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replay_buffer.py
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replay_buffer.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
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, replay_dir):
self._data_specs = data_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):
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)
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, replay_dir, max_size, num_workers, nstep, discount,
fetch_every, save_snapshot):
self._replay_dir = replay_dir
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 = False
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._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
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)
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(replay_dir, max_size, batch_size, num_workers,
save_snapshot, nstep, discount):
max_size_per_worker = max_size // max(1, num_workers)
iterable = ReplayBuffer(replay_dir,
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