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import os
from time import sleep
import torch
import jukebox.utils.dist_adapter as dist
def print_once(msg):
if (not dist.is_available()) or dist.get_rank()==0:
print(msg)
def print_all(msg):
if (not dist.is_available()):
print(msg)
elif dist.get_rank()%8==0:
print(f'{dist.get_rank()//8}: {msg}')
def allgather(x):
xs = [torch.empty_like(x) for _ in range(dist.get_world_size())]
dist.all_gather(xs, x)
xs = torch.cat(xs, dim=0)
return xs
def allreduce(x, op=dist.ReduceOp.SUM):
x = torch.tensor(x).float().cuda()
dist.all_reduce(x, op=op)
return x.item()
def allgather_lists(xs):
bs = len(xs)
total_bs = dist.get_world_size()*len(xs)
lengths = torch.tensor([len(x) for x in xs], dtype=t.long, device='cuda')
lengths = allgather(lengths)
assert lengths.shape == (total_bs,)
max_length = torch.max(lengths).item()
xs = torch.tensor([[*x, *[0]*(max_length - len(x))] for x in xs], device='cuda')
assert xs.shape == (bs, max_length), f'Expected {(bs, max_length)}, got {xs.shape}'
xs = allgather(xs)
assert xs.shape == (total_bs,max_length), f'Expected {(total_bs, max_length)}, got {xs.shape}'
return [xs[i][:lengths[i]].cpu().numpy().tolist() for i in range(total_bs)]
def setup_dist_from_mpi(
master_addr="127.0.0.1", backend="nccl", port=29500, n_attempts=5, verbose=False
):
if dist.is_available():
return _setup_dist_from_mpi(master_addr, backend, port, n_attempts, verbose)
else:
use_cuda = torch.cuda.is_available()
print(f'Using cuda {use_cuda}')
mpi_rank = 0
local_rank = 0
device = torch.device("cuda", local_rank) if use_cuda else torch.device("cpu")
torch.cuda.set_device(local_rank)
return mpi_rank, local_rank, device
def _setup_dist_from_mpi(master_addr, backend, port, n_attempts, verbose):
from mpi4py import MPI # This must be imported in order to get e rrors from all ranks to show up
mpi_rank = MPI.COMM_WORLD.Get_rank()
mpi_size = MPI.COMM_WORLD.Get_size()
os.environ["RANK"] = str(mpi_rank)
os.environ["WORLD_SIZE"] = str(mpi_size)
os.environ["MASTER_ADDR"] = master_addr
os.environ["MASTER_PORT"] = str(port)
os.environ["NCCL_LL_THRESHOLD"] = "0"
os.environ["NCCL_NSOCKS_PERTHREAD"] = "2"
os.environ["NCCL_SOCKET_NTHREADS"] = "8"
# Pin this rank to a specific GPU on the node
local_rank = mpi_rank % 8
if torch.cuda.is_available():
torch.cuda.set_device(local_rank)
if verbose:
print(f"Connecting to master_addr: {master_addr}")
# There is a race condition when initializing NCCL with a large number of ranks (e.g 500 ranks)
# We guard against the failure and then retry
for attempt_idx in range(n_attempts):
try:
dist.init_process_group(backend=backend, init_method=f"env://")
assert dist.get_rank() == mpi_rank
use_cuda = torch.cuda.is_available()
print(f'Using cuda {use_cuda}')
local_rank = mpi_rank % 8
device = torch.device("cuda", local_rank) if use_cuda else torch.device("cpu")
torch.cuda.set_device(local_rank)
return mpi_rank, local_rank, device
except RuntimeError as e:
print(f"Caught error during NCCL init (attempt {attempt_idx} of {n_attempts}): {e}")
sleep(1 + (0.01 * mpi_rank)) # Sleep to avoid thundering herd
pass
raise RuntimeError("Failed to initialize NCCL")
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