_id, .variant_id
obs_batch = tree_map(lambda t: t.to(dev), obs_batch)
acts_batch = acts_batch.to(dev)
if aug_model is not None:
# apply augmentations
obs_batch = obs_batch._replace(
observation=aug_model(obs_batch.observation))
# compute loss & take opt step
opt.zero_grad()
batch_losses = do_loss_forward_back(obs_batch.observation,
obs_batch.task_id,
obs_batch.variant_id,
obs_batch.source_id, acts_batch)
opt.step()
# for logging
progress.update(batches_done)
f_loss = np.mean(batch_losses)
loss_ewma = f_loss if loss_ewma is None \
else 0.9 * loss_ewma + 0.1 * f_loss
losses.append(f_loss)
# also track separately for each task
tv_ids = torch.stack((obs_batch.task_id, obs_batch.variant_id), axis=1)
np_tv_ids = tv_ids.cpu().numpy()
assert len(np_tv_ids.shape) == 2 and np_tv_ids.shape[1] == 2, \
np_tv_ids.shape
for tv_id in np.unique(np_tv_ids, axis=0):
tv_mask = np.all(np_tv_ids == tv_id[None], axis=-1)
rel_losses = batch_losses[tv_mask]
if len(rel_losses) > 0:
task_id, variant_id = tv_id
per_task_losses[(task_id, variant_id)] \
.append(np.mean(rel_losses))
progress.stop()
return loss_ewma, losses, per_task_losses
class MinibatchRl(MinibatchRlBase):
"""Runs RL on minibatches; tracks performance online using learning
trajectories."""
def __init__(self, log_traj_window=100, **kwargs):
super().__init__(**kwargs)
self.log_traj_window = int(log_traj_window)
def train(self):
n_itr = self.startup()
for itr in range(n_itr):
with logger.prefix(f"itr #{itr} "):
self.agent.sample_mode(itr) # Might not be this agent sampling.
samples, traj_infos = self.sampler.obtain_samples(itr)
self.agent.train_mode(itr)
opt_info = self.algo.optimize_agent(itr, samples)
self.store_diagnostics(itr, traj_infos, opt_info)
if (itr + 1) % self.log_interval_itrs == 0:
self.log_diagnostics(itr)
self.shutdown()
def initialize_logging(self):
self._traj_infos = deque(maxlen=self.log_traj_window)
self._cum_completed_trajs = 0
self._new_completed_trajs = 0
logger.log(f"Optimizing over {self.log_interval_itrs} iterations.")
super().initialize_logging()
self.pbar = ProgBarCounter(self.log_interval_itrs)
def store_diagnostics(self, itr, traj_infos, opt_info):
self._cum_completed_trajs += len(traj_infos)
self._new_completed_trajs += len(traj_infos)
self._traj_infos.extend(traj_infos)
for k, v in self._opt_infos.items():
new_v = getattr(opt_info, k, [])
v.extend(new_v if isinstance(new_v, list) else [new_v])
self.pbar.update((itr + 1) % self.log_interval_itrs)
def log_diagnostics(self, itr):
self.pbar.stop()
self.save_itr_snapshot(itr)
new_time = time.time()
samples_per_second = (self.log_interval_itrs *
self.itr_batch_size) / (new_time - self._last_time)
logger.record_tabular('Iteration', itr)
logger.record_tabular('CumSteps', (itr + 1) * self.itr_batch_size)
logger.record_tabular('CumTime (s)', new_time - self._start_time)
logger.record_tabular('SamplesPerSecond', samples_per_second)
logger.record_tabular('CumCompletedTrajs', self._cum_completed_trajs)
logger.record_tabular('NewCompletedTrajs', self._new_completed_trajs)
logger.record_tabular('StepsInTrajWindow',
sum(info["Length"] for info in self._traj_infos))
self._log_infos()
self._last_time = new_time
logger.dump_tabular(with_prefix=False)
self._new_completed_trajs = 0
if itr < self.n_itr - 1:
logger.log(f"Optimizing over {self.log_interval_itrs} iterations.")
self.pbar = ProgBarCounter(self.log_interval_itrs)