def queue(cls,
name: str,
max_size: int,
extensions: Sequence[TableExtensionBase] = (),
signature: Optional[reverb_types.SpecNest] = None):
"""Constructs a Table which acts like a queue.
Args:
name: Name of the priority table (aka queue).
max_size: Maximum number of items in the priority table (aka queue).
extensions: See documentation in the constructor.
signature: See documentation in the constructor.
Returns:
Table which behaves like a queue of size `max_size`.
"""
return cls(
name=name,
sampler=item_selectors.Fifo(),
remover=item_selectors.Fifo(),
max_size=max_size,
max_times_sampled=1,
rate_limiter=rate_limiters.Queue(max_size),
extensions=extensions,
signature=signature)
def make_server():
return reverb_server.Server(
tables=[
reverb_server.Table(
'dist',
sampler=item_selectors.Prioritized(priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000000,
rate_limiter=rate_limiters.MinSize(1)),
reverb_server.Table(
'signatured',
sampler=item_selectors.Prioritized(priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000000,
rate_limiter=rate_limiters.MinSize(1),
signature=tf.TensorSpec(dtype=tf.float32, shape=(None, None))),
reverb_server.Table(
'bounded_spec_signatured',
sampler=item_selectors.Prioritized(priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000000,
rate_limiter=rate_limiters.MinSize(1),
# Currently only the `shape` and `dtype` of the bounded spec
# is considered during signature check.
# TODO(b/158033101): Check the boundaries as well.
signature=tensor_spec.BoundedTensorSpec(dtype=tf.float32,
shape=(None, None),
minimum=(0.0, 0.0),
maximum=(10.0, 10.)),
),
],
port=None,
)
def make_server():
return server.Server(
tables=[
server.Table(
'dist',
sampler=item_selectors.Prioritized(priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000000,
rate_limiter=rate_limiters.MinSize(1)),
server.Table(
'dist2',
sampler=item_selectors.Prioritized(priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000000,
rate_limiter=rate_limiters.MinSize(1)),
server.Table(
'signatured',
sampler=item_selectors.Prioritized(priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000000,
rate_limiter=rate_limiters.MinSize(1),
signature=tf.TensorSpec(dtype=tf.float32, shape=(None, None))),
],
port=None,
)
def test_table_info_signature(self, signature):
table = server.Table(name='table',
sampler=item_selectors.Fifo(),
remover=item_selectors.Fifo(),
max_size=100,
rate_limiter=rate_limiters.MinSize(10),
signature=signature)
self.assertEqual(signature, table.info.signature)
def make_tables_and_server():
tables = [
server.Table('dist',
sampler=item_selectors.Prioritized(priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000000,
rate_limiter=rate_limiters.MinSize(1)),
server.Table('dist2',
sampler=item_selectors.Prioritized(priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000000,
rate_limiter=rate_limiters.MinSize(1)),
]
return tables, server.Server(tables=tables)
def test_duplicate_priority_table_name(self):
with self.assertRaises(ValueError):
server.Server(tables=[
server.Table(name='test',
sampler=item_selectors.Prioritized(1),
remover=item_selectors.Fifo(),
max_size=100,
rate_limiter=rate_limiters.MinSize(2)),
server.Table(name='test',
sampler=item_selectors.Prioritized(2),
remover=item_selectors.Fifo(),
max_size=200,
rate_limiter=rate_limiters.MinSize(1))
])
def queue(cls, name: str, max_size: int):
"""Constructs a Table which acts like a queue.
Args:
name: Name of the priority table (aka queue).
max_size: Maximum number of items in the priority table (aka queue).
Returns:
Table which behaves like a queue of size `max_size`.
"""
return cls(name=name,
sampler=item_selectors.Fifo(),
remover=item_selectors.Fifo(),
max_size=max_size,
max_times_sampled=1,
rate_limiter=rate_limiters.Queue(max_size))
def test_replace(self):
table = server.Table(name='table',
sampler=item_selectors.Fifo(),
remover=item_selectors.Fifo(),
max_size=100,
rate_limiter=rate_limiters.MinSize(10))
rl_info = table.info.rate_limiter_info
new_rate_limiter = rate_limiters.RateLimiter(
samples_per_insert=rl_info.samples_per_insert,
min_size_to_sample=1,
min_diff=rl_info.min_diff,
max_diff=rl_info.max_diff)
new_table = table.replace(rate_limiter=new_rate_limiter)
self.assertEqual(new_table.name, table.name)
self.assertEqual(new_table.info.rate_limiter_info.min_size_to_sample,
1)
def make_server():
return reverb_server.Server(tables=[
reverb_server.Table(name=TABLE,
sampler=item_selectors.Prioritized(
priority_exponent=1),
remover=item_selectors.Fifo(),
max_size=1000,
rate_limiter=rate_limiters.MinSize(1)),
])
def test_in_process_client(self):
my_server = server.Server(tables=[
server.Table(name=TABLE_NAME,
sampler=item_selectors.Prioritized(1),
remover=item_selectors.Fifo(),
max_size=100,
rate_limiter=rate_limiters.MinSize(2)),
])
my_client = my_server.localhost_client()
my_client.reset(TABLE_NAME)
del my_client
my_server.stop()
def setUpClass(cls):
super().setUpClass()
cls.server = server.Server(tables=[
server.Table(
name=TABLE_NAME,
sampler=item_selectors.Prioritized(1),
remover=item_selectors.Fifo(),
max_size=1000,
rate_limiter=rate_limiters.MinSize(3),
signature=tf.TensorSpec(dtype=tf.int64, shape=()),
),
],
port=None)
cls.client = client.Client(f'localhost:{cls.server.port}')
def _make_selector_from_key_distribution_options(
options) -> reverb_types.SelectorType:
"""Returns a Selector from its KeyDistributionOptions description."""
one_of = options.WhichOneof('distribution')
if one_of == 'fifo':
return item_selectors.Fifo()
if one_of == 'uniform':
return item_selectors.Uniform()
if one_of == 'prioritized':
return item_selectors.Prioritized(options.prioritized.priority_exponent)
if one_of == 'heap':
if options.heap.min_heap:
return item_selectors.MinHeap()
return item_selectors.MaxHeap()
if one_of == 'lifo':
return item_selectors.Lifo()
raise ValueError(f'Unknown distribution field: {one_of}')
def test_can_sample(self):
table = server.Table(name=TABLE_NAME,
sampler=item_selectors.Prioritized(1),
remover=item_selectors.Fifo(),
max_size=100,
max_times_sampled=1,
rate_limiter=rate_limiters.MinSize(2))
my_server = server.Server(tables=[table], port=None)
my_client = my_server.in_process_client()
self.assertFalse(table.can_sample(1))
self.assertTrue(table.can_insert(1))
my_client.insert(1, {TABLE_NAME: 1.0})
self.assertFalse(table.can_sample(1))
my_client.insert(1, {TABLE_NAME: 1.0})
self.assertTrue(table.can_sample(2))
# TODO(b/153258711): This should return False since max_times_sampled=1.
self.assertTrue(table.can_sample(3))
del my_client
my_server.stop()
def setUpClass(cls):
super().setUpClass()
cls.tables = [
server.Table(
name=TABLE_NAME,
sampler=item_selectors.Prioritized(1),
remover=item_selectors.Fifo(),
max_size=1000,
rate_limiter=rate_limiters.MinSize(3),
signature=tf.TensorSpec(dtype=tf.int64, shape=[]),
),
server.Table.queue(
name=NESTED_SIGNATURE_TABLE_NAME,
max_size=10,
signature=QUEUE_SIGNATURE,
),
server.Table.queue(SIMPLE_QUEUE_NAME, 10),
]
cls.server = server.Server(tables=cls.tables)
cls.client = cls.server.localhost_client()
def test_can_sample(self):
table = server.Table(name=TABLE_NAME,
sampler=item_selectors.Prioritized(1),
remover=item_selectors.Fifo(),
max_size=100,
max_times_sampled=1,
rate_limiter=rate_limiters.MinSize(2))
my_server = server.Server(tables=[table])
my_client = my_server.localhost_client()
self.assertFalse(table.can_sample(1))
self.assertTrue(table.can_insert(1))
my_client.insert(1, {TABLE_NAME: 1.0})
self.assertFalse(table.can_sample(1))
my_client.insert(1, {TABLE_NAME: 1.0})
for _ in range(100):
if table.info.current_size == 2:
break
time.sleep(0.01)
self.assertEqual(table.info.current_size, 2)
self.assertTrue(table.can_sample(2))
# TODO(b/153258711): This should return False since max_times_sampled=1.
self.assertTrue(table.can_sample(3))
del my_client
my_server.stop()
