def rpc_worker(rank, world_size, init_file, func, *args):
if torch_version() == (1, 8, 0):
if torch.cuda.is_available():
# Workaround for https://github.com/pytorch/pytorch/issues/53844
options = rpc.TensorPipeRpcBackendOptions(
init_method="file://" + init_file, _transports=["ibv", "uv"])
else:
# Workaround for https://github.com/pytorch/pytorch/issues/54266
options = rpc.TensorPipeRpcBackendOptions(
init_method="file://" + init_file,
_channels=[
"mpt_uv", "basic", "cuda_ipc", "cuda_gdr", "cuda_xth",
"cuda_basic"
],
)
else:
options = rpc.TensorPipeRpcBackendOptions(init_method="file://" +
init_file)
rpc.init_rpc(
"worker" + str(rank),
rank=rank,
world_size=world_size,
backend=rpc.BackendType.TENSORPIPE,
rpc_backend_options=options,
)
if rank == 0:
func(*args)
rpc.shutdown()
def run_worker(rank, world_size, num_split):
# os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_ADDR'] = '172.10.0.2'
# os.environ['MASTER_PORT'] = '29500'
os.environ['MASTER_PORT'] = '12345'
options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=256)
if rank == 0:
print("Init master")
rpc.init_rpc("master",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
print(rank)
run_master(num_split)
else:
print("init worker rank ", rank)
rpc.init_rpc(f"worker{rank}",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
pass
"""
# source: https://pytorch.org/tutorials/intermediate/dist_tuto.html
dist.init_process_group(
init_method='tcp://10.1.1.20:23456',
rank=args.rank,
world_size=4)
"""
# block until all rpcs finish
rpc.shutdown()
def import_pipe():
global TORCH_PIPE
global RPC_INIT
try:
from torch.distributed.pipeline.sync import Pipe # noqa
global Pipe
from torch.distributed.pipeline.sync.utils import partition_model
global partition_model
from torch.distributed import rpc
import tempfile
TORCH_PIPE = True
# Initialize single process RPC agent since TORCH_PIPE requires
# RRef. RRef depends on RPC being initialized and as a result we initialize
# RPC with a single node.
tmpfile = tempfile.NamedTemporaryFile()
if not RPC_INIT:
rpc.init_rpc(name="worker",
rank=0,
world_size=1,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
init_method="file://{}".format(tmpfile.name), ))
RPC_INIT = True
logger.info('Using torch pipe')
except ImportError:
try:
from fairscale.nn import Pipe # noqa
logger.info('Using fairscale pipe')
except ImportError:
raise ImportError(
"Please install fairscale with: pip install fairscale")
def run_worker(rank, world_size):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'
options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=256,
rpc_timeout=600)
import psutil
p = psutil.Process()
if rank == 0:
p.cpu_affinity([0])
print(
f"Child #{rank}: Set my affinity to {rank}, affinity now {p.cpu_affinity()}",
flush=True)
rpc.init_rpc("master",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
run_master()
else:
p.cpu_affinity([rank - 1])
print(
f"Child #{rank}: Set my affinity to {rank}, affinity now {p.cpu_affinity()}",
flush=True)
rpc.init_rpc(f"worker{rank}",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
pass
# block until all rpcs finish
rpc.shutdown()
def test_init_pg_and_rpc_with_same_socket(self):
addr = DEFAULT_HOSTNAME
port = common.find_free_port()
os.environ["MASTER_ADDR"] = addr
os.environ["MASTER_PORT"] = str(port)
# We internally use a multi-tenant TCP store. Both PG and RPC should successfully
# initialize even when using the same socket address.
dist.init_process_group(
backend="gloo",
init_method="env://",
rank=0,
world_size=1,
)
backend_opts = rpc.TensorPipeRpcBackendOptions(
init_method=f"tcp://{addr}:{port}")
rpc.init_rpc(
name="worker0",
rank=0,
world_size=1,
rpc_backend_options=backend_opts,
)
rpc.shutdown()
def run(rank, world_size):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'
options=rpc.TensorPipeRpcBackendOptions(
num_worker_threads=30,
rpc_timeout=0 # infinite timeout
)
if rank != 0:
rpc.init_rpc(
f"trainer{rank}",
rank=rank,
world_size=world_size,
rpc_backend_options=options
)
# trainer passively waiting for ps to kick off training iterations
else:
rpc.init_rpc(
"ps",
rank=rank,
world_size=world_size,
rpc_backend_options=options
)
run_ps([f"trainer{r}" for r in range(1, world_size)])
# block until all rpcs finish
rpc.shutdown()
def run_worker(rank, world_size, num_split):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'
options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=256, rpc_timeout=600)
import psutil
p = psutil.Process()
if rank == 0:
p.cpu_affinity([rank])
rpc.init_rpc(
"master",
rank=rank,
world_size=world_size,
rpc_backend_options=options
)
run_master(num_split)
else:
p.cpu_affinity([rank])
rpc.init_rpc(
f"worker{rank}",
rank=rank,
world_size=world_size,
rpc_backend_options=options
)
pass
# block until all rpcs finish
rpc.shutdown()
def start(args):
rpc.init_rpc(args.name,
rank=args.rank,
world_size=args.world_size,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
num_worker_threads=args.workers))
rpc.shutdown()
def dist_init(rank: int,
world_size: int,
filename: str,
filename_rpc: str = "") -> bool:
"""
Initialize torch distributed, based on a temporary file shared across ranks, which makes it possible for unrelated
tests to be run concurrently.
Return false if not enough GPUs present in the system.
.. warning: This limits the usecase to all ranks being on the same node
"""
print(f"dist init r={rank}, world={world_size}")
os.environ["WORLD_SIZE"] = str(world_size)
os.environ["RANK"] = str(rank)
url = "file://" + filename
if torch_version() >= (1, 6, 0):
backend = "nccl" if torch.cuda.is_available() else "gloo"
if backend == "nccl" and torch.cuda.device_count() < world_size:
logging.warning(
"Requested world size cannot be reached on this machine, not enough GPUs"
)
return False
torch.distributed.init_process_group(backend=backend,
rank=rank,
world_size=world_size,
init_method=url)
url_rpc = "file://" + filename_rpc
rpc.init_rpc(
f"Test{rank}",
rank=rank,
world_size=world_size,
backend=rpc.BackendType.TENSORPIPE,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
init_method=url_rpc),
)
else:
if world_size > 1:
rpc.init_rpc(f"Test{rank}", rank=rank, world_size=world_size)
elif torch.cuda.is_available():
torch.distributed.init_process_group(backend="nccl",
rank=rank,
world_size=world_size,
init_method=url)
else:
return False
if torch.cuda.is_available() and torch.cuda.device_count():
torch.cuda.set_device(rank % torch.cuda.device_count())
return True
def setup_rpc(scope="session"):
file = tempfile.NamedTemporaryFile()
rpc.init_rpc(name="worker0",
rank=0,
world_size=1,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
init_method="file://{}".format(file.name), ))
yield
rpc.shutdown()
def init_rpc():
os.environ["MASTER_PORT"] = "10639"
init_method = f"tcp://{os.environ['MASTER_ADDR']}:{os.environ['MASTER_PORT']}"
rpc.init_rpc(
f"Test{torch.distributed.get_rank()}",
rank=torch.distributed.get_rank(),
world_size=torch.distributed.get_world_size(),
backend=rpc.BackendType.TENSORPIPE,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
init_method=init_method),
)
def init_rpc(self):
rpc_backend_options = rpc.TensorPipeRpcBackendOptions()
rpc_backend_options.init_method = f"file://{self.file_name}"
for rank in range(self.world_size):
rpc_backend_options.set_device_map(f'worker{rank}', {rank : self.rank, self.rank : rank})
rpc.init_rpc(
name="worker%d" % self.rank,
rank=self.rank,
world_size=self.world_size,
rpc_backend_options=rpc_backend_options,
)
def run(rank, num_workers, data_dir, model, batch_size, test_batch_size, lr,
num_epochs, job_name, target_loss):
logging.basicConfig(level=logging.INFO)
world_size = num_workers + 2
options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=16,
rpc_timeout=0)
if rank == 0:
logging.info(f"PS{rank} initializing")
rpc.init_rpc(f"PS{rank}",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
logging.info(f"PS{rank} initialized")
workers = [f"worker{r}" for r in range(1, world_size - 1)]
ps_rref = rpc.RRef(ParameterServer(model, num_workers, lr, job_name))
futs = []
futs.append(
rpc.rpc_async(to="tester",
func=get_accuracy,
args=(ps_rref, data_dir, test_batch_size, job_name,
target_loss)))
for worker in workers:
futs.append(
rpc.rpc_async(to=worker,
func=run_worker,
args=(ps_rref, data_dir, batch_size, num_epochs,
worker, job_name)))
torch.futures.wait_all(futs)
logging.info(f"Finish training")
elif rank == world_size - 1:
logging.info(f"Tester initializing")
rpc.init_rpc("tester",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
logging.info(f"Tester initialized")
else:
logging.info(f"Worker{rank} initializing")
rpc.init_rpc(f"worker{rank}",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
logging.info(f"Worker{rank} initialized")
rpc.shutdown()
def rpc_worker(rank, world_size, init_file, func, *args):
options = rpc.TensorPipeRpcBackendOptions(init_method="file://" + init_file)
for i in range(world_size):
options.set_device_map("worker" + str(i), {rank: i})
rpc.init_rpc(
"worker" + str(rank),
rank=rank,
world_size=world_size,
backend=rpc.BackendType.TENSORPIPE,
rpc_backend_options=options,
)
if rank == 0:
func(*args)
rpc.shutdown()
def rpc_worker(rank, world_size, batch_size, microbatch_size):
options = rpc.TensorPipeRpcBackendOptions(_transports=["ibv", "uv"])
logging.info(f"Initting worker{rank}")
rpc.init_rpc(
"worker" + str(rank),
rank=rank,
world_size=world_size,
backend=rpc.BackendType.TENSORPIPE,
rpc_backend_options=options,
)
if rank == 0:
run_master(world_size, batch_size, microbatch_size)
rpc.shutdown()
def init_procs(rank, world_size, tr_args=DEFAULTS):
# DDP info
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '42069'
# RPC info
rpc_backend_options = rpc.TensorPipeRpcBackendOptions()
rpc_backend_options.init_method = 'tcp://localhost:42068'
# Master (RNN module)
if rank == world_size - 1:
torch.set_num_threads(M_THREADS)
# Master gets 16 threads and 4x4 threaded workers
# In theory, only 16 threads should run at a time while
# master sleeps, waiting on worker procs
#torch.set_num_threads(16)
rpc.init_rpc('master',
rank=rank,
world_size=world_size,
rpc_backend_options=rpc_backend_options)
rrefs = init_workers(world_size - 1, tr_args['h_size'], TR_START,
TR_END, DELTA, False)
model, zs, h0 = train(rrefs, tr_args)
get_cutoff(model, h0, tr_args)
test(model, zs, h0, rrefs)
# Slaves
else:
# If there are 4 workers, give them each 4 threads
# (Total 16 is equal to serial model)
torch.set_num_threads(W_THREADS)
# Slaves are their own process group. This allows
# DDP to work between these processes
dist.init_process_group('gloo', rank=rank, world_size=world_size - 1)
rpc.init_rpc('worker' + str(rank),
rank=rank,
world_size=world_size,
rpc_backend_options=rpc_backend_options)
# Block until all procs complete
rpc.shutdown()
def test_invalid_pg_rpc_ranks(self):
self.init_pg()
# Init RPC with different ranks.
rpc_backend_options = rpc.TensorPipeRpcBackendOptions()
rpc_backend_options.init_method = f"file://{self.file_name}"
rank = (self.rank + 1) % self.world_size
rpc.init_rpc(
name=f'worker{rank}',
rank=rank,
world_size=self.world_size,
rpc_backend_options=rpc_backend_options,
)
spec = ChunkShardingSpec(dim=0, placements=["rank:1/cuda:1"])
with self.assertRaisesRegex(ValueError, 'Default ProcessGroup and RPC ranks must be the same'):
_sharded_tensor.empty(spec, 10, 20)
def bench_mpi(args):
guess_rank = int(os.environ["OMPI_COMM_WORLD_RANK"])
world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"])
local_rank = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"])
os.environ["UCX_NET_DEVICES"] = best_device_map[local_rank]
os.environ["MASTER_ADDR"] = args.host
os.environ["MASTER_PORT"] = "10638"
if args.socket_name:
os.environ["GLOO_SOCKET_IFNAME"] = args.socket_name
os.environ["TP_SOCKET_IFNAME"] = args.socket_name
torch.distributed.init_process_group(backend="gloo",
rank=guess_rank,
world_size=world_size)
os.environ["MASTER_ADDR"] = args.host
os.environ["MASTER_PORT"] = "10639"
init_method = f"tcp://{os.environ['MASTER_ADDR']}:{os.environ['MASTER_PORT']}"
rank = torch.distributed.get_rank()
world_size = torch.distributed.get_world_size()
rpc.init_rpc(
f"Test{rank}",
rank=rank,
world_size=world_size,
backend=rpc.BackendType.TENSORPIPE,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
rpc_timeout=20, init_method=init_method),
)
backends = {
"model_parallel_backend": "nccl",
"pipeline_backend": "mpi",
"ddp_backend": "nccl"
}
initialize_model_parallel(1, world_size, **backends)
init_random_seed(0)
run_mp_worker(args, world_size)
rpc.shutdown()
torch.distributed.destroy_process_group()
def run(arg, data):
name = arg.name
remote = arg.remote
rpc.init_rpc(name,
rank=arg.rank,
world_size=arg.world_size,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
num_worker_threads=args.workers))
result = rpc.rpc_sync(remote, collect, args=(data, 2))
async_result = []
t1 = time.time()
for i in range(args.workers):
async_result.append(rpc.rpc_async(remote, collect, args=(data, 2)))
for i in range(args.workers):
async_result[i].wait()
t2 = time.time()
print('RTT of {} tensor is {}'.format(result.shape, t2 - t1))
rpc.shutdown()
def run_remote(config_path: Path, rank: int, nic=None, host=None, prefix: str=None):
print(config_path, rank)
config = Config.FromYamlFile(config_path)
config.world_size = config.num_clients + 1
config.replication_id = prefix
nic, host = retrieve_network_params_from_config(config, nic, host)
if not nic or not host:
print('Missing rank, host, world-size, or nic argument when in \'remote\' mode!')
parser.print_help()
exit(1)
retrieve_env_params(nic, host)
print(f'Starting with host={os.environ["MASTER_ADDR"]} and port={os.environ["MASTER_PORT"]} and interface={nic}')
options = rpc.TensorPipeRpcBackendOptions(
num_worker_threads=16,
rpc_timeout=0, # infinite timeout
# init_method=f'tcp://{os.environ["MASTER_ADDR"]}:{os.environ["MASTER_PORT"]}'
init_method='env://',
_transports=["uv"]
)
if rank != 0:
print(f'Starting worker {rank} with world size={config.world_size}')
rpc.init_rpc(
f"client{rank}",
rank=rank,
world_size=config.world_size,
rpc_backend_options=options,
)
client_node = Client(f'client{rank}', rank, config.world_size, config)
client_node.remote_registration()
else:
print(f'Starting the ps with world size={config.world_size}')
rpc.init_rpc(
"federator",
rank=rank,
world_size=config.world_size,
rpc_backend_options=options
)
federator_node = Federator('federator', 0, config.world_size, config)
federator_node.run()
federator_node.stop_all_clients()
print('Ending program')
def run_worker(rank, world_size, num_split):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'
options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=256)
if rank == 0:
rpc.init_rpc("master",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
run_master(num_split)
else:
rpc.init_rpc(f"worker{rank}",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
pass
# block until all rpcs finish
rpc.shutdown()
def run_single(rank, world_size, host=None, args=None, nic=None):
logging.info(f'Starting with rank={rank} and world size={world_size}')
if host:
os.environ['MASTER_ADDR'] = host
else:
os.environ['MASTER_ADDR'] = '0.0.0.0'
os.environ['MASTER_PORT'] = '5000'
if nic:
os.environ['GLOO_SOCKET_IFNAME'] = nic
os.environ['TP_SOCKET_IFNAME'] = nic
else:
os.environ['GLOO_SOCKET_IFNAME'] = 'wlo1'
os.environ['TP_SOCKET_IFNAME'] = 'wlo1'
logging.info(
f'Starting with host={os.environ["MASTER_ADDR"]} and port={os.environ["MASTER_PORT"]}'
)
options = rpc.TensorPipeRpcBackendOptions(
num_worker_threads=16,
rpc_timeout=0, # infinite timeout
init_method=
f'tcp://{os.environ["MASTER_ADDR"]}:{os.environ["MASTER_PORT"]}')
if rank != 0:
logging.info(f'Starting worker {rank}')
rpc.init_rpc(
f"client{rank}",
rank=rank,
world_size=world_size,
rpc_backend_options=options,
)
# trainer passively waiting for ps to kick off training iterations
else:
logging.info('Starting the ps')
rpc.init_rpc("ps",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
run_ps([(f"client{r}", r, world_size) for r in range(1, world_size)],
args)
# block until all rpc finish
rpc.shutdown()
def dist_init(rank: int,
world_size: int,
hostname: Optional[str] = None) -> None:
if hostname is None:
hostname = "localhost"
print(f"dist init r={rank}, world={world_size}, host={hostname}")
os.environ["MASTER_ADDR"] = hostname
os.environ["MASTER_PORT"] = "10638"
os.environ["WORLD_SIZE"] = str(world_size)
os.environ["RANK"] = str(rank)
if torch_version() >= (1, 6, 0):
init_method = f"tcp://{os.environ['MASTER_ADDR']}:{os.environ['MASTER_PORT']}"
backend = "nccl" if torch.cuda.is_available() else "gloo"
torch.distributed.init_process_group(backend=backend,
rank=rank,
world_size=world_size,
init_method=init_method)
os.environ["MASTER_ADDR"] = hostname
os.environ["MASTER_PORT"] = "10639"
init_method = f"tcp://{os.environ['MASTER_ADDR']}:{os.environ['MASTER_PORT']}"
rpc.init_rpc(
f"Test{rank}",
rank=rank,
world_size=world_size,
backend=rpc.BackendType.TENSORPIPE,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
init_method=init_method),
)
else:
if world_size > 1:
rpc.init_rpc(f"Test{rank}", rank=rank, world_size=world_size)
else:
torch.distributed.init_process_group(backend="nccl",
rank=rank,
world_size=world_size)
if torch.cuda.is_available() and torch.cuda.device_count():
torch.cuda.set_device(rank % torch.cuda.device_count())
def run_worker(rank, world_size):
# DDP info
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '42069'
# RPC info
rpc_backend_options = rpc.TensorPipeRpcBackendOptions()
rpc_backend_options.init_method = 'tcp://localhost:42068'
# Master
if rank == world_size - 1:
rpc.init_rpc('master',
rank=rank,
world_size=world_size,
rpc_backend_options=rpc_backend_options)
X_tr, y_tr = gen_toy_data()
X_te, y_te = gen_toy_data()
rrefs = []
for i in range(world_size - 1):
rrefs.append(
rpc.remote('worker' + str(i),
init_embedder,
args=(X_tr.size(2), 10)))
train_loop(rrefs, X_tr, y_tr, X_te, y_te)
# Slaves
else:
# Slaves are their own process group. This allows
# DDP to work between these processes
dist.init_process_group('gloo', rank=rank, world_size=world_size - 1)
rpc.init_rpc('worker' + str(rank),
rank=rank,
world_size=world_size,
rpc_backend_options=rpc_backend_options)
# Block until all procs complete
rpc.shutdown()
def run_worker(rank, world_size, num_split):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'
# Higher timeout is added to accommodate for kernel compilation time in case of ROCm.
options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=256,
rpc_timeout=300)
if rank == 0:
rpc.init_rpc("master",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
run_master(num_split)
else:
rpc.init_rpc(f"worker{rank}",
rank=rank,
world_size=world_size,
rpc_backend_options=options)
pass
# block until all rpcs finish
rpc.shutdown()
def _init_torch_rpc_tp(
self,
master_addr,
master_port,
worker_idx,
worker_num,
):
# https://github.com/pytorch/pytorch/issues/55615
# [BC-Breaking][RFC] Retire ProcessGroup Backend for RPC #55615
str_init_method = "tcp://" + str(master_addr) + ":10000"
logging.info("str_init_method = {}".format(str_init_method))
options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=16,
rpc_timeout=1800,
init_method=str_init_method,
_transports=["uv"])
rpc.init_rpc(
WORKER.format(worker_idx),
backend=rpc.BackendType.TENSORPIPE,
rank=worker_idx,
world_size=worker_num,
rpc_backend_options=options,
)
logging.info("_init_torch_rpc_tp finished.")
def init_procs(rank, world_size, rnn_constructor, rnn_args, worker_constructor, worker_args,
times, just_test, fw, static, single_emb, run_speed_test, load_fn, manual,
tr_args=DEFAULTS):
# DDP info
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '42069'
# RPC info
rpc_backend_options = rpc.TensorPipeRpcBackendOptions()
rpc_backend_options.init_method='tcp://localhost:42068'
# This is a lot easier than actually changing it in all the methods
# at this point
global LOAD_FN
LOAD_FN = load_fn
# Master (RNN module)
if rank == world_size-1:
torch.set_num_threads(M_THREADS)
rpc.init_rpc(
'master', rank=rank,
world_size=world_size,
rpc_backend_options=rpc_backend_options
)
# Speed test doesn't need to train the model,
# just use random state it starts with and check
# how long it takes to run, then return that data
if run_speed_test:
rrefs = init_empty_workers(
world_size-1,
worker_constructor, worker_args
)
rnn = rnn_constructor(*rnn_args)
model = StaticRecurrent(rnn, rrefs) if static\
else DynamicRecurrent(rnn, rrefs)
stats = speed_test(model, rrefs, times)
stats['delta'] = times['delta']
# Evaluating a pre-trained model, so no need to train
elif just_test:
rrefs = init_empty_workers(
world_size-1,
worker_constructor, worker_args
)
rnn = rnn_constructor(*rnn_args)
model = StaticRecurrent(rnn, rrefs) if static\
else DynamicRecurrent(rnn, rrefs)
states = pickle.load(open('model_save.pkl', 'rb'))
model.load_states(states['gcn'], states['rnn'])
h0 = states['h0']
tpe = 0
# Building and training a fresh model
else:
rrefs = init_workers(
world_size-1,
times['tr_start'], times['tr_end'], times['delta'], False,
worker_constructor, worker_args
)
model, h0, tpe = train(rrefs, tr_args, rnn_constructor, rnn_args, static)
if not run_speed_test:
h0, zs = get_cutoff(model, h0, times, tr_args, fw)
if single_emb:
stats = test_single_embed(zs, rrefs, times, model.cutoff)
else:
stats = test(model, h0, times, rrefs, manual=manual)
stats['TPE'] = tpe
# Slaves
else:
torch.set_num_threads(W_THREADS)
# Slaves are their own process group. This allows
# DDP to work between these processes
dist.init_process_group(
'gloo', rank=rank,
world_size=world_size-1
)
rpc.init_rpc(
'worker'+str(rank),
rank=rank,
world_size=world_size,
rpc_backend_options=rpc_backend_options
)
# Block until all procs complete
rpc.shutdown()
# Write output to a tmp file to get it back to the parent process
if rank == world_size-1:
pickle.dump(stats, open(TMP_FILE, 'wb+'), protocol=pickle.HIGHEST_PROTOCOL)
def __init__(
self,
name: str,
rank: int = -1,
world_size: int = -1,
init_dist: bool = True,
init_rpc: bool = True,
dist_backend: str = "gloo",
dist_init_method: str = "tcp://localhost:9100",
rpc_init_method: str = "tcp://localhost:9101",
dist_timeout: float = 60,
rpc_timeout: float = 60,
):
"""
Args:
name: A unique name to identify current process.
rank: A unique rank of the current process. You do not need to specify
it if you are using `torch.distributed.launch` or `torchelastic`
world_size: Size of the distributed world. You do not need to specify
it if you are using `torch.distributed.launch` or `torchelastic`
dist_timeout: Distributed package timeout in seconds.
rpc_timeout: Global rpc call timeout in seconds.
"""
self.world_size = world_size
self.rank = rank
self.name = name
self.groups = {}
self.group_create_signals = {}
if init_dist:
dist.init_process_group(
backend=dist_backend,
init_method=dist_init_method,
timeout=timedelta(seconds=dist_timeout),
rank=rank,
world_size=world_size,
)
if init_rpc:
rpc.init_rpc(
self.name,
rank=rank,
world_size=world_size,
backend=rpc.BackendType.TENSORPIPE,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
init_method=rpc_init_method, rpc_timeout=rpc_timeout
),
)
# get rank-name mapping
self.rank_name_map = {}
for wi in rpc._get_current_rpc_agent().get_worker_infos():
self.rank_name_map[wi.id] = wi.name
# Start role dispatching.
self.started = True
self.rpc_timeout = rpc_timeout
# map for paired values and registered services
self.value_lut = {}
self.service_lut = {}
self.lut_lock = Lock()
self.lut_manager = self.rank_name_map[0]
nhid = 4096 # the dimension of the feedforward network model in nn.TransformerEncoder
nlayers = 12 # the number of nn.TransformerEncoderLayer in nn.TransformerEncoder
nhead = 16 # the number of heads in the multiheadattention models
dropout = 0.2 # the dropout value
from torch.distributed import rpc
tmpfile = tempfile.NamedTemporaryFile()
rpc.init_rpc(
name="worker",
rank=0,
world_size=1,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
init_method="file://{}".format(tmpfile.name),
# Specifying _transports and _channels is a workaround and we no longer
# will have to specify _transports and _channels for PyTorch
# versions >= 1.8.1
_transports=["ibv", "uv"],
_channels=["cuda_ipc", "cuda_basic"],
))
num_gpus = 2
partition_len = ((nlayers - 1) // num_gpus) + 1
# Add encoder in the beginning.
tmp_list = [Encoder(ntokens, emsize, dropout).cuda(0)]
module_list = []
# Add all the necessary transformer blocks.
for i in range(nlayers):
transformer_block = TransformerEncoderLayer(emsize, nhead, nhid, dropout)
if i != 0 and i % (partition_len) == 0:
def run_worker(rank, world_size):
######################################################################
# Load and batch data
# -------------------
#
######################################################################
# The training process uses Wikitext-2 dataset from ``torchtext``. The
# vocab object is built based on the train dataset and is used to numericalize
# tokens into tensors. Starting from sequential data, the ``batchify()``
# function arranges the dataset into columns, trimming off any tokens remaining
# after the data has been divided into batches of size ``batch_size``.
# For instance, with the alphabet as the sequence (total length of 26)
# and a batch size of 4, we would divide the alphabet into 4 sequences of
# length 6:
#
# .. math::
# \begin{bmatrix}
# \text{A} & \text{B} & \text{C} & \ldots & \text{X} & \text{Y} & \text{Z}
# \end{bmatrix}
# \Rightarrow
# \begin{bmatrix}
# \begin{bmatrix}\text{A} \\ \text{B} \\ \text{C} \\ \text{D} \\ \text{E} \\ \text{F}\end{bmatrix} &
# \begin{bmatrix}\text{G} \\ \text{H} \\ \text{I} \\ \text{J} \\ \text{K} \\ \text{L}\end{bmatrix} &
# \begin{bmatrix}\text{M} \\ \text{N} \\ \text{O} \\ \text{P} \\ \text{Q} \\ \text{R}\end{bmatrix} &
# \begin{bmatrix}\text{S} \\ \text{T} \\ \text{U} \\ \text{V} \\ \text{W} \\ \text{X}\end{bmatrix}
# \end{bmatrix}
#
# These columns are treated as independent by the model, which means that
# the dependence of ``G`` and ``F`` can not be learned, but allows more
# efficient batch processing.
#
# In 'run_worker'
def print_with_rank(msg):
print('[RANK {}]: {}'.format(rank, msg))
from torchtext.datasets import WikiText2
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
train_iter = WikiText2(split='train')
tokenizer = get_tokenizer('basic_english')
vocab = build_vocab_from_iterator(map(tokenizer, train_iter),
specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])
def data_process(raw_text_iter):
data = [
torch.tensor(vocab(tokenizer(item)), dtype=torch.long)
for item in raw_text_iter
]
return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))
train_iter, val_iter, test_iter = WikiText2()
train_data = data_process(train_iter)
val_data = data_process(val_iter)
test_data = data_process(test_iter)
device = torch.device(2 * rank)
def batchify(data, bsz, rank, world_size, is_train=False):
# Divide the dataset into bsz parts.
nbatch = data.size(0) // bsz
# Trim off any extra elements that wouldn't cleanly fit (remainders).
data = data.narrow(0, 0, nbatch * bsz)
# Evenly divide the data across the bsz batches.
data = data.view(bsz, -1).t().contiguous()
# Divide the data across the ranks only for training data.
if is_train:
data_per_rank = data.size(0) // world_size
data = data[rank * data_per_rank:(rank + 1) * data_per_rank]
return data.to(device)
batch_size = 20
eval_batch_size = 10
train_data = batchify(train_data, batch_size, rank, world_size, True)
val_data = batchify(val_data, eval_batch_size, rank, world_size)
test_data = batchify(test_data, eval_batch_size, rank, world_size)
######################################################################
# Functions to generate input and target sequence
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
######################################################################
# ``get_batch()`` function generates the input and target sequence for
# the transformer model. It subdivides the source data into chunks of
# length ``bptt``. For the language modeling task, the model needs the
# following words as ``Target``. For example, with a ``bptt`` value of 2,
# we’d get the following two Variables for ``i`` = 0:
#
# .. image:: ../_static/img/transformer_input_target.png
#
# It should be noted that the chunks are along dimension 0, consistent
# with the ``S`` dimension in the Transformer model. The batch dimension
# ``N`` is along dimension 1.
#
# In 'run_worker'
bptt = 35
def get_batch(source, i):
seq_len = min(bptt, len(source) - 1 - i)
data = source[i:i + seq_len]
target = source[i + 1:i + 1 + seq_len].view(-1)
# Need batch dimension first for pipeline parallelism.
return data.t(), target
######################################################################
# Model scale and Pipe initialization
# -----------------------------------
#
######################################################################
# To demonstrate training large Transformer models using pipeline parallelism,
# we scale up the Transformer layers appropriately. We use an embedding
# dimension of 4096, hidden size of 4096, 16 attention heads and 8 total
# transformer layers (``nn.TransformerEncoderLayer``). This creates a model with
# **~1 billion** parameters.
#
# We need to initialize the `RPC Framework <https://pytorch.org/docs/stable/rpc.html>`__
# since Pipe depends on the RPC framework via `RRef <https://pytorch.org/docs/stable/rpc.html#rref>`__
# which allows for future expansion to cross host pipelining. We need to
# initialize the RPC framework with only a single worker since we're using a
# single process to drive multiple GPUs.
#
# The pipeline is then initialized with 8 transformer layers on one GPU and 8
# transformer layers on the other GPU. One pipe is setup across GPUs 0 and 1 and
# another across GPUs 2 and 3. Both pipes are then replicated using DistributedDataParallel.
# In 'run_worker'
ntokens = len(vocab) # the size of vocabulary
emsize = 4096 # embedding dimension
nhid = 4096 # the dimension of the feedforward network model in nn.TransformerEncoder
nlayers = 8 # the number of nn.TransformerEncoderLayer in nn.TransformerEncoder
nhead = 16 # the number of heads in the multiheadattention models
dropout = 0.2 # the dropout value
from torch.distributed import rpc
tmpfile = tempfile.NamedTemporaryFile()
rpc.init_rpc(
name="worker",
rank=0,
world_size=1,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
init_method="file://{}".format(tmpfile.name),
# Specifying _transports and _channels is a workaround and we no longer
# will have to specify _transports and _channels for PyTorch
# versions >= 1.8.1
_transports=["ibv", "uv"],
_channels=["cuda_ipc", "cuda_basic"],
))
# Num gpus for model parallelism.
num_gpus = 2
partition_len = ((nlayers - 1) // num_gpus) + 1
# Add encoder in the beginning.
tmp_list = [Encoder(ntokens, emsize, dropout).cuda(2 * rank)]
module_list = []
# Add all the necessary transformer blocks.
for i in range(nlayers):
transformer_block = TransformerEncoderLayer(emsize, nhead, nhid,
dropout)
if i != 0 and i % (partition_len) == 0:
module_list.append(nn.Sequential(*tmp_list))
tmp_list = []
device = i // (partition_len)
tmp_list.append(transformer_block.to(2 * rank + device))
# Add decoder in the end.
tmp_list.append(Decoder(ntokens, emsize).cuda(2 * rank + num_gpus - 1))
module_list.append(nn.Sequential(*tmp_list))
# Need to use 'checkpoint=never' since as of PyTorch 1.8, Pipe checkpointing
# doesn't work with DDP.
from torch.distributed.pipeline.sync import Pipe
chunks = 8
model = Pipe(torch.nn.Sequential(*module_list),
chunks=chunks,
checkpoint="never")
# Initialize process group and wrap model in DDP.
from torch.nn.parallel import DistributedDataParallel
import torch.distributed as dist
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'
dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
model = DistributedDataParallel(model)
def get_total_params(module: torch.nn.Module):
total_params = 0
for param in module.parameters():
total_params += param.numel()
return total_params
print_with_rank('Total parameters in model: {:,}'.format(
get_total_params(model)))
######################################################################
# Run the model
# -------------
#
######################################################################
# `CrossEntropyLoss <https://pytorch.org/docs/master/nn.html?highlight=crossentropyloss#torch.nn.CrossEntropyLoss>`__
# is applied to track the loss and
# `SGD <https://pytorch.org/docs/master/optim.html?highlight=sgd#torch.optim.SGD>`__
# implements stochastic gradient descent method as the optimizer. The initial
# learning rate is set to 5.0. `StepLR <https://pytorch.org/docs/master/optim.html?highlight=steplr#torch.optim.lr_scheduler.StepLR>`__ is
# applied to adjust the learn rate through epochs. During the
# training, we use
# `nn.utils.clip_grad_norm\_ <https://pytorch.org/docs/master/nn.html?highlight=nn%20utils%20clip_grad_norm#torch.nn.utils.clip_grad_norm_>`__
# function to scale all the gradient together to prevent exploding.
#
# In 'run_worker'
criterion = nn.CrossEntropyLoss()
lr = 5.0 # learning rate
optimizer = torch.optim.SGD(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
import time
def train():
model.train() # Turn on the train mode
total_loss = 0.
start_time = time.time()
ntokens = len(vocab)
# Train only for 50 batches to keep script execution time low.
nbatches = min(50 * bptt, train_data.size(0) - 1)
for batch, i in enumerate(range(0, nbatches, bptt)):
data, targets = get_batch(train_data, i)
optimizer.zero_grad()
# Since the Pipe is only within a single host and process the ``RRef``
# returned by forward method is local to this node and can simply
# retrieved via ``RRef.local_value()``.
output = model(data).local_value()
# Need to move targets to the device where the output of the
# pipeline resides.
loss = criterion(output.view(-1, ntokens),
targets.cuda(2 * rank + 1))
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
optimizer.step()
total_loss += loss.item()
log_interval = 10
if batch % log_interval == 0 and batch > 0:
cur_loss = total_loss / log_interval
elapsed = time.time() - start_time
print_with_rank('| epoch {:3d} | {:5d}/{:5d} batches | '
'lr {:02.2f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch, nbatches // bptt,
scheduler.get_last_lr()[0],
elapsed * 1000 / log_interval, cur_loss,
math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
def evaluate(eval_model, data_source):
eval_model.eval() # Turn on the evaluation mode
total_loss = 0.
ntokens = len(vocab)
# Evaluate only for 50 batches to keep script execution time low.
nbatches = min(50 * bptt, data_source.size(0) - 1)
with torch.no_grad():
for i in range(0, nbatches, bptt):
data, targets = get_batch(data_source, i)
output = eval_model(data).local_value()
output_flat = output.view(-1, ntokens)
# Need to move targets to the device where the output of the
# pipeline resides.
total_loss += len(data) * criterion(
output_flat, targets.cuda(2 * rank + 1)).item()
return total_loss / (len(data_source) - 1)
######################################################################
# Loop over epochs. Save the model if the validation loss is the best
# we've seen so far. Adjust the learning rate after each epoch.
# In 'run_worker'
best_val_loss = float("inf")
epochs = 3 # The number of epochs
best_model = None
for epoch in range(1, epochs + 1):
epoch_start_time = time.time()
train()
val_loss = evaluate(model, val_data)
print_with_rank('-' * 89)
print_with_rank(
'| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
val_loss, math.exp(val_loss)))
print_with_rank('-' * 89)
if val_loss < best_val_loss:
best_val_loss = val_loss
best_model = model
scheduler.step()
######################################################################
# Evaluate the model with the test dataset
# -------------------------------------
#
# Apply the best model to check the result with the test dataset.
# In 'run_worker'
test_loss = evaluate(best_model, test_data)
print_with_rank('=' * 89)
print_with_rank(
'| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(
test_loss, math.exp(test_loss)))
print_with_rank('=' * 89)
