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replay_buffer.py
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replay_buffer.py
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import datetime
import io
import random
import traceback
from collections import defaultdict
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import IterableDataset
def episode_len(episode):
# subtract -1 because the dummy first transition
return next(iter(episode.values())).shape[0] - 1
def save_episode(episode, fn):
with io.BytesIO() as bs:
np.savez_compressed(bs, **episode)
bs.seek(0)
with fn.open('wb') as f:
f.write(bs.read())
def load_episode(fn):
with fn.open('rb') as f:
episode = np.load(f)
episode = {k: episode[k] for k in episode.keys()}
return episode
class ReplayBufferStorage:
def __init__(self, data_specs, meta_specs, replay_dir):
self._data_specs = data_specs
self._meta_specs = meta_specs
self._replay_dir = replay_dir
replay_dir.mkdir(exist_ok=True)
self._current_episode = defaultdict(list)
self._preload()
def __len__(self):
return self._num_transitions
def add(self, time_step, meta):
for key, value in meta.items():
self._current_episode[key].append(value)
for spec in self._data_specs:
value = time_step[spec.name]
if np.isscalar(value):
value = np.full(spec.shape, value, spec.dtype)
assert spec.shape == value.shape and spec.dtype == value.dtype
self._current_episode[spec.name].append(value)
if time_step.last():
episode = dict()
for spec in self._data_specs:
value = self._current_episode[spec.name]
episode[spec.name] = np.array(value, spec.dtype)
for spec in self._meta_specs:
value = self._current_episode[spec.name]
episode[spec.name] = np.array(value, spec.dtype)
self._current_episode = defaultdict(list)
self._store_episode(episode)
def _preload(self):
self._num_episodes = 0
self._num_transitions = 0
for fn in self._replay_dir.glob('*.npz'):
_, _, eps_len = fn.stem.split('_')
self._num_episodes += 1
self._num_transitions += int(eps_len)
def _store_episode(self, episode):
eps_idx = self._num_episodes
eps_len = episode_len(episode)
self._num_episodes += 1
self._num_transitions += eps_len
ts = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')
eps_fn = f'{ts}_{eps_idx}_{eps_len}.npz'
save_episode(episode, self._replay_dir / eps_fn)
class ReplayBuffer(IterableDataset):
def __init__(self, storage, max_size, num_workers, nstep, discount,
fetch_every, save_snapshot):
self._storage = storage
self._size = 0
self._max_size = max_size
self._num_workers = max(1, num_workers)
self._episode_fns = []
self._episodes = dict()
self._nstep = nstep
self._discount = discount
self._fetch_every = fetch_every
self._samples_since_last_fetch = fetch_every
self._save_snapshot = save_snapshot
def _sample_episode(self):
eps_fn = random.choice(self._episode_fns)
return self._episodes[eps_fn]
def _store_episode(self, eps_fn):
try:
episode = load_episode(eps_fn)
except:
return False
eps_len = episode_len(episode)
while eps_len + self._size > self._max_size:
early_eps_fn = self._episode_fns.pop(0)
early_eps = self._episodes.pop(early_eps_fn)
self._size -= episode_len(early_eps)
early_eps_fn.unlink(missing_ok=True)
self._episode_fns.append(eps_fn)
self._episode_fns.sort()
self._episodes[eps_fn] = episode
self._size += eps_len
if not self._save_snapshot:
eps_fn.unlink(missing_ok=True)
return True
def _try_fetch(self):
if self._samples_since_last_fetch < self._fetch_every:
return
self._samples_since_last_fetch = 0
try:
worker_id = torch.utils.data.get_worker_info().id
except:
worker_id = 0
eps_fns = sorted(self._storage._replay_dir.glob('*.npz'), reverse=True)
fetched_size = 0
for eps_fn in eps_fns:
eps_idx, eps_len = [int(x) for x in eps_fn.stem.split('_')[1:]]
if eps_idx % self._num_workers != worker_id:
continue
if eps_fn in self._episodes.keys():
break
if fetched_size + eps_len > self._max_size:
break
fetched_size += eps_len
if not self._store_episode(eps_fn):
break
def _sample(self):
try:
self._try_fetch()
except:
traceback.print_exc()
self._samples_since_last_fetch += 1
episode = self._sample_episode()
# add +1 for the first dummy transition
idx = np.random.randint(0, episode_len(episode) - self._nstep + 1) + 1
meta = []
for spec in self._storage._meta_specs:
meta.append(episode[spec.name][idx - 1])
obs = episode['observation'][idx - 1]
action = episode['action'][idx]
next_obs = episode['observation'][idx + self._nstep - 1]
reward = np.zeros_like(episode['reward'][idx])
discount = np.ones_like(episode['discount'][idx])
for i in range(self._nstep):
step_reward = episode['reward'][idx + i]
reward += discount * step_reward
discount *= episode['discount'][idx + i] * self._discount
return (obs, action, reward, discount, next_obs, *meta)
def __iter__(self):
while True:
yield self._sample()
def _worker_init_fn(worker_id):
seed = np.random.get_state()[1][0] + worker_id
np.random.seed(seed)
random.seed(seed)
def make_replay_loader(storage, max_size, batch_size, num_workers,
save_snapshot, nstep, discount):
max_size_per_worker = max_size // max(1, num_workers)
iterable = ReplayBuffer(storage,
max_size_per_worker,
num_workers,
nstep,
discount,
fetch_every=1000,
save_snapshot=save_snapshot)
loader = torch.utils.data.DataLoader(iterable,
batch_size=batch_size,
num_workers=num_workers,
pin_memory=True,
worker_init_fn=_worker_init_fn)
return loader