def test_client_anomaly_detection():
HID_DIM = 16
experts = {}
for i in range(4):
expert = layers.name_to_block['ffn'](HID_DIM)
experts[f'expert.{i}'] = hivemind.ExpertBackend(
name=f'expert.{i}',
expert=expert,
optimizer=torch.optim.Adam(expert.parameters()),
args_schema=(hivemind.BatchTensorDescriptor(HID_DIM), ),
outputs_schema=hivemind.BatchTensorDescriptor(HID_DIM),
max_batch_size=16,
)
experts['expert.3'].expert.ffn.weight.data[0, 0] = float('nan')
dht = hivemind.DHT(start=True)
server = hivemind.Server(dht, experts, num_connection_handlers=1)
server.start()
try:
server.ready.wait()
dmoe = hivemind.RemoteMixtureOfExperts(in_features=16,
grid_size=(3, ),
dht=dht,
k_best=3,
uid_prefix='expert.',
detect_anomalies=True)
input = torch.randn(1, 16)
input[0, 0] = float('nan')
with pytest.raises(ValueError):
dmoe(input)
input[0, 0] = 0
output = dmoe(input)
inf_loss = float('inf') * output.sum()
with pytest.raises(ValueError):
inf_loss.backward()
dmoe = hivemind.RemoteMixtureOfExperts(in_features=16,
grid_size=(4, ),
dht=dht,
k_best=4,
uid_prefix='expert.',
detect_anomalies=True)
output = dmoe(input)
assert output.isfinite().all()
finally:
server.shutdown()
def benchmark_throughput(num_experts=16,
num_handlers=None,
num_clients=128,
num_batches_per_client=16,
expert_cls='ffn',
hid_dim=1024,
batch_size=2048,
max_batch_size=None,
backprop=True,
device=None,
port=None):
assert not hasattr(torch.cuda, 'is_initialized') or not torch.cuda.is_initialized() \
or torch.device(device) == torch.device('cpu')
assert expert_cls in layers.name_to_block
port = port or find_open_port()
max_batch_size = max_batch_size or batch_size * 4
num_handlers = max(1, num_handlers or num_clients // 2)
benchmarking_failed = mp.Event()
can_start = mp.Event()
timestamps = dict(started=time.perf_counter())
try:
# start clients and await server
# Note: client processes must be launched BEFORE touching gpu, even torch.cuda.is_available can cause trouble
clients = [
mp.Process(target=client_process,
name=f'client_process-{i}',
args=(can_start, benchmarking_failed, port, num_experts,
batch_size, hid_dim, num_batches_per_client,
backprop)) for i in range(num_clients)
]
for client in clients:
client.daemon = True
client.start()
timestamps['launched_clients'] = timestamps[
'began_launching_server'] = time.perf_counter()
# start server
device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
experts = {}
for i in range(num_experts):
expert = torch.jit.script(
layers.name_to_block[expert_cls](hid_dim))
experts[f'expert{i}'] = hivemind.ExpertBackend(
name=f'expert{i}',
expert=expert,
opt=torch.optim.Adam(expert.parameters()),
args_schema=(hivemind.BatchTensorProto(hid_dim), ),
outputs_schema=hivemind.BatchTensorProto(hid_dim),
max_batch_size=max_batch_size,
)
timestamps['created_experts'] = time.perf_counter()
server = hivemind.Server(None,
experts,
port=port,
conn_handler_processes=num_handlers,
device=device)
server.start()
server.ready.wait()
timestamps['server_ready'] = time.perf_counter()
can_start.set()
for client in clients:
client.join()
timestamps['clients_finished'] = time.perf_counter()
except BaseException as e:
benchmarking_failed.set()
raise e
finally:
for client in clients:
if client.is_alive():
client.terminate()
server.shutdown()
timestamps['server_shutdown_finished'] = time.perf_counter()
server.join()
sys.stdout.flush()
sys.stderr.flush()
time_between = lambda key1, key2: \
abs(timestamps[key2] - timestamps[key1]) if (key1 in timestamps and key2 in timestamps) else float('nan')
total_examples = batch_size * num_clients * num_batches_per_client
print('\n' * 3)
print("Benchmark finished, status:".format(
["Success", "Failure"][benchmarking_failed.is_set()]))
print(
"Server parameters: num_experts={}, num_handlers={}, max_batch_size={}, expert_cls={}, hid_dim={}, device={}"
.format(num_experts, num_handlers, max_batch_size, expert_cls, hid_dim,
device))
print(
"Client parameters: num_clients={}, num_batches_per_client={}, batch_size={}, backprop={}"
.format(num_clients, num_batches_per_client, batch_size, backprop))
startup_time = time_between('began_launching_server', 'server_ready')
experts_time = time_between('began_launching_server', 'created_experts')
networking_time = time_between('created_experts', 'server_ready')
process_examples_time = time_between('server_ready', 'clients_finished')
overall_time = time_between('started', 'server_shutdown_finished')
stage = 'forward + backward' if backprop else 'forward'
print("Results: ")
print("\tServer startup took {} s. ({} s. experts + {} s. networking)".
format(startup_time, experts_time, networking_time, '.3f'))
print("\tProcessed {} examples in {}".format(
total_examples, time_betweenprocess_examples_time, '.3f'))
print("\tThroughput for {} passes: {} samples / s.".format(
stage, total_examples / process_examples_time, '.3f'))
print("\tBenchmarking took {} s.".format(overall_time, '.3f'))
if benchmarking_failed.is_set():
print(
"Note: benchmark code failed, timing/memory results only indicate time till failure!"
)
print_device_info(device)
print(flush=True)
assert not benchmarking_failed.is_set()
def create(cls, listen_on='0.0.0.0:*', num_experts: int = None, expert_uids: str = None, expert_pattern: str = None,
expert_cls='ffn', hidden_dim=1024, optim_cls=torch.optim.Adam, scheduler: str = 'none',
num_warmup_steps=None, num_total_steps=None, clip_grad_norm=None, num_handlers=None, max_batch_size=4096,
device=None, no_dht=False, initial_peers=(), dht_port=None, checkpoint_dir: Optional[Path] = None,
compression=CompressionType.NONE, stats_report_interval: Optional[int] = None, *, start: bool,
**kwargs) -> Server:
"""
Instantiate a server with several identical experts. See argparse comments below for details
:param listen_on: network interface with address and (optional) port, e.g. "127.0.0.1:1337" or "[::]:80"
:param num_experts: run this many identical experts
:param expert_pattern: a string pattern or a list of expert uids, example: myprefix.[0:32].[0:256]\
means "sample random experts between myprefix.0.0 and myprefix.255.255;
:param expert_uids: spawn experts with these exact uids, overrides num_experts and expert_pattern
:param expert_cls: expert type from hivemind.server.layers, e.g. 'ffn', 'transformer', 'det_dropout' or 'nop';
:param hidden_dim: main dimension for expert_cls
:param num_handlers: server will use this many parallel processes to handle incoming requests
:param max_batch_size: total num examples in the same batch will not exceed this value
:param device: all experts will use this device in torch notation; default: cuda if available else cpu
:param optim_cls: uses this optimizer to train all experts
:param scheduler: if not `none`, the name of the expert LR scheduler
:param num_warmup_steps: the number of warmup steps for LR schedule
:param num_total_steps: the total number of steps for LR schedule
:param clip_grad_norm: maximum gradient norm used for clipping
:param no_dht: if specified, the server will not be attached to a dht
:param initial_peers: a list of peers that will introduce this node to the dht,\
e.g. ('123.11.22.33:1337', '[fe80::abe2:db1c:be7d:5a85]:4567'), default = no peers
:param dht_port: DHT node will listen on this port, default = find open port
You can then use this node as initial peer for subsequent servers.
:param checkpoint_dir: directory to save and load expert checkpoints
:param compression: if specified, use this compression to pack all inputs, outputs and gradients by all experts
hosted on this server. For a more fine-grained compression, start server in python and specify compression
for each BatchTensorProto in ExpertBackend for the respective experts.
:param start: if True, starts server right away and returns when server is ready for requests
:param stats_report_interval: interval between two reports of batch processing performance statistics
"""
if len(kwargs) != 0:
logger.info("Ignored kwargs:", kwargs)
assert expert_cls in name_to_block
if no_dht:
dht = None
else:
dht_endpoint = replace_port(listen_on, dht_port or hivemind.find_open_port())
dht = hivemind.DHT(initial_peers=initial_peers, start=True, listen_on=dht_endpoint)
logger.info(f"Running DHT node on port {dht.port}, initial peers = {initial_peers}")
assert ((expert_pattern is None and num_experts is None and expert_uids is not None) or
(num_experts is not None and expert_uids is None)), \
"Please provide either expert_uids *or* num_experts (possibly with expert_pattern), but not both"
if expert_uids is None:
if checkpoint_dir is not None:
assert is_directory(checkpoint_dir)
expert_uids = [child.name for child in checkpoint_dir.iterdir() if
(child / 'checkpoint_last.pt').exists()]
total_experts_in_checkpoint = len(expert_uids)
logger.info(f"Located {total_experts_in_checkpoint} checkpoints for experts {expert_uids}")
if total_experts_in_checkpoint > num_experts:
raise ValueError(
f"Found {total_experts_in_checkpoint} checkpoints, but num_experts is set to {num_experts}, "
f"which is smaller. Either increase num_experts or remove unneeded checkpoints.")
else:
expert_uids = []
uids_to_generate = num_experts - len(expert_uids)
if uids_to_generate > 0:
logger.info(f"Generating {uids_to_generate} expert uids from pattern {expert_pattern}")
expert_uids.extend(generate_uids_from_pattern(uids_to_generate, expert_pattern, dht))
num_experts = len(expert_uids)
num_handlers = num_handlers if num_handlers is not None else num_experts * 8
optim_cls = optim_cls if optim_cls is not None else partial(torch.optim.SGD, lr=0.0)
device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
sample_input = name_to_input[expert_cls](4, hidden_dim)
if isinstance(sample_input, tuple):
args_schema = tuple(hivemind.BatchTensorDescriptor.from_tensor(arg, compression) for arg in sample_input)
else:
args_schema = (hivemind.BatchTensorDescriptor.from_tensor(sample_input, compression),)
scheduler = schedule_name_to_scheduler[scheduler]
# initialize experts
experts = {}
for expert_uid in expert_uids:
expert = name_to_block[expert_cls](hidden_dim)
experts[expert_uid] = hivemind.ExpertBackend(name=expert_uid, expert=expert,
args_schema=args_schema,
outputs_schema=hivemind.BatchTensorDescriptor(
hidden_dim, compression=compression),
optimizer=optim_cls(expert.parameters()),
scheduler=scheduler,
num_warmup_steps=num_warmup_steps,
num_total_steps=num_total_steps,
clip_grad_norm=clip_grad_norm,
max_batch_size=max_batch_size)
if checkpoint_dir is not None:
load_experts(experts, checkpoint_dir)
return cls(dht, experts, listen_on=listen_on, num_connection_handlers=num_handlers, device=device,
checkpoint_dir=checkpoint_dir, stats_report_interval=stats_report_interval, start=start)
def make_dummy_server(host='0.0.0.0',
port=None,
num_experts=1,
expert_cls='ffn',
hidden_dim=1024,
num_handlers=None,
expert_prefix='expert',
expert_offset=0,
max_batch_size=16384,
device=None,
no_optimizer=False,
no_dht=False,
initial_peers=(),
dht_port=None,
root_port=None,
verbose=True,
start=False,
UID_DELIMETER=hivemind.DHTNode.UID_DELIMETER,
**kwargs) -> hivemind.Server:
""" A context manager that creates server in a background thread, awaits .ready on entry and shutdowns on exit """
if verbose and len(kwargs) != 0:
print("Ignored kwargs:", kwargs)
assert expert_cls in name_to_block
num_handlers = num_handlers if num_handlers is not None else num_experts * 8
device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
# initialize dht
dht = None
if not no_dht:
if not len(initial_peers):
print(
"No initial peers provided. Starting additional dht as an initial peer."
)
dht_root = hivemind.DHTNode(*initial_peers,
port=root_port
or hivemind.find_open_port(),
start=True)
print(f"Initializing DHT with port {dht_root.port}")
initial_peers = (('localhost', dht_root.port), )
else:
print("Bootstrapping dht with peers:", initial_peers)
if root_port is not None:
print(
f"Warning: root_port={root_port} will not be used since we already have peers."
)
dht = hivemind.DHTNode(*initial_peers,
port=dht_port or hivemind.find_open_port(),
start=True)
if verbose:
print(f"Running dht node on port {dht.port}")
# initialize experts
experts = {}
for i in range(num_experts):
expert = torch.jit.script(name_to_block[expert_cls](hidden_dim))
opt = torch.optim.SGD(expert.parameters(),
0.0) if no_optimizer else torch.optim.Adam(
expert.parameters())
expert_uid = f'{expert_prefix}{UID_DELIMETER}{i + expert_offset}'
experts[expert_uid] = hivemind.ExpertBackend(
name=expert_uid,
expert=expert,
opt=opt,
args_schema=(hivemind.BatchTensorProto(hidden_dim), ),
outputs_schema=hivemind.BatchTensorProto(hidden_dim),
max_batch_size=max_batch_size,
)
# actually start server
server = hivemind.Server(dht,
experts,
addr=host,
port=port or hivemind.find_open_port(),
conn_handler_processes=num_handlers,
device=device)
if start:
server.run_in_background(await_ready=True)
if verbose:
print(f"Server started at {server.addr}:{server.port}")
print(
f"Got {num_experts} active experts of type {expert_cls}: {list(experts.keys())}"
)
return server
def create(listen_on='0.0.0.0:*', num_experts: int = None, expert_uids: str = None, expert_pattern: str = None,
expert_cls='ffn', hidden_dim=1024, optim_cls=torch.optim.Adam, num_handlers=None, max_batch_size=4096,
device=None, no_dht=False, initial_peers=(), dht_port=None, verbose=True,
compression=CompressionType.NONE, *, start: bool, **kwargs) -> Server:
"""
Instantiate a server with several identical experts. See argparse comments below for details
:param listen_on: network interface with address and (optional) port, e.g. "127.0.0.1:1337" or "[::]:80"
:param num_experts: run this many identical experts
:param expert_pattern: a string pattern or a list of expert uids, example: myprefix.[0:32].[0:256]\
means "sample random experts between myprefix.0.0 and myprefix.255.255;
:param expert_uids: spawn experts with these exact uids, overrides num_experts and expert_pattern
:param expert_cls: expert type from hivemind.server.layers, e.g. 'ffn', 'transformer', 'det_dropout' or 'nop';
:param hidden_dim: main dimension for expert_cls
:param num_handlers: server will use this many parallel processes to handle incoming requests
:param max_batch_size: total num examples in the same batch will not exceed this value
:param device: all experts will use this device in torch notation; default: cuda if available else cpu
:param optim_cls: uses this optimizer to train all experts
:param no_dht: if specified, the server will not be attached to a dht
:param initial_peers: a list of peers that will introduce this node to the dht,\
e.g. ('123.11.22.33:1337', '[fe80::abe2:db1c:be7d:5a85]:4567'), default = no peers
:param dht_port: DHT node will listen on this port, default = find open port
You can then use this node as initial peer for subsequent servers.
:param verbose: whether to print server started / finished / terminated events
:param start: if True, starts server right away and returns when server is ready for requests
"""
if verbose and len(kwargs) != 0:
print("Ignored kwargs:", kwargs)
assert expert_cls in name_to_block
# initialize dht
dht = None
if not no_dht:
logger.info(f"Bootstrapping DHT node, initial peers = {initial_peers}")
dht = hivemind.DHT(initial_peers=initial_peers, start=True,
listen_on=f"{hivemind.LOCALHOST}:{dht_port or hivemind.find_open_port()}")
if verbose:
logger.info(f"Running dht node on port {dht.port}")
# get expert uids
assert (expert_pattern is None and num_experts is None) or (expert_uids is None), \
"Please provide either expert_uids *or* num_experts and expert_pattern, but not both"
if expert_uids is None:
assert num_experts is not None, "Please specify either expert_uids or num_experts [and expert_pattern]"
expert_uids = generate_uids_from_pattern(num_experts, expert_pattern, dht=dht)
num_experts = len(expert_uids)
num_handlers = num_handlers if num_handlers is not None else num_experts * 8
optim_cls = optim_cls if optim_cls is not None else partial(torch.optim.SGD, lr=0.0)
device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
sample_input = name_to_input[expert_cls](4, hidden_dim)
if isinstance(sample_input, tuple):
args_schema = tuple(hivemind.BatchTensorDescriptor.from_tensor(arg, compression) for arg in sample_input)
else:
args_schema = (hivemind.BatchTensorDescriptor.from_tensor(sample_input, compression),)
# initialize experts
experts = {}
for expert_uid in expert_uids:
expert = name_to_block[expert_cls](hidden_dim)
experts[expert_uid] = hivemind.ExpertBackend(name=expert_uid, expert=expert,
args_schema=args_schema,
outputs_schema=hivemind.BatchTensorDescriptor(
hidden_dim, compression=compression),
opt=optim_cls(expert.parameters()),
max_batch_size=max_batch_size,
)
# actually start server
server = Server(
dht, experts, listen_on=listen_on,
num_connection_handlers=num_handlers, device=device)
if start:
server.run_in_background(await_ready=True)
if verbose:
logger.info(f"Server started at {server.listen_on}")
logger.info(f"Got {len(experts)} active experts of type {expert_cls}: {list(experts.keys())}")
return server
def create(listen_on='0.0.0.0:*', num_experts: int = None, expert_uids: str = None, expert_pattern: str = None,
expert_cls='ffn', hidden_dim=1024, optim_cls=torch.optim.Adam, num_handlers=None, max_batch_size=4096,
device=None, no_dht=False, initial_peers=(), dht_port=None, checkpoint_dir: Optional[Path] = None,
load_experts=False, compression=CompressionType.NONE, *, start: bool, **kwargs) -> Server:
"""
Instantiate a server with several identical experts. See argparse comments below for details
:param listen_on: network interface with address and (optional) port, e.g. "127.0.0.1:1337" or "[::]:80"
:param num_experts: run this many identical experts
:param expert_pattern: a string pattern or a list of expert uids, example: myprefix.[0:32].[0:256]\
means "sample random experts between myprefix.0.0 and myprefix.255.255;
:param expert_uids: spawn experts with these exact uids, overrides num_experts and expert_pattern
:param expert_cls: expert type from hivemind.server.layers, e.g. 'ffn', 'transformer', 'det_dropout' or 'nop';
:param hidden_dim: main dimension for expert_cls
:param num_handlers: server will use this many parallel processes to handle incoming requests
:param max_batch_size: total num examples in the same batch will not exceed this value
:param device: all experts will use this device in torch notation; default: cuda if available else cpu
:param optim_cls: uses this optimizer to train all experts
:param no_dht: if specified, the server will not be attached to a dht
:param initial_peers: a list of peers that will introduce this node to the dht,\
e.g. ('123.11.22.33:1337', '[fe80::abe2:db1c:be7d:5a85]:4567'), default = no peers
:param dht_port: DHT node will listen on this port, default = find open port
You can then use this node as initial peer for subsequent servers.
:param checkpoint_dir: directory to save expert checkpoints
:param load_experts: whether to load expert checkpoints from checkpoint_dir
:param compression: if specified, use this compression to pack all inputs, outputs and gradients by all experts
hosted on this server. For a more fine-grained compression, start server in python and specify compression
for each BatchTensorProto in ExpertBackend for the respective experts.
:param start: if True, starts server right away and returns when server is ready for requests
"""
if len(kwargs) != 0:
logger.info("Ignored kwargs:", kwargs)
assert expert_cls in name_to_block
if no_dht:
dht = None
else:
dht_endpoint = replace_port(listen_on, dht_port or hivemind.find_open_port())
dht = hivemind.DHT(initial_peers=initial_peers, start=True, listen_on=dht_endpoint)
logger.info(f"Running DHT node on port {dht.port}, initial peers = {initial_peers}")
if load_experts:
assert dir_is_correct(checkpoint_dir)
assert expert_uids is None, "Can't both load saved experts and create new ones from given UIDs"
expert_uids = [child.name for child in checkpoint_dir.iterdir() if (child / 'checkpoint_last.pt').exists()]
if expert_uids:
logger.info(f"Located checkpoints for experts {expert_uids}, ignoring UID generation options")
else:
logger.info(f"No expert checkpoints found in {checkpoint_dir}, generating...")
assert (expert_pattern is None and num_experts is None) or (expert_uids is None) or (num_experts == 0), \
"Please provide either expert_uids *or* num_experts and expert_pattern, but not both"
# get expert uids if not loaded previously
if expert_uids is None:
assert num_experts is not None, "Please specify either expert_uids or num_experts [and expert_pattern]"
logger.info(f"Generating expert uids from pattern {expert_pattern}")
expert_uids = generate_uids_from_pattern(num_experts, expert_pattern, dht=dht)
num_experts = len(expert_uids)
num_handlers = num_handlers if num_handlers is not None else num_experts * 8
optim_cls = optim_cls if optim_cls is not None else partial(torch.optim.SGD, lr=0.0)
device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
sample_input = name_to_input[expert_cls](4, hidden_dim)
if isinstance(sample_input, tuple):
args_schema = tuple(hivemind.BatchTensorDescriptor.from_tensor(arg, compression) for arg in sample_input)
else:
args_schema = (hivemind.BatchTensorDescriptor.from_tensor(sample_input, compression),)
# initialize experts
experts = {}
for expert_uid in expert_uids:
expert = name_to_block[expert_cls](hidden_dim)
experts[expert_uid] = hivemind.ExpertBackend(name=expert_uid, expert=expert,
args_schema=args_schema,
outputs_schema=hivemind.BatchTensorDescriptor(
hidden_dim, compression=compression),
opt=optim_cls(expert.parameters()),
max_batch_size=max_batch_size)
if load_experts:
load_weights(experts, checkpoint_dir)
server = Server(dht, experts, listen_on=listen_on, num_connection_handlers=num_handlers, device=device,
start=start)
return server
