I start 2 processes because I only have 2 gpus but then it gives me a Exception: process 0 terminated with signal SIGSEGV. This code does work with multiple cpus (or at least no error is thrown). Also, it works with a single GPU. Besides that is fails when world_size > 0 and multiple cuda/gpus are present.
My error message this this:
(automl-meta-learning) miranda9~/ML4Coq $ python playground/multiprocessing_playground/ddp_hello_world.py
world_size=2
Traceback (most recent call last):
File "playground/multiprocessing_playground/ddp_hello_world.py", line 49, in <module>
main()
File "playground/multiprocessing_playground/ddp_hello_world.py", line 43, in main
mp.spawn(example,
File "/home/miranda9/miniconda3/envs/automl-meta-learning/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 199, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/home/miranda9/miniconda3/envs/automl-meta-learning/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 157, in start_processes
while not context.join():
File "/home/miranda9/miniconda3/envs/automl-meta-learning/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 105, in join
raise Exception(
Exception: process 0 terminated with signal SIGSEGV
This is the code that gives the error:
import os
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel as DDP
def example(rank, world_size):
# create default process group
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '8888'
dist.init_process_group("gloo", rank=rank, world_size=world_size)
# create local model
model = nn.Linear(10, 10).to(rank)
# construct DDP model
ddp_model = DDP(model, device_ids=[rank])
# define loss function and optimizer
loss_fn = nn.MSELoss()
optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)
# forward pass
outputs = ddp_model(torch.randn(20, 10).to(rank))
labels = torch.randn(20, 10).to(rank)
# backward pass
loss_fn(outputs, labels).backward()
# update parameters
optimizer.step()
def main():
# world_size = 2
world_size = torch.cuda.device_count()
mp.spawn(example,
args=(world_size,),
nprocs=world_size,
join=True)
if __name__=="__main__":
main()
print('Done\n\a')
[Optional] Larger self-contained example (gives same error)
Note however, that this slightly more complete example (only missing a distributed dataloader) also gives me the same issue:
"""
Based on: https://pytorch.org/tutorials/intermediate/ddp_tutorial.html
Correctness of code: https://stackoverflow.com/questions/66226135/how-to-parallelize-a-training-loop-ever-samples-of-a-batch-when-cpu-is-only-avai
Note: as opposed to the multiprocessing (torch.multiprocessing) package, processes can use
different communication backends and are not restricted to being executed on the same machine.
"""
import time
from typing import Tuple
import torch
from torch import nn, optim
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
import os
num_epochs = 5
batch_size = 8
Din, Dout = 10, 5
data_x = torch.randn(batch_size, Din)
data_y = torch.randn(batch_size, Dout)
data = [(i*data_x, i*data_y) for i in range(num_epochs)]
class PerDeviceModel(nn.Module):
"""
Toy example for a model ran in parallel but not distributed accross gpus
(only processes with their own gpu or hardware)
"""
def __init__(self):
super().__init__()
self.net1 = nn.Linear(Din, Din)
self.relu = nn.ReLU()
self.net2 = nn.Linear(Din, Dout)
def forward(self, x):
return self.net2(self.relu(self.net1(x)))
def setup_process(rank, world_size, backend='gloo'):
"""
Initialize the distributed environment (for each process).
gloo: is a collective communications library (https://github.com/facebookincubator/gloo). My understanding is that
it's a library/API for process to communicate/coordinate with each other/master. It's a backend library.
"""
# set up the master's ip address so this child process can coordinate
# os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12355'
# - use NCCL if you are using gpus: https://pytorch.org/tutorials/intermediate/dist_tuto.html#communication-backends
if torch.cuda.is_available():
backend = 'nccl'
# Initializes the default distributed process group, and this will also initialize the distributed package.
dist.init_process_group(backend, rank=rank, world_size=world_size)
def cleanup():
""" Destroy a given process group, and deinitialize the distributed package """
dist.destroy_process_group()
def get_batch(batch: Tuple[torch.Tensor, torch.Tensor], rank):
x, y = batch
if torch.cuda.is_available():
x, y = x.to(rank), y.to(rank)
else:
x, y = x.share_memory_(), y.share_memory_()
return x, y
def get_ddp_model(model: nn.Module, rank):
"""
Moves the underlying storage to shared memory.
This is a no-op if the underlying storage is already in shared memory
and for CUDA tensors. Tensors in shared memory cannot be resized.
:return:
TODO: does this have to be done outside or inside the process? my guess is that it doesn't matter because
1) if its on gpu once it's on the right proc it moves it to cpu with id rank via mdl.to(rank)
2) if it's on cpu then mdl.share_memory() or data.share_memory() is a no op if it's already in shared memory o.w.
"""
# if gpu avail do the standard of creating a model and moving the model to the GPU with id rank
if torch.cuda.is_available():
# create model and move it to GPU with id rank
model = model.to(rank)
ddp_model = DDP(model, device_ids=[rank])
else:
# if we want multiple cpu just make sure the model is shared properly accross the cpus with shared_memory()
# note that op is a no op if it's already in shared_memory
model = model.share_memory()
ddp_model = DDP(model) # I think removing the devices ids should be fine...?
return ddp_model
# return OneDeviceModel().to(rank) if torch.cuda.is_available() else OneDeviceModel().share_memory()
def run_parallel_training_loop(rank, world_size):
"""
Distributed function to be implemented later.
This is the function that is actually ran in each distributed process.
Note: as DDP broadcasts model states from rank 0 process to all other processes in the DDP constructor,
you don’t need to worry about different DDP processes start from different model parameter initial values.
"""
setup_process(rank, world_size)
print()
print(f"Start running DDP with model parallel example on rank: {rank}.")
print(f'current process: {mp.current_process()}')
print(f'pid: {os.getpid()}')
# get ddp model
model = PerDeviceModel()
ddp_model = get_ddp_model(model, rank)
# do training
for batch_idx, batch in enumerate(data):
x, y = get_batch(batch, rank)
loss_fn = nn.MSELoss()
optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)
optimizer.zero_grad()
outputs = ddp_model(x)
# Gradient synchronization communications take place during the backward pass and overlap with the backward computation.
loss_fn(outputs, y).backward() # When the backward() returns, param.grad already contains the synchronized gradient tensor.
optimizer.step() # TODO how does the optimizer know to do the gradient step only once?
print()
print(f"Start running DDP with model parallel example on rank: {rank}.")
print(f'current process: {mp.current_process()}')
print(f'pid: {os.getpid()}')
# Destroy a given process group, and deinitialize the distributed package
cleanup()
def main():
print()
print('running main()')
print(f'current process: {mp.current_process()}')
print(f'pid: {os.getpid()}')
# args
if torch.cuda.is_available():
world_size = torch.cuda.device_count()
else:
world_size = mp.cpu_count()
print(f'world_size={world_size}')
mp.spawn(run_parallel_training_loop, args=(world_size,), nprocs=world_size)
if __name__ == "__main__":
print('starting __main__')
start = time.time()
main()
print(f'execution length = {time.time() - start}')
print('Done!\a\n')
cross posted: https://discuss.pytorch.org/t/why-is-mp-spawn-spawning-4-processes-when-i-only-want-2/112299
